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Power system operation mode calculation (OMC) is the basis for unit commitment, scheduling arrangement, and stability analyses. In dispatch centers at all levels, OMC is usually realized by manually adjusting the parameters of power system components. In a new-type power system scenario, a large number of new energy sources lead to a significant increase in the complexity and uncertainty of a system structure, thus further increasing the workload and difficulty of manual adjustment. Therefore, improving efficiency and quality is of particular importance for power system OMC. This paper first considers generator power adjustment and line switching, and it then models the power flow adjustment process in OMC as a Markov decision process. Afterward, an improved deep Q-network (improved DQN) method is proposed for OMC. A state space, action space, and reward function that conform to the rules of the power system are designed. In addition, the action mapping strategy for generator power adjustment is improved to reduce the number of action adjustments and to speed up the network training process. Finally, 14 load levels under normal and N-1 fault conditions are designed. The experimental results on an IEEE-118 bus system show that the proposed method can effectively generate the operation mode under a given load level, and that it has good robustness.

Circular RNA (circRNA) has been reported to be involved in the pathogenesis of cardiovascular disease; however, it is unclear whether circRNA carried by exosomes (exos) can be used as biomarkers for chronic coronary syndrome (CCS). High-throughput sequencing was carried out in the plasma exosomal RNA of 15 CCS patients and 15 non-cardiac chest pain patients (NCCP, control group) to screen for differentially expressed circRNAs. Selected differentially expressed exo-circRNAs were further verified by real-time polymerase chain reaction in a small-sample cohort and a large-sample cohort. A total of 276 circRNAs were differentially expressed in the plasma exosomes of CCS patients, with 103 up-regulated and 173 down-regulated. Among the 103 up-regulated circRNAs, 5 circRNAs with high expression levels were selected for validation. Real time quantitative PCR of the first and second validation cohort demonstrated that exo-hsa_circ_0075269 and exo-hsa_circ_0000284 were significantly up-regulated in patients with CCS. Circulating exo-hsa_circ_0075269 and exo-hsa_circ_0000284 yielded the area under the curve values of 0.761 (p < 0.001, 95%CI = 0.669, 0.852) and 0.623 (p = 0.015, 95%CI = 0.522, 0.724) for CCS, respectively, by ROC curve analysis. In conclusion, the expression profile of circRNA in plasma exosomes of patients with CCS was significantly different from that of the control group. Plasma exo-hsa_circ_0075269 and exo-hsa_circ_0000284 have the potential to be new biomarkers for CCS.

We used statistical methods to study the classification of high-potassium glass and lead–barium glass and analyzed the correlation between the chemical composition of different types of glass samples. We investigated the categorization methodology of glass cultural relics, conducted a principal component analysis on the chemical composition data of the glass, and developed a case-specific clustering algorithm (K-Means++) to further categorize the glass cultural relics. K-Means++ was developed to reduce the sensitivity of a traditional K-Means clustering algorithm, by choosing the next clustering center with probability inversely proportional to the distance from the current clustering center. Then we verified the validity of the six subcategories we defined by inertia and silhouette score and evaluated the sensitivity of the clustering algorithm. We obtained a robustness ratio that maintained over 0.9 in the random noise test and a silhouette score of 0.525 in the clustering, which illustrated significant divergence among different clusters and showed the result is reasonable. With our proposed algorithm and classification result, a more comprehensive understanding of glass relics can be gained.

Ribes L. belongs to the Grossulariaceae family and has important edible, medicinal, ornamental, and landscaping values. Taxonomic classification within this genus is difficult due to its large variety of species, wide distribution, large morphological variations, and presence of two complex taxonomic groups with bisexual or unisexual flowers. Our study aims to clarify the phylogenetic relationships of Ribes L. taxa in China, and further, to provide a reference for a revised global classification of it. The phylogenetic analysis of 52 Ribes L. samples from 30 species was constructed based on restriction site-associated DNA sequencing and single nucleotide polymorphisms. Afterward, two important taxonomic characters were selected for ancestral state reconstruction over the molecular phylogeny. The results showed that the 52 samples could be divided into six branches, i.e., six subgenera, which caused some controversy regarding the morphological classification of Ribes L. in China. The molecular phylogeny supported the separation of subg. Coreosma from subg. Ribesia and subg. Hemibotrya from subg. Berisia and validated the rationale for recognizing subg. Grossularia as an independent subgenus, the rationality of which was further verified by the reconstruction of ancestor traits. Gene flow among Ribes L. was identified and further confirmed our results.

Encrypted traffic accounts for 95% of the total traffic in the backbone network environment with Tbps bandwidth. As network traffic becomes more and more encrypted and link rates increase in modern networks, the measurement of encrypted traffic relies more on collecting and analyzing massive network traffic data that can be separated from the support of high-speed network traffic measurement technology. Finding top-k elephant flows is a critical task with many applications in congestion control, anomaly detection, and traffic engineering. Owing to this, designing accurate and fast algorithms for online identification of elephant flows becomes more and more challenging. Existing methods either use large-size counters, i.e., 20 bit, to prevent overflows when recording flow sizes or require significant space overhead to measure the sizes of all flows. Thus, we adopt a novel strategy, called count-with-uth-level-sampling, in this paper, to find top-k elephant flows in limited resource environments. Moreover, the proposed algorithm, called MSLCFinder, incurs lightweight counter and uth-level multi-sampling with small, constant processing for millions of flows. Experimental results show that MSLCFinder can achieve more than 97% precision with an extremely limited hardware resource. Compared to the state-of-the-art, our method realizes the statistics and filtering of millions of data streams with less memory.

Many studies have achieved efficient and accurate methods for identifying crop lodging under homogeneous field surroundings. However, under complex field conditions, such as diverse fertilization methods, different crop growth stages, and various sowing periods, the accuracy of lodging identification must be improved. Therefore, a maize plot featuring different growth stages was selected in this study to explore an applicable and accurate lodging extraction method. Based on the Akaike information criterion (AIC), we propose an effective and rapid feature screening method (AIC method) and compare its performance using indexed methods (i.e., variation coefficient and relative difference). Seven feature sets extracted from unmanned aerial vehicle (UAV) images of lodging and nonlodging maize were established using a canopy height model (CHM) and the multispectral imagery acquired from the UAV. In addition to accuracy parameters (i.e., Kappa coefficient and overall accuracy), the difference index (DI) was applied to search for the optimal window size of texture features. After screening all feature sets by applying the AIC method, binary logistic regression classification (BLRC), maximum likelihood classification (MLC), and random forest classification (RFC) were utilized to discriminate among lodging and nonlodging maize based on the selected features. The results revealed that the optimal window sizes of the gray-level cooccurrence matrix (GLCM) and the gray-level difference histogram statistical (GLDM) texture information were 17 × 17 and 21 × 21, respectively. The AIC method incorporating GLCM texture yielded satisfactory results, obtaining an average accuracy of 82.84% and an average Kappa value of 0.66 and outperforming the index screening method (59.64%, 0.19). Furthermore, the canopy structure feature (CSF) was more beneficial than other features for identifying maize lodging areas at the plot scale. Based on the AIC method, we achieved a positive maize lodging recognition result using the CSFs and BLRC. This study provides a highly robust and novel method for monitoring maize lodging in complicated plot environments.

ZnO and carbon quantum dots (CQDs) were synthesized by a hydrothermal method, and CQDs were doped into ZnO by a grinding method to fabricate a ZnO/CQDs composite. The X-ray diffraction and the scanning electron microscope revealed that the as-prepared ZnO has a structure of wurtzite hexagonal ZnO and a morphology of a flower-like microsphere which can provide more surface areas to adsorbed gases. The ZnO/CQDs composite has a higher gas sensitivity response to NO gas than ZnO microspheres. A gas sensitivity test of the ZnO/CQDs composite showed that the sensor had a high NO response (238 for 100 ppm NO) and NO selectivity. The detection limit of the ZnO/CQDs composite to NO was 100 ppb and the response and recovery times were 34 and 36 s, respectively. The active functional group provided by CQDs has a significant effect on NO gas sensitivity, and the gas sensitivity mechanism of the ZnO/CQDs composite is discussed.

The objective of this study was to develop a low-cost method for rice growth information obtained quickly using digital images taken with smartphone. A new canopy parameter, namely, the canopy volume parameter (CVP), was proposed and developed for rice using the leaf area index (LAI) and plant height (PH). Among these parameters, the CVP was selected as an optimal parameter to characterize rice yields during the growth period. Rice canopy images were acquired with a smartphone. Image feature parameters were extracted, including the canopy cover (CC) and numerous vegetation indices (VIs), before and after image segmentation. A rice CVP prediction model in which the CC and VIs served as independent variables was established using a random forest (RF) regression algorithm. The results revealed the following. The CVP was better than the LAI and PH for predicting the final yield. And a CVP prediction model constructed according to a local modelling method for distinguishing different types of rice varieties was the most accurate (coefficient of determination (R2) = 0.92; root mean square error (RMSE) = 0.44). These findings indicate that digital images can be used to track the growth of crops over time and provide technical support for estimating rice yields.

In this work, a lettuce-like ZnO gas sensor with high sensitivity for H2S detection was successfully fabricated by a one-step hydrothermal method. Characterization analysis of the phases, crystallinities, morphology, and chemical compositions indicated that lettuce-like ZnO has a lettuce-like microsphere structure composed of wurtzite hexagonal ZnO sheets. A gas sensitivity test of the lettuce-like ZnO showed that the sensor had a high H2S response (113.04 for 100 ppm H2S) and H2S selectivity. The lettuce-like ZnO sensor has fast response characteristics while maintaining high sensitivity, and has a response time as low as 15 seconds and a recovery time of 90 seconds, and the detection limit reaches 1 ppm. The sensitive mechanism of lettuce-like ZnO sensor to H2S is also discussed.

The current-induced domain wall (DW) motion in a racetrack memory with a synthetic antiferromagnets (SAFs) structure has attracted attention because of the ultrahigh velocity of the DW. However, since there is little stray field due to the zero net magnetization in a pair of antiferromagnetically (AFM) coupled domains, how to read the information stored in the pair of domains is still challenging. In the present work, we propose a readable SAF racetrack memory composed of two ferromagnetic (FM) layers with distinct uniaxial-anisotropy constants. As a result, a region of staggered domains formed between two neighboring DWs in the two layers. In this region, there is a parallel alignment of the moments in the two FM layers. This parallel magnetization is readable and can be exploited to label the structure of the nearby AFM-coupled domains for the racetrack with DWs moving in a fixed direction. This function can be realized by connecting a Schmitt Trigger to a sensor for reading. The stability and the length of the staggered region can be well-tuned by changing the magnetic parameters, such as the interlayer exchange coupling constants, the Dzyaloshinskii−Moriya interaction (DMI) constants, and the uniaxial-anisotropy constants of the two FM layers, in a range that is experimentally achievable.

In this work, V1−xZrxO2 powder (x = 0, 0.01, 0.02, 0.04) was synthesized by two step hydrothermal method. The micro-topography, magnetic and phase transition properties have been investigated using various measurement techniques. All prepared V1−xZrxO2 powder samples exhibit monoclinic structure at room temperature. With the Zr4+ ions doping concentration increased, the shapes of VO2 particles change from spherical to rectangular slice. Besides, the saturation magnetic moment of the samples decrease with the increase of doped Zr4+ ions concentration, while their phase transition temperature increase gradually with Zr ions doping at a rate of around 2 °C/at% on average. We investigated the Zr doping effects on V-V dimers and confirmed the role of V-V dimers in phase transition. We speculate that more V-V dimers form with Zr doping by magnetic measurements, which result in the monoclinic phase of Zr-doped VO2 sample is more stable than rutile phase. Therefore the phase transition temperature is elevated by Zr doping in our experiment. We further consider that the VO2 phase transition should be ascribed to Peierls transition caused by the changing of V-V dimers.

The internal defects and shape of cyclotrimethylenetrinitramine (RDX) crystal are critical parameters for the preparation of reduced sensitivity RDX (RS-RDX). In the current study, RDX was re-crystallized and spheroidized to form the high-quality RDX that was further characterized by purity, apparent density, size distribution, specific surface area, impact sensitivity, and shock sensitivity. The effects of re-crystallization solvent on the growth morphology of RDX crystal were investigated by both theoretical simulation and experiment test, and consistent results were obtained. The high-quality RDX exhibited a high purity (≥99.90%), high apparent density (≥1.811 g/cm3), spherical shape, and relatively low impact sensitivity (6%). Its specific surface area was reduced more than 30%. Compared with conventional RDXs, the high-quality RDX reduced the shock sensitivities of PBXN-109 and PBXW-115 by more than 30%, indicating that it was a RS-RDX. The reduced sensitivity and good processability of the high-quality RDX would be significant in improving the performances of RDX-based PBXs.

Metal seals play a crucial role in ensuring the dependable functioning of the bearing system in roller cone bits. Due to the intricate nature of downhole conditions, the microstructure design of sealing end faces, specifically the surface texture, is rarely used in metal seals. This study focuses on examining the applicability of surface texture technology in metal seals for cone bits to enhance the lubrication characteristics and friction and wear properties of liquid film between end faces. A multi-field coupling model of surface-textured metal seals was established and experimentally verified. The liquid film carrying the capacity and sealing performance of five different shapes of surface-textured end faces (circle, ellipse I (horizontal), ellipse II (vertical), triangle, and chevron) were investigated under varying rotational speeds and environmental pressures. The influences of texture parameters, including depth and area ratio, on the lubrication characteristics and friction reduction effects of surface-textured metal seals were investigated, and the microscopic wear morphology characteristics of the sealing end faces were analyzed. The results show that surface textures can effectively improve the liquid film state and comprehensive performance of metal seals. Specifically, triangle textures significantly enhance the hydrodynamic pressure effect and weaken the abrasive wear and adhesive wear of the end faces. However, when the environmental pressure is p ≥ 30 MPa, the surface textures gradually lose the advantages of dynamic lubrication but can still reduce friction. The proper texture depth and area ratio can achieve zero leakage (obtained theoretically) and minimal friction in metal seals.

Magnetorheological damper (MRD) has been successfully applied to vehicle suspension systems as an intelligent core component. Most conventional MRDs have closed rectangle-shaped magnetic circuits, resulting in a short effective working length and negligible damping force. To address the above issues, a novel full-channel effective MRD with trapezoidal magnetic rings (FEMRD_TMR) is proposed. The trapezoidal magnetic ring can shunt the magnetic circuit, distributing it evenly along the damping channel and increasing the effective working length. Additionally, which has the same variation trend as the magnetic flux through it, makes the magnetic induction intensity distribution more uniform to reduce the magnetic saturation problem. Theoretically analyzing the damping characteristics of the FEMRD_TMR, a quasi-static model is developed to forecast the output damping force. The structural design of MRD is challenging since conventional quasi-static models rely on the yield stress of magnetorheological fluid (MRF) to reflect the rheological property, which cannot be directly observed and is challenging to calculate. The Takagi–Sugeno (T–S) fuzzy neural network and a unique magnetic circuit computation are offered as a novel quasi-static modeling approach to address the issue. The MRF’s yield stress is linearized into magnetic induction intensity functions by the T–S fuzzy neural network and then converted into the MRD’s structural size by the special magnetic circuit calculation. Therefore, the proposed quasi-static model can directly reflect the relationship between the damping force and structure size, simplifying MRD’s structure design. The novel quasi-static model is shown to be more straightforward and understandable than the conventional Bingham quasi-static model and to have approximately accurate damping force prediction when compared to experimental data.

This paper provides several error estimations for total variation (TV) type regularization, which arises in a series of areas, for instance, signal and imaging processing, machine learning, etc. In this paper, some basic properties of the minimizer for the TV regularization problem such as stability, consistency and convergence rate are fully investigated. Both a priori and a posteriori rules are considered in this paper. Furthermore, an improved convergence rate is given based on the sparsity assumption. The problem under the condition of non-sparsity, which is common in practice, is also discussed; the results of the corresponding convergence rate are also presented under certain mild conditions.

As a promising terahertz radar imaging technology, phaseless terahertz coded-aperture imaging (PL-TCAI) has many advantages such as simple system structure, forward-looking imaging and staring imaging and so forth. However, it is very difficult to recover a target only from its intensity measurements. Although some methods have been proposed to deal with this problem, they require a large number of intensity measurements for both sparse and extended target reconstruction. In this work, we propose a method for PL-TCAI by modeling target scattering coefficient as being in the range of a generative model. Theoretically, we analyze and model the system structure, derive the matrix imaging equation, and then study the deep phase retrieval algorithm. Numerical tests based on different generative models show that the targets with the different spareness can achieve high resolution reconstruction when the number of intensity measurements are smaller than the number of target grids. Also, we find that the proposed method has good anti-noise and stability.