Search Results (10)

Fault diagnosis is one of the most important issues for a modular multilevel converter (MMC). However, conventional solutions are deficient in two aspects. Firstly, they lack the necessary feature information. Secondly, they are incapable of performing open-circuit fault diagnosis of the modular multilevel converter with the requisite degree of accuracy. To solve this problem, an intelligent diagnosis method is proposed to integrate the modal time–frequency diagram and FFT-CNN-BiGRU-Attention. By selecting the phase current and bridge arm voltage as the core fault parameters, the particle swarm algorithm is used to optimize the Variational Modal Decomposition parameters, and the fault signal is decomposed and reconstructed into sensitive feature components. The reconstructed signals are further transformed into modal time–frequency diagrams via continuous wavelet transform to fully retain the time–frequency domain features. In the model construction stage, the frequency–domain features are first extracted using the fast Fourier transform (FFT), and the local patterns are captured through a combination with a convolutional neural network; subsequently, the timing correlations are analyzed using bidirectional gated loop cells, and the Attention Mechanism is introduced to strengthen the key features. Simulations show that the proposed method achieves 98.63% accuracy in locating faulty insulated gate bipolar transistors (IGBTs) in the sub-module, with second-level real-time response capability. Compared with the recently published scheme, it maintains stable performance under complex working conditions such as noise interference and data imbalances, showing stronger robustness and practical value. This study provides a new idea for the intelligent operation and maintenance of power electronic devices, which can be extended to the fault diagnosis of other power equipment in the future. Full article

With the government’s strong promotion of the transformation of new and old driving forces, the electrification of buses has developed rapidly. In order to improve resource utilization, many cities have decided to open bus charging stations (CSs) to private vehicles, thus leading to the problems of high electricity costs, long waiting times, and increased grid load during peak hours. To address these issues, a dual-layer optimization model was constructed and solved using the Golden Sine Algorithm, balancing the construction cost of CSs and user costs. In addition, the problem was alleviated by combining energy storage scheduling and the M/M/c queue model to reduce grid pressure and shorten waiting times. The study shows that energy storage scheduling effectively reduces grid load, and the electricity cost is reduced by 6.0007%. The average waiting time is reduced to 2.1 min through the queue model, reducing the electric vehicles user’s time cost. The bi-level programming model and energy storage scheduling strategy have positive implications for the operation and development of bus CSs. Full article

Lipidomics refers to the full characterization of lipids present within a cell, tissue, organism, or biological system. One of the bottlenecks affecting reliable lipidomic analysis is the extraction of lipids from biological samples. An ideal extraction method should have a maximum lipid recovery and the ability to extract a broad range of lipid classes with acceptable reproducibility. The most common lipid extraction relies on either protein precipitation (monophasic methods) or liquid–liquid partitioning (bi- or triphasic methods). In this study, three monophasic extraction systems, isopropanol (IPA), MeOH/MTBE/CHCl₃ (MMC), and EtOAc/EtOH (EE), alongside three biphasic extraction methods, Folch, butanol/MeOH/heptane/EtOAc (BUME), and MeOH/MTBE (MTBE), were evaluated for their performance in characterization of the mouse lipidome of six different tissue types, including pancreas, spleen, liver, brain, small intestine, and plasma. Sixteen lipid classes were investigated in this study using reversed-phase liquid chromatography/mass spectrometry. Results showed that all extraction methods had comparable recoveries for all tested lipid classes except lysophosphatidylcholines, lysophosphatidylethanolamines, acyl carnitines, sphingomyelines, and sphingosines. The recoveries of these classes were significantly lower with the MTBE method, which could be compensated by the addition of stable isotope-labeled internal standards prior to lipid extraction. Moreover, IPA and EE methods showed poor reproducibility in extracting lipids from most tested tissues. In general, Folch is the optimum method in terms of efficacy and reproducibility for extracting mouse pancreas, spleen, brain, and plasma. However, MMC and BUME methods are more favored when extracting mouse liver or intestine. Full article

The melanocortin receptors are involved in numerous physiological pathways, including appetite, skin and hair pigmentation, and steroidogenesis. In particular, the melanocortin-3 receptor (MC3R) is involved in fat storage, food intake, and energy homeostasis. Small-molecule ligands developed for the MC3R may serve as therapeutic lead compounds for treating disease states of energy disequilibrium. Herein, three previously reported pyrrolidine bis-cyclic guanidine compounds with five sites for molecular diversity (R1–R5) were subjected to parallel structure–activity relationship studies to identify the common pharmacophore of this scaffold series required for full agonism at the MC3R. The R2, R3, and R5 positions were required for full MC3R efficacy, while truncation of either the R1 or R4 positions in all three compounds resulted in full MC3R agonists. Two additional fragments, featuring molecular weights below 300 Da, were also identified that possessed full agonist efficacy and micromolar potencies at the mMC5R. These SAR experiments may be useful in generating new small-molecule ligands and chemical probes for the melanocortin receptors to help elucidate their roles in vivo and as therapeutic lead compounds. Full article

The increase in the average global temperature is a consequence of high greenhouse gas emissions. Therefore, using alternative energy carriers that can replace fossil fuels, especially for automotive applications, is of high importance. Introducing more electronics into an automotive battery pack provides more precise control and increases the available energy from the pack. Battery-integrated modular multilevel converters (BI-MMCs) have high efficiency, improved controllability, and better fault isolation capability. However, integrating the battery and inverter influences the maximum DC charging power. Therefore, the DC charging capabilities of 5 3-phase BI-MMCs for a 40-ton commercial vehicle designed for a maximum tractive power of 400 kW was investigated. Two continuous DC charging scenarios are considered for two cases: the first considers the total number of submodules during traction, and the second increases the total number of submodules to ensure a maximum DC charging voltage of 1250 V. The investigation shows that both DC charging scenarios have similar maximum power between 1 and 3 MW. Altering the number of submodules increases the maximum DC charging power at the cost of increased losses. Full article

Objectives: To create a novel preoperative prediction model based on a deep learning algorithm to predict neoplasm T staging and grading in patients with upper tract urothelial carcinoma (UTUC). Methods: We performed a retrospective cohort study of patients diagnosed with UTUC between 2001 and 2012 at our institution. Five deep learning algorithms (CGRU, BiGRU, CNN-BiGRU, CBiLSTM, and CNN-BiLSTM) were used to develop a preoperative prediction model for neoplasm T staging and grading. The Matthews correlation coefficient (MMC) and the receiver-operating characteristic curve with the area under the curve (AUC) were used to evaluate the performance of each prediction model. Results: The clinical data of a total of 884 patients with pathologically confirmed UTUC were collected. The T-staging prediction model based on CNN-BiGRU achieved the best performance, and the MMC and AUC were 0.598 (0.592–0.604) and 0.760 (0.755–0.765), respectively. The grading prediction model [1973 World Health Organization (WHO) grading system] based on CNN-BiGRU achieved the best performance, and the MMC and AUC were 0.612 (0.609–0.615) and 0.804 (0.801–0.807), respectively. The grading prediction model [2004 WHO grading system] based on BiGRU achieved the best performance, and the MMC and AUC were 0.621 (0.616–0.626) and 0.824 (0.819–0.829), respectively. Conclusions: We developed an accurate UTUC preoperative prediction model to predict neoplasm T staging and grading based on deep learning algorithms, which will help urologists to make appropriate treatment decisions in the early stage. Full article

In recent years and due to advanced fabrication techniques of composites, many of these functional materials have been brought to the forefront with more benefits. Amongst composites, special attention has been paid to metal matrix composites (MMCs). Reinforced aluminum MMCs with nanoparticles are among the new MMCs with a wide range of industry applications. The combination of aluminum as a soft, lightweight, and low-strength material with silicon carbide (SiC), bismuth (Bi), and tin (Sn) particles, which are hard and high-strength materials, may lead to the generation of high-strength and lightweight material, which can be classified as difficult to cut material. According to literature, limited studies have been reported on the effects of various reinforcing elements on the machinability of Al-MMC, in principle tool wear morphology and size and surface quality. According to statistical analysis, the effect of cutting parameters and reinforcing particles on the surface quality attributes is not statistically significant. In contrast, the effect of cutting parameters and reinforcing particles on the tool flank wear is significant and reliable. In addition, it is observed that the reinforcing particles and cutting speed have the most significant effects, and the lubrication mode has a minor impact on the tool flank wear. Full article

The Modular Multilevel Converter-High Voltage Direct Current (MMC-HVDC) system is recognized worldwide as a highly efficient strategy for transporting renewable energy across regions. As most of the MMC-HVDC system electronics are weak against overcurrent, protections of the MMC-HVDC system are the major focus of research. Because of the insufficiencies of the conventioned fault diagnosis method of MMC-HVDC system, such as hand-designed fault thresholds and complex data pre-processing, this paper proposes a new method for fault detection and location based on Bidirectional Gated Recurrent Unit (Bi-GRU). The proposed method has obvious advantages of feature extraction on the bi-directional structure, and it simplifies the pre-processing of fault data. The simplified pre-processing avoids the loss of valid information in the data and helps to extract detailed fault characteristics, thus improving the accuracy of the method. Extensive simulation experiments show that the proposed method meets the speed requirement of MMC-HVDC protections (2 ms) and the accuracy rate reaches 99.9994%. In addition, the method is not affected by noise and has a high potential for practical applications. Full article

Fault detection and classification are two of the challenging tasks in Modular Multilevel Converters in High Voltage Direct Current (MMC-HVDC) systems. To directly classify the raw sensor data without certain feature extraction and classifier design, a long short-term memory (LSTM) neural network is proposed and used for seven states of the MMC-HVDC transmission power system simulated by Power Systems Computer Aided Design/Electromagnetic Transients including DC (PSCAD/EMTDC). It is observed that the LSTM method can detect faults with 100% accuracy and classify different faults as well as provide promising fault classification performance. Compared with a bidirectional LSTM (BiLSTM), the LSTM can get similar classification accuracy, requiring less training time and testing time. Compared with Convolutional Neural Networks (CNN) and AutoEncoder-based deep neural networks (AE-based DNN), the LSTM method can get better classification accuracy around the middle of the testing data proportion, but it needs more training time. Full article

The continuous substitution of tin by lead (M ${}^{\mathrm{IV}}$ ) allows for the exploration geometric criteria for the stability of the different stacking variants of alkaline-earth tri-tetrelides $A^{\mathrm{II}}{M}_3^{\mathrm{IV}}$ . A series of ternary Sr and Ba mixed tri-stannides/plumbides A ${}^{\mathrm{II}}$ (Sn ${}_{1-x}$ Pb ${}_x$ ) ${}_3$ (A ${}^{\mathrm{II}}$ = Sr, Ba) was synthesized from stoichiometric mixtures of the elements. Their structures were determined by means of single crystal X-ray data. All structures exhibit close packed ordered $A{M}_3$ layers containing M kagomé nets. Depending on the stacking sequence, the resulting M polyanion resembles the oxygen substructure of the hexagonal (face-sharing octahedra, h stacking, Ni ${}_3$ Sn-type, border compound BaSn ${}_3$ ) or the cubic (corner-sharing octahedra, c stacking, Cu ${}_3$ Au-type, border compound SrPb ${}_3$ ) perovskite. In the binary compound BaSn ${}_3$ (Ni ${}_3$ Sn-type) up to 28% of Sn can be substituted against Pb (hP8, P $6_3/$ mmc, x = 0.28(4): a = 726.12(6), c = 556.51(6) pm, R1 = 0.0264). A further increased lead content of 47 to 66% causes the formation of the BaSn ${}_{2.57}$ Bi ${}_{0.43}$ -type structure with a ${\left(\mathit{hhhc}\right)}_2$ stacking [hP32, P $6_3/$ mmc, x = 0.47(3): a = 726.80(3), c = 2235.78(14) pm, R1 = 0.0437]. The stability range of the BaPb ${}_3$ -type sequence ${\left(\mathit{hhc}\right)}_3$ starts at a lead proportion of 78% (hR36, R $\overline{3}$ m, a = 728.77(3), c = 2540.59(15) pm, R1= 0.0660) and reaches up to the pure plumbide BaPb ${}_3$ . A second new polymorph of BaPb ${}_3$ forms the Mg ${}_3$ In-type structure with a further increased amount of cubic sequences [ ${\left(\mathit{hhcc}\right)}_3$ ; hR48, a = 728.7(2), c = 3420.3(10) pm, R1 = 0.0669] and is thus isotypic with the border phase SrSn ${}_3$ of the respective strontium series. For the latter, a Pb content of 32% causes a small existence region of the PuAl ${}_3$ -type structure [hP24, P $6_3/$ mmc, a = 696.97(6), c = 1675.5(2) pm, R1 = 0.1182] with a ${\left(\mathit{hcc}\right)}_2$ stacking. The series is terminated by the pure c stacking of SrPb ${}_3$ , the stability range of this structure type starts at 75% Pb (cP4, Pm $\overline{3}$ m; a = 495.46(9) pm, R1 = 0.0498). The stacking of the close packed layers is evidently determined by the ratio of the atomic radii of the contributing elements. The Sn/Pb distribution inside the polyanion (’coloring’) is likewise determined by size criteria. The electronic stability ranges, which are discussed on the basis of the results of FP-LAPW band structure calculations are compared with the Zintl concept and Wade’s/mno electron counting rules. Still, due to the presence of only partially occupied steep M-p bands the compounds are metals exhibiting pseudo band gaps close to the Fermi level. Thus, this structure family represents an instructive case for the transition from polar ionic/covalent towards (inter)metallic chemistry. Full article