Accuracy Assessment Research Articles

Evaluating fructose content in poultry feed: electrochemical insights

AbstractThe global demand for poultry meat has increased, but consumers have become more picky in their nutritional choices. The fat content of poultry meat has risen since genetic advancement has resulted in higher rates of body weight gain and fat deposition, which are closely linked. The increase in further processing for fast-food poultry items has resulted in larger birds with more body fat. Usually, carbohydrates are used for easy weight gain in poultry. This study focuses on the electrochemical detection of fructose as a feed additive in poultry. The catalyst used was silver–silver oxide–zinc oxide (Ag–AgO–ZnO) and it was prepared by sol–gel method. Further, the surface morphology of the catalyst was analysed using various techniques, including X-ray diffraction, Fourier-transform infrared spectroscopy and transmission electron microscopy (XRD, FTIR and TEM). Cyclic voltammetry was carried out to find out the effectiveness of this catalyst in detecting fructose and our results revealed a sensitivity of 0.3 M with an electrochemical current of 1 mA cm−2, indicating the effectiveness of the chosen electrochemical approach. And further investigation was carried out to monitor effects of various parameters including scan rate, catalyst loading and fructose concentration. This research contributes valuable insights into assessing fructose levels in poultry feed, with potential implications for optimising nutritional formulations and enhancing overall poultry health. The electrochemical method demonstrates promise as a reliable tool for sensitively analysing fructose in feed additives. Precision and accuracy assessments further underscore the reliability of our electrochemical approach in differentiating fructose content within the poultry feed matrix. Graphical Abstract

Multi-hazard modeling of erosion and landslide susceptibility at the national scale in the example of North Macedonia

Abstract Due to favorable natural conditions and human impact, the territory of North Macedonia is very susceptible to natural hazards. Steep hillslopes combined with soft rocks (schists on the mountains; sands and sandstones in depressions), erodible soils, semiarid continental climate, and sparse vegetation cover give a high potential for soil erosion and landslides. For this reason, this study presents a multi-hazard approach to geohazard modeling on the national extent in the example of North Macedonia. Utilizing Geographic Information Systems, relevant data about the entire research area were employed to analyze and assess soil erosion and susceptibility to landslides and identify areas prone to both hazards. Using the Gavrilović Erosion Potential Method (EPM), an average value of 0.36 was obtained for the erosion coefficient Z, indicating low to moderate susceptibility to erosion. However, a significant area of the country (9.6%) is susceptible to high and excess erosion rates. For the landslide susceptibility assessment (LSA), the Analytical hierarchy process approach is combined with the statistical method (frequency ratio), showing that 29.3% of the territory belongs to the zone of high and very high landslide susceptibility. Then, the accuracy assessment is performed for both procedures (EPM and LSA), showing acceptable reliability. By overlapping both models, a multi-hazard map is prepared, indicating that 22.3% of North Macedonia territory is highly susceptible to erosion and landslides. The primary objective of multi-hazard modeling is to identify and delineate hazardous areas, thereby aiding in activities to reduce the hazards and mitigate future damage. This becomes particularly significant when considering the impact of climate change, which is associated with increased landslide and erosion susceptibility. The approach based on a national level presented in this work can provide valuable information for regional planning and decision-making processes.

Assessment of diagnostic accuracy of Gazelle: A point-of-care testing device for screening β-thalassemia trait.

Background & objectives Timely detection of population with β-thalassemia trait (BTT) followed by genetic counselling is an advocated method of preventing the birth of a child with β-thalassemia major. In this study we aim to assess the diagnostic accuracy of Gazelle, a point-of-care (POC) testing device, in screening for BTT in hospital laboratory setting. Methods Standards for Reporting Diagnostic Accuracy (STARD) guidelines were followed in developing study design, recruiting study participants and sample size calculation for the current research. A consecutive sample of 446 participants was recruited for this study and was tested for the reference test Gazelle as well as the gold standard cation exchange high performance liquid chromatography (CE-HPLC). Low serum ferritin levels are known to interfere with the production of HbA2 and in turn lead to false negative results. Hence, the study population was divided into two categories with respect to a cut off value of 15ng/dl of serum ferritin and the results were analyzed. Results Overall diagnostic accuracy of Gazelle for detecting BTT was found to be 95.3 per cent with a confidence interval (CI) of (92.9 - 97.1) and the sensitivity was 94.5 per cent with a 95% CI of (84.8 - 98.8). When analyzed by the serum ferritin level the diagnostic accuracy was found to be 94.7 per cent (91.1% - 97.1%) and 95.7 per cent (91.8% - 98.1%) for participants with serum ferritin level as > 15 ng/ml and < 15 ng/ml, respectively. Interpretation & conclusions This study found Gazelle to be a good screening tool for β-thalassemia trait with high sensitivity, specificity and accuracy. However, it is recommended that the final confirmation of the diagnosis done by a diagnostic test like HPLC or Capillary Zone Electrophoresis (CZE).

Advanced HPLC Method with Diode Array Detection Using a Phenyl-Bonded Column for Simultaneous Quantitation of Three Sunscreen Filters in a Moisturizing Sunscreen Cream for Acne-Prone Skin

This study introduces a novel, robust, and efficient method for the simultaneous quantitative determination of three sunscreen filters, namely, 4-methylbenzylidene camphor, octyl methoxycinnamate, and avobenzone, in a moisturizing sunscreen cream specifically designed for acne-prone skin. The method employs high-performance liquid chromatography with photodiode-array detection, providing a reliable separation of the analytes. Chromatographic separation was achieved using a Fortis Phenyl analytical column (150.0 × 2.1 mm, 5 μm), with isocratic elution at a flow rate of 0.4 mL/min. The mobile phase was composed of a 57/43 (v/v) mixture of acetonitrile/45 mM aqueous ammonium formate solution, ensuring sufficient resolution and peak symmetry for the target compounds. The method was validated comprehensively for critical performance parameters, including linearity, precision, accuracy, and robustness. Linearity was established across a suitable range for all three analytes, with high correlation coefficients. Precision was confirmed with intra-run and total precision coefficients of variation of ≤4.6%, while accuracy assessments yielded a percent recovery between 98.6 and 100.4, for all quality control levels. Additionally, the method was able to effectively separate the sunscreen filters from other cosmetic ingredients, such as [β-(1.3), (1.6)-D-glucan], low molecular weight (LMW) hyaluronic acid and plant extracts ensuring specificity in complex formulations. This straightforward and time efficient sample preparation process, involving methanol extraction followed by serial dilution, makes the method suitable for routine quality control in cosmetic laboratories. The method was successfully applied to the analysis of two different lots of a commercial sunscreen cream, achieving excellent recovery for all filters, ranging between 94.6% and 99.8%, thus demonstrating its reliability and applicability for the quality control of cosmetics.

Modified ICP based Framework for 3D Localization and Mapping in GNSS-denied Environments using LiDAR

Abstract. Lidar odometry and mapping has emerged as fundamental technology in various fields, including accurate positioning and mapping, autonomous vehicles, robotics, and environmental monitoring. The ability to create accurate maps and calculate trajectories is essential for localization and navigation in complex environments, particularly in scenarios where GNSS signal are unavailable. In this context, we propose a novel method for simultaneous localization and mapping (SLAM) specifically designed for GNSS-denied environments and without the need for high-accuracy ranging or inertial measurement units (IMUs). Our approach aims to address the challenges posed by GNSS-denied environments and the cost constraints associated with using multiple sensors. By using only lidar sensors, specifically the Velodyne VLP 16 sensor, we offer a cost-effective solution. Our method utilizes a modified version of the Iterative Closest Point (ICP) algorithm to handle consecutive lidar scans seamlessly. In addition to our method, we conducted thorough accuracy assessments on the created map. The reason behind extensive accuracy evaluation is to ensure the quality of registration. By comparing alignment between the features of different frames within the map and determining the error between the registered features and the original, we gain a more comprehensive understanding of registration quality.

Spatial Analysis of Land Use Land Cover Dynamics in the Madurai District Using Sentinel-2 Data and Supervised Learning Algorithms

Abstract. LULC, or Land Use and Land Cover, refers to the classification and description of different types of land and its usage patterns, including urban areas, forests, agricultural land, etc. In remote sensing, satellite imagery for LULC mapping is becoming more widespread. Numerous studies examine various approaches to improve mapping efficiency and accuracy, highlighting the significance of various data sources, machine learning algorithms, and categorization techniques. This study employs machine learning classifiers, namely Random Forest (RF), Support Vector Machine (SVM), Gradient Boosted Trees (GTB), Classification and Regression Trees (CART), and K-Nearest Neighbors (KNN) for land use and land cover (LULC) classification of Madurai district utilizing Google Earth Engine. The findings reveal the impressive performance of Random Forest, boasting an overall accuracy of 99.01 percent coupled with a commendable Kappa coefficient of 98.68. Conversely. However, amidst these commendable achievements, it’s noteworthy to highlight the nuanced variations observed between the accuracy of training and validation sets. This discrepancy is attributed to the intrinsic intricacies of the learning processes inherent within the algorithms, underscoring the nuanced nature of algorithmic methodologies and their implications on accuracy assessment within spatial analysis frameworks. The generated land use and land cover (LULC) map allows for a comparison between the ground truth data and the surveys conducted to assess issues such as water scarcity and the drying of natural and man-made water bodies.

Assessing acute effects of two motor-cognitive training modalities on cognitive functions, postural control, and gait stability in older adults: a randomized crossover study.

The process of aging often accompanies a decline in cognitive function, postural control, and gait stability, consequently increasing the susceptibility to falls among older individuals. In response to these challenges, motor-cognitive training has emerged as a potential intervention to mitigate age-related declines. This study aims to assess the acute effects of two distinct motor-cognitive training modalities, treadmill dual-task training (TMDT) and interactive motor-cognitive training (IMCT), on cognitive function, postural control, walking ability, and dual-task performance in the elderly population. In this randomized crossover study, 35 healthy elderly individuals (aged 60-75) participated in three acute training sessions involving TMDT, IMCT, and a control reading condition. Assessments of executive function, postural control, gait performance, and cognitive accuracy were conducted both before and after each session. Both TMDT and IMCT improved executive functions. Notably, IMCT resulted in a significant enhancement in correct response rates and a reduction in reaction times in the Stroop task (p < 0.05) compared to TMDT and the control condition. IMCT also led to an increase in dual-task gait speed (p < 0.001) and showed a trend towards improved cognitive accuracy (p = 0.07). Conversely, TMDT increased postural sway with eyes open (p = 0.013), indicating a potential detriment to postural control. The findings suggest that IMCT holds greater immediate efficacy in enhancing cognitive function and gait stability among older adults compared to TMDT, with a lesser adverse impact on postural control. This underscores the potential of IMCT as a preferred approach for mitigating fall risk and enhancing both cognitive and physical functions in the elderly population.

Electronic structure simulations in the cloud computing environment.

The transformative impact of modern computational paradigms and technologies, such as high-performance computing (HPC), quantum computing, and cloud computing, has opened up profound new opportunities for scientific simulations. Scalable computational chemistry is one beneficiary of this technological progress. The main focus of this paper is on the performance of various quantum chemical formulations, ranging from low-order methods to high-accuracy approaches, implemented in different computational chemistry packages and libraries, such as NWChem, NWChemEx, Scalable Predictive Methods for Excitations and Correlated Phenomena, ExaChem, and Fermi-Löwdin orbital self-interaction correction on Azure Quantum Elements, Microsoft's cloud services platform for scientific discovery. We pay particular attention to the intricate workflows for performing complex chemistry simulations, associated data curation, and mechanisms for accuracy assessment, which is demonstrated with the Arrows automated workflow for high throughput simulations. Finally, we provide a perspective on the role of cloud computing in supporting the mission of leadership computational facilities.

Automated Pipeline for Multi-LiDAR Extrinsic Calibration: Experimental Evaluation and Performance Analysis

Abstract. In recent years, 3D LiDAR (Light Detection and Ranging) has become a crucial sensor in various applications such as autonomous vehicles, robotics, object detection, precision forestry, and agriculture. However, specific LiDAR sensors, such as Velodyne VLP16, exhibit some drawbacks, such as a limited field of view and sparse data density, making them inadequate for certain specific applications. Hence, this research proposes a method for calibrating two VLP16 LiDAR sensors to improve coverage and reduce blind spots. The pipeline for performing the calibration begins with mounting LiDARs correctly in a rod at a specific orientation and distance, followed by the selection of multiple sites for data collection, then performing a registration algorithm for estimating calibration parameters, and then an accuracy assessment of the calibrated point clouds. The registration algorithm used here is a modified version of ICP (Iterative Closest Point), which overcomes the need for initialization and eliminates manual intervention in installing targets or retro-reflectors. Finally, we evaluated the accuracy of the fused point cloud collected in an open environment using two calibrated Velodyne VLP16 sensors. For accuracy assessment, we used the PCA eigenvalue and RMSE value to observe how tightly point clouds are fused. As a calibration result, we got the orientation and translation parameters, which are used to achieve the common coordinate system, and accomplished calibration accuracy up to single-digit precision from all the experimental sites. Now the system of two calibrated VLP16 sensors will provide higher coverage and increased data density and might be useful for forest applications.

A Virtual Reality Tool for Accuracy Assessment of 3D Models in an Immersive Virtual Environment

Abstract. Accurate validation and assessment techniques are essential for ensuring the reliability of spatial reconstructions derived from photogrammetry, enabling well-informed decision-making across diverse domains. This study presents a Virtual Reality (VR) based accuracy assessment tool tailored for evaluating the accuracy and quality of 3D models generated by Unmanned Aerial Vehicles (UAVs). Leveraging the Unity game engine platform, our workflow entails three key steps: aligning real-world coordinates with an arbitrary Unity coordinate system, transforming the positions of Ground Control Points (GCPs) from field survey to the arbitrary system using a reference GCP, and marking observed points on the 3D models. Absolute and Relative Root Mean Square Errors (RMSE), Mean Errors (ME), and Standard Deviation of errors (SD) are computed within the virtual environment via the game object transform properties. The error distributions around each GCP are visually depicted using Unity game engine components for enhanced interaction and comprehension. The efficacy of the tool is validated through experimentation on four 3D models generated from varying camera angles during UAV data capture. The tool provides the opportunity to directly interact with the 3D models and visualize the errors, which is quite distinct from traditional methods. Using the developed tool, results were obtained to indicate that configurations employing camera angles of 60° + 75º exhibit notable performance in terms of relative and absolute accuracy.

A fast hybrid approach for continuous land cover change monitoring and semantic segmentation using satellite time series

Land cover change detection and classification, including both inter-class changes (land cover conversion, LCC) and intra-class changes (land cover modification, LCM), is critical for understanding the Earth’s dynamic processes and promoting sustainability. However, previous studies have predominantly focused on LCC, with less emphasis on LCM. Land cover classification remains challenging, and its mapping results are often affected by salt and pepper noise. Here, we propose a hybrid approach for continuous change detection and classification of LCC and LCM using Jinchang City, China, as a case study. Firstly, we combined the Continuous Change Detection and Classification (CCDC) and the Bayesian Estimator of Abrupt change, Seasonal change, and Trend (BEAST) algorithms to identify LCC and LCM using all available Landsat time series (TS) data from 2000 to 2020. Then, the harmonic regression coefficients and RMSE values derived from CCDC (hereafter called CCDC features) were fed into the DCNN model for LCC classification. Our findings indicate: (1) For LCC and LCM accuracy assessment, the CCDC and BEAST ensemble achieved a spatial F1 score of 82.7% and an average temporal F1 score of 79.7%. (2) In LCC classification, the DCNN model with CCDC features, particularly DeepLabV3+, outperformed the pixel-based XGBoost and other multi-year land cover products, with frequency-weighted intersection over union (FWIoU), overall accuracy, and Kappa scores of 88.7%, 94%, and 0.87, respectively. (3) Seasonal LCM showed a more concentrated distribution than trend LCM. (4) In Jinchang City, LCM larger than LCC in area, and grassland and cultivated land are the most distributed. Our approach can be contributed to wall-to-wall land surface monitoring and enhance land management capabilities.

Flood risk assessment using modern models integrated with GIS for susceptibility mapping of the M'zab Valley watershed (Algeria)

ABSTRACT Comprehensive flood control is needed because flooding can be catastrophic for people's lives and ways of living. This management must provide information on the hydrologic, geotechnical, environmental, social, and economic elements of floods. This work created maps for the flood susceptibility of the M'zab Valley using logistic regression (LR) and frequency ratio (FR). The main objective of the research was to strengthen the bivariate probability capabilities of FR and LR. A flood inventory map was created by extracting flood sites from multiple sources. The flood inventory was divided randomly into two parts: 30% was used for validation and the remaining 70% was used to train the models. Independent variable datasets included the distance from the river, rainfall, soil type, land use/land cover (LULC), elevation, drainage density, flow accumulation, and slope. The effect of each variable on flooding was assessed by comparing each independent variable with the dependent flood layer. The flood susceptibility map was assessed using the prediction rate approach on the validation dataset, which was not used for model construction. The accuracy assessment's findings revealed a prediction rate of FR 0.86 and LR 0.881, as well as a success rate of FR 0.924 and LR 0.924.

Continuous flow microfluidic system with magnetic nanoparticles for the spectrophotometric quantification of urea in urine and plasma samples.

Urea, synthesized exclusively in the liver, is primarily transported through the bloodstream to the kidneys, where it is excreted in urine, accounting for 80-90% of nitrogen excretion in humans. Elevated blood urea levels, indicative of kidney dysfunction, make it a crucial biomarker for assessing renal function. Previous studies on urea detection using microdevices have largely focused on conductometric methods. In this study, we demonstrated the application of a continuous flow miniaturized system for rapid spectrophotometric urea quantification using polydimethylsiloxane (PDMS) microdevices. The microdevice featured two distinct zones: an enzymatic reaction zone, where urease-conjugated magnetic nanoparticles were immobilized, and a detection zone, where reagents were incorporated to produce a colored reaction product via a modified Berthelot reaction. Integrating magnetic nanoparticles as a solid support for the enzyme enabled the reuse of PDMS microdevices without compromising the analytical signal. Spectrophotometric detection was performed in an additional microdevice acting as a microflow cell coupled with optical fibers. A calibration curve was constructed using urea standards diluted in phosphate buffer solution (PBS), yielding a linear range of 0.12-3.00 mg dL-1. The method demonstrated detection and quantification limits of 0.04 mg dL-1 and 0.12 mg dL-1, respectively. Precision and accuracy assessments yielded a repeatability of 0.90% and intermediate precision of 4.52%, with recovery rates near 100%. The method was applied to plasma and urine samples, showing urea concentrations within normal physiological ranges and an analysis throughput of 36 measurements per hour.

Accuracy Assessment of NOAA IMS 4 km Products on the Tibetan Plateau with Landsat-8 OLI Images

The NOAA IMS (Interactive Multisensor Snow and Ice Mapping System) is a blended snow and ice product based on active and passive satellite sensors, ground observation, and other auxiliary information, providing the daily cloud-free snow cover extent in the Northern Hemisphere (NH) and having great application potential in snow cover monitoring and research in the Tibetan Plateau (TP). However, accuracy assessment of products is crucial for various aspects of applications. In this study, Landsat-8 OLI images were used to evaluate and validate the accuracy of IMS products in snow cover monitoring on the TP. The results show that (1) average overall accuracy of IMS 4 km products is 76.0% and average mapping accuracy is 88.3%, indicating that IMS 4 km products are appropriate for large-scale snow cover monitoring on the TP. (2) IMS 4 km products tend to overestimate actual snow cover on the TP, with an average commission rate of 45.4% and omission rate of 11.7%, and generally present that the higher the proportion of snow-covered area, the lower the probability of omission rate and the higher the probability of commission rate. (3) Mapping accuracy of IMS 4 km snow cover on the TP generally is higher at the high altitudes, and commission and omission errors increase with the decrease of elevation. (4) Compared with less regional representativeness of ground observations, the spatial characteristics of snow cover based on high-resolution remote sensing data are much more detailed, and more reliable verification results can be obtained. (5) In addition to commission and omission error metrics, the overall accuracy and mapping accuracy based on the reference image instead of classified image can better reveal the general monitoring accuracy of IMS 4 km products on the TP area.

Assessment of the vessel position dead reckoning method error

A method of the calculation of vessel dead reckoning coordinates with the accuracy assessment is proposed. It is based on previously derived formulas for the assessment of the uncertainty boundaries for vessel geodetic latitude and longitude during its motion along the ellipsoid surface. The above formulas are used as follows. At first, the latitude and its precision index are calculated. After, they are used for the calculation of the longitude and its precision index. As the result, it is possible to determine uncertainty intervals for vessel coordinates derived with dead reckoning. The implementation of this method for testing an electronic chart display and information system Navi-Sailor 4000 as to the calculation of vessel dead reckoning coordinates with it is shown. The provision of the information about vessel heading and speed through water is carried out to the system in real-time mode by means of a signal simulator. The speed and heading are remaining unchanged during one-hour time interval. During the above time interval, the data about dead reckoning coordinates are recording into a log-file, then the data are converted in a text format and used for the test. The dead reckoning coordinates, calculated by the system, are in the limits of uncertainty assessed with the proposed method. A limitation of the method is that it does not take into account the influence of errors due to rounding because of the finiteness of the computer bit grid. After the elimination of the above limitation, it seems to be possible to use a modernized version of the method as a part of a procedure for testing the navigational systems which give the information about vessel dead reckoning coordinates.

A New Pilot Hole Preparation System for Percutaneous Pedicle Screw Placement - A Randomized Controlled Study.

Randomized controlled study. To introduce a new pilot hole preparation system for percutaneous pedicle screw placement and investigate its efficiency and safety in comparison with the conventional method. Placing screws accurately, rapidly, and safely with less radiation exposure is critical for minimally invasive Lumbar interbody fusion (LIF). Optimizing pilot hole preparation instruments has important clinical implications. A total of 60 patients (180 screws) were included in this study. All patients were randomized into two groups (new system vs conventional method) and performed single-level minimally invasive percutaneous fixation, interbody fusion, and unilateral decompression. Basic information, time of pilot hole preparation, time of screw placement, and fluoroscopy time were recorded. Screw placement accuracy was graded based on the Gertzbein-Robbins scale, and the angle between the screw axis and the pedicle axis was collected in postoperative CT. There was no statistical difference in basic information between the two groups. The mean time of single pilot hole preparation was 4.08±1.01 min in the new system group and 5.34±1.30 min in the conventional method group (P<0.001). The time of single screw placement was significantly shorter in the new system group (0.82±0.20 vs 1.72±0.33min), and the fluoroscopy time was also less in the new system group (13.70±3.42 vs 19.95±5.50 s) (P<0.001). Screw placement accuracy assessment showed that there were 85 (94.45%) A-grade screws in the new system group while 76 (84.44%) A-grade screws in the conventional method group (P=0.027). The new pilot hole preparation system has shown significant reductions in the time of pilot hole preparation, time of screw placement, and radiation exposure, and has good clinical application value.

Risk Factors for Musculoskeletal Disorders in Korean Farmers: Survey on Occupational Diseases in 2020 and 2022.

This study investigated factors influencing the prevalence of musculoskeletal disorders (MSDs) resulting from agricultural work, utilizing the 2020 and 2022 occupational disease survey data collected by the Rural Development Administration. The combined data from these years indicated a 6.02% prevalence of MSDs, reflecting a significant class imbalance in the binary response variables. This imbalance could lead to classifiers overlooking rare events, potentially inflating accuracy assessments. We evaluated five distinct models to compare their performance using both original and synthetic data and assessing the models' performance based on synthetic data generation. In the multivariate logistic model, we focused on the main effects of the covariates as there were no statistically significant second-order interactions. Focusing on the random over-sampling examples (ROSE) method, gender, age, and pesticide use were particularly impactful. The odds of experiencing MSDs were 1.29 times higher for females than males. The odds increased with age: 2.66 times higher for those aged 50-59, 4.60 times higher for those aged 60-69, and 7.16 times higher for those aged 70 or older, compared to those under 50. Pesticide use was associated with 1.26 times higher odds of developing MSDs. Among body part usage variables, all except wrists and knees were significant. Farmers who frequently used their necks, arms, and waist showed 1.27, 1.11, and 1.23 times higher odds of developing MSDs, respectively. The accuracy of the raw method was high, but the ROSE method outperformed it for precision and F1 score, and both methods showed similar AUC.

Interior Profile Accuracy Assessment Method of Deep-Hole Parts Based on Servo Drive System.

Dimensional and profile measurements of deep-hole parts are key processes both in manufacturing and product lifecycle management. Due to the particularity of the space conditions of deep-hole parts, the existing measurement instruments and methods exhibit some limitations. Based on the multi-axis, highly precise servo drive system, a novel measuring device is developed. The laser displacement sensors are fed by the flux-switching permanent magnet linear motor, and the part is rotated by the servo motor. On this basis, the assessment methods of roundness, straightness, and cylindricity are proposed by employing the least square method (LSM). Additionally, considering the axial center deviation between the sensors and the part, the rotating center coordinate is optimized by the gradient descent algorithm (GDM). Then, the measurement system is constructed and the experiment study is conducted. The results indicate favorable evaluation error of the LSM fitting and GDM iteration. Compared with the coordinate measuring machine (CMM), the measured results show good consistency. In the error analysis, the angle positioning error of measured point is less than 0.01°, and the axial positioning error is less than 0.05 mm. The proposed system and assessment method are regarded as a feasible and promising solution for deep-hole part measurements.

Influence of compliance and resistance of the test lung on the accuracy of the tidal volume delivered by the ventilator

BackgroundLarge variations in respiratory system compliance and resistance may cause the accuracy of tidal volume (VT) delivery beyond the declared range. This study aimed at evaluating the accuracy of VT delivery using a test lung model to simulate pulmonary mechanics under normal or disease conditions.MethodsIn vitro assessment of the VT delivery accuracy was carried out on two commercial ventilators. Measurements of the inspired and expired VT from the ventilator and FlowAnalyser were compared to evaluate the separated and combined influences of compliance and resistance on the delivered VT accuracy. To do this, the errors of five delivered volumes (30 ml, 50 ml, 100 ml, 300 ml, and 500 ml) were checked under 29 test conditions involving a total of 27 combinations of resistance and compliance.ResultsFor the tested ventilator S1 with a flow sensor near the expiratory valve, the average of expired VT errors (ΔVTexp) in three measurements (4 test conditions for each measurement) correlated to test lung compliance (r=-0.96, p = 0.044), and the average of inspired VT errors (ΔVTins) correlated to compliance (r = 0.89, p = 0.106); for the tested ventilator S2 with a flow sensor located at the Y piece, no clear relationship between compliance and ΔVTexp or ΔVTins was found. Furthermore, on two ventilators tested, the current measurements revealed a poor correlation between test lung resistance and ΔVTins or ΔVTexp, and the maximum values of ΔVTexp and ΔVTins correspond to the maximum resistance of 200 cmH2O/(L/s), at which the phenomenon of the flap fluttering in the variable orifice flow senor was observed, and the recorded peak inspiratory pressure (Ppeak) was much higher than the Ppeak estimated by the classical equation of motion. In contrast, at the lower resistance values of 5, 20, 50 and 100 cmH2O/(L/s), the recorded Ppeak was very close to the estimated Ppeak. Overall, the delivered VT errors were in the range of ± 14% on two ventilators studied.ConclusionsDepending on the placement site of the flow sensor in the ventilator circuit, the compliance and resistance of the test lung have different influences on the accuracy of VT delivery, which is further attributed to different fluid dynamics effects of the compliance and resistance. The main influence of compliance is to raise the peak inspiratory pressure Ppeak, thereby increasing the compression volume within the ventilator circuit; whereas a high resistance not only contributes to elevating Ppeak, but more importantly, it governs the gas flow conditions. Ppeak is a critical predictive indicator for the accuracy of the VT delivered by a ventilator.

7798 A Prospective Within-Patient Comparison of Molecular Imaging Ligands for Detection Of Surgically Curable Primary Aldosteronism (PA), and an Assessment of Accuracy in Cortisol Co-Secretors

Abstract Disclosure: E. Goodchild: None. X. Wu: None. R. Senanayake: None. J. MacFarlane: None. G. Argentesi: None. K. Laycock: None. C. Cabrera: None. S.M. O'Toole: None. J. Salsbury: None. D. Benu: None. Y. Lee: None. A. Chua: None. M. Mattson: None. P. Laila: None. N. Hilliard: None. M. Alison: None. D. Berney: None. K. Drew: None. C. Mein: None. E. Wozniak: None. J. Kearney: None. A. Sahdev: None. N. Bird: None. G. Smith: None. M. Hird: None. V. Warnes: None. D. Gillett: None. F. Aigbirhio: None. A. McIntosh: None. A. McConnachie: None. K. Cruickshank: None. H. Cheow: None. M. Gurnell: None. W. Drake: None. M.J. Brown: None. Introduction: Molecular imaging could reduce the need for invasive, scarcely-available adrenal sampling (AVS) to identify aldosterone-producing adenomas (APAs). Easily-distributable ligands for widespread use will be needed. Imidazole ligands lacking selectivity ex vivo for CYP11B1 and CYP11B2 are rendered selective in vivo by pre-treatment with dexamethasone. Aims To determine whether post-dexamethasone para-chloro-2-[[1]8F]fluoroethyletomidate PET-CT (CETO) (T ½ 110min), is interchangeable with previously validated [[1][1]C]-metomidate PET-CT (MTO) (T ½ 20min) for the diagnosis of APA. To identify whether a non-fully-suppressed cortisol after prolonged (72h) low-dose dexamethasone forewarns of an increased risk of a false-positive MTO result, in the MATCH study cohort.1Experimental design Prospective within-patient comparison of diagnostic interventions (n=31), with independent scoring of each as high, medium, low probability of unilateral PA, blind to scoring of the other scan or AVS. Prospective measurements of cortisol after prolonged low-dose dexamethasone suppression (PLDDF) and urinary hybrid steroids, with 6-month post-operative PASO biochemical outcomes and APA somatic genotyping when high probability (MTO or AVS) mandated surgery. Major results MTO and CETO agreed in 29/31, kappa=0.85 [95%CI 0.68,1.00]. Scatter was evenly distributed on either side of zero on Bland-Altman analysis (bias -0.04, [95% CI -0.12,0.04]. Of all 174 MATCH-study patients (n=174) PLDDF was available in 156, 32 had PLDDF &amp;gt;25nmol/L. Of these, MTO scored 16 as high probability of unilateral PA, in whom there was complete PASO biochemical success in 14 (88%), and absent in 2 (12%), indicating possible false-positives (AVS had failed in both). In patients with PLDDF &amp;lt;/=25nmol/L, MTO identified unilateral disease in 22, 19 (86.3%) of whom had complete biochemical success at 6 months, 3 (13.6%) with partial or absent success. 22 patients had both an elevated PLDDF and urinary hybrid steroid profiles measured, of whom 4 had an elevated 18OH/F ratio. PLDDF in unilateral and not-unilateral MTO outcome were comparable (unpaired T test p= 0.92). A known somatic genotype mutation was found in 21 patients with PLDDF &amp;gt;25nmol/l. PLDDF was highest in KCNJ5 mutants (12/21, mean 55.3nmol/L), compared to ATP1A1 (3/21, mean 44nmol/L), CACNA1D (4/21, mean 31nmol/L), ATP2B3 (1/21, 36nmol/L) and GNAQ (1/21, 28nmol/L). All had complete biochemical success at 6 months post-op. Conclusion: CETO is interchangeable with MTO, which is non-inferior to AVS, in lateralising aldosterone excess in PA patients. False-positives rarely occurred by MTO, and autonomous cortisol secretion did not increase the chance of a false-positive MTO result, compared to patients without, in this cohort. Reference: (1) Wu X et al. Nature Medicine. 2023;29:190-202. Presentation: 6/3/2024