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"Liu, Ge"
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Reducing Profit‐Seeking Default Behaviors in Demand Response via a Deep Learning‐Based Credit Scoring System
The participation of distributed energy resources in demand response enriches the manners of power grid dispatching. However, due to the profit‐seeking behaviors of some demand response suppliers, the cleared trading volumes may not be executed as expected. Therefore, this paper proposes a deep learning‐based credit scoring method for reducing profit‐seeking default behaviors in demand response. First, a modified demand response model is constructed considering the default probability of DRSs to simulate the interaction between the distribution network trading coordinator and demand response suppliers. Then, to evaluate the default probability, a transaction credit and market value‐based default probability evaluation model is proposed. Because of the complexity of data acquisition, the credit evaluation may be biased. Thus, a deep learning‐based credit scoring method is proposed for objectively quantifying credit of the transaction stakeholders, which can be efficiently applied in a variety of complex data types. Finally, simulations on an urban area with an industrial zone, two commercial zones, and a residential zone are shown to illustrate the validity of the proposed method. A demand response model considering profit‐seeking default behaviors. A transaction credit and market value‐based default probability evaluation method. A deep learning‐based credit scoring method integrating a gradient boosting decision tree and artificial neural network.
Journal Article
Knowledge, attitudes, and practices towards COVID-19 among Chinese residents during the rapid rise period of the COVID-19 outbreak: a quick online cross-sectional survey
Unprecedented measures have been adopted to control the rapid spread of the ongoing COVID-19 epidemic in China. People's adherence to control measures is affected by their knowledge, attitudes, and practices (KAP) towards COVID-19. In this study, we investigated Chinese residents' KAP towards COVID-19 during the rapid rise period of the outbreak. An online sample of Chinese residents was successfully recruited via the authors' networks with residents and popular media in Hubei, China. A self-developed online KAP questionnaire was completed by the participants. The knowledge questionnaire consisted of 12 questions regarding the clinical characteristics and prevention of COVID-19. Assessments on residents' attitudes and practices towards COVID-19 included questions on confidence in winning the battle against COVID-19 and wearing masks when going out in recent days. Among the survey completers (n=6910), 65.7% were women, 63.5% held a bachelor degree or above, and 56.2% engaged in mental labor. The overall correct rate of the knowledge questionnaire was 90%. The majority of the respondents (97.1%) had confidence that China can win the battle against COVID-19. Nearly all of the participants (98.0%) wore masks when going out in recent days. In multiple logistic regression analyses, the COVID-19 knowledge score (OR: 0.75-0.90, P<0.001) was significantly associated with a lower likelihood of negative attitudes and preventive practices towards COVID-2019. Most Chinese residents of a relatively high socioeconomic status, in particular women, are knowledgeable about COVID-19, hold optimistic attitudes, and have appropriate practices towards COVID-19. Health education programs aimed at improving COVID-19 knowledge are helpful for Chinese residents to hold optimistic attitudes and maintain appropriate practices. Due to the limited sample representativeness, we must be cautious when generalizing these findings to populations of a low socioeconomic status.
Journal Article
Mitigating sub-synchronous oscillation using intelligent damping control of DFIG based on improved TD3 algorithm with knowledge fusion
The occurrence of sub-synchronous oscillation (SSO) phenomenon in doubly-fed induction generators (DFIGs)-based wind turbines threatens the secure and stable operation of the power grid. Conventional sub-synchronous damping controllers encounter challenges in adapting to the dynamic operating conditions of power systems. This paper introduces an Intelligent Sub-Synchronous Damping Controller (I-SSDC) for DFIGs that integrates deep reinforcement learning (DRL) and knowledge to address the limitations of conventional methods for SSO mitigation. The initial step involves formulating a framework for I-SSDC using the improved twin delayed deep deterministic policy gradient (TD3) algorithm incorporating Softmax. Following this, a surrogate model is constructed, employing Weighted Linear Regression and regularization. This model is designed to identify the predominant influencing factors of SSO, focusing on the selection of the output signal (installation position) to optimize decision-making in I-SSDC. The objective is to enhance the controller’s environmental adaptability and interpretability. Moreover, knowledge and experience related to SSOs are integrated into agent training to improve the exploration efficiency of the agent. Case studies under various operating conditions of the test power system validate the efficacy of the proposed I-SSDC in suppressing SSOs.
Journal Article
A two-stage subsynchronous oscillation assessment method for DFIG-based wind farm grid-connected system
In the power system, the wind farm based on Doubly-Fed Induction Generator (DFIG) may lead to Subsynchronous Oscillation (SSO), which poses a challenge to the stability of the power grid. In order to accurately evaluate SSO, this paper proposes a new evaluation method. It is divided into two main stages: firstly, the interference level of Phasor Measurement Unit (PMU) data is identified by using the classification model based on Upper Confidence Bound (UCB) and Double Deep Q Network (DDQN). Then, an SSO parameter estimation model based on Local Feature Fusion Transformer (LFF-Transformer) network is designed for data with different interference levels. Experimental results show that the errors of eRMSE-f and EMAPE-F are 0.001 and 0.003 respectively, and the errors of eRMSE-δ and EMAPE-δ are 0.009 and 0.015 respectively. In terms of training and testing time, this method is 90 s and 18 s respectively, which is significantly better than Multi-SVR and Multi-CNN. After application, the frequency deviation decreased from 0.05 to 0.02 Hz, the voltage deviation decreased from 3.5 to 1.5%, the power fluctuation decreased from 10 to 5 MW, the SSO frequency decreased from 1.5 Hz to less than 0.5 Hz, and the SSO damping ratio increased from 0.08 to 0.15. This shows that the proposed method effectively increases the stability of the power grid.
Journal Article
Manganese-mediated reductive functionalization of activated aliphatic acids and primary amines
Alkyl carboxylic acids as well as primary amines are ubiquitous in all facets of biological science, pharmaceutical science, chemical science and materials science. By chemical conversion to redox-active esters (RAE) and Katritzky’s
N
-alkylpyridinium salts, respectively, alkyl carboxylic acids and primary amines serve as ideal starting materials to forge new connections. In this work, a Mn-mediated reductive decarboxylative/deaminative functionalization of activated aliphatic acids and primary amines is disclosed. A series of C-X (X = S, Se, Te, H, P) and C-C bonds are efficiently constructed under simple and mild reaction conditions. The protocol is applicable to the late-stage modification of some structurally complex natural products or drugs. Preliminary mechanistic studies suggest the involvement of radicals in the reaction pathway.
Alkyl carboxylic acids and primary amines are ubiquitous and useful for synthesis of new compounds. Here, the authors report a manganese-mediated reductive decarboxylative/deaminative functionalization of activated aliphatic acids and primary amines for construction of C-C and C-X bonds under mild conditions.
Journal Article
Regional disparities and influencing factors of high quality medical resources distribution in China
Background
With the gradual increase of residents’ income and the continuous improvement of medical security system, people’s demand for pursuing higher quality and better medical and health services has been released. However, so far little research has been published on China's high quality medical resources (HQMR). This study aims to understand the spatiotemporal variation trend of HQMR from 2006 to 2020, analyze regional disparity of HQMR in 2020, and further explore the main factors influencing the distribution of HQMR in China.
Methods
The study selected Class III level A hospitals (the highest level medical institutions in China) to represent HQMR. Descriptive statistical methods were used to address the changes in the distribution of HQMR from 2006 to 2020. Lorentz curve, Gini coefficient (G), Theil index (T) and High-quality health resource density index (HHRDI) were used to calculate the degree of inequity. The geographical detector method was used to reveal the key factors influencing the distribution of HQMR.
Results
The total amount of HQMR in China had increased year by year, from 647 Class III level A hospitals in 2006 to 1580 in 2020. In 2020, G for HQMR by population was 0.166, while by geographic area was 0.614. T was consistent with the results for G, and intra-regional contribution rates were higher than inter-regional contribution rates. HHRDI showed that Beijing, Shanghai, and Tianjin had the highest allocated amounts of HQMR. The results of the geographical detector showed that total health costs, government health expenditure, size of resident populations, GDP, number of medical colleges had a significant impact on the spatial distribution of HQMR and the q values were 0.813, 0.781, 0.719, 0.661, 0.492 respectively. There was an interaction between the influencing factors.
Conclusions
China's total HQMR is growing rapidly but is relatively inadequate. The distribution of HQMR by population is better than by geography, and the distribution by geography is less equitable. Population size and geographical area both need to be taken into account when formulating policies, rather than simply increasing the number of HQMR.
Journal Article
Controllable all-fiber generation/conversion of circularly polarized orbital angular momentum beams using long period fiber gratings
Mode-division multiplexing (MDM) is a promising technology for increasing the data-carrying capacity of a single few-mode optical fiber. The flexible mode manipulation would be highly desired in a robust MDM network. Recently, orbital angular momentum (OAM) modes have received wide attention as a new spatial mode basis. In this paper, we firstly proposed a long period fiber grating (LPFG) system to realize mode conversions between the higher order LP core modes in four-mode fiber. Based on the proposed system, we, for the first time, demonstrate the controllable all-fiber generation and conversion of the higher order LP core modes to the first and second order circularly polarized OAM beams with all the combinations of spin and OAM. Therefore, the proposed LPFG system can be potentially used as a controllable higher order OAM beam switch and a physical layer of the translating protocol from the conventional LP modes communication to the OAM modes communication in the future mode carrier telecommunication system and light calculation protocols.
Journal Article
Superparamagnetic iron oxide nanoparticles conjugated with Aβ oligomer-specific scFv antibody and class A scavenger receptor activator show therapeutic potentials for Alzheimer's Disease
Alzheimer's disease (AD) is a progressive neurodegenerative disorder. No disease-modifying strategy to prevent or delay AD progression currently exists. Aβ oligomers (AβOs), rather than monomers or fibrils, are considered as the primary neurotoxic species. Therapeutic approaches that direct against AβOs and promote Aβ clearance may have great value for AD treatment.
We here reported a multifunctional superparamagnetic iron oxide nanoparticle conjugated with Aβ oligomer-specific scFv antibody W20 and class A scavenger receptor activator XD4 (W20/XD4-SPIONs). Besides the diagnostic value, W20/XD4-SPIONs retained the anti-Aβ properties of W20 and XD4 by inhibiting Aβ aggregation, attenuating AβO-induced cytotoxicity and increasing microglial phagocytosis of Aβ. When applied to APP/PS1 mice, W20/XD4-SPIONs significantly rescued cognitive deficits and alleviated neuropathology of AD mice.
These results suggest that W20/XD4-SPIONs show therapeutic benefits for AD. In combination with the early diagnostic property, W20/XD4-SPIONs present as a promising agent for early-stage AD diagnosis and intervention.
Journal Article
Mesenchymal Stem Cell Migration and Tissue Repair
Mesenchymal stem cells (MSCs) are multilineage cells with the ability to self-renew and differentiate into a variety of cell types, which play key roles in tissue healing and regenerative medicine. Bone marrow-derived mesenchymal stem cells (BMSCs) are the most frequently used stem cells in cell therapy and tissue engineering. However, it is prerequisite for BMSCs to mobilize from bone marrow and migrate into injured tissues during the healing process, through peripheral circulation. The migration of BMSCs is regulated by mechanical and chemical factors in this trafficking process. In this paper, we review the effects of several main regulatory factors on BMSC migration and its underlying mechanism; discuss two critical roles of BMSCs—namely, directed differentiation and the paracrine function—in tissue repair; and provide insight into the relationship between BMSC migration and tissue repair, which may provide a better guide for clinical applications in tissue repair through the efficient regulation of BMSC migration.
Journal Article