Accurate Analysis Research Articles

Image Recognition of Photovoltaic Cell Occlusion Based on Subpixel Matching

INTRODUCTION: During the operation of large photovoltaic power stations, they are often shielded by dust and bird droppings, which greatly reduce the power generation and even cause fires. Analysis of PV cell occlusion image recognition accuracy based on sub-pixel matching. OBJECTIVES: In order to find the location of the pv cells, we use the method of subpixel image matching. Improve recognition accuracy. METHODS: When the power plant is running normally, taken the original image for photovoltaic power station as the original sample, and then using the subpixel gradient matching algorithm, to match the original image and find out that the minimum matching values. RESULTS: If the calculation results is greater than a specified threshold, When the calculated result is greater than the specified threshold, the power station is considered abnormal. CONCLUSION: The experimental process shows that this method can better judge the operating status of photovoltaic power station, and can find out the location of mismatched photovoltaic cells more accurately, and the calculation accuracy reaches sub-pixel level.

OccTest: An integrated approach for quality control of species occurrence data

AbstractAimSpecies occurrence data are valuable information that enables one to estimate geographical distributions, characterize niches and their evolution, and guide spatial conservation planning. Rapid increases in species occurrence data stem from increasing digitization and aggregation efforts, and citizen science initiatives. However, persistent quality issues in occurrence data can impact the accuracy of scientific findings, underscoring the importance of filtering erroneous occurrence records in biodiversity analyses.InnovationWe introduce an R package, occTest, that synthesizes a growing open‐source ecosystem of biodiversity cleaning workflows to prepare occurrence data for different modelling applications. It offers a structured set of algorithms to identify potential problems with species occurrence records by employing a hierarchical organization of multiple tests. The workflow has a hierarchical structure organized in testPhases (i.e. cleaning vs. testing) that encompass different testBlocks grouping different testTypes (e.g. environmental outlier detection), which may use different testMethods (e.g. Rosner test, jacknife,etc.). Four different testBlocks characterize potential problems in geographic, environmental, human influence and temporal dimensions. Filtering and plotting functions are incorporated to facilitate the interpretation of tests. We provide examples with different data sources, with default and user‐defined parameters. Compared to other available tools and workflows, occTest offers a comprehensive suite of integrated tests, and allows multiple methods associated with each test to explore consensus among data cleaning methods. It uniquely incorporates both coordinate accuracy analysis and environmental analysis of occurrence records. Furthermore, it provides a hierarchical structure to incorporate future tests yet to be developed.Main conclusionsoccTest will help users understand the quality and quantity of data available before the start of data analysis, while also enabling users to filter data using either predefined rules or custom‐built rules. As a result, occTest can better assess each record's appropriateness for its intended application.

Acidic Derivatization of Thiols Using Diethyl 2-Methylenemalonate: Thiol-Michael Addition Click Reaction for Simultaneous Analysis of Cysteine and Cystine in Culture Media Using LC-MS/MS.

Cysteine (Cys) and its oxidized form, cystine (Cys2), play crucial roles in biological systems and have considerable applications in cell culture. However, Cys in cell culture media is easily oxidized to Cys2, leading to solubility issues. Traditional analytical methods struggle to maintain the oxidation states of Cys and Cys2 during analysis, posing a significant challenge to accurately measuring and controlling these compounds. To effectively control the Cys and Cys2 levels, a rapid and accurate analytical method is required. Here, we screened derivatizing reagents that can react with Cys even under acidic conditions to realize a novel analytical method for simultaneously determining Cys and Cys2 levels. Diethyl 2-methylenemalonate (EMM) was found to possess the desired traits. EMM, characterized by its dual electron-withdrawing attributes, allowed for a rapid reaction with Cys under acidic conditions, preserving intact information for understanding the functions of target compounds. Combined with LC-MS/MS and an internal standard, this method provided high analytical accuracy in a short analytical time of 9 min. Using the developed method, the rapid oxidation of Cys in cell culture media was observed with the headspace of the storage container considerably influencing Cys oxidation and Cys2 precipitation rates. The developed method enabled the direct and simplified analysis of Cys behavior in practical media samples and could be used in formulating new media compositions, ensuring quality assurance, and real-time analysis of Cys and Cys2 in cell culture supernatants. This novel approach holds the potential to further enhance the media performance by enabling the timely optimal addition of Cys.

The role of body–object interaction in children’s concept processing: insights from two Chinese communities

A rating of body–object interactions (BOIs) reflects the ease with which a human body can interact physically with a word’s referent. Studies with adults have demonstrated a facilitating BOI effect in language tasks, with faster and more accurate responses for high BOI words (e.g., cup) than low BOI words (e.g., coal). A few studies have explored the BOI effect in children. However, these studies have all adopted adult-rated BOIs, which may differ from children’s. Using child-rated BOIs, the present study investigated the BOI effect in Chinese children and its relationship with age, as well as whether there was a community difference in the BOI effect. Children (aged 7–8) from Mainland China (N = 100) and Hong Kong SAR (HK; N = 90) completed a lexical decision task used to measure the BOI effect. The children were asked to judge whether each item was a real Chinese word; each real word was assigned a child-rated BOI score. After controlling nonverbal intelligence, gender, working memory, and Chinese character reading, a significant BOI effect was observed at the response accuracy and speed levels. The accuracy and latency analyses illustrated a community difference; the BOI effect was smaller in the HK children. This study suggests that BOI measures may be sensitive to the ecological differences between tested communities. The findings support the need for further investigations into the BOI effect across Chinese communities, particularly those in Mainland China.

A novel feature selection method based on quantum support vector machine

Feature selection is critical in machine learning to reduce dimensionality and improve model accuracy and efficiency. The exponential growth in feature space dimensionality for modern datasets directly results in ambiguous samples and redundant features, which can severely degrade classification accuracy. Quantum machine learning offers potential advantages for addressing this challenge. In this paper, we propose a novel method, quantum support vector machine feature selection (QSVMF), integrating quantum support vector machines with multi-objective genetic algorithm. QSVMF optimizes multiple simultaneous objectives: maximizing classification accuracy, minimizing selected features and quantum circuit costs, and reducing feature covariance. We apply QSVMF for feature selection on a breast cancer dataset, comparing the performance of QSVMF against classical approaches with the selected features. Experimental results show that QSVMF achieves superior performance. Furthermore, the Pareto front solutions of QSVMF enable analysis of accuracy versus feature set size trade-offs, identifying extremely sparse yet accurate feature subsets. We contextualize the biological relevance of the selected features in terms of known breast cancer biomarkers. This work highlights the potential of quantum-based feature selection to enhance machine learning efficiency and performance on complex real-world data.

Impurity profile analysis of amphotericin B using on-line two-dimensional high performance liquid chromatography-quadrupole time-of-flight mass spectrometry

Amphotericin B (AmB) is a polyene-macrolide antimicrobial drug with a broad antibacterial spectrum and remarkable efficacy against deep fungal infections. It binds to ergosterol on the fungal cell membrane and alters its permeability, thereby destroying the membrane. AmB is a multicomponent antimicrobial medication that contains a wide range of impurities, rendering quality analysis extremely difficult. In the current Chinese Pharmacopoeia (Edition 2020) and European Pharmacopoeia (EP10.3), high performance liquid chromatography (HPLC) is applied to examine related substances in AmB. However, this technique presents a number of issues. For instance, the mobile phases used in the HPLC method described in both references contain nonvolatile inorganic salts, which cannot be coupled with a mass spectrometry (MS) detector. In addition, because the mobile phases used have a low pH, the component/impurities of AmB drug can easily be degraded or interconverted during the analytical process, leading to reduced analytical accuracy. Therefore, the accuracy and sensitivity of this method must be improved. In this study, a method based on on-line two-dimensional high performance liquid chromatography-quadrupole time-of-flight mass spectrometry (2D HPLC-Q TOF/MS) was developed to analyze the impurity profile of AmB in accordance with the Chinese Pharmacopoeia (Edition 2020) and European Pharmacopoeia (EP10.3). The method combines on-line dilution and a multiple-capture HPLC system to achieve the efficient separation of AmB component/impurities. It also resolves the issue of poor solvent compatibility in 2D HPLC, increases the analytical flux, enhances the automation capability, reduces the mutual conversion of AmB and its impurities during the analytical process, and increases the detection sensitivity of the method. MS was also used to determine the structural inference of unstable components and impurities. An XBridge Shield C18 column (250 mm×4.6 mm, 3 μm) was used for first-dimensional-liquid chromatography with gradient elution using methanol-acetonitrile-4.2 g/L citric acid monohydrate solution (10∶30∶60, v/v/v, pH 4.7) as mobile phase A and methanol-acetonitrile-4.2 g/L citric acid monohydrate solution (12∶68∶20, v/v/v, pH 3.9) as mobile phase B. An Xtimate C8 column (10 mm×2.1 mm, 5 μm) was used as the trap column, and trapping and desalting were performed using 10 mmol/L ammonium formate aqueous solution containing 0.1% formic acid-acetonitrile (95∶5, v/v). An Xtimate C8 column (250 mm×2.1 mm, 5 μm) was used for second-dimensional-liquid chromatography with gradient elution using 10 mmol/L ammonium formate aqueous solution containing 0.1% formic acid-acetonitrile (95∶5, v/v) and 10 mmol/L ammonium formate aqueous solution containing 0.1% formic acid-acetonitrile (5∶95, v/v) as mobile phases. The data were collected in positive-ion mode. In this study, the structures of six impurities in amphotericin B were inferred, according to the fragmentation, the MS and MS2 spectra of each impurity. The developed method can be used to quickly and sensitively analyze the impurity profile of AmB. Furthermore, the research results on impurity profiles can be applied to guide improvements in AmB production.

VIRTUAL MOUSE WITH INTEGRATED CHATBOT

Notably, the research addresses challenges in achieving precision and user adaptability within Virtual Gesture Systems. Employing a systematic methodology, the study utilizes cutting-edge hardware and software to capture and analyze data related to hand motion. Additionally, this research integrates a voice assistant-based chatbot with limited functions, expanding the scope of interaction possibilities. The results offer a comprehensive evaluation of the combined Virtual Gesture System and voice-assisted chatbot. This evaluation includes a comparative analysis of accuracy, efficiency, and user satisfaction against existing systems in the field. The ensuing discussion interprets the findings, addressing initial research questions and outlining implications for future development. The integration of a voice assistant-based chatbot enhances the versatility of the system, opening avenues for streamlined and user-friendly interactions. Despite inherent limitations, this research significantly contributes to the understanding of Virtual Gesture Systems, presenting a novel approach to human-computer interaction across diverse domains.

A comprehensive evaluation of marker-based, markerless methods for loose garment scenarios in varying camera configurations

In support of smart wearable researchers striving to select optimal ground truth methods for motion capture across a spectrum of loose garment types, we present an extended benchmark named DrapeMoCapBench (DMCB+). This augmented benchmark incorporates a more intricate limb-wise Motion Capture (MoCap) accuracy analysis, and enhanced drape calculation, and introduces a novel benchmarking tool that encompasses multicamera deep learning MoCap methods. DMCB+ is specifically designed to evaluate the performance of both optical marker-based and markerless MoCap techniques, taking into account the challenges posed by various loose garment types. While high-cost marker-based systems are acknowledged for their precision, they often require skin-tight markers on bony areas, which can be impractical with loose garments. On the other hand, markerless MoCap methods driven by computer vision models have evolved to be more cost-effective, utilizing smartphone cameras and exhibiting promising results. Utilizing real-world MoCap datasets, DMCB+ conducts 3D physics simulations with a comprehensive set of variables, including six drape levels, three motion intensities, and six body-gender combinations. The extended benchmark provides a nuanced analysis of advanced marker-based and markerless MoCap techniques, highlighting their strengths and weaknesses across distinct scenarios. In particular, DMCB+ reveals that when evaluating casual loose garments, both marker-based and markerless methods exhibit notable performance degradation (>10 cm). However, in scenarios involving everyday activities with basic and swift motions, markerless MoCap outperforms marker-based alternatives. This positions markerless MoCap as an advantageous and economical choice for wearable studies. The inclusion of a multicamera deep learning MoCap method in the benchmarking tool further expands the scope, allowing researchers to assess the capabilities of cutting-edge technologies in diverse motion capture scenarios.

Assembly accuracy analysis and phase optimization of aero-engine multistage rotors considering surface morphology and non-uniform contact deformation

The assembly accuracy of multistage rotors is crucial to guarantee the quality and performance of aero engines. However, traditional assembly accuracy analysis methods usually oversimplify geometric deviations as the degrees of freedom (DOF) variations relative to the ideal position, ignoring real surface morphology and the resulting non-uniform contact deformation. Therefore, this paper proposes an assembly accuracy analysis method that integrates the effects of surface morphology and non-uniform contact deformation on error propagation process, and further adopts a phase optimization method to improve the assembly accuracy of multistage rotors. Specifically, the surface morphology of rotor end faces is generated using the random midpoint displacement method. The Conjugate Gradient-Fast Fourier Transform (CG-FFT) algorithm is employed to calculate the non-uniform contact deformation. According to the simulation results, a fitting deformation surface (FDS) is constructed to determine the assembly position and orientation. On this basis, a novel error propagation model is established and the mounting phase of each stage rotor is optimized by a genetic algorithm (GA). A case study of a specific rotor system is carried out to verify the proposed error propagation model and give the optimized assembly combination with the objective of minimum coaxiality. This research effectively improves the prediction and actual assembly accuracy of aero-engine multistage rotors and provide a theoretical basis for the precision assembly of multistage rotors in aero-engine applications.

Application of Deep Learning on Structure Displacement Measurement and Accuracy Analysis

Displacement serves as a crucial indicator in structural health monitoring within civil engineering, with computer vision being a commonly employed tool due to its cost-effectiveness and convenience. This paper introduces a methodology for tracking feature points computed via computer vision techniques SuperPoint and SuperGlue. Two test scenarios are conducted: a bridge loading test characterized by a low uniform stress state, and a tensile strength test marked by a high stress state. However, a notable drawback of this method is the potential misalignment between the survey point (the point under measurement) and the feature point, leading the errors. To address this issue, a fundamental principle for selecting feature points is presented. In the bridge loading test, the displacement of the survey point is recorded, and the influence of misalignment between survey point and feature point on accuracy is analyzed under low-stress uniform conditions. The findings demonstrate the feasibility of this method for field measurements in civil engineering. Subsequently, a tensile strength test is conducted to assess the influence of different feature point selection criteria on accuracy under high-stress conditions. Concurrently, the DIC software Ncorr is applied to analyze the displacement field of feature points near the survey point. The results highlight the significant influence of selecting feature points within regions of high stress concentration on measurement accuracy.

Antenatal Determinants of Postnatal Renal Function in Fetal Megacystis: A Systematic Review.

Introduction: To evaluate the clinical usefulness of demographic data, fetal imaging findings and urinary analytes were used for predicting poor postnatal renal function in children with congenital megacystis. Materials and methods: A systematic review was conducted in MEDLINE's electronic database from inception to December 2023 using various combinations of keywords such as "luto" [All Fields] OR "lower urinary tract obstruction" [All Fields] OR "urethral valves" [All Fields] OR "megacystis" [All Fields] OR "urethral atresia" [All Fields] OR "megalourethra" [All Fields] AND "prenatal ultrasound" [All Fields] OR "maternal ultrasound" [All Fields] OR "ob-stetric ultrasound" [All Fields] OR "anhydramnios" [All Fields] OR "oligohydramnios" [All Fields] OR "renal echogenicity" [All Fields] OR "biomarkers" [All Fields] OR "fetal urine" [All Fields] OR "amniotic fluid" [All Fields] OR "beta2 microglobulin" [All Fields] OR "osmolarity" [All Fields] OR "proteome" [All Fields] AND "outcomes" [All Fields] OR "prognosis" [All Fields] OR "staging" [All Fields] OR "prognostic factors" [All Fields] OR "predictors" [All Fields] OR "renal function" [All Fields] OR "kidney function" [All Fields] OR "renal failure" [All Fields]. Two reviewers independently selected the articles in which the accuracy of prenatal imaging findings and fetal urinary analytes were evaluated to predict postnatal renal function. Results: Out of the 727 articles analyzed, 20 met the selection criteria, including 1049 fetuses. Regarding fetal imaging findings, the predictive value of the amniotic fluid was investigated by 15 articles, the renal appearance by 11, bladder findings by 4, and ureteral dilatation by 2. The postnatal renal function showed a statistically significant relationship with the occurrence of oligo- or anhydramnion in four studies, with an abnormal echogenic/cystic renal cortical appearance in three studies. Single articles proved the statistical prognostic value of the amniotic fluid index, the renal parenchymal area, the apparent diffusion coefficient (ADC) measured on fetal diffusion-weighted MRI, and the lower urinary tract obstruction (LUTO) stage (based on bladder volume at referral and gestational age at the appearance of oligo- or anhydramnios). Regarding the predictive value of fetal urinary analytes, sodium and β2-microglobulin were the two most common urinary analytes investigated (n = 10 articles), followed by calcium (n = 6), chloride (n = 5), urinary osmolarity (n = 4), and total protein (n = 3). Phosphorus, glucose, creatinine, and urea were analyzed by two articles, and ammonium, potassium, N-Acetyl-l3-D-glucosaminidase, and microalbumin were investigated by one article. The majority of the studies (n = 8) failed to prove the prognostic value of fetal urinary analytes. However, two studies showed that a favorable urinary biochemistry profile (made up of sodium < 100 mg/dL; calcium < 8 mg/dL; osmolality < 200 mOsm/L; β2-microglobulin < 4 mg/L; total protein < 20 mg/dL) could predict good postnatal renal outcomes with statistical significance and urinary levels of β2-microglobulin were significantly higher in fetuses that developed an impaired renal function in childhood (10.9 ± 5.0 mg/L vs. 1.3 ± 0.2 mg/L, p-value < 0.05). Conclusions: Several demographic data, fetal imaging parameters, and urinary analytes have been shown to play a role in reliably triaging fetuses with megacystis for the risk of adverse postnatal renal outcomes. We believe that this systematic review can help clinicians for counseling parents on the prognoses of their infants and identifying the selected cases eligible for antenatal intervention.

Development and Evaluation of a New Self-Administered Near Visual Acuity Chart: Accuracy and Feasibility of Usage.

Background: Visual acuity (VA) assessments are crucial in ophthalmology but traditionally rely on in-clinic evaluations. The emergence of telemedicine has spurred interest in creating dependable self-administered VA tests for use beyond standard clinical environments. This study evaluated the practicality and validity of a self-administered near VA card test against traditional Snellen and Rosenbaum Pocket Vision Screener (RPVS) methods for home monitoring and enhancing clinical workflow. Methods: In a cross-sectional study, a near VA card (Hadassah Self-Visual Acuity Screener (HSVA)) was developed with written and videotaped instructions for self-use. Patients with a minimal best-corrected VA (BCVA) of 1.0 LogMAR in at least one eye were recruited from ophthalmology and optometry clinics. Outcomes included the mean BCVA difference between the self-administered values and those obtained by the examiner, and correlations between BCVA values obtained by the Snellen, RPVS, HSVA, and previous distance BCVA methods according to the patients' electronic medical records. Results: A total of 275 participants (mean age: 42.5 ± 19.4 years; range: 18-89 years; 47% female) were included. Test-retest reliability analysis of the HSVA demonstrated a very good correlation and repeatability (n = 38 patients; Rs = 1.0; p < 0.001). Accuracy analysis revealed the mean LogMAR BCVA values of an additional 237 patients obtained by the Snellen, RPVS, and HSVA methods were similar (p = 0.10). The self-test BCVA results obtained by the HSVA agreed with the masked examiner-tested VA results (n = 67 patients; p = 0.17; Rs = 0.87; ICC = 0.96). Similar results were obtained when stratification by median age (42 years) was performed. Bland-Altman analysis of the HSVA and RPVS methods demonstrated a good agreement. To assess whether the HSVA could predict the VA results in the clinically used charts, multivariate analysis was used and revealed that the HSVA predicted the RPVS results (β = 0.91; p = 0.001; R2 = 0.88), and the self-test HSVA predicted the Snellen VA results within two lines (β = 0.93; p = 0.01; R2 = 0.36). Conclusions: The home-based HSVA assessment exhibited high test-retest reliability, accuracy, and alignment with clinical-standard VA tests. Its efficacy in self-testing mirrored examiner-conducted VA assessments and accurately predicted Snellen VA outcomes, indicating the HSVA's suitability for self-monitoring in chronic ocular conditions or when access to conventional examinations is limited. The utility of self-administered VA tests may extend beyond ophthalmology and optometry, potentially benefiting primary care, emergency medicine, and neurology. Further research is needed to explore and validate the practical applications of remote VA testing.

Cascaded Interleaved DC–DC Converter for a Bidirectional Electric Vehicle Charging Station

The dc-dc bidirectional step-up interleaved converter coupled by a central capacitor in a cascaded topology is used as a Bidirectional Electric Vehicle Charging Station (BEVCS) for charging and discharging the battery of an Electric Vehicle (EV). In this paper, a unified (coupled) model is proposed combining all switching intervals in a single model, allowing an accurate analysis and a better design for the controllers. In addition, this solution enables not only stage 1 to regulate the dc-link voltage on the terminals of stage 2, but also to use a classical Proportional-Integral ( <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$PI$</tex-math></inline-formula> ) controller or an adaptive <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$\boldsymbol{SoC}$</tex-math></inline-formula> -sharing function as a control structure. Finally, stability analysis, simulations, and experimental results are accomplished to evaluate the effectiveness of the proposed approach.

Error Modeling and Accuracy Analysis of Parallel Robots for Spacecraft Docking Motion Simulation System

Background:: A large number of on-orbit docking dynamics experiments are conducted in the spacecraft docking motion simulation system with the development of Chinese space science and technology, and therefore, the requirement for high-precision parallel robots has increased. The pose accuracy of parallel robots is one of the most important problems in this application. Objective:: In order to evaluate the pose accuracy of parallel robots in advance, a comprehensive pose accuracy analysis method considering major error sources is presented in this paper, and the influence of major error sources on the pose accuracy is also investigated to summarize the statistics and propagation characteristics of the pose error. Methods:: The first-order error model for the parallel robot is established based on a generalized error model for each hydraulic cylinder and a sensitivity analysis method. Using the error model, a statistical approach to the parallel robot pose accuracy analysis is presented, and the influence of different parameter errors and different poses on the pose accuracy of the parallel robot is investigated. Sensitivity analysis is applied to evaluate the contribution of each parameter error to the position and orientation error of the parallel robot. An automated pose accuracy analysis program that computes and graphically displays the position and orientation error distributions and the sensitivity analysis results is developed. Results: The statistical analysis results of the influence of different parameter errors and different poses on pose accuracy are obtained by using the automated pose accuracy analysis program. The means of the position and orientation errors are close to zero. The standard deviations in the x and y directions are larger than those in the z direction, and these standard deviations are amplified with the increase of the parameter errors. For the given elevation, sensitivity analysis to various parameter errors is performed. It is found that the length error sensitivities of the hydraulic cylinders are less than one, and position error sensitivities of the hook joints A4, A5, and A6 are much greater than those of hook joints A1, A2 and A3. Conclusion:: The elemental error sources belong to one of two groups, i.e., those affecting the hydraulic cylinder length and those affecting the hook joints. The distributions of the position and orientation errors are consistent with statistics theories. The parallel robot is more sensitive to the errors affecting the hook joints than those affecting the hydraulic cylinder length. These will help the designers and users of the parallel robot understand the statistics and propagation characteristics of the pose error. Some recent patents on error modeling and kinematic calibration of parallel robots are also discussed in this article.

Establishing the optimal number of passes during EUS-FNB for diagnosis of pancreatic solid lesions: Prospective multicenter study.

Background and study aims The optimal number of needle passes during endoscopic ultrasound-guided fine-needle biopsy (EUS-FNB) is not yet established. We aimed to perform a per-pass analysis of the diagnostic accuracy of EUS-FNB of solid pancreatic lesions using a 22G Franseen needle. Patients and methods Consecutive patients with solid pancreatic lesions referred to 11 Italian centers were prospectively enrolled. Three needle passes were performed; specimens were collected after each pass and processed individually as standard histology following macroscopic on-site evaluation (MOSE) by the endoscopist. The primary endpoint was diagnostic accuracy of each sequential pass. Final diagnosis was established based on surgical pathology or a clinical course of at least 6 months. Secondary endpoints were specimen adequacy, MOSE reliability, factors impacting diagnostic accuracy, and procedure-related adverse events. Results A total of 504 samples from 168 patients were evaluated. Diagnostic accuracy was 90.5% (85.0%-94.1%) after one pass and 97.6% (94.1%-99.3%) after two passes ( P =0.01). Similarly, diagnostic sensitivity and sample adequacy were significantly higher adding the second needle pass (90.2%, 84.6%-94.3% vs 97.5%, 93.8%-99.3%, P =0.009 and 91.1%, 85.7%-94.9% vs 98.2%, 95.8%-99.3%, P =0.009, one pass vs two passes, respectively). Accuracy, sensitivity, and adequacy remained the same after the third pass. The concordance between MOSE and histological evaluation was 89.9%. The number of passes was the only factor associated with accuracy. One case of mild acute pancreatitis (0.6%) was managed conservatively. Conclusions At least two passes should be performed for the diagnosis of solid pancreatic lesions. MOSE is a reliable tool to predict the histological adequacy of specimens.

Driving behavior-guided battery health monitoring for electric vehicles using extreme learning machine

An accurate estimation of the state of health (SOH) of batteries is critical to ensuring the safe and reliable operation of electric vehicles (EVs). Feature-based machine learning methods have exhibited enormous potential for rapidly and precisely monitoring battery health status. However, simultaneously using various health indicators (HIs) may weaken estimation performance due to feature redundancy. Furthermore, ignoring real-world driving behaviors can lead to inaccurate estimation results as some features are rarely accessible in practical scenarios. To address these issues, we proposed a feature-based machine learning pipeline for reliable battery health monitoring, enabled by evaluating the acquisition probability of features under real-world driving conditions. We first summarized and analyzed various individual HIs with mechanism-related interpretations, which provide insightful guidance on how these features relate to battery degradation modes. Moreover, all features were carefully evaluated and screened based on estimation accuracy and correlation analysis on three public experimental battery degradation datasets. Finally, the scenario-based feature fusion and acquisition probability-based practicality evaluation method construct a useful tool for feature extraction with consideration of driving behaviors. This work highlights the importance of balancing the performance and practicality of HIs during the development of feature-based battery health monitoring algorithms.

Research on the rapid 3D measurement of satellite antenna reflectors using stereo tracking technique

Measurement and analysis of surface accuracy are crucial steps in the manufacturing process of satellite antenna reflectors. The conventional close-range photogrammetry requires attaching target points on the surface, which is time-consuming and may damage the surface quality. To overcome these drawbacks, a rapid measurement technique of non-contact three-dimensional (3D) shapes is proposed, which is based on the integration of a stereo tracking method and a structured light measurement method. This paper focuses on addressing the issue of obtaining pose transformation matrices for measurement devices in motion within this method. Initially, a plane tracking scheme and tracking plate pattern is designed. Furthermore, the feature points of the tracking plate are extracted from the target area of image processing. Moreover, the pose transformation matrix of the tracking plate relative to the fixed world coordinate system at different positions is obtained using the triangulation method. Finally, a measurement experiment is conducted, resulting in a stereo tracking scheme error within 0.5%.

Adjusting dynamic and damping performance in fiber-reinforced magnetorheological elastomer composite conical shells subjected to compressive loads

This study explores the dynamic behavior of axially loaded conical shells composed of magnetorheological elastomer composites (MRECs). The investigation focuses on analyzing the impact of compressive loads on the frequencies and loss factors of these composites. The MRECs consist of laminates reinforced with fibers, enabling tunable viscoelastic properties through the utilization of magnetic fields. The equivalent mechanical characteristics of the MREC are calculated using the modified Voigt and Halpin–Tsai micromechanical rules. By employing the first-order shear deformation theory and Sanders-based strains, the kinematics of the conical shell are accurately modeled. A semi-analytical analysis, combining trigonometric expansion and the generalized differential quadrature (GDQ) methods, is employed to solve the system's highly coupled partial differential equations. Two consecutive analyses are performed. Firstly, the critical buckling load of the MREC conical shell is obtained through a stability analysis. Subsequently, the frequencies and loss factors of the shell in the pre-buckling zone are computed. Parametric analyses are conducted to study the impact of key factors, including magnetic field strength, compressive load magnitude, composite properties, geometric parameters, and boundary conditions. The proposed methodology is rigorously validated through meticulous accuracy and convergence analysis. The outcomes provide valuable insights into the dynamic response of axially loaded magnetorheological elastomer composite conical shells, highlighting their potential in adjustable dynamic and damping systems.

Physical Education Teachers’ Swimming Skill Analysis in 6- to 12-Year-Old Children: Findings From an Online Survey

Purpose: This study aims to investigate how physical education teachers analyze their students’ swimming skills. Particular attention is given to information gathering within the diagnostic process. Methods: Data were collected from a quantitative online survey of German physical education teachers from primary and secondary schools (n = 551). This survey’s questionnaire is based on evaluated statements from a qualitative interview study (n = 10). Findings: Teachers’ diagnostic approaches vary greatly and differ in terms of quality criteria and usability. The predominant method used is movement observation, but 50.3% of the teachers do it rather rarely or without the use of criteria. Many of them (63.8%) would like to be supported by a diagnostic tool for the analysis of swimming skills. Discussion/Conclusion: It has been concluded that an accurate analysis of the students’ swimming skills as a precondition for adaptive lesson structuring is not achieved. It is necessary to determine whether a diagnostic tool could improve this process.

Diagnostic accuracy of cartridge-based nucleic acid amplification test and resistance pattern in new and previously treated tuberculosis cases: A prospective observational study

Background: Diagnostic methods, particularly sputum microscopy, are the standard method for diagnosing tuberculosis (TB) in many settings. The emergence of cartridge-based nucleic acid amplification test (CBNAAT) as a newer diagnostic tool for TB has shown promising results. CBNAAT is a rapid molecular test that detects the genetic material of Mycobacterium tuberculosis (MTB), the bacterium causing TB, in sputum samples. It offers higher sensitivity and faster results compared to sputum microscopy. Aim: The study aimed to 1) compare the diagnostic accuracy of CBNAAT in detecting smear-positive and smear-negative pulmonary TB cases, and 2) compare rifampicin resistance prevalence between newly diagnosed and previously treated pulmonary TB patients. Methodology: A hospital-based prospective study was conducted in the Respiratory Medicine Department of Guru Gobind Singh Government Hospital, Jamnagar, on PTB patients from August 2019 to December 2020. Data were collected through a self-structured questionnaire that included detailed clinical history, radiological, biochemical, and microbiological investigations. Descriptive statistical analyses were performed to present data as percentages and frequencies. Diagnostic accuracy analysis was conducted. A P-value &lt;0.05 was considered significant. Results: Out of 150 cases, 104 (69.3 %) were male, while 46 (30.7%) were female. Sixty-one percent of new and 86% of previously treated cases were male, whereas 39% of new and 14% of previously treated cases were female. The sensitivity of sputum smear microscopy is 77.33% in cases of PTB, whereas sputum CBNAAT detected 94% of cases of PTB. The sensitivity of CBNAAT for MTB detection in smear microscopy–positive samples was 93.97%, whereas the sensitivity in smear microscopy–negative samples was 94.12%. Regarding rifampicin resistance, the prevalence was lower in newly diagnosed (3/100) compared to previously treated patients with rifampicin-resistant PTB (3/50). Conclusion: These findings suggest that CBNAAT can be a valuable tool for the detection of TB, particularly in cases where sputum smear microscopy results are negative. However, further research is needed to validate these results and evaluate the clinical implications of using CBNAAT in routine TB diagnostics.