Support For Further Analysis Research Articles

DC voltage prestrike characteristics of single VI and series VIs with AMF and TMF contacts

The double-break vacuum circuit breaker (VCB) effectively solves the problem of voltage saturation of the single-break VCB and improves the performance of the VCB. However, there is currently a lack of research on the prestrike characteristics of combined series vacuum interrupters (VIs) composed of different contact structures when closing under DC voltage. In this paper, the prestrike characteristics of a single VI and a combined series VIs with a cup-type axial magnetic field (AMF) contact and a spiral-type transverse magnetic field (TMF) contact are studied experimentally and numerically. The prestrike characteristics include 50 % prestrike gap (d50), prestrike gap dispersion (σpre), prestrike voltage (Uf) and prestrike field strength (E). Two single VIs and four series VIs are AMF single VI, TMF single VI, AMF-AMF series VIs, AMF-TMF series VIs, TMF-AMF series VIs and TMF-TMF series VIs. This research can provide a basis for the design of the series VIs applied in the phase-controlled closing, and provide theoretical support for further analysis of the prestrike characteristics of the series VIs under capacitive switching.

Deep learning phase retrieval in x-ray single-particle imaging for biological macromolecules

Abstract Phase retrieval is an important optimization problem that occurs, for example, in the analysis of coherent diffraction patterns from isolated proteins. All iterative algorithms employed for phase retrieval in this context require some a priori knowledge of the object, usually in the form of a support that describes the extent of the particle. Phase retrieval is a time-consuming task that can often fail, particularly if the support is too loose or of bad quality. This paper presents a neural network that can produce low-resolution estimates of the phased object in a fraction of the time it takes for a full phase retrieval. It can also successfully be used as support for further analysis. Our network is trained on simulated data from biological macromolecules and is thus tailored to the type of data seen in a typical CDI experiment. Other approaches to support finding require very accurate data without missing regions or the full phase-retrieval algorithm to be run for a long time. Our network could speed up offline analysis and provide real-time feedback during data collection.

Bowel Sounds Detection Method Based on ResNet-BiLSTM and Attention Mechanism

Bowel sounds can reflect the movement and health status of the gastrointestinal tract. However, the traditional manual auscultation method has subjective deviation and is time-consuming and labor-intensive. In order to better assist doctors in diagnosing bowel sounds and improve the reliability and efficiency of bowel sound detection, this study proposed a deep neural network model that combines a residual neural network (ResNet), a bidirectional long short-term memory network (BiLSTM), and an attention mechanism. Firstly, a large number of labeled clinical data was collected using the self-developed multi-channel bowel sound acquisition system, and the multi-scale wavelet decomposition and reconstruction method was used to preprocess the bowel sounds. Then, log Mel spectrogram features were extracted and sent to the network for training. Finally, the performance and effectiveness of the model were evaluated and verified by 10-fold cross-validation and an ablation experiment. The experimental results showed that the precision, recall, and F 1 score of the model reached 83%, 76%, and 79%, respectively, and it could effectively detect bowel sound segments and locate their start and end times, performing better than previous algorithms. This algorithm can not only provide auxiliary information for doctors in clinical practice but also offer technical support for further analysis and research of bowel sounds.

Comparison of Three Machine Learning Algorithms Using Google Earth Engine for Land Use Land Cover Classification

Google Earth Engine (GEE) is presently the most innovative international open-source platform for the advanced-level analysis of geospatial big data. In this study, we used three machine learning algorithms to apply this cloud platform for Land Use Land Cover (LULC) research in the Mardan, Pakistan. The machine learning algorithm in GEE is the most advanced technique to generate reliable and informative LULC maps from various satellite data to present reliable results. The primary goal of the present study is to compare the performance of various machine learning models (i.e., classification and regression trees [CART], support vector machine [SVM], and random forest [RF]) in GEE for the reliable four classes LULC maps using the Sentinel-2 imageries of 2022. In the current study, three satellite indices like the Normalized Difference Vegetation Index, Modified Normalized Difference Water Index, and Normalized Difference Built Index were applied to detect the features (i.e., vegetation, built, barren land, and water bodies in the study area). The performance of all three models was evaluated by validation and accuracy assessments. The Kappa coefficients of CART, SVM, and RF for Sentinel-2 images were 94%, 95%, and 97%, while the average overall accuracy is 96.25%, 97%, and 98.68%, respectively. The present study illustates that in this classification and comparison, RF performed better than SVM and CART. The current research study revealed that GEE has speedily processed the satellite imageries to develop the four classes of reliable LULC maps of the study area with the best accuracy results and deliver excellent support for further analysis.

Combustion Regime Identification in Turbulent Non-Premixed Flames with Principal Component Analysis, Clustering and Back-Propagation Neural Network

Identifying combustion regimes is important for understanding combustion phenomena and the structure of flames. This study proposes a combustion regime identification (CRI) method based on rotated principal component analysis (PCA), clustering analysis and the back-propagation neural network (BPNN) method. The methodology is tested with large-eddy simulation (LES) data of two turbulent non-premixed flames. The rotated PCA computes the principal components of instantaneous multivariate data obtained in LES, including temperature, and mass fractions of chemical species. The frame front results detected using the clustering analysis do not rely on any threshold, indicating the quantitative characteristic given by the unsupervised machine learning provides a perspective towards objective and reliable CRI. The training and the subsequent application of the BPNN rely on the clustering results. Five combustion regimes, including environmental air region, co-flow region, combustion zone, preheat zone and fuel stream are well detected by the BPNN, with an accuracy of more than 98% using 5 scalars as input data. Results showed the computational cost of the trained supervised machine learning was low, and the accuracy was quite satisfactory. For instance, even using the combined data of CH4-T, the method could achieve an accuracy of more than 95% for the entire flame. The methodology is a practical method to identify combustion regime, and can provide support for further analysis of the flame characteristics, e.g., flame lift-off height, flame thickness, etc.

Effect of Polygonal Agglomerated Ice Crystals on Laser Scattering

Cirrus clouds contain a large number of irregular small ice crystals. These solid ice crystals cause energy loss and reduce the signal-to-noise ratio at the receiver, causing errors in reception. Considering the random motion and structural diversity of ice particles in cirrus clouds, the discrete dipole approximation method was used to establish sphere-sphere, sphere–ellipsoid, sphere-hexagonal prism, and sphere-hexagonal plate ice particle models. The effects of different agglomerated ice particles on the laser extinction, absorption, and scattering efficiency, as well as the laser intensity and Mueller matrix elements, were analyzed, and the scattering characteristics of agglomerated ice particles in different spatial orientations were preliminarily explored. The results show that the spatial orientation of the clustered particles has great influence on the scattering characteristics. The maximum relative error of the scattering efficiency was 200%, and the maximum relative error value of the elements of the Mueller matrix reaches 800-fold. The results of this study provide theoretical support for further analysis of the scattering characteristics of ice crystal particles with complex agglomeration structures and for further study of the scattering characteristics of randomly moving agglomeration particles in cirrus clouds.

The Trickle-Down Effect of Responsible Leadership on Employees' Pro-Environmental Behaviors: Evidence from the Hotel Industry in China.

Based on the trickle-down effect model, social learning theory and trait activation theory, this study explores the mechanisms of multi-level responsible leadership on employees’ pro-environmental behaviors in the hotel industry in China. The results show that responsible leadership positively influences employees’ pro-environmental behaviors; mid-level responsible leadership significantly mediates the positive relationship between high-level responsible leadership and employees’ pro-environmental behaviors; and the perceived role of ethics and social responsibility positively moderates the relationship between responsible leadership and employees’ pro-environmental behaviors. The results of this study provide empirical support for further analysis of the “black box” of responsible leadership on employees’ pro-environmental behaviors, fill the gap of the trickle-down model in leadership, and provide new directions for sustainable value creation in hospitality industry organizations.

Study on a novel backlash-adjustable worm drive via the involute helical beveloid gear meshing with dual-lead involute cylindrical worm

This paper focuses on a novel backlash-adjustable worm drive, and it is composed of a dual-lead involute cylindrical (DIC) worm and an involute helical beveloid (IHB) gear. It is characterized by the low error sensitivity, easy to manufacture and with the backlash-adjustable. The design criteria for the novel worm drive are presented based on a variable-lead and variable-thickness media rack. Then the mathematical model and meshing performance analysis model are deduced and established, and the theory of backlash-adjustable is proposed. The tooth contact analysis (TCA) is conducted by the numerical calculation and stress analysis. Moreover, the experimental gearbox is manufactured, and the contact tests and transmission accuracy tests are completed. These results indicate that the novel worm drive can effectively adjust backlash without affecting the transmission accuracy. This theoretical research provides the theoretical support for further analysis and application of the novel backlash-adjustable worm drive.

Management and Visualization of Geoscience Big Data based on Hadoop Platform

This study attempts to use big data technology to manage and analyze the geoscience data with large amount, wide sources and various formats accumulated for many years. The traditional data processing methods are mostly based on pure database and single machine, which is slow and difficult to achieve good results of data service. Hadoop is a distributed, semi-structured data management platform, which can be used to access large data quickly and conveniently. It has been proved by practice that the Hadoop platform can not only effectively complete all kinds of geoscience data processing, but also greatly improve the processing speed. It has good practicability and versatility, and provides technical support for further analysis and application of geoscience big data.

Identification and Evolutionary Analysis of FAD2 Gene Family in Green Plants

Fatty acid desaturase 2 (FAD2) is a crucial enzyme in the biosynthesis of polyunsaturated fatty acids in plants, which introduce a double bond into oleic acid to produce linoleic acid. FAD2 is imperative to the application and nutritional value of vegetable oil as both oleic acid and linoleic acid are the primary constituent fatty acids and their relative proportion determines the nutritional value and oxidative stability in most temperate vegetable oils. In the study described herein, we have identified a total of 253 candidate FAD2 genes from the published genomes of 79 plants. Phylogenetic analysis showed that FAD2 genes in green plants can be divided into three categories, namely, eudicots, monocots and lower plants (except Amborella trichopoda). Through the analysis of its conserved domains, all FAD2 genes contain motifs harbouring HECGH, HRRHH and HVAHH, −the three histidine clusters that are important functional structures of FAD2 gene. According to exon/intron structure analysis, the number of introns varies greatly in lower plants. With the evolution of green plants, the number of introns decreases. This study found that introns were mainly located in the 5’UTR and CDs regions, and their length ranged from 4 bp to 11,233 BP. In addition, about 56% of the 3 ‘and 5’ ends of FAD2 are located at Ag: GT, and the total content of a and t is generally about 60%.Through other bioinformatics methods, we concluded that FAD2 was more conservative in the process of plant evolution. This study provides theoretical support for further analysis of FAD2 gene family evolution.

Characterization of Two-Phase Flow Structure by Deep Learning-Based Super Resolution

Two-phase flow exists widely in the industrial field, and its research is of great significance for industrial production. In this brief, we design a wire-mesh sensor system with 256 measurement points and conduct vertical upward gas-liquid two-phase flow experiments to capture the flow information under different flow conditions. We find that flow structure can be characterized by the gas-liquid distribution imaging obtained through the wire-mesh sensor. To obtain more detailed imaging, we apply super-resolution methods based on deep learning to improve the imaging quality and choose No-Reference Structural Sharpness to evaluate the effects. The results show that compared with traditional interpolation method, the deep learning-based super resolution can restore more abundant details such as edges and textures, thereby characterizing the flow structure more finely. This provides an important support for further analysis of the flow characteristics and flow regularity, which can effectively optimize the industrial process.

Analysis on the kinematical characteristics of compound motion curve-face gear pair

Recently, more and more non-circular gear pairs have been used in gear transmission mechanism. The transmission ratio of some non-circular gear pairs have been presented, however, the relative motions of the two gears are different in different gear pairs. The analytical model and mathematical equations for the classification of the transmission pattern, transmission ratio and relative motions of non-circular gear pair have been established. The classification model illustrates the non-circular gear pairs with both fixed axis and movable axis are existing, presents some other possible new transmission patterns. This paper focuses on the complex governing equations for the compound motion of this curve-face gear pair and shows that the kinematical response of pinion-spring system is continuous intermittent collisions with incomplete vibrations in the meshing process. The limitation conditions for the critical velocity of this gear pair are proposed in this paper. The results indicate that the vibration of this gear pair at low velocity is obvious and the critical velocity should be limited to not more than 700 rpm, which is affected by the parameters of this gear pair. These theoretical results, which have been verified by the experimental rolling, provide the theoretical support for further analysis and application of this compound motion face gear pair.

Analysis of loaded characteristics of helical curve face gear

As a new gear pair, the helical curve face gear pair have not been widely applied as normal face gear. It can be used to substitute crank-slider mechanism to achieve variable transmission ratio and improve the structure and characteristics to some extent, such as grinders, planers and so on. The mathematical model of the helical curve face gear pair and the tooth contact analysis are necessary for the theoretical analysis of this gear pair. Based on the theory of gear meshing and coordinate transmission, the meshing equations for non-circular gear contacting with helical curve face gear without misalignment are obtained. On the basis of Hertz's theory, the principal curvatures at contact point are represented. And the contact area on every tooth and the half cycle of helical curve face gear have been established separately. As well as the contact stress on every tooth and half cycle of helical curve face gear. However, there may be misalignment during the meshing of this gear pair, this paper also covers the contact path and the functions of transmission errors with misalignment, includes the comparison of helical curve face gear and straight curve face gear. The developed theories for complex tooth surface are verified by experiment. These analysis show the contact characteristics of this gear pair during the meshing process and provide the theoretical support for further analysis and application of helical curve face gear pair.

Preliminary mechanical analysis of an improved amphibious spherical father robot

Amphibious micro-robots are being developed for complicated missions in limited spaces found in complex underwater environments. Therefore, compact structures able to perform multiple functions are required. The robots must have high velocities, long cruising times, and large load capacities. It is difficult to meet all these requirements using a conventional underwater micro-robot, so we previously proposed an amphibious spherical father---son robot system that includes several micro-robots as son robots and an amphibious spherical robot as a father robot. Our father robot was designed to carry and power the son robots. This paper discusses improvements to the structure and mechanism of the father robot, which was designed to have a spherical body with four legs. Based on recent experiments in different environments, we have improved the father robot by adding four passive wheels, and we have redesigned its structure by means of three-dimensional printing technology, resulting in greatly improved velocity and stability. Moreover, due to the complexity and uncertainty of many underwater environments, it is essential for the father robot to have adequate structural strength. We analyzed the movement mechanisms and structural strength using finite element analysis to obtain the deformation and equivalent stress distributions of the improved robot. The results provide support for further analysis of the structural strength and optimal design of our amphibious spherical father robot.