With the advancement of technology and human movement towards evolution, intelligent control methods are becoming more important. One of the areas of progress is related to the development of new ...frameworks for electricity generation and distribution systems, and the microgrid structure with economic capabilities is one of the frameworks. Accordingly, this paper presents a new practical method for controlling the frequency of microgrids and is able to cover the following issues at the same time. 1- It considers the nonlinear model of provisional microgrid which has a hybrid structure (AC and DC) in addition to renewable energy sources. 2- Introduces a method for microgrid frequency control under different operational conditions that performs based on the brain emotional learning. 3- Ensures the operation and applicability of the control method for the provisional microgrid through implementation of FPGA for the first time 4- Confirms the robustness of the proposed method under extreme load changes. So, in the simulation scenarios, the effects of wind turbine and solar energy are considered and under the influence of various changes in load and system uncertainties, the robustness and efficiency of the proposed method are well demonstrated.
Urban flooding is a devastating natural hazard for cities around the world. Flood risk mapping is a key tool in flood management. However, it is computationally expensive to produce flood risk maps ...using hydrodynamic models. To this end, this paper investigates the use of machine learning for the assessment of surface water flood risks in urban areas. The factors that are considered in machine learning models include coordinates, elevation, slope gradient, imperviousness, land use, land cover, soil type, substrate, distance to river, distance to road, and normalized difference vegetation index. The machine learning models are tested using the case study of Exeter, UK. The performance of machine learning algorithms, including naïve Bayes, perceptron, artificial neural networks (ANNs), and convolutional neural networks (CNNs), is compared based on a spectrum of indicators, e.g., accuracy, F-beta score, and receiver operating characteristic curve. The results obtained from the case study show that the flood risk maps can be accurately generated by the machine learning models. The performance of models on the 30-year flood event is better than 100-year and 1000-year flood events. The CNNs and ANNs outperform the other machine learning algorithms tested. This study shows that machine learning can help provide rapid flood mapping, and contribute to urban flood risk assessment and management.
Ferroptosis, a novel form of regulating cell death, is related to various cancers. However, the role of ferroptosis-related genes (FRGs) on the occurrence and development of colon cancer (CC) needs ...to be further elucidated.
CC transcriptomic and clinical data were downloaded from TCGA and GEO databases. The FRGs were obtained from the FerrDb database. The consensus clustering was performed to identify the best clusters. Then, the entire cohort was randomly divided into the training and testing cohorts. Univariate Cox, LASSO regression and multivariate Cox analyses were used to construct a novel risk model in training cohort. The testing and the merged cohorts were performed to validate the model. Moreover, CIBERSORT algorithm analyze TIME between high- and low- risk groups. The immunotherapy effect was evaluated by analyzing the TIDE score and IPS between high- and low- risk groups. Lastly, RT-qPCR were performed to analyze the expression of the three prognostic genes, and the 2-years OS and DFS between the high- and low- risk groups of 43 clinical CC samples to further validate the value of the risk model.
SLC2A3, CDKN2A, and FABP4 were identified to construct a prognostic signature. Kaplan-Meier survival curves showed that OS between the high- and low-risk groups were statistically significant (p
<0.001, p
<0.001, p
<0.001). TIDE score and IPS were higher in the high-risk group (p
<0.005, p
<0.005, p
<0.001, p
= 3e-08, p
= 4.1e-10). The clinical samples were divided into high- and low- risk groups according to the risk score. There was a statistical difference in DFS (p=0.0108).
This study established a novel prognostic signature and provided more insight into the immunotherapy effect of CC.
Entity linking in knowledge-based question answering (KBQA) is intended to construct a mapping relation between a mention in a natural language question and an entity in the knowledge base. Most ...research in entity linking focuses on long text, but entity linking in open domain KBQA is more concerned with short text. Many recent models have tried to extract the features of raw data by adjusting the neural network structure. However, the models only perform well with several datasets. We therefore concentrate on the data rather than the model itself and created a model DME (Domain information Mining and Explicit expressing) to extract domain information from short text and append it to the data. The entity linking model will be enhanced by training with DME-processed data. Besides, we also developed a novel negative sampling approach to make the model more robust. We conducted experiments using the large Chinese open source benchmark KgCLUE to assess model performance with DME-processed data. The experiments showed that our approach can improve entity linking in the baseline models without the need to change their structure and our approach is demonstrably transferable to other datasets.
In this article, self-adjusted Multi-sensor Information Fusion measuring method of electric energy based on neural networks has been thoroughly given. This paper studies the method of automatic error ...correction of electric power measurement also. The effective learning algorithm of the neural network based on gradient algorithm and Newton algorithm is combined with the LEA discriminant method.The results show that the method can improve the learning efficiency. The hardware model of adaptive real-time fast power measurement is constructed by using DSP device. The experimental results show that the adaptive power measurement model is better than the traditional power meter.
Laparoscopic surgery (LS) requires CO
insufflation to establish the operative field. Patients with worsening pain post-operatively often undergo computed tomography (CT). CT is highly sensitive in ...detecting free air-the hallmark sign of a bowel injury. Yet, the clinical significance of free air is often confounded by residual CO
and is not usually due to a visceral injury. The aim of this study was to attempt to quantify the residual pneumoperitoneum (RPP) after a robotic-assisted laparoscopic prostatectomy (RALP).
We prospectively enrolled patients who underwent RALP between August 2018 and January 2020. CT scans were performed on postoperative days (POD) 3, 5, and 7. To investigate potential factors influencing the quantity of RPP, correlation plots were made against common variables.
In total, 31 patients with a mean age of 66 years (median 67, IQR 62-70.5) and mean BMI 26.59 (median 25.99, IQR: 24.06-29.24) underwent RALP during the study period. All patients had a relatively unremarkable post-operative course (30/31 with Clavien-Dindo class 0; 1/31 with class 2). After 3, 5, and 7 days, 3.2%, 6.4%, and 32.3% were completely without RPP, respectively. The mean RPP at 3 days was 37.6 mL (median 9.58 mL, max 247 mL, IQR 3.92-31.82 mL), whereas the mean RPP at 5 days was 19.85 mL (median 1.36 mL, max 220.77 mL, IQR 0.19-5.61 mL), and 7 days was 10.08 mL (median 0.09 mL, max 112.42 mL, IQR 0-1.5 mL). There was a significant correlation between RPP and obesity (
= 0.04665), in which higher BMIs resulted in lower initial insufflation volumes and lower RPP.
This is the first study to systematically assess RPP after a standardized laparoscopic procedure using CT. Larger patients tend to have smaller residuals. Our data may help surgeons interpreting post-operative CTs in similar patient populations.
•Structure establishing of EHP.•System modeling and simulating.•Experiments on prototype.
Electro-hydraulic pumps (EHP) possess the advantages of integrated structure and high working efficiency, ...which thereby attract widespread attention. For the purpose of airborne deployment, an axial piston pump based EHP is established in this work. The modeling of the EHP is carried out in detail considering its structure and working principle via AMESim. Specifically, the model is performed on the basis of three aspects, which are electromagnetic driving, mechanical transmission and fluid dynamic. The computational simulation is conducted based on the modeling. Further, the prototype is designed and machined for working performance evaluation. The properties under different configurations of the EHP are investigated by experiments. Experimental results indicate that the proposed EHP provides a decent working performance in line with the modeling and simulating. The effectiveness of the EHP is verified and the scheme for further improvement is presented.
Data processing in military field is in the stage of fusion disambiguation. The way of addressing the same thing is complex and difficult to integrate. Entity coreference resolution can effectively ...solve this problem. At the same time, entity coreference resolution is also an important part of knowledge fusion stage in the process of building military knowledge graph. This paper studies and implements Chinese coreference resolution from two aspects: Named Entity Recognition and coreference resolution. Firstly, use the BiLSTM+CRF model of neural network to realize NER in military field. By mining Wikipedia corpus, construct a pattern base, and iteratively find the coreference relationship in text based on pattern, and finally establish a model. To complete the rapid and effective construction of a total of 220,000 thesaurus, covering the military field of aircraft and ships two types of objectives, to achieve the military field entity coreference resolution, to provide strong support for the construction of military knowledge graph.
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