A fast multi-step prediction and rolling optimization excitation control method for multi-machine power system

Predstavlja se metoda brzog reguliranja predviđanja uzbude za elektro-energetski sustav s više strojeva. Ta metoda predviđanja u nekoliko koraka ostvaruje se dinamičkim modelom sustava. Neka ograničenja neujednačenosti stanja, ulaza i izlaza razmatraju se u optimizaciji valjanja. U svrhu uštede vremena optimizacije otvorene petlje prediktivnog upravljanja modela primijenjuje se Gramian metoda balansirane redukcije i poboljšani algoritam optimizacije. Za provjeru učinkovitosti tog pristupa koristi se elektro-energetski sustav s 50 strojeva. U usporedbi sa simuliranim rezultatima uz različite regulatore, ovom se metodom može znatno reducirati vrijeme računanja. Naponi terminala generatora zadržani su u zadanim točkama. Poboljšana je stabilnost elektroenergetskog sustava.A fast excitation predictive control method for multi-machine power system is presented. The multi-step prediction technique is realized via system dynamic model. Some inequality constraints on states, inputs and outputs are considered in rolling optimization. The Gramian balanced reduction technique and the improved optimization algorithm are used in order to save the time of open-loop optimization in model predictive control. A 50-machine power system is used to verify the effectiveness of this approach. Compared with simulated results under different controllers, this method can greatly reduce the calculating-time. The voltages of generator terminals are maintained within the set points. The stability of power system is improved

Vitamin D status in tuberculosis patients with diabetes, prediabetes and normal blood glucose in China: a cross-sectional study.

OBJECTIVE: The association between tuberculosis (TB), diabetes mellitus (DM) and vitamin D status is poorly characterised. We therefore: (1) determined vitamin D status in patients with TB in relation to whether they had normal fasting blood glucose (FBG), pre-DM or DM and (2) assessed whether baseline characteristics in patients with TB, including their DM status, were associated with vitamin D deficiency. METHODS: In patients with TB consecutively attending six clinics or hospitals in China, we measured 25-hydroxycholecalciferol (25-(OH)D3) at the time of registration using electrochemiluminescence in a COBASE 601 Roche analyser by chemiluminescence immunoassay. Data analysis was performed using the χ2 test, ORs and multivariate logistic regression. RESULTS: There were 306 eligible patients with TB, including 96 with smear positive pulmonary TB, 187 with smear negative pulmonary TB and 23 with extrapulmonary TB. Of these, 95 (31%) had normal blood glucose, 83 (27%) had pre-DM and 128 (42%) had DM. Median serum vitamin D levels were 16.1 ng/mL in patients with TB with normal FBG, 12.6 ng/mL in patients with TB with pre-DM and 12.1 ng/mL in patients with TB with DM (p<0.001). The study highlighted certain baseline characteristics associated with vitamin D deficiency (25-(OH)D3<20 ng/mL). After adjusting for confounders, serum vitamin D deficiency was significantly more common in patients being registered in the cold season (November to April) (p=0.006) and in those with DM (p=0.003). CONCLUSION: Vitamin D levels are lower in patients with TB with pre-DM and DM and are also affected by certain baseline characteristics that include being registered in the cold season and having DM. TB programmes need to pay more attention to vitamin D status in their patients, especially if there is coexisting pre-DM or DM

Deficient O-GlcNAc Glycosylation Impairs Regulatory T Cell Differentiation and Notch Signaling in Autoimmune Hepatitis

Post-translational modifications such as glycosylation play an important role in the functions of homeostatic proteins, and are critical driving factors of several diseases; however, the role of glycosylation in autoimmune hepatitis is poorly understood. Here, we established an O-GlcNAc glycosylation-deficient rat model by knocking out the Eogt gene by TALEN-mediated gene targeting. O-GlcNAc glycosylation deficiency overtly aggravated liver injury in concanavalin-A induced autoimmune hepatitis, and delayed self-recovery of the liver. Furthermore, flow cytometry analysis revealed increased CD4+ T cell infiltration in the liver of rats with O-GlcNAc glycosylation deficiency, and normal differentiation of regulatory T cells (Tregs) in the liver to inhibit T cell infiltration could not be activated. Moreover, in vitro experiments showed that O-GlcNAc glycosylation deficiency impaired Treg differentiation to inhibit the Notch signaling pathway in CD4+ T cells. These finding indicate that O-GlcNAc glycosylation plays a critical role in the activation of Notch signaling, which could promote Treg differentiation in the liver to inhibit T cell infiltration and control disease development in autoimmune hepatitis. Therefore, this study reveals a regulatory role for glycosylation in the pathogenesis of autoimmune hepatitis, and highlights glycosylation as a potential treatment target

Machine learning for the prediction of all-cause mortality in patients with sepsis-associated acute kidney injury during hospitalization

BackgroundSepsis-associated acute kidney injury (S-AKI) is considered to be associated with high morbidity and mortality, a commonly accepted model to predict mortality is urged consequently. This study used a machine learning model to identify vital variables associated with mortality in S-AKI patients in the hospital and predict the risk of death in the hospital. We hope that this model can help identify high-risk patients early and reasonably allocate medical resources in the intensive care unit (ICU).MethodsA total of 16,154 S-AKI patients from the Medical Information Mart for Intensive Care IV database were examined as the training set (80%) and the validation set (20%). Variables (129 in total) were collected, including basic patient information, diagnosis, clinical data, and medication records. We developed and validated machine learning models using 11 different algorithms and selected the one that performed the best. Afterward, recursive feature elimination was used to select key variables. Different indicators were used to compare the prediction performance of each model. The SHapley Additive exPlanations package was applied to interpret the best machine learning model in a web tool for clinicians to use. Finally, we collected clinical data of S-AKI patients from two hospitals for external validation.ResultsIn this study, 15 critical variables were finally selected, namely, urine output, maximum blood urea nitrogen, rate of injection of norepinephrine, maximum anion gap, maximum creatinine, maximum red blood cell volume distribution width, minimum international normalized ratio, maximum heart rate, maximum temperature, maximum respiratory rate, minimum fraction of inspired O2, minimum creatinine, minimum Glasgow Coma Scale, and diagnosis of diabetes and stroke. The categorical boosting algorithm model presented significantly better predictive performance [receiver operating characteristic (ROC): 0.83] than other models [accuracy (ACC): 75%, Youden index: 50%, sensitivity: 75%, specificity: 75%, F1 score: 0.56, positive predictive value (PPV): 44%, and negative predictive value (NPV): 92%]. External validation data from two hospitals in China were also well validated (ROC: 0.75).ConclusionsAfter selecting 15 crucial variables, a machine learning-based model for predicting the mortality of S-AKI patients was successfully established and the CatBoost model demonstrated best predictive performance

A Study on the Impact of Consumer Ethics on Apparel Purchasing Behavior

As the Chinese economy grows fast and the Chinese residents' spending power increases, "ethical consumers" have been lavished with increasing attention. In fact, consumption has become a decision-making process closely tied to ethics or morality. This article describes consumers' basic cognition of environmental issues and ethical apparel in a quantitative manner. The research covers mainly the following topics: consumers' understanding and recognition of ethical apparels, factors bearing on the behavior of purchasing ethical apparels, surveys of the intentions of consumers to purchase ethical apparels as well as the personal information of survey respondents. The purpose is to provide forward-looking bases and reference for apparel enterprises to plan ethical marketing

Blur Kernel Estimation and Non-Blind Super-Resolution for Power Equipment Infrared Images by Compressed Sensing and Adaptive Regularization

Infrared sensing technology is more and more widely used in the construction of power Internet of Things. However, due to cost constraints, it is difficult to achieve the large-scale installation of high-precision infrared sensors. Therefore, we propose a blind super-resolution method for infrared images of power equipment to improve the imaging quality of low-cost infrared sensors. If the blur kernel estimation and non-blind super-resolution are performed at the same time, it is easy to produce sub-optimal results, so we chose to divide the blind super-resolution into two parts. First, we propose a blur kernel estimation method based on compressed sensing theory, which accurately estimates the blur kernel through low-resolution images. After estimating the blur kernel, we propose an adaptive regularization non-blind super-resolution method to achieve the high-quality reconstruction of high-resolution infrared images. According to the final experimental demonstration, the blind super-resolution method we proposed can effectively reconstruct low-resolution infrared images of power equipment. The reconstructed image has richer details and better visual effects, which can provide better conditions for the infrared diagnosis of the power system

Improving Neural Network Detection Accuracy of Electric Power Bushings in Infrared Images by Hough Transform

To improve the neural network detection accuracy of the electric power bushings in infrared images, a modified algorithm based on the You Only Look Once version 2 (YOLOv2) network is proposed to achieve better recognition results. Specifically, YOLOv2 corresponds to a convolutional neural network (CNN), although its rotation invariance is poor, and some bounding boxes (BBs) exhibit certain deviations. To solve this problem, the standard Hough transform and image rotation are utilized to determine the optimal recognition angle for target detection, such that an optimal recognition effect of YOLOv2 on inclined objects (for example, bushing) is achieved. With respect to the problem that the BB is biased, the shape feature of the bushing is extracted by the Gap statistic algorithm, based on K-means clustering; thereafter, the sliding window (SW) is utilized to determine the optimal recognition area. Experimental verification indicates that the proposed rotating image method can improve the recognition effect, and the SW can further modify the BB. The accuracy of target detection increases to 97.33%, and the recall increases to 95%

Marketing Resource Allocation Strategy Optimization Based on Dynamic Game Model

Game theory has become an important tool to study the competition between oligopolistic enterprises. After combing the existing literature, it is found that there is no research combining two-stage game and nonlinear dynamics to analyze the competition between enterprises for advertising. Therefore, this paper establishes a two-stage game model to discuss the effect of the degree of firms’ advertising input on their profits. And the complexity of the system is analyzed using nonlinear dynamics. This paper analyzes and studies the dynamic game for two types of application network models: data transmission model and transportation network model. Under the time-gap ALOHA protocol, the noncooperative behavior of the insiders in the dynamic data transmission stochastic game is examined as well as the cooperative behavior. In this paper, the existence of Nash equilibrium and its solution algorithm are proved in the noncooperative case, and the “subgame consistency” of the cooperative solution (Shapley value) is discussed in the cooperative case, and the cooperative solution satisfying the subgame consistency is obtained by constructing the “allocation compensation procedure.” The cooperative solution is obtained by constructing the “allocation compensation procedure” to satisfy the subgame consistency. In this paper, we propose to classify the packets transmitted by the source nodes, and by changing the strategy of the source nodes at the states with different kinds of packets, we find that the equilibrium payment of the insider increases in the noncooperative game with the addition of the “wait” strategy. In the transportation dynamic network model, the problem of passenger flow distribution and the selection of service parameters of transportation companies are also studied, and a two-stage game theoretical model is proposed to solve the equilibrium price and optimal parameters under Wardrop’s criterion

Characteristic Impedance Analysis of Medium-Voltage Underground Cables with Grounded Shields and Armors for Power Line Communication

The characteristic impedance of a power line is an important parameter in power line communication (PLC) technologies. This parameter is helpful for understanding power line impedance characteristics and achieving impedance matching. In this study, we focused on the characteristic impedance matrices (CIMs) of the medium-voltage (MV) cables. The calculation and characteristics of the CIMs were investigated with special consideration of the grounded shields and armors, which are often neglected in current research. The calculation results were validated through the experimental measurements. The results show that the MV underground cables with multiple grounding points have forward and backward CIMs, which are generally not equal unless the whole cable structure is longitudinally symmetrical. Then, the resonance phenomenon in the CIMs was analyzed. We found that the grounding of the shields and armors not only affected their own characteristic impedances but also those of the cores, and the resonance present in the CIMs should be of concern in the impedance matching of the PLC systems. Finally, the effects of the grounding resistances, cable lengths, grounding point numbers, and cable branch numbers on the CIMs of the MV underground cables were discussed through control experiments