Accurate Interpretation Research Articles

Assessing knowledge gaps and educational needs in urine drug test interpretation among health care professionals.

Urine drug testing (UDT) is a critical tool used in medical, forensic, and occupational settings, but interpreting results can be challenging. We performed a study to assess the ability of health care professionals to interpret UDT results accurately. In total, 911 clinical and laboratory professionals in the United States and Canada responded to a survey with questions gauging expertise in UDT interpretation. Responses were analyzed to identify knowledge gaps. Toxicologists and laboratory PhD scientists performed well, with means of 4.82 and 4.63 questions answered correctly (out of 6 possible), respectively. Physicians specializing in pathology, emergency medicine, primary care, and internal medicine, however, displayed concerning knowledge gaps, as did laboratorians with nondoctoral degrees. Experience and training correlated with interpretation accuracy. Identification of simulated compliance as well as understanding opioid exposure, metabolism, and immunoassay cross-reactivity were among the most clinicallysignificant knowledge gaps. More than 30% of survey respondents indicated that they would seek UDT information from the internet or peers rather than clinical or laboratory experts. The study highlighted the need for targeted education and better collaboration between clinical and laboratory experts and other health care professionals to ensure that when physicians order UDT, they can accurately interpret results and reduce harm.

Adaptive dual-domain filtering for random seismic noise removal

Random noise significantly reduces the signal-to-noise ratio of seismic data and affects the accuracy of seismic interpretation. Traditional denoising methods typically require manual parameter tuning to increase robustness and accuracy across various random noise levels. In this study, based on the statistical definition of random noise, we use the variance of random noise as the level of random noise and develop an adaptive dual-domain filter (ADDF). The ADDF method estimates the random noise variance in the seismic data and uses this estimation to effectively denoise the seismic data. First, we use a difference operator in two directions to remove the useful structures from the seismic data. The processed data are then used to estimate the global random noise variance through iterative statistical processing. In the denoising process of the ADDF, the seismic data are masked by a bilateral filter in the spatial domain, followed by a short-time Fourier transform with wavelet shrinkage in the frequency domain, both controlled by the adaptively estimated random noise variance. The dual-domain filter is applied iteratively for the best performance. Synthetic experiments demonstrate the robustness of the ADDF in accurately estimating the noise variance without tuning parameters, and its superior denoising performance is evident in synthetic examples and field data when compared with two typical denoising methods: f- x deconvolution and curvelet domain thresholding. As an adaptive random noise estimation and removal method, ADDF relies only on seismic data, making denoising random noise more objective and accurate without manual adjustment.

A Decade Analysis (2013-2023) of Paddy's Yield Productivity by Using Landsat 8 Imagery in Sukoharjo District, Indonesia

Sukoharjo District has the highest rice productivity in Central Java. Sukoharjo has a strategic location. It makes this area prone to land use changes. It makes a severe impact on Paddy's productivity. This condition needs to be monitored continually. Remote sensing can provide an efficient and accurate method to solve this condition. Using the NDVI from Landsat 8 imagery and ubinan data, the model can be built to calculate and analyze paddy’s productivity. The steps were 1) interpretation of paddy fields area; 2) calculation of NDVI’s mean values a month before harvesting; 3) interpretation accuracy test; 4) correlation value between NDVI and ubinan data in 2022; 5) calculation of paddy productivity; and 6) analysis. Within a decade (2013-2023), there was a reduction in paddy's yield area for 981.90 Ha. During that period, there was an increase in paddy's productivity, around 38.2 x 103 tons. Almost all sub-districts in Sukoharjo’s yield had been reduced except for Tawangsari and Weru. Kartasura and Grogol have experienced an intensive change in paddy yields to non-paddy yields. Intensive land use changes affected paddy’s productivity. Multi-temporal imagery combined with ubinan data can be used to monitor paddy’s productivity. Forty-one points were calculated for the y (productivity) and x (NDVI value) equation. The equation resulting from this method (y=6.2212x+6.7444) can be used as a reference for calculating productivity in Sukoharjo District in different periods. From different calculations, the accuracy obtained from this method was 86%.Keywords: Landsat 8; NDVI; Paddy’s Productivity; Sukoharjo; Ubinan data

Automated high-precision alignment of geologic maps with well orientation

The 3D reflection seismic interpretation entails the creation of structure maps of subsurface horizons across extensive areas, often spanning tens of thousands of square kilometers within sedimentary basins. To be correlated with well measurements, maps made from 3D reflection seismic are routinely assigned the correct depth at well control points. However, integrating orientation information measured from wells remains a challenge due to the absence of upscaling guidelines. We address the scale-dependent nature of geologic structure and well orientation information in subsurface mapping, particularly focusing on integrating kilometer-scale seismic interpretation with centimeter-scale well-measured dip and azimuth data. Our pioneering methodology streamlines this integration process through three essential steps: first, transforming well dip and azimuth into a gradient representation; second, smoothly extending gradient differences between the map and well within a user-defined radius of influence; and third, constructing an updated horizon map by bicubic spline interpolation to ensure automated and high-precision alignment. In addition, a structure-oriented interpolation technique is introduced to preserve faults and local structures during orientation correction. Application of the methodology to a 3D reflection seismic horizon demonstrates its effectiveness in automating the complex task of tying subsurface maps to well orientation information. This not only reduces the need for manual interventions but also introduces a new perspective on subsurface mapping. It provides a robust framework that enhances the accuracy and reliability of geologic interpretation, offering significant advancements beyond previous efforts in the literature.

A graph-theoretic approach for the analysis of lesion changes and lesions detection review in longitudinal oncological imaging

Radiological follow-up of oncology patients requires the detection of lesions and the quantitative analysis of lesion changes in longitudinal imaging studies of patients, which is time-consuming and requires expertise.We present here a new method and workflow for the analysis and review of lesions and volumetric lesion changes in longitudinal scans of a patient. The generic graph-based method consists of lesion matching, classification of changes in individual lesions, and detection of patterns of lesion changes computed from the properties of the graph and its connected components. The workflow guides clinicians in the detection of missed lesions and wrongly identified lesions in manual and computed lesion annotations using the analysis of lesion changes. It serves as a heuristic method for the automatic revision of ground truth lesion annotations in longitudinal scans.The methods were evaluated on longitudinal studies of patients with three or more examinations of metastatic lesions in the lung (19 patients, 83 CT scans, 1178 lesions), the liver (18 patients, 77 CECT scans, 800 lesions) and the brain (30 patients, 102 T1W-Gad MRI scans, 317 lesions) with ground-truth lesion annotations. Lesion matching yielded a precision of 0.92–1.0 and recall of 0.91–0.99. The classification of changes in individual lesions yielded an accuracy of 0.87–0.97. The classification of patterns of lesion changes yielded an accuracy of 0.80–0.94. The lesion detection review workflow applied to manual and computed lesion annotations yielded 120 and 55 missed lesions and 20 and 164 wrongly identified lesions for all longitudinal studies of patients, respectively.The automatic analysis of lesion changes and review of lesion detection in longitudinal studies of oncological patients helps detect missed lesions and wrongly identified lesions. This method may help improve the accuracy of radiological interpretation and the disease status evaluation.

An artificial intelligence approach for interpreting creative combinational designs

Combinational creativity, a form of creativity involving the blending of familiar ideas, is pivotal in design innovation. While most research focuses on how combinational creativity in design is achieved through blending elements, this study focuses on the computational interpretation, specifically identifying the ‘base’ and ‘additive’ components that constitute a creative design. To achieve this goal, the authors propose a heuristic algorithm integrating computer vision and natural language processing technologies, and implement multiple approaches based on both discriminative and generative artificial intelligence architectures. A comprehensive evaluation was conducted on a dataset created for studying combinational creativity. Among the implementations of the proposed algorithm, the most effective approach demonstrated a high accuracy in interpretation, achieving 87.5% for identifying ‘base’ and 80% for ‘additive’. We conduct a modular analysis and an ablation experiment to assess the performance of each part in our implementations. Additionally, the study includes an analysis of error cases and bottleneck issues, providing critical insights into the limitations and challenges inherent in the computational interpretation of creative designs.

Study on the adsorption mechanism of fluorescent nano-tracer in sandstone core

Chemical tracer testing technology has increasingly become the preferred method for reservoir monitoring in oil fields due to its low cost and high efficiency. Understanding the migration behavior and adsorption mechanisms of chemical tracers in porous media is crucial for designing tracer injection processes and enhancing the accuracy of tracer test interpretation. This study employs an enhanced Stöber sol-gel method to synthesize fluorescent silica nanoparticles. After surface modification with the silane coupling agent γ-(methacryloyloxy) propyltrimethoxysilane (KH570) and doping with two fluorescent dyes, a multicolor fluorescent nano-tracer (PMFN) was synthesized. This study conducted dynamic and equilibrium adsorption experiments to investigate the adsorption behavior of PMFN on sandstone surfaces. To elucidate the adsorption mechanism of PMFN, we fitted the adsorption equilibrium experimental data using the Langmuir, Freundlich, and Temkin isothermal adsorption models. The adsorption kinetics were studied using the Lagergren, Elovich, and pseudo-first-order kinetic models. Additionally, we assessed the influence of ionic strength and particle size on the adsorption of PMFN by calculating the DLVO interaction forces between PMFN and the sandstone surface. The results showed that PMFN adsorption on sandstone primarily occurs in the pre-contact stage and is characterized by monolayer adsorption. When the ionic strength of the solution is 0.001 mol/L, the maximum repulsive energy barrier between PMFN and the sandstone surface hinders adsorption.

Improving the technology for Inflow Profile Evaluation in horizontal Wells by temperature changes Due to calorimetric Mixing

The paper is devoted to the problem of increasing the effectiveness of temperature logging quantitative interpretation for the inflow profile evaluation in horizontal production wells draining heterogeneous reservoirs with low permeability. Such wells are characterized by an extremely uneven distribution of inflow along the length of the wellbore. One of the ways of quantitative interpretation of temperature logging is based on the effect of calorimetric mixing. It’s widely used to quantify the share of local producing intervals in the total flow rate.The low accuracy of interpretation is associated with the lack of reliable information about the temperature of the fluid flowing into the wellbore. The authors propose an estimate of this parameter based on the similarity of the behavior of the temperature field vs time in the near-wellbore zone of a reservoir during periods of stable production and the periods of the well shut-in. This relationship is confirmed by the results of modeling the temperature field of the “well – reservoir” system, taking into account changes in a wide range of reservoir permeability and thermal properties of the reservoir, the geometry of hydraulic fractures in the reservoir, the flowing fluids, as well as the parameters of the well production targets. The logging technology recommended by the authors involves the registration of several temperature profiles along the length of the wellbore at the beginning of the well production with the maximum rate and drawdown and during the well shut-in. Their integrated analysis based on the behavior of the temperature field features in time identified on the basis of modeling makes it possible to evaluate with a high degree of certainty the dynamics of the temperature of the gas-liquid mixture coming from reservoirs to the wellbore during production periods. This provides the required accuracy in the quantitative assessment of the inflow profile from mixing anomalies.The proposed approaches to the interpretation of thermograms are applicable in the analysis of the results of non-stationary temperature logging results in horizontal and vertical wells during the production from heterogeneous reservoirs both through perforation and multi-stage hydraulic fracturing.

PECGreview: Assessment of a Novel Tool to Evaluate the Accuracy of Pediatric ECG Interpretation Skills.

The skill of interpretation of the electrocardiogram (ECG) remains poor despite existing educational initiatives. We sought to evaluate the validity of using a subjective scoring system to assess the accuracy of ECG interpretations submitted by pediatric cardiology fellows, trainees, and faculty to the Pediatric ECG Review (pECGreview), a web-based ECG interpretation training program. We conducted a retrospective, cross-sectional study of responses submitted to pECGreview. ECG interpretations were assessed independently by four individuals with a range of experience. Accuracy was assessed using a 3-point scale: 100% for generally correct interpretations, 50% for over- or underdiagnosis of minor ECG abnormalities, and 0% for over- or underdiagnosis of major ECG abnormalities. Inter-rater agreement was assessed using expanded Bland-Altman plots, Pearson correlation coefficients, and Intraclass Correlation Coefficients (ICC). 1460 ECG interpretations by 192 participants were analyzed. 107 participants interpreted at least five ECGs. The mean accuracy score was 76.6 ± 13.7%. Participants were correct in 66.1 ± 5.1%, had minor over- or underdiagnosis in 21.5 ± 4.6% and major over- or underdiagnosis in 12.3 ± 3.9% of interpretations. Validation of agreement between evaluators demonstrated limits of agreement of 11.3%. Inter-rater agreement exhibited consistent patterns (all correlations ≥ 0.75). Absolute agreement was 0.74 (95% CI 0.69-0.80), and average measures agreement was 0.92 (95% CI 0.89-0.94). Accuracy score analysis of as few as five ECG interpretations submitted to pECGreview yielded good inter-rater reliability for assessing and ranking ECG interpretation skills in pediatric cardiology fellows in training.

Unlocking Optimal Glycemic Interpretation: Redefining HbA1c Analysis in Female Patients With Diabetes and Iron-Deficiency Anemia Using Machine Learning Algorithms.

In response to the nuanced glycemic challenges faced by women with iron deficiency anemia (IDA) associated with diabetes, this study uses advanced machine learning algorithms to redefine hemoglobin (Hb)A1c measurement values. We aimed to improve the accuracy of glycemic interpretation by recognizing the critical interaction between erythrocytes, iron, and glycemic levels in this specific demographic group. This retrospective observational study included 17,526 adult women with HbA1c levels recorded from 2017 to 2022. Samples were classified as diabetic, prediabetic, or non-diabetic based on HbA1c and fasting blood glucose (FBG) levels for distribution analysis without impacting model training. Support Vector Machines, Linear Regression, Random Forest, and K-Nearest Neighbor algorithms as machine learning (ML) methods were used to predict HbA1c levels. Following the training of the model, HbA1c values were predicted for the IDA samples using the trained model. According to our results, there has been a 0.1 unit change in HbA1c values, which has resulted in a clinical decision change in some patients. Using ML to analyze HbA1c results in women with IDA may unveil distinctions among patients whose HbA1c values hover near critical medical decision thresholds. This intersection of technology and laboratory science holds promise for enhancing precision in medical decision-making processes.

Study on quantitative interpretation of uranium spectral gamma-ray logging based on machine learning algorithm

Spectral gamma-ray logging stands out as an efficacious methodology in the exploration of uranium(U). In order to address the challenges associated with low statistical efficacy in spectral analysis and mitigate the impact of spectral drift, machine learning algorithms such as Back Propagation (BP) neural network, Generalized Regression Neural Network (GRNN), and Support Vector Machine (SVM) have been employed for the quantitative interpretation of uranium. A method rooted in machine learning algorithms for energy spectral analysis has been proposed, catering to the analysis of high-speed spectra logging and gamma-ray spectra characterized by spectral drift. Examinations conducted on standard model wells containing uranium revealed that the BP neural network exhibited commendable accuracy in uranium interpretation, achieving quantification accuracy rates of 86.986 % and 93.478 % for low-grade and medium-high-grade uranium ores in the Testing Set, respectively. Notably, marginal variations in the model's quantification errors were observed under diverse logging speeds and spectral drift conditions, underscoring the capacity of gamma-ray spectral quantitative interpretation through machine learning algorithms to effectively surmount the impacts of logging speed and spectral drift. This machine learning-based approach to energy spectral analysis proved instrumental in enhancing traditional logging speeds to 6 m/min, presenting a novel perspective for the quantitative interpretation of uranium spectral gamma-ray logging at heightened logging speeds.

Automated Building Information Modeling Compliance Check through a Large Language Model Combined with Deep Learning and Ontology

Ensuring compliance with complex industry standards and regulations during the design and implementation phases of construction projects is a significant challenge in the building information modeling (BIM) domain. Traditional manual compliance checking methods are inefficient and error-prone, failing to meet modern engineering demands. Natural language processing (NLP) and deep learning methods have improved efficiency and accuracy in rule interpretation and compliance checking. However, these methods still require extensive manual feature engineering, large, annotated datasets, and significant computational resources. Large language models (LLMs) provide robust language understanding with minimal labeled data due to their pre-training and few-shot learning capabilities. However, their application in the AEC field is still limited by the need for fine-tuning for specific tasks, handling complex texts with nested clauses and conditional statements. This study introduces an innovative automated compliance checking framework that integrates LLM, deep learning models, and ontology knowledge models. The use of LLM is motivated by its few-shot learning capability, which significantly reduces the need for large, annotated datasets required by previous methods. Deep learning is employed to preliminarily classify regulatory texts, which further enhances the accuracy of structured information extraction by the LLM compared to directly feeding raw data into the LLM. This novel combination of deep learning and LLM significantly enhances the efficiency and accuracy of compliance checks by automating the processing of regulatory texts and reducing manual intervention. This approach is crucial for architects, engineers, project managers, and regulators, providing a scalable and adaptable solution for automated compliance in the construction industry with broad application prospects.

3D Marine Controlled-Source Electromagnetic Numerical Simulation Considering Terrain and Static Effect

The marine controlled-source electromagnetic (CSEM) method is an important geophysical method for the exploration of marine hydrocarbon resources. In marine CSEM forward modeling the uplift terrain, such as submarine hills and seamounts, the static effects caused by polymetallic nodules and hydrothermal sulfide are ignored which can lead to the deviation of marine CSEM data. To improve the accuracy of data processing and interpretation, this study realizes efficient 3D numerical simulation considering submarine uplift terrain and static effect based on the finite difference (FD) algorithm in a fictitious wave domain. First, based on the correspondence principle between the fictitious wave domain and a real diffusive domain, we derive the FD electromagnetic field iteration equations of the fictitious wave domain, and apply the fictitious emission source and the boundary condition of complex frequency shifted-perfectly matched layer (CFS-PML). We use the inverse transformation method to convert the electromagnetic response to the diffusive domain. Then, we carry out simulations on typical 1D and 3D reservoir models to verify the correctness and effectiveness of the algorithm. Furthermore, we design an uplift terrain model and a static effect model and study the influence of parameters such as top width, bottom width, height and volume of uplift terrain on the CSEM field response characteristics through the forward modeling of multiple models and discuss the influence of parameters such as electrical conductivity, length, width, thickness, depth and volume of a shallow anomaly on the marine CSEM response. Finally, we analyze the characteristics and rules of electromagnetic field propagation of uplift terrain and static effect, which provides theoretical guidance for the design of marine CSEM exploration systems.

SYNCHRONIZATION PROBLEMS OF SIMULTANEOUS TRANSLATION

Among the many difficulties encountered in simultaneous interpreting - an important feature of multilingual communication in various international contexts - synchronization issues stand out. This article analyzes many synchronization problems and the nature of synchronization problems, their causes, effects, and potential solutions. We highlight the technological, environmental, and cognitive components that create these challenges. We will also discuss modern strategies and methods to reduce the difficulties and problems during synchronization, while improving the accuracy and efficiency of simultaneous interpretation.

Pulmonary Function Tests: Easy Interpretation in Three Steps.

Pulmonary function tests (PFTs) are pivotal in diagnosing and managing a broad spectrum of respiratory disorders. These tests provide critical insights into lung health, guiding diagnoses, assessing disease severity, and shaping patient management strategies. This review addresses the complexities and nuances inherent in interpreting PFT data, particularly in light of recent updates from the European Respiratory Society (ERS) and American Thoracic Society (ATS). These updates have refined interpretive strategies, moving away from definitive diagnostic uses of spirometry to a more probabilistic approach that better accounts for individual variability through the use of Z-scores and lower limits of normal (LLNs). Significantly, this narrative review delves into the philosophical shift in spirometry interpretation, highlighting the transition from direct clinical diagnostics to a more nuanced evaluation geared towards determining the likelihood of disease. It critiques the reliance on fixed ratios and emphasizes the need for reference values that consider demographic variables such as age, sex, height, and ethnicity, in line with the latest Global Lung Function Initiative (GLI) equations. Despite these advances, challenges remain in ensuring uniformity across different predictive models and reference equations, which can affect the accuracy and consistency of interpretations. This paper proposes a streamlined three-step framework for interpreting PFTs, aiming to unify and simplify the process to enhance clarity and reliability across various medical specialties. This approach not only aids in accurate patient assessments but also mitigates the potential for misdiagnosis and ensures more effective patient management. By synthesizing contemporary guidelines and integrating robust physiological principles, this review fosters a standardized yet flexible approach to PFT interpretation that is both scientifically sound and practically feasible.

Stored energy density solution for TSV-Cu structure deformation under thermal cyclic loading based on PINN

TSV-Cu is widely used for chip interconnects, where high testing costs and complex crystal plasticity finite element (CPFE) limit the research of its deep microplastic evolution process. Considering the stored energy density (SED) based on dislocation density and plastic work as a fatigue indicator factor (FIP), this paper proposes for the first time a physics-informed neural network (PINN) framework based on SED, which aims to achieve the solution of SED distributions quickly and efficiently to save computational time and cost. The grain orientation, geometrical compatibility factor, back stress, and effective plastic strain are taken into account as inputs to the PINN model. The total dislocation density is used as an indirect solution variable to construct the associated loss boundary terms, which results in the solution of the SED. The results of the two real EBSD tests show that the PINN model is able to accurately and sensitively predict the SED concentration distribution for different thermal cycle loadings, and maintains a high degree of agreement with the CPFE calculation results. Moreover, The superiority of PINN over other machine learning algorithms in terms of physical model interpretation and prediction accuracy is verified. These make it a reality for PINN to solve for the FIP distribution for the first time, and to accurately and quickly predict the location of crack initiation.

Effects of note-taking on the accuracy and fluency of consecutive interpreters' immediate free recall of source texts: A three-stage developmental study

Note-taking for consecutive interpreting (CI) is a special type of note-taking in that selective notes are taken in a highly multi-tasking condition under time pressure during a CI speech segment but are used to enable a complete recall of the source speech immediately. Given the specialty of interpreters' note-taking, it is still unclear in what ways note-taking affects interpreters' memory performance. Moreover, the effect of note-taking is theoretically assumed to depend on interpreting expertise, which has not been substantiated by empirical research. This study addresses these questions by investigating the effect of note-taking on the accuracy and fluency of interpreters' memory of the source text at different training stages. An oral recall experiment was administered to student interpreters at three critical note-taking training stages: the initial stage (with one-month training in note-taking), after the critical development stage (with six-month training in note-taking), and the advanced stage (with two-year training in note-taking), with a sample size of 53, 53 and 35 at each stage respectively. Participants listened to and immediately recalled speech segments consecutively in two note-taking conditions: with and without note-taking. Recall accuracy was measured by completeness of information recalled, and recall fluency was measured by oral fluency during recall. Results showed that note-taking significantly enhanced both accuracy and fluency of interpreters' recall of the source text at all the three stages, revealing a facilitative effect of note-taking on source text memory. The size of the note-taking effect increased as interpreting training experience accumulated, indicating that interpreting training enhanced the facilitative role of note-taking. However, the developmental trajectories of the note-taking effect on recall accuracy and fluency differed. As interpreting training proceeded, the note-taking effect on recall fluency was first improved (from Stage 1 to Stage 2), and later was the note-taking effect on recall accuracy (from Stage 1 to Stage 3). The findings shed light on the cognitive mechanisms of how note-taking functions for consecutive interpreters. A potential practical implication for interpreter training is that a step-by-step note-taking training approach may be beneficial.

Accuracy of focused cardiac ultrasound interpretation among emergency and critical care medicine residents in Ethiopia: A multi-center cross-sectional study

IntroductionFocused cardiac ultrasound (FoCUS) has emerged as a valuable tool in emergency and critical care medicine, allowing for rapid assessment of cardiac function and structure at the bedside. This rapid diagnostic technique holds particular promise in resource-limited settings like Ethiopia, where access to standard echocardiography may be limited and delayed. However, the accuracy of FoCUS interpretation is highly dependent on the operator's skills and expertise. To inform the design of effective interventions, the study aimed to assess the accuracy of FoCUS interpretation and associated factors among senior Emergency Medicine residents at two large referral teaching hospitals in Ethiopia. MethodsA cross-sectional study was conducted from October to December 2023 among 80 residents at Tikur Anbessa Specialized Hospital and St. Paul's Hospital Millennium Medical College. To assess diagnostic accuracy, 15 pre-selected cardiac ultrasound videos (normal and pathological cases) were selected from American College of Emergency Physicians website and the PoCUS Atlas, and accurate interpretation was defined as correctly answering at least 12 out of 15 readings. A binary logistic regression model was fitted to identify significant factors at the 5% level of significance, where significant results were interpreted using adjusted odds ratio (AOR) with 95% confidence interval (CI). ResultThe overall accuracy in interpreting FoCUS findings was 47.5% (95% CI: 38.8–60.0%), with highest for collapsing Inferior Vena Cava (91.3%) and standstill (90.0%), and lowest for Regional Wall Motion Abnormality of Left Ventricle (46.3%). Residents who received training (AOR=4.14, 95%CI:1.32–13.04, p = 0.015), perceived themselves as skilled (AOR=4.81, 95%CI=1.06–21.82, p = 0.042), and felt confident in acquiring and interpretation (AOR=3.16, 95%CI=1.01–9.82, p = 0.047) demonstrated significantly higher accuracy. ConclusionThe study identified a low overall accuracy in FoCUS interpretation, with accuracy improving with training and better perceived skill and confidence. Training programs with simulation, continuous education, and mentorship are crucial to enhance these critical skills.

A logging evaluation method for shale and calcium-bearing reservoirs based on the effective medium HB resistivity model—a case study on the Sartu Formation and Putaohua Formation West of the Daqing Placanticline

Abstract To solve the problems of low logging interpretation accuracy and fluid identification in shale and calcium-bearing formations in the study area, this paper uses the effective medium theory and electric double layer theory, combined with artificial core technology, to carry out the first quantitative study of the influence of calcareous on rock resistivity, and establishes a conductive model for the evaluation of the oil and gas properties of shale and calcium-bearing formations. First, considering the salt exclusion effect of clay pore water and its dispersion distribution, an improved Berg model was established. Compared with the original Berg model, the improved Berg model achieved a higher interpretation accuracy. Second, artificial cores with different calcium and clay content were designed and prepared using artificial core technology. A quantitative formula describing the relationship between the cementation exponent m and the calcium and clay content was established and combined with the improved Berg model to build an effective medium HB resistivity model suitable for shale and calcium-bearing formations. Finally, the hierarchical decomposition crossplot approach was used to identify the calcareous cemented rock layers, then the reservoir parameters were calculated. The new model is applied to the quantitative evaluation of reservoir logging, and the comparison with the oil test results indicates that the new model established in this paper can be applied to the quantitative evaluation of reservoir logging in this area, and the corresponding evaluation techniques provided a good reference for logging interpretation and evaluation in other corresponding areas.

MBRB: Micro-belief rule Base model based on cautious conjunctive rule for interpretable fault diagnosis

—In aerospace engineering, implementing interpretable fault diagnosis technology is critical for improving the credibility of diagnostic results and ensuring the reliability of complex electromechanical systems. As a typical interpretable modeling method, the belief rule base (BRB) approach still faces two challenges that need to be addressed for its application in interpretable fault diagnosis: 1) The lengthy rule structures raise the cost of model interpretability, and 2) over-optimization of parameters diminishes the interpretability. To solve these problems, this paper proposes a modified micro-belief rule structure and develops a new micro-belief rule base (MBRB) model based on this structure. In addition, considering the independence of rules and the correlations between multiple BRBs, a cautious conjunctive rule-based reasoning process is established as the inference engine of MBRB. Moreover, an interpretable optimization method based on projection covariance matrix adaptive evolutionary strategy (PCMAES-I) is proposed for the MBRB model to balance model interpretability and diagnostic accuracy. Finally, the availability and effectiveness of the proposed MBRB is verified through an electromagnetic relay experiment.