Characteristics Of Different Types Research Articles

Discussion on reliability and test number of switches in Tokamak fast discharge units

High-power switches, as the important active part of Fast Discharge Unit (FDU) in Tokamak, are designed to interrupt the circuit current and transfer the quench energy for protecting expensive superconducting magnets. Hence, their reliability has always been highly underlined. In this paper, the reliability and test number of FDU switches are discussed, mainly including three issues. First, the FDU configuration characteristics of different types of superconducting magnets are introduced, and explanations and calculations on reliability are provided. Second, the reliability interval estimation after a certain test number is discussed by using the binomial distribution theory. Three, the compliance test based on the claimed reliability is studied, and two types of test plans are discussed and compared to derive the test number. The discussion in this paper focuses on the FDU switches and provides valuable guidance for their reliability test and assessment.

Clustering Methods for the Characterization of Synchrotron Radiation X‐Ray Fluorescence Images of Human Carotid Atherosclerotic Plaque

This study employs computational algorithms to automatically identify and classify features in X‐Ray fluorescence (XRF) microscopy images. Principal component analysis (PCA) and unsupervised machine learning algorithms, such as Gaussian mixture (GM) clustering, are implemented to label features on a collection of XRF maps of human atherosclerotic plaque samples. The investigation involves the hard X‐Ray nanoprobe (NanoMAX) at MAX IV synchrotron radiation facility, utilizing scanning transmission X‐Ray microscopy (STXM) and XRF techniques. The analysis covers regions of interest scanned by the beam with a step size of 200 nm, yielding XRF maps of elements like calcium, iron, and zinc. These maps reveal intricate structures unsuitable for manual labeling. However, they can be accurately classified in an automated fashion using GM. Prior to clustering, PCA is used to deal with repeated patterns and background areas. The resulting clusters are associated with different types of features, which can be identified as specific tissues confirmed by histology. Regions of high concentrations of phosphorus, sulfur, calcium, and iron are found in the samples. These regions are also observed in the STXM results as spots of low transmission that typically are associated with calcium deposits only.

Dynamic Change Characteristics and Main Controlling Factors of Pore Gas and Water in Tight Reservoir of Yan’an Gas Field in Ordos Basin

Tight sandstone gas has become an important field of natural gas development in China. The tight sandstone gas resources of Yan’an gas field in Ordos Basin have made great progress. However, due to the complex gas–water relationship, its exploration and development have been seriously restricted. The occurrence state of water molecules in tight reservoirs, the dynamic change characteristics of gas–water two-phase seepage and its main controlling factors are still unclear. In this paper, the water-occurrence state, gas–water two-phase fluid distribution and dynamic change characteristics of different types of tight reservoir rock samples in Yan’an gas field were studied by means of water vapor isothermal adsorption experiment and nuclear magnetic resonance methane flooding experiment, and the main controlling factors were discussed. The results show that water molecules in different types of tight reservoirs mainly occur in clay minerals and their main participation is in the formation of fractured and parallel plate pores. The adsorption characteristics of water molecules conform to the Dent model; that is, the adsorption is divided into single-layer adsorption, multi-layer adsorption and capillary condensation. In mudstone, limestone and fine sandstone, water mainly occurs in small-sized pores with a diameter of 0.001 μm–0.1 μm. The dynamic change characteristics of gas and water are not obvious and no longer change under 7 MPa displacement pressure, and the gas saturation is low. The gas–water dynamic change characteristics of conglomerate and medium-coarse sandstone are obvious and no longer change under 9 MPa displacement pressure. The gas saturation is high, and the water molecules mainly exist in large-sized pores with a diameter of 0.1 μm–10 μm. The development of organic matter in tight reservoir mudstone is not conducive to the occurrence of water molecules. Clay minerals are the main reason for the high water saturation of different types of tight reservoir rocks. Tight rock reservoirs with large pore size and low clay mineral content are more conducive to natural gas migration and occurrence, which is conducive to tight sandstone gas accumulation.

Lobar and Segmental Atrophy of the Liver: Differential Diagnoses and Treatments

Segmental or lobar liver atrophy is a common but not well-understood clinical condition. Hepatic atrophy can be classified into hepatic atrophy secondary to other pathologies and primary segmental hepatic atrophy, which is a benign intrahepatic lesion (pseudotumor) not associated with any other pathology. The pathophysiological mechanisms underlying atrophy can be divided into three main situations: obstruction of biliary outflow, obstruction of the systemic venous outflow, and obstruction of incoming portal venous flow. For what may concern secondary hepatic atrophy, there are many pathologies that could underlie this condition, ranging from benign to intrahepatic malignancies, with particular reference to particularly hepatocellular carcinoma and biliary duct carcinoma. An accurate and prompt differential diagnosis between the various forms and causes of atrophy is important for early identification and adequate treatment of underlying pathologies. A comprehensive review of the literature on the etiology and the radiological and histological characteristics of different types of hepatic atrophy is currently unavailable. Therefore, the aim of this review is to summarize the primary and secondary causes of segmental or lobar liver atrophy (excluding forms involving the entire liver parenchyma) and to provide practical tools for clinical and radiological differential diagnosis.

Analysis of Mixed Traffic Flow Characteristics Based on Fleet Composition

In urban road networks, the integration of connected and autonomous vehicles (CAV) significantly influences traffic flow patterns, with the Fleet Composition—representing the positioning of these vehicles within convoys—being crucial in dictating the symmetry of information exchange amongst them. First, the vehicle composition of the mixed traffic flow is analyzed, and the mathematical analytical expressions of the random distribution characteristics of different types of vehicles are constructed. Second, we analyze the vehicle according to human characteristics in different situations. Then, consider the following characteristics and reaction times of manual drivers, establish a mixed traffic flow following model, and validate the established following. Finally, the basic graph model considering Fleet Composition is derived, and the effects of reaction time, Fleet Composition, driver following characteristics, and other parameters on road capacity under different penetration rates of CAV are analyzed by a Python and SUMO joint simulation. Finally, the characteristics of mixed traffic flow at intersections were analyzed.

Analysis of Mixed Traffic Flow Characteristics Based on Fleet Composition

In urban road networks, the integration of connected and autonomous vehicles (CAV) significantly influences traffic flow patterns, with the Fleet Composition—representing the positioning of these vehicles within convoys—being crucial in dictating the symmetry of information exchange amongst them. First, the vehicle composition of the mixed traffic flow is analyzed, and the mathematical analytical expressions of the random distribution characteristics of different types of vehicles are constructed. Second, we analyze the vehicle according to human characteristics in different situations. Then, consider the following characteristics and reaction times of manual drivers, establish a mixed traffic flow following model, and validate the established following. Finally, the basic graph model considering Fleet Composition is derived, and the effects of reaction time, Fleet Composition, driver following characteristics, and other parameters on road capacity under different penetration rates of CAV are analyzed by a Python and SUMO joint simulation. Finally, the characteristics of mixed traffic flow at intersections were analyzed.

Grain size characteristics of different types of surface sediments around Qixing Lake in Kubuqi Desert

Grain size characteristics of different types of surface sediments around Qixing Lake in Kubuqi Desert

Energy Consumption Calculation of Civil Buildings in Regional Integrated Energy Systems: A Review of Characteristics, Methods and Application Prospects

Civil buildings play a critical role in urban energy consumption. The energy consumption of civil buildings significantly affects energy allocation and conservation management within regional integrated energy systems (RIESs). This paper first analyzes the influencing factors of civil building energy consumption, as well as the energy consumption characteristics of different types of buildings such as office buildings, shopping malls, hospitals, hotels, and residential buildings. Subsequently, it reviews methodologies for calculating operational energy consumption, offering valuable insights for the optimization and strategic adjustments of an RIES. Finally, the paper assesses the application potential of these calculation methods within an RIES and discusses the future development trend of calculating civil building energy consumption.

Study of the Safety Characteristics of Different Types of Pepper Powder (Capsicum L.)

This research was aimed at comparing the fire characteristics of different types of pepper in the context of explosion prevention. The following characteristics were studied: explosion pressure Pmax and Kst at selected concentrations, ignition temperature of the deposited dust layer from the hot surface, and minimum ignition energy. The comparison of the chemical properties of the used types of pepper was performed using TG/DSC. The results of the measurements suggest that different types of peppers exhibit different explosion characteristics. Each sample reached the maximum value of the explosion pressure and rate of pressure rise at different concentrations. The volume of the explosion chamber used also influenced the explosion characteristics. It is a consequence of the fact that the explosion characteristics strongly depend on the mechanism of action of a particular igniter. The minimum effect on the safety characteristics was observed when measuring the minimum ignition energy and the minimum ignition temperature of the dust layer from the hot surface. The results of the measurements suggest that different types of peppers exhibit different explosion characteristics. This information should then be considered in explosion prevention.

Thyroid Disorders among Type 2 Diabetes Mellitus: A Cross-sectional Study from a District Hospital in West Bengal.

The coexistence of diabetes mellitus (DM) (both type 1 and type 2) and thyroid dysfunction, two common endocrine problems, is scientifically predictable. The prevalence of thyroid disorders among type 2 diabetics (more prevalent in India) was studied among patients visiting the outpatient department (OPD) of a district hospital in West Bengal to assess its relation with different characteristics of type 2 DM. A total of 120 patients suffering from type 2 DM (already diagnosed and on treatment) were randomly selected from the OPD (irrespective of their glycemic status). The thyroid status of all those patients was assessed. All diabetic patients were studied with a predesigned schedule and lab investigations for the prevalence of thyroid dysfunction and its association with pertinent variables from January to December 2019. This study found a 28.3% prevalence of thyroid dysfunction among diabetics. It was significantly associated with poor glycemic control [rising hemoglobin A1c (HbA1c) level] (actual p-value for HbA1c vs abnormal thyroid status = 2.4 E-21) but not with other variables, including the duration of diabetes. Screening for thyroid dysfunction among diabetic persons should be routine, and strict glycemic control is essential.

A novel multi-feature fusion attention neural network for the recognition of epileptic EEG signals.

Epilepsy is a common chronic brain disorder. Detecting epilepsy by observing electroencephalography (EEG) is the main method neurologists use, but this method is time-consuming. EEG signals are non-stationary, nonlinear, and often highly noisy, so it remains challenging to recognize epileptic EEG signals more accurately and automatically. This paper proposes a novel classification system of epileptic EEG signals for single-channel EEG based on the attention network that integrates time-frequency and nonlinear dynamic features. The proposed system has three novel modules. The first module constructs the Hilbert spectrum (HS) with high time-frequency resolution into a two-channel parallel convolutional network. The time-frequency features are fully extracted by complementing the high-dimensional features of the two branches. The second module constructs a grayscale recurrence plot (GRP) that contains more nonlinear dynamic features than traditional RP, fed into the residual-connected convolution module for effective learning of nonlinear dynamic features. The third module is the feature fusion module based on a self-attention mechanism to assign optimal weights to different types of features and further enhance the information extraction capability of the system. Therefore, the system is named HG-SANet. The results of several classification tasks on the Bonn EEG database and the Bern-Barcelona EEG database show that the HG-SANet can effectively capture the contribution degree of the extracted features from different domains, significantly enhance the expression ability of the model, and improve the accuracy of the recognition of epileptic EEG signals. The HG-SANet can improve the diagnosis and treatment efficiency of epilepsy and has broad application prospects in the fields of brain disease diagnosis.

Deep learning for enhanced index tracking

We develop a novel deep learning method for the enhanced index tracking problem, which aims to outperform an index while effectively controlling the tracking error. We generate a dynamic trading policy from a neural network that accepts a set of features as inputs. We design four blocks in the neural network architecture to handle different types of features, including regimes of the index and stocks, their short-term characteristics, and the current allocation. Outputs from the blocks are integrated into the final output that changes the portfolio allocation. We test our model on several indexes in empirical studies based on real market data. Out-of-sample results reveal the importance of different features and demonstrate the ability of our method in obtaining excess returns while effectively controlling the tracking error, downside risk, and transaction costs.

DCNeT: A disease comorbidity network-based temporal deep learning framework to predict cardiovascular risk in patients with mental disorders

Patients with mental disorders (MDs) are at higher subsequent risk of developing cardiovascular diseases (CVDs) than the general population. Early identification of cardiovascular risk in patients with MDs is beneficial for timely intervention and reducing disease burden. Recently, deep learning approaches have been increasingly applied in CVDs risk prediction. However, these methods have three major issues: 1) mostly relying on multiple types of clinical data, 2) not sufficiently mining and utilizing comorbidity patterns hidden in complex correlations among various diseases, and 3) not fully leveraging the time information, including the irregular intervals. To address these issues, we propose a disease comorbidity network-based temporal deep learning framework (DCNeT) to predict the subsequent CVDs risk for patients with MDs based on routinely collected administrative health data. Firstly, to identify the comorbidity patterns of MDs, we construct a disease comorbidity network (DCN) for MDs and apply graph embedding method to generate disease embeddings for each disease in the DCN. Then, a code attention mechanism is proposed to obtain the weight of each disease which is embedded into dense vectors based on the structure of DCN. We present a view attention mechanism to compute the attention weights of different types of features including disease embeddings, basic features, and disease indicators for generating the final representations of patients’ hospitalizations. Furthermore, to fully utilize the information on the irregular time intervals between hospitalizations, a time encoding module is designed, and the time-aware LSTM is adopted to model the irregular time intervals and capture the temporal patterns of patients’ hospitalizations. The experimental results show that DCNeT outperforms the state-of-the-art methods, with the area under the receiver operating characteristic curve of 0.7658, 0.8143, 0.8110, and 0.7839 on four datasets, respectively. The ablation experiments further demonstrate that each module of DCNeT, including the code attention, view attention, and time encoding module, contributes to its superior performance, with average improvements of 1.20 %, 1.65 %, and 1.13 % in accuracy, respectively. Our DCNeT could be utilized as an efficient framework for identifying high-risk groups of CVDs among patients with MDs that may benefit from screening and preventive strategies.

SelfMixed: Self-supervised Mixed Noise Attenuation for Distributed Acoustic Sensing Data

Distributed acoustic sensing (DAS) is an emerging data acquisition technique known for its high sensing density, cost-effectiveness, and environmental friendliness, making it a technology with significant future application potential in many fields. However, DAS signals are often contaminated by various types of noise, such as high-frequency, high-amplitude erratic, and horizontal noise, making their processing challenging. Therefore, it is crucial to leverage the physical characteristics of these diverse types of noise in DAS data and effectively attenuate them. In this work, we propose SelfMixed, a novel self-supervised learning method for mixed noise suppression of DAS data. We fully exploit the physical characteristics of different types of noise in DAS data and introduce a physical characteristic-based training strategy. Specifically, we utilize the ℓ2-norm to characterize {random noise}, the ℓ1-norm for erratic noise, and horizontal smoothness and vertical non-smoothness for horizontal noise. Additionally, we employ a blind-spot-based training strategy for DAS denoising, relying solely on observed noisy data. To more effectively attenuate horizontal noise, we also introduce a Fourier transform-based parameterization method. By combining self-supervised deep priors with the physical characteristics of mixed DAS noise, our method effectively {attenuates} complex mixed noise in field DAS data. Extensive experiments on synthetic and field data from various geographic scenarios validate the superiority of SelfMixed over seven state-of-the-art DAS denoising approaches.

Classification of Biomedical Images with Mined Statistical Features and Dynamic Programming

Background:: In the research and practice of medical sciences, accurate classification of biomedical images with computer programs may provide an important basis for the study and diagnosis of many diseases. Methods:: This paper proposes a new statistical approach that can accurately classify biomedical images based on their statistical features. In the first step of the proposed approach, a number of SIFT features of different types are computed for each pixel in a biomedical image and a statistical feature that describes the distribution of each type of SIFT features is obtained for the image. In the second step, a dynamic programming approach is used to efficiently analyze the dependence among different statistical features associated with an image and compute the probability for an image to belong to each possible class; the class with the largest probability is determined as the result of classification. Results:: Experimental results show that the proposed approach can lead to classification results with accuracy higher than that of a few state-of-the-art approaches for the classification of biomedical images Conclusion:: The proposed approach can achieve classification accuracy comparable to that of several state-of-the-art classification approaches. It is thus potentially useful for applications where large models are not appropriate for classification tasks due to limitations in computational or communication resources

Investigation of key morphological parameters of pores in different grades of asphalt mixture based on CT scanning technology

The internal pore structure of asphalt mixtures directly influences their permeability characteristics. Current research has not yet clarified the quantitative characterization of different types of meso-pore structures and their impact mechanisms on permeability characteristics. Therefore, this study, based on the acquisition of the internal pore structure of asphalt mixtures through CT non-destructive scanning technology, classifies pores according to their characteristics. It conducts a multi-dimensional and multi-scale qualitative and quantitative analysis of the meso-pore structures of asphalt mixtures in terms of pore distribution, equivalent radius of pore throats, inclination angle of pore throats, tortuosity of pore throats, and pore connectivity. The results indicate that the internal pore distributions of AC-16, SMA-16, OGFC-13, and OGFC-16 gradation asphalt mixtures are uniform, without noticeable segregation. The results of porosity obtained through non-destructive CT scanning technology demonstrate good consistency with those of macroscopic experiments. This demonstrates that analyzing CT images can accurately acquire pores and quantify pore characteristics. The number of pores in AC-16 gradation specimens is approximately 3–5 times that of other groups of gradation specimens, but the average volume of the pores is only 0.98 mm3. The average pore volume for SMA-16 gradation is 5.1 mm3, while for OGFC-13 and OGFC-16 gradation, the average pore volumes are 28.3 mm3 and 29.6 mm3, respectively. In SMA-16 grade specimens, the volume of open pores accounts for 95% of the total pore volume, despite the fact that open pores make up only 1% of the total number of pores. In the OGFC-13 and OGFC-16 graded asphalt mixtures, the volumes of interconnected pores account for 88.7% and 88.9% of the total pore volume, respectively. The number and volume proportion of pore throats decrease with the increase in pore throats angle, indicating that there are more pore throats with smaller angles, which are conducive to longitudinal drainage, and fewer pore throats with larger angles, which are conducive to lateral drainage. This suggests that the vertical permeability of samples formed by traditional Marshall compaction is usually greater than their horizontal permeability.

Analysis of Two Kinds of MV Trailer Creation Content

MV trailer is the MV inside the more eye-catching or eye-catching, to attract everyone's attention, it is a very effective way to promote the MV. This paper focuses on two different types of MV trailer type creation features, and analyzes the visual effect of MV trailer to attract the public with theoretical and practical significance. Using two representative female groups thumb amount of the first five MV forecast data in this paper found: (1) dance type MV forecast play characteristic is the music of chorus accompaniment with a short dance and different perspective scene to catch the public eye; (2) Story type MV forecast is characterized by some specific scene and perspective transformation suggested that the subsequent MV story. This paper aims to analyze the theoretical and practical significance of the visual effects of MV trailers in attracting the public by studying the creative characteristics of different types of MV trailers, and provide reference for the subsequent MV releases for better publicity.

Different types of tumor microvessels in stage I-IIIA squamous cell lung cancer and their clinical significance.

Lung cancer (LC) is the leading cause of morbidity and mortality among malignant neoplasms. Improving the diagnosis and treatment of LC remains an urgent task of modern oncology. Previously, we established that in gastric, breast and cervical cancer, tumor microvessels (MVs) differ in morphology and have different prognostic significance. The connection between different types of tumor MVs and the progression of LC is not well understood. To evaluate the morphological features and clinical significance of tumor MVs in lung squamous cell carcinoma (LUSC). A single-center retrospective cohort study examined medical records and archival paraffin blocks of 62 and 180 patients with stage I-IIIA LUSC in the training and main cohorts, respectively. All patients underwent radical surgery (R0) at the Orenburg Regional Cancer Clinic from May/20/2009 to December/14/2021. Tumor sections were routinely processed, and routine Mayer's hematoxylin and eosin staining and immunohistochemical staining for cluster of differentiation 34 (CD34), podoplanin, Snail and hypoxia-inducible factor-1 alpha were performed. The morphological features of different types of tumor MVs, tumor parenchyma and stroma were studied according to clinicopathological characteristics and LUSC prognosis. Statistical analysis was performed using Statistica 10.0 software. Univariate and multivariate logistic regression analyses were performed to identify potential risk factors for LUSC metastasis to regional lymph nodes (RLNs) and disease recurrence. Receiver operating characteristic curves were constructed to discriminate between patients with and without metastases in RLNs and those with and without disease recurrence. The effectiveness of the predictive models was assessed by the area under the curve. Survival was analyzed using the Kaplan-Meier method. The log-rank test was used to compare survival curves between patient subgroups. A value of P < 0.05 was considered to indicate statistical significance. Depending on the morphology, we classified tumor vessels into the following types: normal MVs, dilated capillaries (DCs), atypical DCs, DCs with weak expression of CD34, "contact-type" DCs, structures with partial endothelial linings, capillaries in the tumor solid component and lymphatic vessels in lymphoid and polymorphocellular infiltrates. We also evaluated the presence of loose, fine fibrous connective tissue (LFFCT) and retraction clefts in the tumor stroma, tumor spread into the alveolar air spaces (AASs) and fragmentation of the tumor solid component. According to multivariate analysis, the independent predictors of LUSC metastasis in RLNs were central tumor location (P < 0.00001), the presence of retraction clefts (P = 0.003), capillaries in the tumor solid component (P = 0.023) and fragmentation in the tumor solid component (P = 0.009), whereas the independent predictors of LUSC recurrence were tumor grade 3 (G3) (P = 0.001), stage N2 (P = 0.016), the presence of LFFCT in the tumor stroma (P < 0.00001), fragmentation of the tumor solid component (P = 0.0001), and the absence of tumor spread through the AASs (P = 0.0083). The results obtained confirm the correctness of our previously proposed classification of different types of tumor vessels and may contribute to improving the diagnosis and treatment of LUSC.

Characteristics of fibres based on secondary raw materials and their use in concrete technology

Different types of fibres in cementitious composites, particularly in concrete, are currently used for a number of reasons. Fibres are being added to improve mechanical properties (especially steel and glass fibres), to increase the durability – to reduce occurrence of microcracks during the concrete aging (mainly synthetic and cellulose fibres), or to increase the fire resistance (polypropylene fibres). Within the study, characterization of different types of alternative fibres (fibres generated during waste recycling that would otherwise end in incinerators or landfills) with possible use in cementitious composites. These were fibres from recycled PET bottles, paper, and mineral wool, whose properties were compared to the traditionally used cellulose and polypropylene fibres. In the experimental part, the thickness, length, shape, and surface of individual fibres were monitored by an optical microscope. Furthermore, the amount of heat of combustion was determined by the calorimetric method, and the differential thermal analysis (DTA) was carried out for determination of the impact of high temperature on monitored fibres. The microstructure of fibres was monitored using a scanning electron microscope. The focus of the experimental study was on fibres usable in concrete and capable of enduring high temperature stress.

Identification and morphological characterization of different types of plastic microparticles

The knowledge of the polymeric composition of microplastics (MPs) is interesting because offers useful information on the resistance, durability, and degradability of these materials, also allowing progress in the control of this contamination. However, there is currently a lack of reliable standardized methods for the identification, and characterization of the plastic microparticles. This work uses different techniques in a complementary manner for the identification, and characterization of MPs that more frequently are found in the environment. A total of 10 types of plastics were collected (polystyrene (PS), polyethylene terephthalate (PETE), polyethylene (PE), high- and low-density polyethylene (HDPE and LDPE, respectively), polyvinyl chloride (PVC), polypropylene (PP), polytetrafluoroethylene (PTFE), Polyamide (PA, Nylon 6,6) and poly-carbonate (PC)) and their chemical identification were analyzed by reflectance-attenuated infrared (FTIR-ATR). Furthermore, the samples were observed using light microscopy, and scan-ning electron microscopy (SEM). Also, staining with 12 different dyes was performed to improve the identification of microplastics. The results of this study revealed that PETE, PE, HDPE and LDPE, whose SEM images exhibited smoothness and flat uniformity of their surface, were not (or less) susceptible to adsorb staining solutions while PP, PA, PVC, and PTFE, were capable of adsorbing the dye solutions.