Reliability Evaluation of a Distribution Network with Microgrid Based on a Combined Power Generation System

Distributed generation (DG), battery storage (BS) and electric vehicles (EVs) in a microgrid constitute the combined power generation system (CPGS). A CPGS can be applied to achieve a reliable evaluation of a distribution network with microgrids. To model charging load and discharging capacity, respectively, the EVs in a CPGS can be divided into regular EVs and ruleless EVs, according to their driving behavior. Based on statistical data of gasoline-fueled vehicles and the probability distribution of charging start instant and charging time, a statistical model can be built to describe the charging load and discharging capacity of ruleless EVs. The charge and discharge curves of regular EVs can also be drawn on the basis of a daily dispatch table. The CPGS takes the charge and discharge curves of EVs, daily load and DG power generation into consideration to calculate its power supply time during islanding. Combined with fault duration, the power supply time during islanding will be used to analyze and determine the interruption times and interruption duration of loads in islands. Then the Sequential Monte Carlo method is applied to complete the reliability evaluation of the distribution system. The RBTS Bus 4 test system is utilized to illustrate the proposed technique. The effects on the system reliability of BS capacity and V2G technology, driving behavior, recharging mode and penetration of EVs are all investigated

Optimal Dispatch Strategy of a Virtual Power Plant Containing Battery Switch Stations in a Unified Electricity Market

A virtual power plant takes advantage of interactive communication and energy management systems to optimize and coordinate the dispatch of distributed generation, interruptible loads, energy storage systems and battery switch stations, so as to integrate them as an entity to exchange energy with the power market. This paper studies the optimal dispatch strategy of a virtual power plant, based on a unified electricity market combining day-ahead trading with real-time trading. The operation models of interruptible loads, energy storage systems and battery switch stations are specifically described in the paper. The virtual power plant applies an optimal dispatch strategy to earn the maximal expected profit under some fluctuating parameters, including market price, retail price and load demand. The presented model is a nonlinear mixed-integer programming with inter-temporal constraints and is solved by the fruit fly algorithm

MDFF: A Method for Fine-Grained UFZ Mapping With Multimodal Geographic Data and Deep Network

As basic units of urban areas, urban functional zones (UFZs) are fundamental to urban planning, management, and renewal. UFZs are mainly determined by human activities, economic behaviors, and geographical factors, but existing methods 1) do not fully use multimodal geographic data owing to a lack of semantic modeling and feature fusion of geographic objects and 2) are composed of multiple stages, which lead to the accumulation of errors through multiple stages and increase the mapping complexity. Accordingly, this study designs a multimodal data fusion framework (MDFF) to map fine-grained UFZs end-to-end, which effectively integrates very-high-resolution remote sensing images and social sensing data. The MDFF extracts physical attributes from remote sensing images and models socioeconomic semantics of geographic objects from social sensing data, and then fuses multimodal information to classify UFZs where object semantics guide the fine-grained classification. Experimental results in Beijing and Shanghai, two major cities of China, show that the MDFF greatly improves the quality of UFZ mapping with the accuracy about 5% higher than state-of-the-art methods. The proposed method significantly reduces the complexity of UFZ mapping to complete the urban structure analysis conveniently

Long non-coding RNA LINC01215 promotes epithelial-mesenchymal transition and lymph node metastasis in epithelial ovarian cancer through RUNX3 promoter methylation

Epithelial ovarian cancer (EOC) still remains the most lethal gynaecological malignancy in women, despite the recent progress in the management, including surgery and chemotherapy. According to the microarray data of the GSE18520 and GSE54388 datasets, LINC01215 was identified as an upregulated long noncoding RNA (lncRNA) in EOC. Therefore, this study aimed to figure out the involvement of LINC01215 in the progression of EOC. RT-qPCR was conducted to select the EOC cell line with the highest expression of LINC01215. Methylation of RUNX3 was then examined in EOC cells by MS-PCR. Furthermore, the interaction between LINC01215 and methylation-related proteins was revealed according to the results of RIP and RNA pull down assays. Subsequently, the involvement of LINC01215 and RUNX3 in regulating biological behaviors of EOC cells was investigated. Finally, the effects of the ectopic expression of LINC01215 and RUNX3 on the tumor formation and lymph node metastasis (LNM) of EOC cells were assessed in the xenograft tumors of nude mice. Overexpressing LINC01215 contributed to downregulated levels of RUNX3, as demonstrated by the recruitment of methylation-related proteins. Silencing of LINC01215 elevated the expression of RUNX3, thus suppressing cell proliferation, migration, invasion and EMT and decreasing the expressions of MMP-2, MMP-9 and Vimentin, but increased the expression of E-cadherin. The tumor growth and LNM were suppressed by downregulated levels of LINC01215 through inducing the expression of RUNX3. Collectively, the down-regulating LINC01215 could upregulate the expression of RUNX3 by promoting its methylation, thus suppressing EOC cell proliferation, migration and invasion, EMT, tumor growth and LNM

Water extraction from optical high-resolution remote sensing imagery: a multi-scale feature extraction network with contrastive learning

Accurately spatiotemporal distribution of water bodies is of great importance in the fields of ecology and environment. Recently, convolutional neural networks (CNN) have been widely used for this purpose due to their powerful features extraction ability. However, the CNN methods have two limitations in extracting water bodies. First, the large variations in both the spatial and spectral characteristics of water bodies require that the CNN-based methods have the ability of extracting multi-scale features and using multi-layer features. Second, collecting enough samples is a difficult problem in the training phase of CNN. Therefore, this paper proposed a multi-scale features extraction network (MSFENet) for water extraction, and its advantages are contributed to two distinct features: (1) scale features extractor (MSFE) is designed to extract multi-layer multi-scale features of water bodies; (2) contrastive learning (CL) is adopted to reduce the sample size requirement. Experimental results show that MSFE can effectively improve the small water body extraction performance, and the CL can significantly improve the extraction accuracy when the training sample size is small. Compared with other methods, MSFENet achieves the highest F1-score and kappa coefficient in two datasets. Furthermore, spectral variability analysis shows that MSFENet is more robust than other neural networks in a spectrum variation scenario

Microstructure and Lamellae Phase of Raw Natural Rubber via Spontaneous Coagulation Assisted by Sugars

Natural rubber (NR) as a renewable biopolymer is often produced by acid coagulation of fresh natural latex collected from Hevea brasiliensis. However, this traditional process is facing a huge economic and environmental challenge. Compared with the acid coagulation, spontaneous or microorganism coagulation is an ecofriendly way to obtain NR with excellent performance. To clarify the influence of different sugars on NR quality, several sugars were used to assist the coagulation process. Influence of different sugars on microstructure and cold crystallization were examined by 1H NMR, DSC, etc. The results indicated that sugars exhibit different biological activity on terminal components of fresh field latex and can influence the resultant molecular structure and basic properties. Brown sugar exhibits higher metabolic activity and is inclined to decompose the protein and phospholipids crosslinking compared with other sugars. The larger molecular weight of sugar molecule is beneficial for the formation of the stable α lamellae phase and higher overall degree of crystallization

Uniform Loading of Nickel Phosphide Nanoparticles in Hierarchical Carbonized Wood Channel for Efficient Electrocatalytic Hydrogen Evolution

The development of self-supporting high-efficiency catalysts is a major challenge for the efficient production of H2 via water splitting. In this manuscript, a freestanding Ni2P-Ni12P5/carbonized wood (CW) composite electrode was prepared by a simple hydrothermal method and high-temperature calcination using pine wood with uniform channel as support and a large number of hydroxyl groups as nucleation center. The morphology and structural characteristics indicated that the Ni2P and Ni12P5 nanoparticles were uniformly distributed within the hierarchical porous structure of the CW. In acid media, the as-prepared Ni2P-Ni12P5/CW exhibits an excellent catalytic activity with a low overpotential of 151 mV at 10 mA cm−2 and a reasonably good long-term stability

Geographic mapping with unsupervised multi-modal representation learning from VHR images and POIs

Most supervised geographic mapping methods with very-high-resolution (VHR) images are designed for a specific task, leading to high label-dependency and inadequate task-generality. Additionally, the lack of socio-economic information in VHR images limits their applicability to social/human-related geographic studies. To resolve these two issues, we propose an unsupervised multi-modal geographic representation learning framework (MMGR) using both VHR images and points-of-interest (POIs), to learn representations (regional vector embeddings) carrying both the physical and socio-economic properties of the geographies. In MMGR, we employ an intra-modal and an inter-modal contrastive learning module, in which the former deeply mines visual features by contrasting different VHR image augmentations, while the latter fuses physical and socio-economic features by contrasting VHR image and POI features. Extensive experiments are performed in two study areas (Shanghai and Wuhan in China) and three relevant while distinctive geographic mapping tasks (i.e., mapping urban functional distributions, population density, and gross domestic product), to verify the superiority of MMGR. The results demonstrate that the proposed MMGR considerably outperforms seven competitive baselines in all three tasks, which indicates its effectiveness in fusing VHR images and POIs for multiple geographic mapping tasks. Furthermore, MMGR is a competent pre-training method to help image encoders understand multi-modal geographic information, and it can be further strengthened by fine-tuning even with a few labeled samples. The source code is released at https://github.com/bailubin/MMGR.National Research Foundation (NRF)The work presented in this paper is supported by the International Research Center of Big Data for Sustainable Development Goals (No. CBAS2022GSP06), the National Natural Science Foundation of China (No. 42001327, 42271469), the China Postdoctoral Science Foundation (No. 2019M660003 and No. 2020T130005), the National Key Research and Development Program of China (No. 2021YFE0117100), the Knut and Alice Wallenberg Foundation (to W.H.), and the National Research Foundation, Singapore under its Industry Alignment Fund – Prepositioning (IAF-PP) Funding Initiative