Verification Of Accuracy Research Articles

Performance verification of Siemens Atellica DL IM1600 automatic immune analyzer detection system: A-TPO and A-TG.

To study the performance of the Siemens Atellica DL IM1600 automatic immune analyzer detection system for thyroid peroxidase antibodies (A-TPO) and anti-thyroglobulin antibodies (A-TG). Perform performance verification based on CNAS and CLSI related documents, including precision, accuracy, reference interval, and linearity. In precision validation, intra-batch imprecision coefficient of variation values of A-TG and A-TPO were 3.60%, 5.40%, and 3.20%, 2.20%, inter-batch imprecision coefficient of variation values were 5.40%, 4.40%, and 5.00%, 2.50%, all within the specified range. In the accuracy verification, the A-TG comparison values were -10.94%, -1.49%, -1.14%, -7.78%, -3.28%, and the A-TPO values were 21.20%, 6.42%, 14.63%, 12.40%, and 6.55%, The absolute values were all lower than the comparison requirements. The reference interval verifies that the A-TG and A-TPO test results of health patients were within the normal reference interval of 0 to 60 U/mL. In the linear range validation, A-TG linear regression y = 1.0145x - 10.093, correlation coefficient R2 = 0.9963 > 0.95, A-TPO linear regression y = 0.9985x - 18.172, correlation coefficient R2 = 0.9954 > 0.95, indicating linear linearity. The performance verification of Siemens Atellica DL IM1600 automatic immune analyzer detection system for A-TPO and A-TG was satisfactory, suitable for clinical application.

Influence of Spatial Scale Effect on UAV Remote Sensing Accuracy in Identifying Chinese Cabbage (Brassica rapa subsp. Pekinensis) Plants

The exploration of the impact of different spatial scales on the low-altitude remote sensing identification of Chinese cabbage (Brassica rapa subsp. Pekinensis) plants offers important theoretical reference value in balancing the accuracy of plant identification with work efficiency. This study focuses on Chinese cabbage plants during the rosette stage; RGB images were obtained by drones at different flight heights (20 m, 30 m, 40 m, 50 m, 60 m, and 70 m). Spectral sampling analysis was conducted on different ground backgrounds to assess their separability. Based on the four commonly used vegetation indices for crop recognition, the Excess Green Index (ExG), Red Green Ratio Index (RGRI), Green Leaf Index (GLI), and Excess Green Minus Excess Red Index (ExG-ExR), the optimal index was selected for extraction. Image processing methods such as frequency domain filtering, threshold segmentation, and morphological filtering were used to reduce the impact of weed and mulch noise on recognition accuracy. The recognition results were vectorized and combined with field data for the statistical verification of accuracy. The research results show that (1) the ExG can effectively distinguish between soil, mulch, and Chinese cabbage plants; (2) images of different spatial resolutions differ in the optimal type of frequency domain filtering and convolution kernel size, and the threshold segmentation effect also varies; (3) as the spatial resolution of the imagery decreases, the optimal window size for morphological filtering also decreases, accordingly; and (4) at a flight height of 30 m to 50 m, the recognition effect is the best, achieving a balance between recognition accuracy and coverage efficiency. The method proposed in this paper is beneficial for agricultural growers and managers in carrying out precision planting management and planting structure optimization analysis and can aid in the timely adjustment of planting density or layout to improve land use efficiency and optimize resource utilization.

Intelligent Verification Tool for Surgical Information of Ophthalmic Patients-A Study Based on Artificial Intelligence Technology.

With the development of day surgery, the characteristics of "short, frequent and fast" ophthalmic surgery are becoming more prominent. However, nurses are not efficient in verifying patients' surgical information, and problems such as patient privacy leakage are becoming more prominent. To improve the situation, we developed a new augmented reality (AR)-based tool for visual recognition and artificial intelligent (AI) interpretation of the pattern and location of patient surgical skin markings for the verification of the correct surgical site and procedure. The tool can also display a variety of other verbally requested patient information. The purpose of this proposal is to evaluate its feasibility of use by surgical nurses in a real clinical setting. We developed a tool with image recognition technologies to interpretation patient surgical skin markings and match the information obtained with the patients surgical records, thus, verify the patient's surgical information. Verification includes the proper surgical site and type of procedure to be performed. Nurses can interact with the device through its speech recognition capabilities and the device provides them with a variety of other requested patient information via a heads-up display. Three hundred patients in an outpatient ophthalmology clinic were divided into an AR intelligent verification experimental group and a manual verification control group. The accuracy of information verification, work time consumption, and economic cost data were compared between the 2 groups to evaluate the effectiveness of the AR Surgical Information Intelligent Verification Tool in clinical patient surgical information verification. There was no statistically difference in the correct rates of patient surgical information review between the experimental group (95.33%) and the control group (98.67%) (χ2 = 2.934, P = 0.087). The median time for information verification was 10.00 (10.00, 11.00) seconds in the experimental group and 21.00 (20.00, 24.00) seconds in the control group, a statistically difference (Z = 0.000, P < 0.001). The experimental group saved 11 seconds per patient per review compared with the control group. Considering 10,531 surgeries in 2023, printing 1 page of surgical information per 9 patients and requiring 4 copies, 4680 pages of printing paper could be saved. The AR Surgical Information Intelligent Verification Tool has advantages in assisting medical staff in patient surgical information verification, improving nursing efficiency, preventing surgical mark errors or nonstandardization, protecting patient privacy, and saving costs. It has certain research and application value in the scenario of patient surgical information verification in ophthalmic day ward.

Comprehensive Analysis of Bile Medicines Based on UHPLC-QTOF-MSE and Machine Learning.

Based on UHPLC-QTOF-MSE analysis and quantized processing, combined with machine learning algorithms, data modeling was carried out to realize digital identification of bear bile powder (BBP), chicken bile powder (CIBP), duck bile powder (DBP), cow bile powder (CBP), sheep bile powder (SBP), pig bile powder (PBP), snake bile powder (SNBP), rabbit bile powder (RBP), and goose bile powder (GBP). First, 173 batches of bile samples were analyzed by UHPLC-QTOF-MSE to obtain the retention time-exact mass (RTEM) data pair to identify bile acid-like chemical components. Then, the data were modeled by combining support vector machine (SVM), random forest (RF), artificial neural network (ANN), gradient boosting (GB), AdaBoost (AB), and Naive Bayes (NB), and the models were evaluated by the parameters of accuracy (Acc), precision (P), and area under the curve (AUC). Finally, the bile medicines were digitally identified based on the optimal model. The results showed that the RF model constructed based on the identified 12 bile acid-like chemical constituents and random forest algorithm is optimal with ACC, P, and AUC > 0.950. In addition, the accuracy of external identification verification of 42 batches of bile medicines detected at different times is 100.0%. So based on UHPLC-QTOF-MSE analysis and combined with the RF algorithm, it can efficiently and accurately realize the digital identification of bile medicines, which can provide reference and assistance for the quality control of bile medicines. In addition, hyodeoxycholic acid, glycohyodeoxycholic acid, and taurochenodeoxycholic acid, and so forth are the most important bile acid constituents for the identification of nine bile medicines.

RETRACTION: Propofol-Induced miR-219-5p Inhibits Growth and Invasion of Hepatocellular Carcinoma Through Suppression of GPC3-Mediated Wnt/β-Catenin Signalling Activation.

T. Gong, X. Ning, Z. Deng, M. Liu, B. Zhou, X. Chen, S. Huang, Y. Xu, Z. Chen, and R. Luo, "Propofol-Induced miR-219-5p Inhibits Growth and Invasion of Hepatocellular Carcinoma Through Suppression of GPC3-Mediated Wnt/β-Catenin Signalling Activation," Journal of Cellular Biochemistry 120, no. 10 (2019): 16934-16945, https://doi.org/10.1002/jcb.28952. The above article, published online on 19 May 2019 in Wiley Online Library (wileyonlinelibrary.com), has been retracted by agreement between the authors; the journal Editor-in-Chief, Christian Behl; and Wiley Periodicals LLC. The retraction has been agreed due to concerns raised by third parties on the data presented in the article. Specifically, multiple areas of overlap were detected within and across Figures 1D, 2E and 2F. The authors acknowledged that unintentional mistakes were made during figure compilation and collaborated on the investigation into the detected issues. Even though they provided the raw data underlying the study, verification of the material's accuracy proved challenging due to the time elapsed since publication. As full accuracy of the provided material could no longer be ascertained, the editors have decided to retract the article, as they consider the identified issues to undermine the reliability of the presented research. The authors agree with the decision of retraction acknowledging that the identified issues may impact the overall interpretation of the research findings.

Accuracy verification of the 2D spectral AIS method by the hull monitoring data

Accuracy validation of the 2D spectral AIS method (AIS method using two-dimensional wave spectrum; 2D-AIS method) using hull monitoring data of a large ore carrier is discussed. The AIS method predicts the response of actual ships using AIS-based ship position data, hindcast wave data, and response characteristics data. The conventional AIS method uses representative parameters(H, T, θ) from wave spectrum modeling. The modeling involves many uncertainties. By using the 2D wave spectrum as it is, the effect of uncertainties associated with wave spectrum modeling is investigated. Compared to the analysis of the monitoring data conducted in this study, it was found that the 2D-AIS method improves the accuracy to about 15 %, while the conventional AIS method shows an error of up to about 35 % compared to the actual measurements in the long-term maximum expected values. This enhancement is attributed to the inability of the modeled wave spectrum to accurately represent the two-peak wave spectrum observed in extreme sea states, leading to a deficiency in reproducing wave frequency components corresponding to peak frequencies of the response. The findings underscore the effectiveness of the 2D-AIS method in refining long-term hull stress estimations, thus offering valuable insights for enhancing the safety and performance of hull structures.

On-orbit spectral characteristics analysis of the greenhouse gases monitoring instrument: Spectral wavelength stability and instrumental line shape stability

The Greenhouse Gases Monitoring Instrument (GMI) is a carbon satellite payload developed based on the principle of spatial heterodyne spectroscopy technology, which is specifically designed for the global analysis of greenhouse gases (CO2 and CH4) “sources” and “sinks”. Due to the low concentration and minimal gradient variations of CO2 and CH4 in the atmosphere, higher precision is required for their retrieval. Vibrations during satellite launch and harsh space environments during on-orbit operation may cause changes in the performance of various payload components, resulting in decreased quality of spectrum products and retrieval precision. This paper proposes a set of on-orbit spectral characteristic evaluation methods tailored for the GMI to monitor the quality of its spectrum. It also develops an adaptive blind pixel detection and correction algorithm specifically for the GMI and optimizes the interferogram processing flow algorithm. On-orbit spectral calibration is performed, and methods to evaluate spectral characteristic parameters have been established. The analysis focuses on the variations of the spectral characteristics in the CO2-1 bands (1.575 μm) of the GMI during one-year of on-orbit calibration spectrum. The initial spectral wavenumber offset is 0.133 cm−1 after entering orbit, and the average spectral wavenumber offset is 0.0313 cm−1 during stable operation. During the one-year period, the maximum variation in the instrumental line shape function of the GMI is 0.006 cm−1. The observed spectrum obtained in nadir observation mode, underwent to accuracy verification. The results demonstrate consistency between the spectral peak wavenumbers and the relative trend changes of the observed and theoretical spectrum, with an average relative residual of 0.987%.

What Do News Disseminators and Audiences in the Digital Age Need Most? — Skills? Knowledge? Or Accuracy-motivated Sceptical Knowing

In the digital age, journalism encounters both challenges and opportunities. Drawing on a wide range of classic discussions and investigative experiments, this article introduces the concept of “accuracy-motivated sceptical knowing.” This concept, driven by the verification of factual accuracy, represents a professional form of sceptical knowing and is crucial for becoming a responsible media professional and an informed citizen. This article discusses the necessity of transitioning from “general scepticism” to accuracy-motivated sceptical knowing. From the perspectives of both the disseminator and the audience, this article proposes methods for accuracy-motivated sceptical knowing: the disseminator should persist and innovate, while the audience should focus on the source, evidence, openness, and education, and both disseminators and audiences should embrace the “Hurtt Six Points” — questioning, suspension, search, empathy, self-esteem, and autonomy. Finally, this article clarifies the difference between accuracy-motivated sceptical knowing and related concepts, as well as the boundaries of its application.

Prevalence of Thyroid Dysfunction among Patients with Type II diabetes Mellitus in Tertiary Care Center: A Descriptive Cross-sectional Study

Introduction: Diabetes mellitus (DM) and thyroid dysfunction (TD) are two of the most prevalent endocrine disorders globally. Both conditions have significant effects on metabolism and are known to influence each other. Thyroid hormones play a crucial role in glucose homeostasis by affecting insulin secretion, sensitivity, and glucose metabolism.Conversely, altered glucose metabolism in diabetes can impact thyroid function, leading to a higher prevalence of thyroid disorders in diabetic patients. Understanding this relationship is important for improving the clinical management of patients with Type II DM. However, there is limited data on the prevalence of thyroid dysfunction in diabetic populations within specific regions. Methods: A cross-sectional descriptive study was conducted from February to September 2023 among 209 patients diagnosed with Type II DM at a tertiary care center after obtaining ethical approval from Institutional Review Committee(Reference No: 367 (6-11) E2). Thyroid function was assessed using thyroid-stimulating hormone (TSH), free T3 (FT3), and free T4 (FT4) levels. Thyroid dysfunction was classified into hypothyroidism and hyperthyroidism based on standard clinical and laboratory criteria. Data management involved entry into Microsoft Excel, verification for accuracy, and subsequent analysis using SPSS version 29.Results: The study included 209 patients (61% female, 39% male), with a mean age of 65.87 ± 13.7 years. The prevalence of thyroid disorders was 77(36.8%): 53(25.3%) hypothyroidism,24 (11.4%) hyperthyroidism among patient with type II diabetes mellitus.Conclusions: Our study shows a high prevalence of thyroid disorders, especially subclinical hypothyroidism, in individuals with type 2 diabetes. Poorly controlled blood glucose (HbA1c ≥ 7.5) significantly increases the risk, underscoring the need for routine thyroid screening in T2DM management

Fusion of Wavelet Features and Gabor Features for SVM-based Iris Verification

Iris verification now become increasingly prominent in biometric-based person verification systems. It has gained a significant role in biometric systems due to its stability, high uniqueness, contactless and non-invasive properties. Iris has more inherent distinctive features than other biometrics. Feature extraction of iris plays a crucial role in this system for accurate person verification. Using the feature extraction process, unique iris features like textural patterns, crypts, and furrows of iris are extracted. In our study, we did a fusion of Discrete Wavelet Transform (DWT) features with multiple wavelet bases (db4, haar, coif3, and sym4) and Gabor features, which contain a good amount of textural and localized information. Fusion here indicates the concatenation of the extracted features using the above techniques. In this work, we studied this method on the full iris only so that a maximum number of features can be extracted. This combined approach yielded a significant 112 number of features. The extracted features are then verified using a support vector machine (SVM) classifier based on radial basis function (RBF) kernel with training vs testing split ratios of 8:2, 6:4, 4:6 and 2:8. In this study, we achieved the highest overall verification accuracy of 95.9% with training vs testing split ratio of 8:2. For other training vs testing split ratios of 6:4, 4:6 and 2:8 we achieved overall verification accuracies of 91.4%, 93.2% and 91.2% respectively. We got an overall verification accuracy of 92.9%, considering training vs testing ratios of 8:2, 6:4, 4:6 and 2:8.

Dataset for Deep Learning-based GEMS Asian Dust Detection

In South Korea, Asian dust frequently occurs during the spring, causing various health issues, including respiratory diseases. Consequently, public awareness and concern about air pollutants have increased, leading to demands for improved air quality and accurate forecasting. To meet these demands, the Ministry of Environment has deployed the Geostationary Environment Monitoring Spectrometer (GEMS) on the GK2B satellite to monitor atmospheric pollutants and climate change-inducing substances in real-time. The current GEMS dust product, generated using thresholds of the UV-aerosol index and visible-aerosol index, has shown limitations in accurately detecting suspended particulate matter. This study aims to develop a comprehensive AI dataset for improving GEMS Asian dust detection. Data were collected from January to May 2021, focusing on dates with significant dust events. Label data were meticulously generated through annotations based on outputs from various satellites and groundbased observations. Subsequent data preprocessing and augmentation techniques, including normalization and cut-mix, were applied to enhance the dataset’s robustness and generalizability. To evaluate the dataset, model training was conducted. The results predicted by the model showed improvements over the detection results of existing algorithms. Future datasets will be developed with improved labeling methods and accuracy verification techniques. These dataset improvements are expected to contribute to the development of deep learning models with superior predictive performance compared to current dust detection algorithms.

Smart-CX – Method of extraction of parasitic capacitances in ICs

This paper proposes a novel rule-based parasitic capacitance extraction methodology, integrated into Smart-CX, to enhance the accuracy of SPICE simulations and physical verification of ICs. This methodology is crucial during the microchip design phase, particularly in preparation for fabrication on mature to advanced process nodes. The proposed enhanced analytical method addresses the accuracy/time tradeoffs between numerical and analytical extraction techniques. This study validates the accuracy of the methodology by comparison of the extracted parasitic parameters with foundry-provided 2D-models. The parasitic capacitances that are extracted within this method include: area capacitances (Ca), coupling capacitances (Cc), including via-to-via capacitances (Cmv), contact-to-gate capacitances (Cmg), contact-to-active capacitances (Cmc) and fringe type capacitances with the top (Cft) and bottom (Cfb) conductive layers.

Inversion Modeling of Chlorophyll Fluorescence Parameters in Cotton Canopy via Moisture Data and Spectral Analysis

The study of chlorophyll fluorescence parameters is very important for understanding plant photosynthesis. Monitoring cotton chlorophyll fluorescence parameters via spectral technology can aid in understanding the photosynthesis, growth, and stress of cotton fields in real time and provide support for cotton growth regulation and planting management. In this study, cotton plot experiments with different water treatments were set up to obtain the spectral reflectance of the cotton canopy, the maximum photochemical quantum yield (Fv/Fm), and the photochemical quenching coefficient (qP) of leaves at different growth stages. Support vector machine regression (SVR), random forest regression (RFR), and artificial neural network regression (ANNR) were used to establish a fluorescence parameter inversion model of the cotton canopy leaves. The results show that the original spectrum was transformed by multivariate scattering correction (MSC), the standard normal variable (SNV), and continuous wavelet transform (CWT), and the model constructed with Fv/Fm passed accuracy verification. The SNV-SVR model at the budding stage, the MSC-SVR model at the early flowering stage, the SNV-SVR model at the full flowering stage, the MSC-SVR model at the flowering stage, and the CWT-SVR model at the full boll stage had the highest estimation accuracy. The accuracies of the three spectral preprocessing and qP models were verified, and the MSC-SVR model at the budding stage, SNV-SVR model at the early flowering stage, MSC-SVR model at the full flowering stage, SNV-SVR model at the flowering stage, and CWT-SVR model at the full boll stage presented the highest estimation accuracies.

Optimization of high-speed reducer in electric vehicle based on analysis of lubrication

PurposeThe purposes of this paper are optimization of high speed reducer in electric vehicles based on the analysis of lubrication and verification of simulation accuracy and optimization results.Design/methodology/approachThe traditional CFD method presents poor applicability to complex geometric problems due to grid deformity. Therefore, moving particle semi-implicit (MPS) method is applied in this study to simulate lubrication of the reducer and analyze the influence of input speed and lubrication system design on the distribution. According to the results, the reducer is optimized. Meanwhile, the experiments for lubrication and churning power loss is carried out to verify the accuracy of simulation and optimization effects.FindingsThe flow field of lubricant inside the reducer is obtained. The lubrication system of reducer needs to be improved. Simulation and experiment show that the optimization is sufficient and efficient.Originality/valueAccording to the simulation of lubrication, the reducer is optimized. The lubrication experimental setup is established. The conclusion of paper can provide the method and tool for reducer in electric vehicle.Peer reviewThe peer review history for this article is available at: https://publons.com/publon/10.1108/ILT-04-2024-0123/

Accuracy Verification of a 2D Adaptive Mesh Refinement Method by the Benchmarks of Lid-Driven Cavity Flows with an Arbitrary Number of Refinements

Accuracy Verification of a 2D Adaptive Mesh Refinement Method by the Benchmarks of Lid-Driven Cavity Flows with an Arbitrary Number of Refinements

Machine learning analysis for detecting late recurrence and loss to follow‐up after renal cell carcinoma surgery

AbstractObjectivesRenal cell carcinoma (RCC) is shown to have a tendency for late recurrence, occurring 5 or more years after curative surgery. Imaging diagnosis is required for follow‐up, and there is no definitive answer as to how long this should continue. Some patients discontinue follow‐up visits at their own discretion. How best to predict late recurrence and loss to follow‐up (LF) remains unclear.Patients and methodsThis study targeted patients diagnosed with non‐metastatic RCC who underwent either radical or partial nephrectomy at Chiba University Hospital between 1988 and 2021. Follow‐up for patients with RCC is typically lifelong. We used random survival forests (RSFs), a machine learning‐based survival analysis method, to predict late recurrence and LF. For verification of prediction accuracy, we applied the time‐dependent area under the receiver operating characteristic curve (t‐AUC). To analyse the risks of late recurrence and LF, SurvSHAP(t) and partial dependence plots were used.ResultsWe analysed 1051 cases in this study. Median follow‐up was 58.5 (range: 0–376) months. The predictive accuracy of recurrence using RSF was t‐AUC 0.806, 0.761, 0.674 and 0.566 at 60, 120, 180 and 240 months postoperatively, respectively. The recurrence risk impact showed a time‐dependent increase up to approximately 50 months postoperatively. Beyond 50 months, there were no distinct risk factors characteristic of late recurrence. The predictive accuracy of LF using RSF was t‐AUC 0.542, 0.699, 0.685, 0.628 and 0.674 at 60, 120, 180, 240 and 300 months postoperatively, respectively. The risk of LF increased with advancing age beyond 70 years.ConclusionIt is difficult to identify factors that predict late recurrence. For long‐term follow‐up observation, it is essential to pay particular attention to patients with RCC aged 70 years and above. Establishing frameworks to facilitate collaboration with local hospitals near patients' residences and providing care within the community is necessary.

Verification of real-time measurement accuracy of spraying thickness using LIDAR in greening foundation spraying method

Verification of real-time measurement accuracy of spraying thickness using LIDAR in greening foundation spraying method

Patient education resources for oral mucositis: a google search and ChatGPT analysis.

Oral mucositis affects 90% of patients receiving chemotherapy or radiation for head and neck malignancies. Many patients use the internet to learn about their condition and treatments; however, the quality of online resources is not guaranteed. Our objective was to determine the most common Google searches related to "oral mucositis" and assess the quality and readability of available resources compared to ChatGPT-generated responses. Data related to Google searches for "oral mucositis" were analyzed. People Also Ask (PAA) questions (generated by Google) related to searches for "oral mucositis" were documented. Google resources were rated on quality, understandability, ease of reading, and reading grade level using the Journal of the American Medical Association benchmark criteria, Patient Education Materials Assessment Tool, Flesch Reading Ease Score, and Flesh-Kincaid Grade Level, respectively. ChatGPT-generated responses to the most popular PAA questions were rated using identical metrics. Google search popularity for "oral mucositis" has significantly increased since 2004. 78% of the Google resources answered the associated PAA question, and 6% met the criteria for universal readability. 100% of the ChatGPT-generated responses answered the prompt, and 20% met the criteria for universal readability when asked to write for the appropriate audience. Most resources provided by Google do not meet the criteria for universal readability. When prompted specifically, ChatGPT-generated responses were consistently more readable than Google resources. After verification of accuracy by healthcare professionals, ChatGPT could be a reasonable alternative to generate universally readable patient education resources.

Gas leakage physical simulation and its distributed optical fiber sensing experiments in the casing annulus of injection-production pipe column for underground gas storage

Aiming at the problems of high-pressure gas leakage monitoring of injection-production pipe columns in deep underground gas storage under the high-temperature and high-pressure environment, and the problems that distributed optical fiber sensing system is not convenient to carry out detection accuracy and sensitivity testing and verification in actual wellbores, an experimental device for the pipe column gas leakage simulation test of gas storage wellbore is designed and developed to simulate the actual operation conditions of injection-production wellbores. A distributed optical fiber sensing testing environment is built, and the gas leakage signal analysis and location in the casing annulus of injection-production pipe column for the wellbore of underground gas storage are realized by using a distributed temperature sensing (DTS) system and a distributed acoustic sensing (DAS) system. The high-pressure gas leakage signals of the small crack in the damaged area of the wellbore connection can be obtained through the armored optical cable arranged along the casing annulus of pipe columns in the physical device. The optical fiber and its systems can monitor and detect the change of temperature difference and acoustic vibration field in the perimeter caused by high-pressure gas leakage. The spatial-temporal evolution of the casing annulus leakage law and the purpose of pipe column gas leakage can be realized for the wellbore safety monitoring of underground gas storage. The temperature measurement accuracy and positioning sensitivity of the DTS and DAS systems were simulated and verified by designing the pressure difference between inside and outside annular tubes and the leakage source with an interval of 1.0 m. The effectiveness of the pipe column leakage physical simulation experimental device and the reliability of DTS/DAS testing positioning accuracy are verified. This experimental device based on physical model test and its DTS/DAS testing system effectively verify the feasibility and reliability of distributed optical fiber wellbore leakage monitoring for underground gas storage, and its application provides a novel way for the evaluation and analysis of the wellbore tightness and safety of underground energy storage.

A Geometry-Scalable Physically-Based SPICE Compact Model for SiC MPS Diodes Including the Snapback Mechanism

In this paper, a simple compact model for the static behavior of SiC MPS diodes is developed in the form of a SPICE-compatible subcircuit. The model is suited to describe the undesired snapback mechanism likely to occur in unoptimized high-voltage MPS structures with narrow width of the PiN portion and/or very thick drift layer; in particular, the model accounts for the snapback mechanism both as the cell extension varies and as individual portions of Schottky and PiN vary. Sentaurus TCAD simulations of a 10-kV MPS diode are used as a reference for the calibration of the model parameters and accuracy verification.