Vision Transformer-Based Photovoltaic Prediction Model

Data Availability Statement: The data presented in this study are available upon request from the corresponding authors.Copyright © 2023 by the authors. Sensing the cloud movement information has always been a difficult problem in photovoltaic (PV) prediction. The information used by current PV prediction methods makes it challenging to accurately perceive cloud movements. The obstruction of the sun by clouds will lead to a significant decrease in actual PV power generation. The PV prediction network model cannot respond in time, resulting in a significant decrease in prediction accuracy. In order to overcome this problem, this paper develops a visual transformer model for PV prediction, in which the target PV sensor information and the surrounding PV sensor auxiliary information are used as input data. By using the auxiliary information of the surrounding PV sensors and the spatial location information, our model can sense the movement of the cloud in advance. The experimental results confirm the effectiveness and superiority of our model

Distributed Generation Forecasting Based on Rolling Graph Neural Network (ROLL-GNN)

Data Availability Statement: The data presented in this study are available on request from the corresponding author.Copyright © 2023 by the authors. The future power grid will have more distributed energy sources, and the widespread access of distributed energy sources has the potential to improve the energy efficiency, resilience, and sustainability of the system. However, distributed energy, mainly wind power generation and photovoltaic power generation, has the characteristics of intermittency and strong randomness, which will bring challenges to the safe operation of the power grid. Accurate prediction of solar power generation with high spatial and temporal resolution is very important for the normal operation of the power grid. In order to improve the accuracy of distributed photovoltaic power generation prediction, this paper proposes a new distributed photovoltaic power generation prediction model: ROLL-GNN, which is defined as a prediction model based on rolling prediction of the graph neural network. The ROLL-GNN uses the perspective of graph signal processing to model distributed generation production timeseries data as signals on graphs. In the model, the similarity of data is used to capture their spatio-temporal dependencies to achieve improved prediction accuracy.Guangdong Basic and Applied Basic Research Foundation (2021A1515010742, 2020A1515011160, 2020A1515010801); National Natural Science Foundation of China (52007032); Basic Research Program of Jiangsu Province (BK20200385)

A dynamical model reveals gene co-localizations in nucleus

Co-localization of networks of genes in the nucleus is thought to play an important role in determining gene expression patterns. Based upon experimental data, we built a dynamical model to test whether pure diffusion could account for the observed co-localization of genes within a defined subnuclear region. A simple standard Brownian motion model in two and three dimensions shows that preferential co-localization is possible for co-regulated genes without any direct interaction, and suggests the occurrence may be due to a limitation in the number of available transcription factors. Experimental data of chromatin movements demonstrates that fractional rather than standard Brownian motion is more appropriate to model gene mobilizations, and we tested our dynamical model against recent static experimental data, using a sub-diffusion process by which the genes tend to colocalize more easily. Moreover, in order to compare our model with recently obtained experimental data, we studied the association level between genes and factors, and presented data supporting the validation of this dynamic model. As further applications of our model, we applied it to test against more biological observations. We found that increasing transcription factor number, rather than factory number and nucleus size, might be the reason for decreasing gene co-localization. In the scenario of frequency-or amplitude-modulation of transcription factors, our model predicted that frequency-modulation may increase the co-localization between its targeted genes

On Arrangements of Orthogonal Circles

In this paper, we study arrangements of orthogonal circles, that is, arrangements of circles where every pair of circles must either be disjoint or intersect at a right angle. Using geometric arguments, we show that such arrangements have only a linear number of faces. This implies that orthogonal circle intersection graphs have only a linear number of edges. When we restrict ourselves to orthogonal unit circles, the resulting class of intersection graphs is a subclass of penny graphs (that is, contact graphs of unit circles). We show that, similarly to penny graphs, it is NP-hard to recognize orthogonal unit circle intersection graphs.Comment: Appears in the Proceedings of the 27th International Symposium on Graph Drawing and Network Visualization (GD 2019

In an in vitro model of human tuberculosis, monocyte-microglial networks regulate matrix metalloproteinase-1 and -3 gene expression and secretion via a p38 mitogen activated protein kinase-dependent pathway.

BACKGROUND: Tuberculosis (TB) of the central nervous system (CNS) is characterized by extensive tissue inflammation, driven by molecules that cleave extracellular matrix such as matrix metalloproteinase (MMP)-1 and MMP-3. However, relatively little is known about the regulation of these MMPs in the CNS. METHODS: Using a cellular model of CNS TB, we stimulated a human microglial cell line (CHME3) with conditioned medium from Mycobacterium tuberculosis-infected primary human monocytes (CoMTb). MMP-1 and MMP-3 secretion was detected using ELISAs confirmed with casein zymography or western blotting. Key results of a phospho-array profile that detects a wide range of kinase activity were confirmed with phospho-Western blotting. Chemical inhibition (SB203580) of microglial cells allowed investigation of expression and secretion of MMP-1 and MMP-3. Finally we used promoter reporter assays employing full length and MMP-3 promoter deletion constructs. Student's t-test was used for comparison of continuous variables and multiple intervention experiments were compared by one-way ANOVA with Tukey's correction for multiple pairwise comparisons. RESULTS: CoMTb up-regulated microglial MMP-1 and MMP-3 secretion in a dose- and time-dependent manner. The phospho-array profiling showed that the major increase in kinase activity due to CoMTb stimulation was in p38 mitogen activated protein kinase (MAPK), principally the α and γ subunits. p38 phosphorylation was detected at 15 minutes, with a second peak of activity at 120 minutes. High basal extracellular signal-regulated kinase activity was further increased by CoMTb. Secretion and expression of MMP-1 and MMP-3 were both p38 dependent. CoMTb stimulation of full length and MMP-3 promoter deletion constructs demonstrated up-regulation of activity in the wild type but a suppression site between -2183 and -1612 bp. CONCLUSIONS: Monocyte-microglial network-dependent MMP-1 and MMP-3 gene expression and secretion are dependent upon p38 MAPK in tuberculosis. p38 is therefore a potential target for adjuvant therapy in CNS TB

Ptch2/Gas1 and Ptch1/Boc differentially regulate Hedgehog signalling in murine primordial germ cell migration.

Gas1 and Boc/Cdon act as co-receptors in the vertebrate Hedgehog signalling pathway, but the nature of their interaction with the primary Ptch1/2 receptors remains unclear. Here we demonstrate, using primordial germ cell migration in mouse as a developmental model, that specific hetero-complexes of Ptch2/Gas1 and Ptch1/Boc mediate the process of Smo de-repression with different kinetics, through distinct modes of Hedgehog ligand reception. Moreover, Ptch2-mediated Hedgehog signalling induces the phosphorylation of Creb and Src proteins in parallel to Gli induction, identifying a previously unknown Ptch2-specific signal pathway. We propose that although Ptch1 and Ptch2 functionally overlap in the sequestration of Smo, the spatiotemporal expression of Boc and Gas1 may determine the outcome of Hedgehog signalling through compartmentalisation and modulation of Smo-downstream signalling. Our study identifies the existence of a divergent Hedgehog signal pathway mediated by Ptch2 and provides a mechanism for differential interpretation of Hedgehog signalling in the germ cell niche