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This paper proposes a cascading failure simulation (CFS) method and a hybrid machine learning method for vulnerability analysis of integrated power-gas systems (IPGSs). The CFS method is designed to study the propagating process of cascading failures between the two systems, generating data for machine learning with initial states randomly sampled. The proposed method considers generator and gas well ramping, transmission line and gas pipeline tripping, island issue handling and load shedding strategies. Then, a hybrid machine learning model with a combined random forest (RF) classification and regression algorithms is proposed to investigate the impact of random initial states on the vulnerability metrics of IPGSs. Extensive case studies are carried out on three test IPGSs to verify the proposed models and algorithms. Simulation results show that the proposed models and algorithms can achieve high accuracy for the vulnerability analysis of IPGSs.

The stochastic cloud cover on photovoltaic (PV) panels affects the solar power outputs, producing high instability in the integrated power systems. It is an effective approach to track cloud motion during short-term PV power forecasting based on data sources of satellite images. However, since temporal variations of these images are noisy and non-stationary, pixel-sensitive prediction methods are critically needed in order to seek a balance between forecast precision and huge computation burden due to a large image size. Hence, a graphical learning framework is proposed for intra-hour PV power prediction. By simulating the cloud motion using bi-directional extrapolation, a directed graph is generated representing the pixel values from multiple frames of historical images. The nodes and edges in the graph denote the shapes and motion directions of the regions of interest (ROIs) in satellite images. A spatial-temporal graph neural network (GNN) is then proposed to deal with the graph. Comparing with conventional deep-learning-based models, GNN is more flexible for varying sizes of input, in order to be able to handle dynamic ROIs. Referring to the comparative studies, the proposed method greatly reduces the redundancy of image inputs without sacrificing the visual scope, and slightly improves the prediction accuracy.

There is growing evidence that air pollution plays a role in TB, but few studies have comprehensively included the six common air pollutants. Our objectives were to investigate the association between short-term exposure to six common air pollutants and the risk of tuberculosis outpatient visits in Fuyang, China, during the period 2015-2020. We combined the two models to explore the effects of exposure to six air pollutants on the risk of tuberculosis outpatient visits, including Poisson generalized linear regression model and distributed lag non-linear model (DLNM). We performed stratified analyses for season, gender and age. We used the lag-specific relative risks and cumulative relative risk obtained by increasing pollutant concentration by per 10 units to evaluate the connection between six air pollutants and TB, PM2.5 (RR=1.0018, 95%CI: 1.0004-1.0032, delay of 12 days) and (RR=1.0169,95%CI: 1.0007-1.0333, lag 0-16 days), SO2 were 0.9549 (95%CI: 0.9389-0.9712, lag 0 days) and 0.8212 (95%CI: 0.7351-0.9173, 0-20 days lag). Stratified analyses showed that seasonal differences had a greater impact on TB, and male more likely to develop TB than female, older people more likely to develop TB than younger people. Exposure to O3, CO, PM10, PM2.5, and NO2 increases the risk of TB outpatient visits, except SO2 reduces the risk. Male and elderly people should be protected. We need to pay more attention to the impact of seasonal effects on TB.

A key process underlying the application of low-dimensional, quantum-confined semiconductors in energy conversion is charge transfer from these materials, which, however, has not been fully understood yet. Extensive studies of charge transfer from colloidal quantum dots reported rates increasing monotonically with driving forces, never displaying an inverted region predicted by the Marcus theory. The inverted region is likely bypassed by an Auger-like process whereby the excessive driving force is used to excite another Coulomb-coupled charge. Herein, instead of measuring charge transfer from excitonic states (coupled electron-hole pairs), we build a unique model system using zero-dimensional quantum dots or two-dimensional nanoplatelets and surface-adsorbed molecules that allows for measuring charge transfer from transiently-populated, single-charge states. The Marcus inverted region is clearly revealed in these systems. Thus, charge transfer from excitonic and single-charge states follows the Auger-assisted and conventional Marcus charge transfer models, respectively. This knowledge should enable rational design of energetics for efficient charge extraction from low-dimensional semiconductor materials as well as suppression of the associated energy-wasting charge recombination., Marcus inverted region for charge transfer from low-dimensional semiconductor materials has been long sought after. Here, the authors reveal this region by directly measuring charge transfer from single-charge states rather than excitonic states.

The variation and uncertainty of renewable energy potentially impact power system stability. This article proposes an efficient uncertainty evaluation algorithm to evaluate the influence of renewable energy uncertainty on power system dynamic performance. This algorithm applies probabilistic collocation method (PCM), which greatly reduces the simulation burden without compromising the result accuracy, to approximate the dynamic simulation results. The proposed algorithm also considers the correlation among adjacent renewable generators by using the Copula function, which is suitable to model nonlinear correlations. Additionally, this article extends the utilization of PCM. The actual historical data of renewable generation productions can be utilized directly, and kernel density estimation (KDE) is used to capture the nonparametric distribution of renewable generation production. To improve the accuracy of the approximation results, the proposed method adopts K-means clustering technique to select the approximation samples of input variables. The proposed probabilistic dynamic simulation algorithm is compared with Monte Carlo simulations (MCS) with the probabilistic results on the IEEE 39-bus system with multiple renewable generators, and the accuracy and efficiency of the proposed algorithm are validated.

Background Some children hospitalized for severe pertussis need intensive care; moreover, some children die because of deterioration alone or in combination with other complications. The purpose of this study was to identify the mortality risk factors among hospitalized children with severe pertussis. Methods This study evaluated the medical records of 144 hospitalized children with severe pertussis at the Guangzhou Women and Children’s Medical Centre between January 2016 and December 2019. Results The median age of patients was 2 months (IQR 1–4 months), with 90.3% of the patients aged 70.0 × 109/L (odds ratio [OR], 230.66; 95% confidence interval [CI], 5.16–10,319.09 P = 0.005) and pulmonary hypertension (PH) (OR 323.29; 95% CI 16.01–6529.42; P Conclusion Severe pertussis mainly occurred in children aged 70.0 × 109/L and PH were the prognostic independent variables associated with death.

Heat strain experienced by individuals wearing chemical protective clothing (CPC) is severe and dangerous especially in hot-humid environment. The development of material science and interdisciplinary studies including ergonomics, physiology and heat transfer is urgently required for the reduction of heat strain. The aim of this paper was to study the relationship among clothing thermal properties, physiological responses and environmental conditions. Three kinds of CPC were selected. Eight participants wore CPC and walked (4 km/h, two slopes with 5% and 10%) on a treadmill in an environment with (35±0.5) °C and RH of (60±5)%. Core temperature, mean skin temperature, heart rate, heat storage and tolerance time were recorded and analyzed. Physiological responses were significantly affected by the clothing thermal properties and activity intensity in hot-humid environment. The obtained results can help further development of heat strain model. New materials with lower evaporative resistance and less weight are necessary to release the heat strain in hot-humid environments.在热湿环境中, 人员穿着化学防护服时会产生危险性极高的严重热应激反应。缓解人员热应激 需要材料学、人机工效学、生理学和传热学等学科交叉协同发展。本文目的是研究服装、生理反应和 环境条件之间的关系。在温度(35±0.5) °C和相对湿度(60±5)%的环境中, 8 名被试人员穿着三种化学防 护服, 在跑台上以4 km/h 的速度运动(运动坡度分别为5%和10%), 记录并分析被试人员体核温度、平 均皮肤温度、心率、热蓄积和耐受时间。研究表明, 在热湿环境中, 服装热性能和运动强度对人体生 理反应有显著影响, 研究结果可为进一步发展化学防护服及热应激模型提供参考。在热湿环境中为缓 解人员热应激, 需要具有更低热阻、湿阻和更轻重量的新型材料。.

The frequency security problem becomes a critical concern in power systems when the system inertia is lowered due to the high penetration of renewable energy sources (RESs). A wind-storage system (WSS) controlled by power electronics can provide the virtual inertia to guarantee the fast frequency response after a disturbance. However, the provision of virtual inertia might be affected by the variability of wind power generation. To address this concern, we propose a two-stage chance-constrained stochastic optimization (TSCCSO) model to find the optimal thermal unit commitment (i.e., economic operation) and the optimal placement of virtual inertia (i.e., frequency stability) in a power grid using representative power system operation scenarios. An enhanced bilinear Benders decomposition method is employed with strong valid cuts to effectively solve the proposed optimization model. Numerical results on a practical power system show the effectiveness of the proposed model and solution method.

Solar energy is a strongly intermittent renewable energy source, which is affected by varied meteorological conditions, and thus produces arbitrary power outputs in photovoltaic (PV) power generation. Complex weather variations make it challenging to develop an efficient PV power forecasting method. In this study, a graph modeling method is proposed for short-term PV power prediction. Unlike many conventional machine-learning models, the proposed model is capable of evaluating interconnections among various meteorological input factors. This study details the design and operation of graph modeling, including graph construction, node feature construction, message transfer, and readout. An entire model is established consisting of spectral graph convolution, multiple graphical edges and a hierarchical output manner. The testing results suggest that the proposed multi-graph model outperforms other benchmark models in terms of accuracy under day-ahead forecasting cases. Besides, the single-graph model achieves a reduced cost of training time comparing to deep-learning benchmark models. This work was supported by National Natural Science Foundation of China under Grant 52077062.

This paper presents a multi-period active distribution network planning (ADNP) with distributed generation (DG). The objective of the proposed ADNP is to minimize the total planning cost, subject to both investment and operation constraints. The paper proposes a multi-stage stochastic optimization model to address DG uncertainties over several periods, in which the decisions are made sequentially by only using the present-stage information. A nested decomposition method is proposed which applies the stochastic dual dynamic integer programming (SDDIP) method to address computational intractabilities of the proposed ADNP approach. The presented numerical results and discussions on a 33-bus distribution system and a large-scale 906-bus system verify the effectiveness of the proposed ADNP method and its solution method.

The water system management problem has been widely investigated. However, the interdependencies between water and energy systems are significant and the effective co-optimization is required considering strong interconnections. This paper proposes a two-stage distributionally robust operation model for integrated water-energy nexus systems including power, gas and water systems networked with energy hub systems at a distribution level considering wind uncertainty. The presence of wind power uncertainty inevitably leads to risks in the optimization model. Accordingly, a coherent risk measure, i.e., conditional value-at-risk, is combined with the optimization objective to determine risk-averse operation schemes. This two-stage mean-risk distributionally robust optimization is solved by Bender's decomposition method. Both the day-ahead and real-time operation cost are minimized with an optimal set of scheduling the multi-energy infrastructures. Case studies focus on investigating the strong interdependencies among the four interconnected energy systems. Numerical results validate the economic effectiveness of IES through optimally coordinating the multi-energy infrastructures. The proposed model can provide system operators a powerful two-stage operation scheme to minimise operation cost under water-energy nexus considering risk caused by renewable uncertainties, thus benefiting customers with lower utility bills.

Background: A valid, reliable, accessible, engaging, and affordable digital cognitive screen instrument for clinical use is in urgent demand. Objective: To assess the clinical utility of the MemTrax memory test for early detection of cognitive impairment in a Chinese cohort. Methods: The 2.5-minute MemTrax and the Montreal Cognitive Assessment (MoCA) were performed by 50 clinically diagnosed cognitively normal (CON), 50 mild cognitive impairment due to AD (MCI-AD), and 50 Alzheimer’s disease (AD) volunteer participants. The percentage of correct responses (MTx-% C), the mean response time (MTx-RT), and the composite scores (MTx-Cp) of MemTrax and the MoCA scores were comparatively analyzed and receiver operating characteristic (ROC) curves generated. Results: Multivariate linear regression analyses indicated MTx-% C, MTx-Cp, and the MoCA score were significantly lower in MCI-AD versus CON and in AD versus MCI-AD groups (all with p≤0.001). For the differentiation of MCI-AD from CON, an optimized MTx-% C cutoff of 81% had 72% sensitivity and 84% specificity with an area under the curve (AUC) of 0.839, whereas the MoCA score of 23 had 54% sensitivity and 86% specificity with an AUC of 0.740. For the differentiation of AD from MCI-AD, MTx-Cp of 43.0 had 70% sensitivity and 82% specificity with an AUC of 0.799, whereas the MoCA score of 20 had 84% sensitivity and 62% specificity with an AUC of 0.767. Conclusion: MemTrax can effectively detect both clinically diagnosed MCI and AD with better accuracy as compared to the MoCA based on AUCs in a Chinese cohort.

Rare-earth-doped upconversion luminescence has attracted extensive attention due to its narrow-band and wide-range emission spectra, which cover biological and optical communication windows. For di...

With the emerging concept of sharing-economy, shared electric vehicles (EVs) are playing a more and more important role in future mobility-on-demand traffic system. This article considers joint charging scheduling, order dispatching, and vehicle rebalancing for large-scale shared EV fleet operator. To maximize the welfare of fleet operator, we model the joint decision making as a partially observable Markov decision process (POMDP) and apply deep reinforcement learning (DRL) combined with binary linear programming (BLP) to develop a near-optimal solution. The neural network is used to evaluate the state value of EVs at different times, locations, and states of charge. Based on the state value, dynamic electricity prices and order information, the online scheduling is modeled as a BLP problem where the decision variables representing whether an EV will 1) take an order, 2) rebalance to a position, or 3) charge. We also propose a constrained rebalancing method to improve the exploration efficiency of training. Moreover, we provide a tabular method with proved convergence as a fallback option to demonstrate the near-optimal characteristics of the proposed approach. Simulation experiments with real-world data from Haikou City verify the effectiveness of the proposed method.

Active plasmonic systems have attracted numerous research interests as they play a central role in functional devices and many emerging applications. However, the current tuning strategies are either static/irreversible or inconvenient with limited tunability and reproducibility. Here, we show a dynamic and reversible tuning of pixelated plasmonic cluster arrays embedded in elastomers that is fast, easy to operate and highly reproducible. This active plasmonic system is fabricated via template-assisted self-assembly, followed by the nanomolding process. These hybrid plasmonic elastomers show a prominent and reversible shift of coupled plasmons with the stimuli of solvents or mechanical forces, which can be monitored in real time using the scattering spectra. Patterning of this hybrid plasmonic system can be facilely achieved via laser direct writing due to the photothermal ablation, which has great implications in high resolution flexible displays, anti-counterfeiting, microfluidic flow catalysis, sensing and diagnosis.

Diabetic nephropathy (DN) is considered the primary causes of end-stage renal disease (ESRD) and is related to abnormal glycolipid metabolism, hemodynamic abnormalities, oxidative stress and chronic inflammation. Antagonism of vascular endothelial growth factor B (VEGF-B) could efficiently ameliorate DN by reducing renal lipotoxicity. However, this pharmacological strategy is far from satisfactory, as it ignores numerous pathogenic factors, including anomalous reactive oxygen species (ROS) generation and inflammatory responses. We found that the upregulation of VEGF-B and downregulation of interleukin-22 (IL-22) among DN patients were significantly associated with the progression of DN. Thus, we hypothesized that a combination of a VEGF-B antibody and IL-22 could protect against DN not only by regulating glycolipid metabolism but also by reducing the accumulation of inflammation and ROS. To meet these challenges, a novel anti-VEGFB/IL22 fusion protein was developed, and its therapeutic effects on DN were further studied. We found that the anti-VEGFB/IL22 fusion protein reduced renal lipid accumulation by inhibiting the expression of fatty acid transport proteins and ameliorated inflammatory responses via the inhibition of renal oxidative stress and mitochondrial dysfunction. Moreover, the fusion protein could also improve diabetic kidney disease by increasing insulin sensitivity. Collectively, our findings indicate that the bifunctional VEGF-B antibody and IL-22 fusion protein could improve the progression of DN, which highlighted a novel therapeutic approach to DN., Graphical abstract The expression of VEGF-B was increased and that of IL-22 was decreased in diabetic nephropathy (DN) patients. Anti-VEGFB/IL22 fusion protein combining VEGF-B antibody and IL22 ameliorated DN by alleviating ectopic lipid accumulation, inflammatory responses and insulin resistance.Image 1

Thermoelectric power generation is an outstanding technology for high- and low-temperature heat recovery. Their packaging, lack of moving parts, direct heat to electrical conversion, and zero-emiss...

Today, the global energy crisis is becoming more serious, which is manifested by the shortage of fossil fuels and considerable environmental pollution. As a supplement to large-scale centralized power generation, distributed energy resources, such as wind and photovoltaic (PV) power, provide a new way to solve the energy crisis.

Interconnected power systems that link several countries and fully utilize their individual resources in a complementary manner are becoming increasingly important. As these systems enhance accommodation of renewable energy, they also represent a move toward low-carbon and low-emission power systems. In this paper, a low-carbon dispatch model is proposed to coordinate the generation output between several countries where the carbon emission constraint is a priority. An adjustable robust optimization approach is used to find the optimal solution under the worst-case scenario to address the uncertainties associated with renewable energy resources. A specific constraint is that the area control error for each country should be self-balanced. Furthermore, a reformation using participation factors is presented to simplify the proposed robust dispatch model. Simulation results for practical interconnected power systems in northeast Asian countries verify the effectiveness of the proposed model.

De Garengeot hernia is a rare femoral hernia that contains the vermiform appendix. It has always been a subject of interest, mainly because of its uncommon entity, but also because of the diagnostic challenge it presents and the lack of consensus in its surgical management. It mainly presents as an emergency, with strangulation of the femoral hernia and acute inflammation of the appendix. The purpose of this study is to present two cases of De Garengeot hernias, the different surgical approach in each one and to perform a systematic literature review on all published accounts of De Garengeot hernias focusing on the surgical management of the hernias that contained an inflamed appendix. A literature search was performed using the keywords 'De Garengeot hernia' and 'appendicitis in femoral hernia'. All the published papers in the English language from 1898 to the end of 2019, as well as two patients from Lewisham and Greenwich NHS Trust, were included. There were 110 female vs 17 males. The majority of the described approaches were via either a Lockwood, or a transinguinal (Lothestein/McVay) incision. Most of the operations were done with an open incision. In the presence of an inflamed appendix, most hernias were repaired with sutures. Forty-one patients had their hernia repaired with non-absorbable sutures and four with absorbable. Most of the patients recovered without complications and were discharged after an average of 4.82 days.

Background: The widespread incidence and prevalence of Alzheimer’s disease and mild cognitive impairment (MCI) has prompted an urgent call for research to validate early detection cognitive screening and assessment. Objective: Our primary research aim was to determine if selected MemTrax performance metrics and relevant demographics and health profile characteristics can be effectively utilized in predictive models developed with machine learning to classify cognitive health (normal versus MCI), as would be indicated by the Montreal Cognitive Assessment (MoCA). Methods: We conducted a cross-sectional study on 259 neurology, memory clinic, and internal medicine adult patients recruited from two hospitals in China. Each patient was given the Chinese-language MoCA and self-administered the continuous recognition MemTrax online episodic memory test on the same day. Predictive classification models were built using machine learning with 10-fold cross validation, and model performance was measured using Area Under the Receiver Operating Characteristic Curve (AUC). Models were built using two MemTrax performance metrics (percent correct, response time), along with the eight common demographic and personal history features. Results: Comparing the learners across selected combinations of MoCA scores and thresholds, Naïve Bayes was generally the top-performing learner with an overall classification performance of 0.9093. Further, among the top three learners, MemTrax-based classification performance overall was superior using just the top-ranked four features (0.9119) compared to using all 10 common features (0.8999). Conclusion: MemTrax performance can be effectively utilized in a machine learning classification predictive model screening application for detecting early stage cognitive impairment.

Objective. To investigate the effects of arteriovenous thrombolysis combined with mechanical thrombectomy on clinical efficacy, neurological function, and the changes of nerve injury markers of acute cerebral infarct (ACI) patients. Methods. A total of 143 cases with ACI admitted to our hospital from June 2017 to June 2019 were elected as research subjects. Among them, 69 cases of patients who received treatment of arteriovenous thrombolysis were considered as group A, and 74 cases of patients who received treatment of arteriovenous thrombolysis combined with mechanical thrombectomy were considered as group B. NIHSS score, clinical efficacy, vascular recanalization, adverse reactions, hemodynamics, neurological injury indexes, duration of coma, length of hospital stay, and prognosis of patients in the two groups were compared. Results. After treatment, the NIHSS score of group A was higher than that of group B (P<0.05), the clinical efficacy of group B was better than that of group A, and the incidence of adverse reactions was lower than that of group A (P<0.05). There was no difference in vascular recanalization rate, duration of coma, and prognosis between the two groups (P>0.05). Length of hospital stay, maximum peak velocity after treatment (Vs), and mean flow rate (Vm) of group A were lower than those of group B, while vascular resistance index (RI), pulsatility index (PI), serum glutamic acid (Glu), neuron-specific enolase (NES), and S100β protein detected by enzyme-linked immunosorbent assay (ELISA) of group A were higher than those of group B (P<0.05). Conclusion. Arteriovenous thrombolysis combined with mechanical thrombectomy has a significant effect on ACI, with high safety and quick effect. In addition, it has a stronger effect on improving and protecting the neurological function of patients, which is worth promoting in clinical practice.

Modern power systems are facing greater challenges in dynamic voltage performance due to the increased wind power penetration and proliferation of induction loads. This paper proposes a multi-stage coordinated var source planning method for voltage resilience enhancement. A set of novel voltage resilience metrics are proposed to quantify the steady-state and short-term voltage stability, as well as the defensive capability and restorative performance of the system. The planning model coordinates the deployment of both fixed capacitor banks and STATCOM with coordinated defensive and restorative control actions including wind power curtailment, excitation system compensation, demand response, and load shedding. Three sub-objectives are optimized simultaneously: 1) planning cost and operation mitigations cost, 2) resilience indices of defensive capability, and 3) resilience indices of restorative performance. Finally, a robust parameter design technique called Taguchi's Orthogonal Array Testing is employed for uncertainty quantification and Pareto optimal solutions are provided for a flexible decision-making. The computation burden is mitigated by candidate buses selection with Morris Screening Method and critical contingency identification. The efficiency and effectiveness of the proposed approach are validated on the New England 39-bus system.

Nowadays, the entire world is facing challenges in energy and environment. To resolve these problems, the power systems are interconnected to promote the development of renewable energy sources (RESs). However, the economic dispatch (ED) problem for the global energy interconnection (GEI) should tackle two issues: 1) handle the uncertainty from RES and allocate the responsibility among the interconnected countries and 2) protect the information privacy through the dispatch. Motivated by the above, this article proposes a zonally adjustable robust decentralized ED model for the GEI. In the model, each country is only responsible for its own uncertainty, and tie-line power flows remain unchanged under uncertainties. Moreover, an alternating direction method of multipliers (ADMM)-based fully distributed algorithm is used, in which only limited information should be exchanged between neighboring countries. Finally, a case study on the Northeast Asian countries verifies the effectiveness of the proposed method. Note to Practitioners —Since the renewable energy generation has a spatial correlation among regional countries, global energy interconnection (GEI) aims to combine several power systems together to promote the renewable energy accommodation. However, two problems need to be considered: 1) Information Privacy: The information privacy of the power system in each country should be preserved, which prevents the GEI from conducting a centralized optimal dispatch framework and 2) Uncertainty: The uncertain output of renewable energy resources brings challenge to the power system secure operation. The main contribution of this article is to set up a zonally robust decentralized optimization for the GEI, where the zonally robust economic dispatch (ED) is conducted by the area control error (ACE) system to manage the difference between scheduled and actual generation under the uncertainties, and the alternating direction method of multipliers (ADMMs) algorithm is adopted for decentralizing the zonally adjustable robust ED model, which only needs limited information. In particular, this article uses a real-world example from Northeast Asian Countries to help engineers understand the advantages of the GEI and the new dispatch framework.

The stability of the modular multilevel converter-based multi-terminal direct current (MMC-based MTDC) transmission system is strongly affected by the coupling among multiple elements and the interaction between the control systems. Improper designed control parameters may deteriorate the dynamic response or even reduce the stability margin of the MMC-based MTDC. This study proposes an optimal control parameters design approach to enhance the stability of MMC-based MTDC. The dynamic model of an MMC-based MTDC consisting of main circuit, control system, AC system and DC system is established, and the small-signal stability model is derived. The impact of control systems on the stability of MMC-based MTDC is studied through participation factor analysis. An optimal control parameters design problem with an eigenvalue-based objective function is formulated and a sensitivity analysis-based optimisation algorithm is proposed to optimise the control parameters jointly. The backtracking line search is applied to determine the feasible step size in each iteration. Stability assessment and time-domain simulations are performed to verify the effectiveness of the proposed method.

Plasmon-induced hot carriers can be used to control both the growth and etching behavior of gold nanoparticles (Au NPs) with laser irradiation. Here, the Au NPs placed on Si substrate are irradiate...

The cooperative characteristics of resistive superconducting fault current limiters (R-SFCLs) and DC circuit breakers (DCCBs) have been investigated with regard to the fault clearing in modular multilevel converter (MMC)-based multiterminal direct current (MTDC) grids. However, research is lacking in complete protection solutions that combine R-SFCLs and mechanical DCCBs while considering the backup protection and the effect of R-SFCLs on the fault characteristics of MMCs. This study investigates a fault current breaking scheme that includes primary and backup protection. Moreover, the fault characteristics of MMCs with R-SFCLs are analysed, and the fault transient process is divided into three stages. Then two fault current limiting schemes with R-SFCLs located at the converter output or the line are proposed. These two protection schemes are applied to the DC test system, verified and compared. The results show that in both schemes, the size of the designed R-SFCL should be generally a few tens of Ω. These R-SFCLs maintain the converters unblocked until the primary breakers or backup breakers interrupt the fault currents, and the M-DCCBs are only required to break several kA currents. After a fault occurs, the system will restore within 200 ms for primary protection or 250 ms for backup protection.

Due to the fast response capabilities, combined-cycle units could provide valuable flexibilities to cope with uncertainties from the renewable energy resources. However, combined-cycle units intensify the coupling between power and gas networks, so operational constraints of the two networks should be explicitly considered simultaneously. Indeed, neglecting the impact of any network may lead to infeasible solutions that will violate physical operational constraints. Compared with the previous stochastic unit commitment model with only combined-cycle units, this paper establishes a stochastic gas-power network constrained unit commitment model considering both combined-cycle units and gas network. Furthermore, an adaptive dynamic programming (ADP) to prevent the curse of dimensionality is proposed. Numerical results verify the effectiveness of the proposed model.

Grid connection of intermittent renewable energy, such as wind power and photovoltaic, results in challenges of keeping power balance for power system operation. In order to solve this problem, this article proposed a multitime scale coordinated scheduling model for the combined system of wind power–photovoltaic–thermal generator–hydro pumped storage–battery (WPTHB) by taking advantages of their complementary operation characteristics. The scheduling model is composed of three time scales: the day-ahead scheduling, the 1-h ahead scheduling, and 15-min ahead scheduling. 1) In the day-ahead scheduling, based on the 24-h ahead forecast data of wind–photovoltaic power and load demand (WPL), the optimal power outputs of thermal power units are solved from a mixed-integer linear programming model to achieve the minimal operation cost of thermal units. 2) In the 1-h ahead scheduling, based on power output of thermal units optimized in the day-ahead scheduling and the hourly forecasted WPL, the hydro-pumped unit power outputs are optimally dispatched to minimize their operation cost. 3) In the 15-min ahead scheduling, based on day-ahead optimal power outputs of thermal units and the 1-h ahead optimal outputs of pumped storage, the battery optimal power generation is obtained from an ac optimal power flow model solved by MATPOWER. The simulation of the New England system has validated that the proposed multitime scale coordinated scheduling model could fully explore the distinguished power regulation speed and capacities of thermal power units, hydro-pumped storage, and batteries to effectively track WPL variations and achieve system economic operation simultaneously.

Decarbonization of the electricity sector is crucial to mitigate the impacts of climate change and global warming over the coming decades. The key challenges for achieving this goal are carbon emission trading and electricity sector regulation, which are also the major components of the carbon and electricity markets, respectively. In this paper, a joint electricity and carbon market model is proposed to investigate the relationships between electricity price, carbon price, and electricity generation capacity, thereby identifying pathways toward a renewable energy transition under the transactional energy interconnection framework. The proposed model is a dynamically iterative optimization model consisting of upper- level and lower-level models. The upper-level model optimizes power generation and obtains the electricity price, which drives the lower-level model to update the carbon price and electricity generation capacity. The proposed model is verified using the Northeast Asia power grid. The results show that increasing carbon price will result in increased electricity price, along with further increases in renewable energy generation capacity in the following period. This increase in renewable energy generation will reduce reliance on carbon-emitting energy sources, and hence the carbon price will decline. Moreover, the interconnection among zones in the Northeast Asia power grid will enable reasonable allocation of zonal power generation. Carbon capture and storage (CCS) will be an effective technology to reduce the carbon emissions and further realize the emission reduction targets in 2030-2050. It eases the stress of realizing the energy transition because of the less urgency to install additional renewable energy capacity.

This paper first presents a comprehensive model of the gas system with detailed formulations on pipelines, short pipes, resistors, valves, compressors, and compressor stations. Furthermore, an optimal energy flow model is proposed for integrated power–gas generation and transmission systems. Specifically, on the generation side, gas-fired units couple the two energy systems as the power generation and gas sink; on the transmission side, gas compressor stations link the two energy systems as the power demand and gas transportation. However, gas flow equations are nonlinear and gas flow directions also need to be optimized. Logical programming and tailored piecewise linearization techniques are performed, leading to a mixed-integer linear program (MILP). Only a logarithmic number of binary variables are introduced to represent the nonlinear quadratic function, and thus, the MILP model can be solved very efficiently. Numerical results on four power–gas test systems demonstrate the effectiveness of the proposed approach.

This paper proposed one novel Thermo-Electric Ventilation (TEV) system driven by the concentrated photovoltaic-thermoelectric generator (CPV-TEG), which could use the electric power converted directly from solar energy by CPV-TEG. The effects of incident solar irradiance, number of thermoelectric generators, and ambient air temperatures on the power output of CPV-TEG have been analytically investigated through energy balance and first law of thermodynamics. Furthermore, input current and number of thermoelectric coolers were sensitively varied to optimize the performance of TEV system respectively in heating and cooling modes. Finally, an integrated theoretical and numerical approach was proposed to match the power output of CPV-TEG with the power input of TEV. Modeling results indicate that the output power from CPV-TEG could satisfy the energy demand of TEV system when the input currents of thermoelectric coolers were no more than 2.5 A and 2.8 A respectively for cooling and heating modes. Minimum energy and exergy efficiencies of the system in winter heating mode were confirmed to be 1.67 and 0.24 respectively, which were far higher than that in summer cooling mode. This research may be helpful for enhancing performance and reducing exergy destruction of thermoelectric ventilation system, simultaneously.

Ancillary services (AS) are the services necessary to support the transmission of electric power from generators to consumers given the obligations of control areas and transmitting utilities within those control areas to maintain reliable operations of the interconnected transmission system. As a result of the increasing penetration of renewable resources, the new reliability needs are emerging so that the changes made to the AS market become necessary. This paper presents a new AS framework being implemented at the Electric Reliability Council of Texas (ERCOT) in order to address the primary frequency control issues associated with the declining system inertia. This new design can balance the need for both reliability and economics while opening up the AS market to the traditional and non-traditional resources, including load resources with under-frequency relays and energy-limited resources like batteries. This paper also introduces a new way to quantify the benefits of the new AS design using ERCOT model and data.

The Fabry–Perot (FP) cavity is one of the basic optical components that has been widely applied in interferometry, spectroscopy, lasers and optoelectronic devices. Currently, tailorable FP cavities are highly desirable for fast and in situ modulation, which is crucial for optoelectronic devices and their integration. Here a simple tuning strategy of FP cavity mode is developed based on the plasmon-assisted laser printing technique. The laser irradiation induces photochemical and/or photothermal degradation of the polymer medium in the cavity, which is highly dependent on the film thickness and laser power. Thus, the FP cavity mode can be precisely and continuously tuned with irradiation time, which is then monitored using reflection spectra. Such a laser printing strategy is facile and general, which is capable of generating different structural colours.

In the environment of rapid social and economic development, the reform of medical informatization is constantly advancing, and the residents pay more and more attention to their own health status while improving their living standards. The traditional medical service system has some disadvantages in providing real-time, cross regional, long-term and easy-to-operate health services, which has become increasingly inadequate to meet the health needs of users. In order to solve the problem of difficulty in seeing a doctor caused by limited medical resources, and to carry out real-time health monitoring for a large number of groups suffering from chronic diseases and sub-health groups, this study conducted in-depth analysis and experimental exploration on the human remote mobile medical information collection method based on the Internet of things and intelligent algorithm. It established the information collection section by using KbaC clustering algorithm based on ant colony point system which, combined with a comparative study on the health indicators of related groups, has successfully proved that the Internet of things technology and intelligent algorithm for medical information collection and follow-up medical services are of certain positive significance, based on the Internet of things and other related technologies of human remote medical information collection system that can accurately and timely detect the patient's blood pressure, blood sugar and other health data, and then provide corresponding medical services. RESUMO No ambiente de rápido desenvolvimento do nível social e econômico, a reforma da informatização médica está constantemente avançando, e os residentes prestam cada vez mais atenção ao seu próprio estado de saúde, melhorando ao Mesmo tempo seu padrão de vida. O sistema tradicional de serviços médicos tem algumas desvantagens em fornecer serviços de saúde em tempo real, transfronteiriços, de longo prazo e fáceis de operar, o que vem se tornando cada vez mais inadequado para satisfazer as necessidades de saúde dos usuários. A fim de resolver o problema a da dificuldade em consultar um médico por devido a recursos médicos limitados, e para realizar a monitorização da saúde em tempo real para um grande número de grupos que sofrem de doenças crônicas e subgrupos de saúde, este estudo conduziu uma análise aprofundada e uma exploração experimental sobre o método de coleta de informações médicas móvel à distância humana baseado na Internet das coisas e algoritmo inteligente. Estabeleceu a seção de coleta de informações utilizando o algoritmo de clustering KbaC baseado no sistema de pontos de colônias de formigas que, juntamente com um estudo comparativo sobre os indicadores de saúde dos Grupos conexos, conseguiu provar que a tecnologia da Internet das coisas e o algoritmo inteligente para a coleta de informações médicas e acompanhamento dos serviços médicos têm certa relevância positiva baseada na Internet das coisas e outras tecnologias relacionadas ao sistema de coleta de informações médicas remotas humanas, podendo detectar com precisão e tempo hábil a pressão arterial do paciente, a glicose e outros dados de saúde, e, em seguida, fornecer o serviço médico correspondente. RESUMEN En un entorno de rápido desarrollo social y económico, la reforma de la informatización médica avanza constantemente y las personas prestan cada vez más atención a su estado de salud mientras mejoran su nivel de vida. El sistema de servicio médico tradicional tiene deficiencias en la prestación de servicios de salud en tiempo real, transregionales, a largo plazo y fáciles de operar, los que se han vuelto cada vez más inadecuados para satisfacer las necesidades de salud de los usuarios. Este estudio realizó un análisis con el objetivo de resolver la dificultad para consultar al médico debido a la limitación de los recursos, y de realizar un seguimiento de la salud en tiempo real de un gran número de grupos que padecen enfermedades crónicas. Dicho trabajo realizó un análisis en profundidad y de exploración experimental acerca del método de recopilación de información médica humana móvil remoto basado en Internet de las cosas y el algoritmo inteligente. Estableció la sección de recopilación de información utilizando el algoritmo de agrupación KbaC basado en el sistema de puntos de colonia de hormigas. Esto, combinado con un estudio comparativo sobre los indicadores de salud de grupos relacionados, ha demostrado con éxito que la tecnología de Internet de las cosas y el algoritmo inteligente para la recopilación y seguimiento de información médica son de importancia positiva, y que pueden detectar de manera precisa y oportuna la presión arterial, el azúcar en sangre y otros datos de salud del paciente, para luego proporcionar la atención médica correspondiente.

Silver nanowires (Ag NWs) present prominent waveguiding properties of subwavelength light due to their nanoconfinement with propagating surface plasmons, which is of great importance for on-chip integration of nanophotonic devices and optical computation. Such propagating plasmons also exert plasmonic forces, which can be utilized to manipulate nanoparticles (NPs) beyond the diffraction limit. However, such controllability is spatially limited to the near fields, whereas a large portion of uncontrolled particles are randomly deposited on the chips, which could be detrimental to the integrated optical devices. Herein we shine continuous wave laser at one end of the Ag NW immersed in AgNO3 solution to launch the propagating surface plasmons. The laser irradiation also induces the photoreduction of Ag+ ions to locally generate tiny Ag NPs, which evolve into large Ag flake branches closer to the other end of the Ag NW. Such a peculiar growth is due to the synergistic effect of plasmonic forces and the thermophoretic/thermo-osmosis forces induced by temperature gradient. These branched Ag NWs with sharp angles are intrinsically chiral, which can be partially controlled by changing the irradiation location, forming plasmonic chiral enantiomers. The circular differential scattering (CDS) response of these branched Ag NWs can be as large as 40%, which can be used for chiral enantiomer sensing with spectral dissymmetric factor up to 4 nm induced by phenylalanine. This plasmon-directed on-wire growth not only offers a facile approach for generating plasmonic chiral nanostructures with remote controllability, but also provides significant insights on the synergistic effect of plasmonic forces and thermal-induced forces, which has great implications for self-assembly and integration of on-chip optics.

Realizing process data traceability of industrial products is an important way to solve the problem of product quality analysis and achieve optimal design. At present, research on product data traceability mainly relies on centralized service provider storage, and product data rarely covers the whole life cycle, resulting in the opacity and incomplete of the product traceability process data. In this paper, blockchain technology is introduced into the traceability management of product data, and a product life cycle data traceability method based on multi-blockchain is proposed. The data sharing and communication mechanisms among blockchains are analyzed. Moreover, the block structure of product traceability data, the upload process of block data and the tracing process of multi-chain product data are elaborated in detail. Finally, based on the open source ethereum architecture, the system application platform is developed to verify the feasibility and rationality of the proposed method.

With a significant increase of load type and distributed generator permeability in multiple community integrated energy systems (CIESs), a hybrid distribution system (HDS) including AC/DC power network and heat network is required to satisfy multi-energy demands. This paper presents a detailed model for CIES and HDS to realize cascade utilization of multi-energy. To overcome the profit conflicts among HDS and multiple CIESs, a coordinated operation model based on hierarchical decentralized scheduling is proposed. Furthermore, the alternating direction method of multipliers (ADMM) algorithm is used to solve this coordinated model. Finally, the proposed model and method have been verified on a test HDS, which consists of an IEEE 33-bus AC power system coupling with a 4-bus DC power system and a 6-node heat system, and multiple CIESs which locate in Jiangsu Province, China.

BackgroundMental health policy makers require evidence-based information to optimise effective care provision based on local need, but tools are unavailable.AimsTo develop and validate a population-level prediction model for need for early intervention in psychosis (EIP) care for first-episode psychosis (FEP) in England up to 2025, based on epidemiological evidence and demographic projections.MethodWe used Bayesian Poisson regression to model small-area-level variation in FEP incidence for people aged 16–64 years. We compared six candidate models, validated against observed National Health Service FEP data in 2017. Our best-fitting model predicted annual incidence case-loads for EIP services in England up to 2025, for probable FEP, treatment in EIP services, initial assessment by EIP services and referral to EIP services for ‘suspected psychosis’. Forecasts were stratified by gender, age and ethnicity, at national and Clinical Commissioning Group levels.ResultsA model with age, gender, ethnicity, small-area-level deprivation, social fragmentation and regional cannabis use provided best fit to observed new FEP cases at national and Clinical Commissioning Group levels in 2017 (predicted 8112, 95% CI 7623–8597; observed 8038, difference of 74 [0.92%]). By 2025, the model forecasted 11 067 new treated cases per annum (95% CI 10 383–11 740). For every 10 new treated cases, 21 and 23 people would be assessed by and referred to EIP services for suspected psychosis, respectively.ConclusionsOur evidence-based methodology provides an accurate, validated tool to inform clinical provision of EIP services about future population need for care, based on local variation of major social determinants of psychosis.

Energy storages can significantly relieve the pressure of the power system brought by a large amount of renewable energy generation. Under this situation, the risk assessment method becomes critical. In this paper, an explicit model for diverse energy storages with battery and Hydrogen Storage Systems (HSS) is built. Further, an optimal load shedding model by utilizing the sequential Monte Carlo (SMC) method is proposed to assess the risk of the power system with diverse energy storages. Then, the proposed method is test on a power system which is adapted from the IEEE 24-bus system. The numerical results show that diverse energy storages can improve risk assessment results of the power system.

As the energy crisis becomes more prominent, the issue of energy conservation and recycling is urgent. For large-scale industrial plants, there are various forms of heat energy resources with different grades and energy storages. This study presents the detail modelling of all equipment in the integrated energy system to specify the physical operational constraints. Furthermore, an optimisation model is set up to minimise the total cost of an individual industrial plant, with the consideration of energy production equipment, energy conversion equipment and energy storage equipment in the plant. In particular, the relationship among the heat energy with different grades is described to characterise the cascade utilisation of the heat energy. Finally, a material processing factory with multi-grade heat energy is simulated to verify the effectiveness of the proposed model with energy cascade utilisation.

Novel phenomena emerge as the sizes of semiconductor nanocrystals (NCs) are tuned into the quantum confinement regime, such as size-dependent optical gaps, enhanced exciton–exciton interactions, an...

Cardiovascular disease has always been the most serious public health problem in China. Although many studies have found that the risk of death caused by cardiovascular disease is related to air pollutants, the existing results are still inconsistent. The aim of this study was to investigate the effects of air pollutants on the risk of daily cardiovascular deaths in Hefei, China. Daily data on cardiovascular deaths, daily air pollutants, and meteorological factors from 2007 to 2016 were collected in this study. A time-series study design using a distributed lag nonlinear model was employed to evaluate the association between air pollutants and cardiovascular deaths. First, a single air pollutant model was established based on the minimum value of Akaike information criterion (AIC), and the single day lag effects and multi-day lag effects were discussed separately. Then, two-pollutant models were fitted. Subgroup analyses were conducted by gender (male and female), age (< 65 age and ≥ 65 age), and disease type (ischemic heart disease and cerebral vascular disease). There were 34,500 cases of cardiovascular deaths during the period 2007-2016, and the average concentrations of air pollutants (PM10, SO2, NO2, PM2.5, CO, O3) were 106.11, 20.34, 30.49, 72.59, 958.7, and 67.88 μg/m3, respectively. An increase of interquartile range (IQR) in PM10, SO2, NO2, PM2.5, CO, and O3 were associated with an increase of 4.34% (95%CI 1.54~7.23%) at lag 0-6, 5.79% (95%CI 2.43~9.27%) at lag 0-5, 4.47% (95%CI 1.64~7.37%) at lag 0-5, 3.14% (95%CI 0.03~6.36%) at lag 0-4, 3.11% (95%CI 0.21~6.10%) at lag 0-3, and 8.17% (95%CI 1.89~14.84%) at lag 0-5 in cardiovascular deaths, respectively. Females, older group (≥ 65 years) and deaths from cerebral vascular disease were more vulnerable to air pollution than males, younger individuals (< 65 years) and deaths from ischemic heart disease. Our results suggest that air pollution increased the risk of cardiovascular deaths in Hefei. These findings can provide evidence for effective air quality interventions in Hefei.

Colloidal nanocrystals (NCs) can be generally classified as either excitonic or plasmonic. Excitonic NCs can find applications in photocatalysis and light emitting devices; in contrast, plasmonic N...

Hot electrons can dramatically improve the efficiency of solar cells and sensitize energetically-demanding photochemical reactions. Efficient hot electron devices have been hindered by sub-picosecond intraband cooling of hot electrons in typical semiconductors via electron-phonon scattering. Semiconductor quantum dots were predicted to exhibit a “phonon bottleneck” for hot electron relaxation as their quantum-confined electrons would couple very inefficiently to phonons. However, typical cadmium selenide dots still exhibit sub-picosecond hot electron cooling, bypassing the phonon bottleneck possibly via an Auger-like process whereby the excessive energy of the hot electron is transferred to the hole. Here we demonstrate this cooling mechanism can be suppressed in copper-doped cadmium selenide colloidal quantum dots due to femtosecond hole capturing by copper-dopants. As a result, we observe a lifetime of ~8.6 picosecond for 1Pe hot electrons which is more than 30-fold longer than that in same-sized, undoped dots (~0.25 picosecond)., Weak electron-phonon scattering that can enable long-lived hot electrons in semiconductors is of interest in hot carrier solar cells. Here, the authors report copper-doped colloidal cadmium-selenide quantum dots with hot electron lifetime extended by more than 30-fold compared to undoped dots.

Capacity market is a long-term clearing model to coordinate the traditional thermal generation and the renewable energy generation, which minimizes the total capacity cost of traditional generation, while satisfying the operational constraints and reliability requirement. Furthermore, to address the uncertainties in the long-term optimal decision, min–max regret is employed to find the optimal solution under the worst regret, generating several representable scenarios that can help generation companies to understand how these scenarios would impact on the future generation planning. Finally, a decomposition method is proposed to solve this reliability based min–max regret stochastic optimization model by bisection. The test results on one regional grid in China show the effectiveness of the proposed model.

Lead halide perovskite quantum dots (PQDs) have recently been proposed as scalable and color-tunable single emitters, but their slow spontaneous emission (1–10 ns) creates a mismatch with high-spee...

We performed a network meta-analysis of randomized controlled trials (RCTs) to evaluate the efficacy of HER2-targeted agents in combination with taxanes and to identify the best strategy for HER2+ metastatic breast cancer (MBC). Pubmed, Embase, the Cochrane Library, and ClinicalTrials.gov were searched for randomized controlled trials that evaluated any taxanes+HER2-targeted agents in the treatment of HER2+ MBC. The primary outcome was overall survival (OS). The secondary outcomes included overall response rate (ORR) and progression-free survival (PFS). A total of 13 RCTs were eligible, involving 4941 patients and 10 regimens. The result showed that single-HER2-targeted agent+a taxane did improve the effect on ORR and PFS than taxane alone, but only trastuzumab+a taxane had a significant improvement in OS outcomes. Single-HER2-targeted agent (trastuzumab) combined with taxane-based doublets (taxane+carboplatin/capecitabine/doxorubicin/bevacizumab) showed no further benefit than trastuzumab+a taxane. Doublet-HER2-targeted agents combined with a taxane(trastuzumab+pertuzumab+a taxane) showed further improvement in ORR, PFS, and all OS outcomes than single-HER2-targeted agent+a taxane. Ranking analysis based on their P-scores suggested that trastuzumab+pertuzumab+a taxane was the best combination treatment for all the efficacy outcomes. These findings demonstrate that combining two HER2-targeted agents (trastuzumab+pertuzumab) with a taxane is much more beneficial for the treatment of HER2+ MBC. Dual HER2-targeted agents combined with a taxane appears to be the preferred application of HER2+ MBC.

Increasing the redundancy of distribution lines and increasing the penetration of distributed energy resources (DERs) both help microgrids ride through contingencies. However, it remains a challenging problem for how to coordinate these two measures for minimising the deployment cost while guaranteeing a pre-specified degree of resilience in case of contingencies. Accordingly, this study proposes a novel microgrid planning model to site and size candidate sets of DERs and distribution lines in close coordination, which is mathematically equivalent to a two-stage robust optimisation problem. In particular, the resilience level of microgrid operations is quantified and maintained such that the load loss is constrained within a given bound under any realisation of N – k contingencies. The proposed model also incorporates a practical strategy to maintain the radial topology of islanding network sections in any N – k contingency. Finally, numerical experiments based on two microgrid test systems are performed. The results show that the system resilience is adaptively enhanced through optimally placing DERs and distribution lines compared with the conventional economics based model. Moreover, the employed robust method is at least ten times faster than the reliability-based method in identifying the worst contingency.

Considering uncertainties from wind power and day-ahead market price, this paper proposes a scenario-based two-stage model for the day-ahead scheduling of integrated energy distribution network which couples various energy systems such as electricity, heat, and natural gas. However, gas network constraints pose a great challenge to the computation because the piecewise linear approximation (PLA) method linearizes the nonlinear flow-pressure constraints (i.e., Weymouth equations) by adding a large number of additional auxiliary variables and constraints into the model, which increases the complexity of the model. To address this problem, we propose a sufficient condition for identifying redundant gas network constraints. We also develop a bounds tightening strategy that combines Weymouth equation relaxation and an optimality-based bounds tightening method to mitigate the conservatism of the proposed sufficient condition. Numerical case studies show that the proposed method can greatly improve the computational performance of the optimization model, making it superior to the conventional PLA method. For a larger-scale test system, the improvement of computational efficiency is more significant.