Inverter System: A Solution to Improve the Efficiency of New Energy Generation in Factories

The report mainly analyzes whether the inverter system could improve the efficiency of converting new energy into factory electricity based on McLuhan's laws of media theory. Firstly, the report asserts the significance of using new energy and the importance of utilizing the inverter system to improve the power conversion of new energy in factories. Secondly, it mainly describes McLuhan's theory from four different aspects. In addition, according to the four aspects of McLuhan's theory, the rationality and feasibility of the inverter system solution are analyzed. Then, it is concluded that the inverter system can well improve the conversion efficiency of new energy generation in factories. Finally, this paper claims suggestions from two different perspectives to promote the development of the inverter system

Vector Control of Three-Phase Solar Farm Converters Based on Fictive-Axis Emulation

In this paper, a new method for adjusting the current of three-phase voltage source DC-AC converter in orthogonal (DQ) reference frame is presented. In the DQ reference system, AC variable appears in the constant form of DC, making the controller design the same as the DC-DC converter [1]. It provides controllable gain benefits at the steady-state operating point, and finally realizes zero steady-state error [2]. In addition, the creative analytical model is dedicated to building up a series of virtual quantities orthogonal to the actual single-phase system. In general, orthogonal imaginary numbers get the reference signal by delaying the real quantity by a quarter period. However, the introduction of such time delay makes the dynamic response of the system worse. In this paper, orthogonal quantities are generated from a virtual axis system parallel to the real axis, which can effectively improve the dynamic performance of traditional methods without increasing the complexity of controller structure. Through PSCAD simulation, the ideal experimental results are obtained

Analyzing the Nonlinear System by Designing an Optimum Digital Filter named Hermitian-Wiener Filter

The classical Wiener filter was engaged into identifying the linear structures, resulting in clear and incredible drawbacks in working with nonlinear integrated system. Currently, the Hermitian-Wiener system are suitable for unpredicted sub-system that consists of numerous and complex inputs. The system introduces a two-stage to analyze the subintervals where the output nonlinearities are noninvertible, through using the unknown orders and parameters. Finally, a practical strategy would be discussed to analyze the nonlinear parameters

Application of LSTM and CONV1D LSTM Network in Stock Forecasting Model

Predicting the direction of the stock market has always been a huge challenge. Also, the way of forecasting the stock market reduces the risk in the financial market, thus ensuring that brokers can make normal returns. Despite the complexities of the stock market, the challenge has been increasingly addressed by experts in a variety of disciplines, including economics, statistics, and computer science. The introduction of machine learning, in-depth understanding of the prospects of the financial market, thus doing many experiments to predict the future so that the stock price trend has different degrees of success. In this paper, we propose a method to predict stocks from different industries and markets, as well as trend prediction using traditional machine learning algorithms such as linear regression, polynomial regression and learning techniques in time series prediction using two forms of special types of recursive neural networks: long and short time memory (LSTM) and spoken short-term memory

Effect of Amorphization Methods on the Properties and Structures of Potato Starch-Monoglyceride Complex

Recently, starch-based fat replacers (FRs) have emerged as unique ingredients, possessing few calories and high vascular scavenger function without adverse organoleptic changes. Here, a two-step modification method for the development of a starch-based FRs is reported. First, native potato starch is amorphized by grinding, alkali and ethanol treatment. Then, the amorphized starch is complexed with monoglyceride. The results show that alkaline amorphous potato starch (AAPS) has the best emulsifying activity; ethanol amorphous potato starch complex (EAPSC) has the highest content of resistant starch (RS) (21.49%), while grinding amorphous potato starch (GAPS) retains the granular structure of the original starch best. The amorphization reduces the amylose content of starch, leading to reduced swelling power and increased digestibility. Complexation, on the other hand, is more like attaching a layer of the hydrophobic membrane. Combined with DSC and XRD, amorphization reduces the value of enthalpy and crystallinity, while the complexation process does the opposite. Overall, EAPSC is the best candidate for novel FRs, due to its greater emulsion stability and enzyme resistance. The experimental results provide a theoretical basis for the application of a novel potato starch-monoglyceride complex in foods such as cakes and snack fillings

Changes of Riverbeds and Water-carrying Capacity of the Yellow River Inner Mongolia Section

This paper introduced the evolution of the section from Bayangaole to Toudaoguai in the Yellow River and analysed the factors influencing erosion, deposition, and water-carrying capacity of the section over years. Through data obtained from observation in the Bayangaole station, Sanhuhekou station, Zhaojunfen station, and Toudaoguai station, after analysis, it has been got that the riverbeds observed at these stations have been silted up over time, and the water-carrying capacity has been reducing. Besides, the construction of reservoirs or power stations may accelerate this trend

Exosomes in pathogenesis, diagnosis, and therapy of ischemic stroke

Ischemic stroke is one of the major contributors to death and disability worldwide. Thus, there is an urgent need to develop early brain tissue perfusion therapies following acute stroke and to enhance functional recovery in stroke survivors. The morbidity, therapy, and recovery processes are highly orchestrated interactions involving the brain with other tissues. Exosomes are natural and ideal mediators of intercellular information transfer and recognized as biomarkers for disease diagnosis and prognosis. Changes in exosome contents express throughout the physiological process. Accumulating evidence demonstrates the use of exosomes in exploring unknown cellular and molecular mechanisms of intercellular communication and organ homeostasis and indicates their potential role in ischemic stroke. Inspired by the unique properties of exosomes, this review focuses on the communication, diagnosis, and therapeutic role of various derived exosomes, and their development and challenges for the treatment of cerebral ischemic stroke

Single-trial phase entrainment of theta oscillations in sensory regions predicts human associative memory performance

Episodic memories are rich in sensory information and often contain integrated information from different sensory modalities. For instance, we can store memories of a recent concert with visual and auditory impressions being integrated in one episode. Theta oscillations have recently been implicated in playing a causal role synchronizing and effectively binding the different modalities together in memory. However, an open question is whether momentary fluctuations in theta synchronization predict the likelihood of associative memory formation for multisensory events. To address this question we entrained the visual and auditory cortex at theta frequency (4 Hz) and in a synchronous or asynchronous manner by modulating the luminance and volume of movies and sounds at 4 Hz, with a phase offset at 0° or 180°. EEG activity from human subjects (both sexes) was recorded while they memorized the association between a movie and a sound. Associative memory performance was significantly enhanced in the 0° compared with the 180° condition. Source-level analysis demonstrated that the physical stimuli effectively entrained their respective cortical areas with a corresponding phase offset. The findings suggested a successful replication of a previous study (Clouter et al., 2017). Importantly, the strength of entrainment during encoding correlated with the efficacy of associative memory such that small phase differences between visual and auditory cortex predicted a high likelihood of correct retrieval in a later recall test. These findings suggest that theta oscillations serve a specific function in the episodic memory system: binding the contents of different modalities into coherent memory episodes

OpenGSL: A Comprehensive Benchmark for Graph Structure Learning

Graph Neural Networks (GNNs) have emerged as the de facto standard for representation learning on graphs, owing to their ability to effectively integrate graph topology and node attributes. However, the inherent suboptimal nature of node connections, resulting from the complex and contingent formation process of graphs, presents significant challenges in modeling them effectively. To tackle this issue, Graph Structure Learning (GSL), a family of data-centric learning approaches, has garnered substantial attention in recent years. The core concept behind GSL is to jointly optimize the graph structure and the corresponding GNN models. Despite the proposal of numerous GSL methods, the progress in this field remains unclear due to inconsistent experimental protocols, including variations in datasets, data processing techniques, and splitting strategies. In this paper, we introduce OpenGSL, the first comprehensive benchmark for GSL, aimed at addressing this gap. OpenGSL enables a fair comparison among state-of-the-art GSL methods by evaluating them across various popular datasets using uniform data processing and splitting strategies. Through extensive experiments, we observe that existing GSL methods do not consistently outperform vanilla GNN counterparts. However, we do observe that the learned graph structure demonstrates a strong generalization ability across different GNN backbones, despite its high computational and space requirements. We hope that our open-sourced library will facilitate rapid and equitable evaluation and inspire further innovative research in the field of GSL. The code of the benchmark can be found in https://github.com/OpenGSL/OpenGSL.Comment: 9 pages, 4 figure