Precise Results Research Articles

Analysis of ultrasonic device influence on the ultrasonic thickness measurement of the steel grade

ABSTRACT Ultrasonic thickness measurement depends on the capability of the equipment and its operating characteristics, as well as on the auxiliary means used to adjust the system and analyse the obtained results. The characteristics of the ultrasonic device commonly used as a flaw detector are crucial for achieving accurate and precise results and are very important for the reliability of the results obtained in the measurement process, especially in production systems. This article aims to determine the quantitative variations in thickness caused by influencing parameters of the ultrasonic device and chosen ultrasonic probe, their quantification of interactions, and the significance of their influence on the accuracy and precision of the results. In the experimental part of the research, a factorial plan of the experiment was carried out to determine the influence of the ultrasonic device and the type of ultrasonic probe on the variability of the results of the ultrasonic thickness measurement. The parameters of the ultrasonic device and probes were analysed, and the experimental plan for developing the regression model was elaborated. The response surface methodology was applied, and the in-service measurement process was simulated using Monte Carlo simulation.

SUITABILITY MAPPING FOR OPTIMAL LANDFILL SITE IN COASTAL MOUNTAIN MUNICIPALITIES. CASE STUDY: COLLO MASSIF (NORTHEASTERN ALGERIA)

The management of Municipal Solid Waste (MSW) in coastal mountainous regions displays a significant challenge to sustainable development in Algeria, particularly in its environmental dimension. Local stakeholders face substantial difficulties in identifying appropriate sites for solid waste disposal in this area due to the fragility and complexity of their ecological systems and their potential to amplify the impacts of waste disposal. This affects directly the quality of life, sustainability of natural resources, and threatens the scenic beauty of this tourist destination. Therefore, developing a mechanism based on scientific knowledge to facilitate decision-making processes in these regions is imperative. This paper aims to underscore the importance of employing Cartographic Suitability Mapping for Optimal Landfill Site Selection in coastal mountainous regions as an effective tool to ensure precise results, focusing on the study case of Collo, Skikda state, Algeria. The results show four categories of land suitability: constraint (5.22-7.76) , unsuitable (3.25-4.26), and suitable area (1.21-3.24), with four candidate areas identified as the most suitable for landfill establishment. It is recommended that future studies incorporate social and economic dimensions to further enhance decision-making processes in this regard.

TopicFM+: Boosting Accuracy and Efficiency of Topic-Assisted Feature Matching.

This study tackles image matching in difficult scenarios, such as scenes with significant variations or limited texture, with a strong emphasis on computational efficiency. Previous studies have attempted to address this challenge by encoding global scene contexts using Transformers. However, these approaches have high computational costs and may not capture sufficient high-level contextual information, such as spatial structures or semantic shapes. To overcome these limitations, we propose a novel image-matching method that leverages a topic-modeling strategy to capture high-level contexts in images. Our method represents each image as a multinomial distribution over topics, where each topic represents semantic structures. By incorporating these topics, we can effectively capture comprehensive context information and obtain discriminative and high-quality features. Notably, our coarse-level matching network enhances efficiency by employing attention layers only to fixed-sized topics and small-sized features. Finally, we design a dynamic feature refinement network for precise results at a finer matching stage. Through extensive experiments, we have demonstrated the superiority of our method in challenging scenarios. Specifically, our method ranks in the top 9% in the Image Matching Challenge 2023 without using ensemble techniques. Additionally, we achieve an approximately 50% reduction in computational costs compared to other Transformer-based methods. Code is available at https://github.com/TruongKhang/TopicFM.

Above ground biomass estimation in the upper Blue Nile basin forests, North-Western Ethiopia

Forest ecosystems play a decisive role in the global climatic condition, as well as, provides a wide range of societal benefits, including fuel-wood, tourism, and ecosystem services are considered as one of the major sources of livelihood for the local people in the upper Blue Nile Basin. Therefore, rapid and accurate estimation of forest biomass is crucial for greatly reducing the uncertainty in carbon stock assessments, and for designing strategic forest management plans. Because, above-ground biomass (AGB) estimation is important in determining the management, environmental, and economic roles of forests in the Blue Nile basin. The study was aimed at estimating above-ground biomass in the Upper Blue Nile Basin forests by integrating field-measured data with predictors from Sentinel-2 image. The relationship between measured AGB and sentinel-2 derived vegetation indices and biophysical parameters showed a good correlation result (r value ranging from 0.67 to 0.74). A stepwise regression analysis was carried out in order to develop AGB estimation model by identifying the most important variable. The result demonstrated that, green normalized difference vegetation index, leaf area index, fraction of absorbed photosynthetic active radiation and fractional vegetation cover achieved good performance in predicting AGB with R2 value > 0.5. AGB was estimated with a coefficient of determination (R2) of 0.59 adjusted R2 of 0.618 and root mean square error of (RMSE) 38.36 t/ha in comparison to field observations. The maximum AGB value of 268.32 t/ha was estimated in the Alemsaga natural forest, which is a highly protected dense forest stand from any entrance and disturbance. Generally, integrating field data with optical remote sensing data provides more reliable result for AGB estimation. Moreover, it is also recommended to employ RADAR and LiDAR remote sensing data products together in order to attain more precise estimate results of AGB with great potential for forest resource monitoring and management.

Developing a machine learning-based methodology for optimal hyperparameter determination—A mathematical modeling of high-pressure and high-temperature drilling fluid behavior

Drilling fluids exhibit complex rheological behavior due to a non-linear response to shear rate variations and high sensitivity to changes in temperature, time, and pressure conditions. The prediction of drilling fluid rheological behavior is crucial for the success of oil well drilling, and it directly impacts the fluid's performance. The dataset used in this study was obtained from extensive rheometric tests of water-based and olefin-based drilling fluids in steady-state flow curves. The optimal hyperparameters were guided by performance metrics and compared with alternative models such as Power-law and Herschel-Bulkley rheological models. Different configurations with different hidden layers, using neuron sequences of 16, 32, and 64, learning rates of 0.001 and 0.01, and the ReLU activation function were used to improve the model's performance. Additionally, the paper delved into the impact of the number of training epochs on the accuracy of shear stress predictions. Finding this equilibrium was identified as a crucial factor in achieving precise results. The neural network model demonstrated remarkable accuracy when using the ML-C3 configuration, with MAE values of 0.535 and R2 of 0.987 in predicting the steady-state flow curves of drilling fluids, establishing itself as a powerful tool for forecasting the rheological behavior of these fluids under diverse operational conditions. The present research significantly contributes to the field of drilling fluid rheology and provides valuable insights for optimizing drilling operations in HPHT environments.

Green Computing Innovation Index Measurement Using Fuzzy Inference System for Young Inventors

Green Computing Innovation (GCI) aims to address the negative effects of traditional computing on the environment and promote sustainable practices in computing. Subsequently, the Green Computing Innovation Index (GCII) was used to measure young inventors' inventiveness to evaluate from green computing perspectives. Nonetheless, one of the most significant challenges evaluators face in the measurement process includes uncertainty and imprecise data in measuring the young inventors' green computing innovation. Thus, this paper measures the Green Computing Innovation Index (GCII) with a novel approach utilizing Fuzzy Inference System (FIS) with the Mamdani method. The study measures the top 10 young inventors' innovations from the Faculty of Computing and Meta-Technology, UPSI. Fuzzy Inference System will assist in providing improved decision-making solutions for evaluators to prevent any uncertainty and subjectivity during the evaluation process. The development of FIS is based on four key innovation elements. It uses a triangular membership function with Centroid, Middle of Maximum (MOM), Smallest of Maximum (SOM), Bisector and Largest of Maximum (LOM) methods for defuzzification. The centroid defuzzification method is observed to perform better than other defuzzification methods. It is due to the fact that the adopted defuzzification approach is equally sensitive to all key innovation criteria inputs and can truly represent the minor change in the values of any parameters across all products. The centroid method was found to be the most efficient for decision-making, providing precise results with the ability to detect discrepancies as small as 0.001. Thus, the centroid method can improve decision-making efficiency, leading to fairness in selecting the best green computing innovation product. Ultimately, GCII rating contribute mainly on encouraging young inventors to innovate and promote sustainable practices in innovations.

Psychometric Properties of the Chinese Version of the Gilliam Autism Rating Scale-Third Edition (CV-GARS-3).

Gilliam Autism Rating Scale-Third Edition (GARS-3) serves as an effective screening tool for autism spectrum disorder (ASD) that is based on the latest and authoritative diagnostic criteria, however, there is a deficiency in adaptive research in China. We aimed to revise the Chinese version of GARS-3 (CV-GARS-3) and evaluate its psychometric characteristics, providing a theoretical basis for the improvement of ASD screening tools in China. This study developed CV-GARS-3 through translation and cultural adaptation of GARS-3. 362 ASD individuals, 126 typical development individuals, and 103 individuals with other disorders were recruited to analyze the psychometric characteristics of CV-GARS-3. The results showed that exploratory structural equation model demonstrated satisfactory goodness-of-fit. Within the non-verbal ASD samples, all items loaded on anticipated factors. Regarding verbal ASD samples, 3 items exhibited considerable cross-loadings and were categorized under unexpected factors. Meanwhile, acceptable criterion validity was reflected in the four subscales (r = 0.71) and the six subscales (r = 0.74). Satisfactory reliability was observed in the four subscales (Cronbach's α = 0.96, inter-rater consistency = 0.86, test-retest consistency = 0.87) and the six subscales (Cronbach's α = 0.94, inter-rater consistency = 0.81, test-retest consistency = 0.81). In addition, receiver operating characteristic analysis demonstrated that both the four subscales (sensitivity = 89%, specificity = 85%, accuracy = 88%) and the six subscales (sensitivity = 86%, specificity = 88%, accuracy = 86%) had outstanding screening effects. Therefore, the results suggested that the CV-GARS-3 is considered as a useful tool for the screening and auxiliary diagnosis of ASD. Notably, the expression of scale should be further improved to adapt the context of Chinese culture and achieve more precise diagnostic results.

Intaglio surface of CNC milled versus 3D printed maxillary complete denture bases – An in vitro investigation of the accuracy of seven systems

ObjectivesThe accuracy of the intaglio surface of maxillary complete dentures produced with a variety of digital manufacturing systems have been comprehensively investigated, however, not for multiple systems in the same study. This in vitro study endeavors to measure and compare the accuracy of the intaglio surface of maxillary complete denture bases constructed with different photopolymerization-based 3D printing and CNC milling systems. Materials and MethodsTwo subtractive manufacturing machines and five additive manufacturing machines were used to manufacture a maxillary denture bases (n = 10) for analysis. The samples were stored in artificial saliva at 37 °C for 7 days to mimic intraoral use before they were air dried for 5 min, and then scanned with an E3 dental laboratory scanner to generate STL files. A 3D metrology software program was used to analyze the data against the original CAD design. Statistical differences were determined with 1-way analysis of variance (ANOVA) and the Kruskal Wallis test (α=0.05). Color deviation heat maps were used to interpret areas of clinical significance. ResultsThe results indicated that the subtractive manufacturing method delivers the truest and most precise intaglio surface of maxillary complete denture bases. The additive manufacturing technique delivers less true and less precise results with inconsistency observed between the different systems as opposed to the subtractive manufacturing groups which showed no significant differences. ConclusionsWhen manufacturing a complete maxillary denture base subtractive manufacturing will deliver the most consistent and true results.

Detection and classification on MRI images of brain tumor using YOLO NAS deep learning model

If a brain tumor is not properly diagnosed, it might result in fatal consequences and major health issues. As a result, a key component of diagnosis is the early identification of brain tumors and the precise categorization of brain tumor types. This research work focusses on detection and classification such as pituitary, meningioma, glioma, and non-tumorous tumors from brain tumor MRI image using YOLO NAS model. Advances in deep learning have led to an increasing awareness of computer-aided diagnosis technology. To improve classification performance, the input data set is acquired from the REMBRANDT repository using the Digital Database of Brain Tumor Magnetic Resonance Images. The primary phases of this task include segmentation, classification, and pre-processing. The researcher preprocessed the RGB image to exclude any undesired spots before locating the ROI. For each brain tumor image, we used the hybrid anisotropic diffusion filtering (HADF) technique to remove noise. The Encoder-Decoder Network (En–DeNet), which uses a deep neural network based on U-Net as the encoder and a pre-trained EfficientNet as the decoder, is then used to segment the MRI images. A proposed model was developed using a deep learning-based YOLO NAS technique to identify brain cancers from MRI images such as meningioma tumors, non-tumor, glioma tumors, and pituitary tumors. The suggested model's classification performance is weighed based on parameters such as precision, F1-score, sensitivity, accuracy, and specificity. The proposed fusion method YOLO NAS has improved classification results of accuracy (AC = 0.997%), specificity (SP = 0.985%), precision (PR = 0.982%), F1-score (F1 = 0.992%), and sensitivity (SE = 0.985%) using 2570 MRI data for training and 630 data for testing and validation of brain tumor cases. The results show that the brain tumor type classification system based on the proposed YOLO NAS technique works better than the DNN, PDCNN, DenseNet-161, and DCNN-SGD model classifiers.

The Versatility of the Taguchi Method: Optimizing Experiments Across Diverse Disciplines

AbstractThe Taguchi method, a robust experimental design technique, establishes a strong connection between input and output variables. Known for its capacity to yield precise results with fewer trials and minimized errors, this method has gained widespread application in various fields such as engineering, physics, chemistry, economics, finance, and more. In this paper, the authors examine the importance of the Taguchi orthogonal array method, its step-by-step optimization procedure, and its potential for future applications. Through a thorough literature review, the authors investigate how the Taguchi method has been effectively employed to identify key factors influencing response variables. The versatility of the Taguchi method becomes apparent when considering its applications across diverse disciplines. Researchers in engineering have successfully utilized this technique to optimize processes and enhance product quality. Furthermore, in scientific fields like physics and chemistry, the Taguchi method has proven invaluable for conducting experiments efficiently, resulting in more accurate and reproducible outcomes. Researchers gain critical insights into the effects of factors on the response variable by employing statistical tools such as mean analysis, variance analysis, and signal-to-noise ratio. The Taguchi method remains a valuable and broadly applicable tool for optimizing experiments and identifying influential factors across multiple disciplines. This paper’s extensive literature review emphasizes its significance in various fields and outlines the step-by-step procedure to leverage its potential for optimization.

Laser-inertial tightly coupled SLAM system for indoor degraded environments

Purpose To address the issues of low localization and mapping accuracy, as well as map ghosting and drift, in indoor degraded environments using light detection and ranging-simultaneous localization and mapping (LiDAR SLAM), a real-time localization and mapping system integrating filtering and graph optimization theory is proposed. By incorporating filtering algorithms, the system effectively reduces localization errors and environmental noise. In addition, leveraging graph optimization theory, it optimizes the poses and positions throughout the SLAM process, further enhancing map accuracy and consistency. The purpose of this study resolves common problems such as map ghosting and drift, thereby achieving more precise real-time localization and mapping results. Design/methodology/approach The system consists of three main components: point cloud data preprocessing, tightly coupled inertial odometry based on filtering and backend pose graph optimization. First, point cloud data preprocessing uses the random sample consensus algorithm to segment the ground and extract ground model parameters, which are then used to construct ground constraint factors in backend optimization. Second, the frontend tightly coupled inertial odometry uses iterative error-state Kalman filtering, where the LiDAR odometry serves as observations and the inertial measurement unit preintegration results as predictions. By constructing a joint function, filtering fusion yields a more accurate LiDAR-inertial odometry. Finally, the backend incorporates graph optimization theory, introducing loop closure factors, ground constraint factors and odometry factors from frame-to-frame matching as constraints. This forms a factor graph that optimizes the map’s poses. The loop closure factor uses an improved scan-text-based loop closure detection algorithm for position recognition, reducing the rate of environmental misidentification. Findings A SLAM system integrating filtering and graph optimization technique has been proposed, demonstrating improvements of 35.3%, 37.6% and 40.8% in localization and mapping accuracy compared to ALOAM, lightweight and ground optimized lidar odometry and mapping and LiDAR inertial odometry via smoothing and mapping, respectively. The system exhibits enhanced robustness in challenging environments. Originality/value This study introduces a frontend laser-inertial odometry tightly coupled filtering method and a backend graph optimization method improved by loop closure detection. This approach demonstrates superior robustness in indoor localization and mapping accuracy.

Satellite-based Spectral Indices for Extracting Village Water Bodies in Ludhiana District of Punjab, India

Ensuring water security is the foremost priority of every nation, and water bodies are the most precious life-sustaining resources on the earth. Availability of reliable and updated information of water bodies including knowledge about their locations is necessary for the sustainable planning and management of water resources at a regional scale. Remote sensing technique has revolutionized extraction of village water bodies as it provides fine spatial resolution, gives rapid and precise results and covers large areas, and hence, it has been widely used in the recent studies. In this study, three remote sensing-based spectral indices, i.e., normalized difference water index (NDWI), modified normalized difference water index (MNDWI), and normalized difference pond index (NDPI) were employed to demarcate the area of ponds in each village of Ludhiana district in Punjab. Accuracy of the spectral indices was evaluated by comparing the area of the ponds delineated by the indices with that of digitized manually. The results indicated that NDWI, MNDWI and NDPI could identify 370, 1263 and 1410 number of village ponds, respectively. On the other hand, a total of 1513 village ponds could be demarcated through manual digitization. Thus, the efficiency of NDWI in identification of correct number of village ponds was 24.5%, while the efficiency of MNDWI and NDPI was 83.5% and 93.2%, respectively. Furthermore, NDPI demonstrated an efficiency of 60-65% in demarcating the area of village ponds.

A-323 Advancing Point-of-Care Testing: Independent Evaluation of the Truvian Platform

Abstract Background To address the deficiencies of long turnaround time, increased cost and delayed medical intervention in the current paradigm of routine blood testing, Truvian has developed an automated blood testing platform that provides timely and lab-accurate results at the point of care. With 300 µL from a single Lithium Heparin blood sample, the Truvian solution generates 34 test results, including CMP, Lipid, HbA1c, TSH and CBC in under 30 minutes (TruWellness PanelTM).The present study focused on rigorous evaluation of the reliability and accuracy of Truvian's platform in a real-world clinical setting by untrained operators. Platform precision was measured over time on multiple Truvian instruments under standardized protocol. Accuracy and reliability were assessed independently at the UC San Diego Anti-Viral Research Center (AVRC) by testing samples from donors with chronic medical conditions and comparing against FDA cleared devices, Roche Cobas and Sysmex-XN. Methods Precision was assessed using the control workflow where three commercially available controls, were loaded as sample inputs into the Truvian consumables to perform the TruWellness Panel(TM). Three levels of controls were run in quadruplicate daily on 4 instruments (N > 100 per instrument). Within and between run, day, instrument, and consumable lot variance components were assessed.Accuracy was assessed as part of an IRB-approved method comparison study at AVRC by untrained operators. A total of 50 unique donors representing various disease states were consented and enrolled. Three 4-mL blood samples were collected from each donor: one Lithium Heparin (LiHep) sample for the Truvian System's TruWellness PanelTM, another LiHep gel tube for Roche Cobas clinical chemistry and TSH assays, and an EDTA sample for the Sysmex complete blood count with 3-part differential and HbA1c. All samples were tested on the Truvian System within an hour of collection or processed for central lab testing. Concordance to comparator devices was assessed via relative difference plots, Passing-Bablok and/or Deming regression analyses. Results Our results indicated that the performance of Truvian's instrument met pre-specified reliability, precision and accuracy criteria (per preliminary analysis of the first 50 patients). Reproducibility results showed 100% (69/69) of all tests met specifications. Interim analysis of the first 50 patients in the method comparison study indicates a robust concordance with predicate devices with 20 out of 23 tests showing a correlation coefficient of ≥ 0.9. During donor testing, the instrument demonstrated 96% (48/50) run reliability with only a single operator-induced error where the blood tube was not loaded correctly, highlighting the robustness and ease of use of the platform. Conclusions The present evaluation underscores Truvian's instrument as an easy-to-operate platform that can be run by untrained operators, maintain reliability in real-world clinical settings, and deliver accurate and precise results. Enrollment and sample testing are ongoing, with plans to expand the sample size to 200 donors and study completion planned for May 2024. Broadening the study to encompass a wider range of analyte measurements among chronic disease patients is expected to enhance performance data. This independent evaluation provided confidence in Truvian Platform’s performance and suitability for practical healthcare applications.

A-318 Validation of umbilical cord blood as an alternate sample type for quantitative in vitro detection of glucose-6-phosphate dehydrogenase in neonates

Abstract Background Detection of glucose-6-phosphate dehydrogenase (G6PD) deficiency provides important information for neonatal clinical management. G6PD newborn screening is recommended using umbilical cord blood specimens in vulnerable patient populations.1 Also, cord blood is used by many clinicians instead of peripheral blood for neonatal screening of G6PD.2 Use of cord blood is a modification to the FDA-approved Pointe Scientific G6PD kinetic-340 nm assay. The aim of this study was to validate cord blood as an alternate specimen type for the Pointe Scientific G6PD assay. Methods Hemoglobin (Hgb) results are from Sysmex XN 9100™ analyzers that use SLS methodology with photometric detection. G6PD results are from Roche cobas® c502 analyzers with Pointe Scientific kinetic method and spectrophotometric detection of activity, expressed as U/g Hgb. Neonatal blood specimens were obtained as paired samples from heel sticks and umbilical cord collected in EDTA lavender top tubes. Accuracy acceptance criteria was defined as Deming regression slope between 0.90 and 1.10, r-value of >0.90 and bias ±15% and mixing study recovery of ±10%. Precision (intra-assay and inter-assay), reportable range and linearity, carryover, and stability were established using cord blood samples or control materials sourced from Thermo Fisher Scientific or Sysmex. Reference intervals3, analytical sensitivity, specificity, and clinical validity were transferred to cord blood due to comparability with whole blood FDA-approved method. EP Evaluator software by Data Innovations® was used for calculations. Results Direct comparison of G6PD results (U/g Hgb) from 125 paired samples yielded a 0.9025 correlation coefficient, 1.024 Deming slope, and -0.25% bias. Mixing and recovery of admixed samples gave % recoveries of 102, 103, 101, 102, 100, and 100 for G6PD (U/g Hgb), and 102, 102, 101, 104, 99, and 105 for Hgb (g/dL). Intra-assay precision results were 2.4, 0.6, 0.8, 0.5, 0.4 % CV for G6PD and Hgb, respectively. Inter-assay precision results were 3.1, 3.3, 0.7, 0.7, 0.5 % CV for G6PD and Hgb, respectively. Reportable range and linearity evaluation resulted in a maximum deviation from target recovery of 4%, and a slope of 0.979 for a range of 50 - 2740 U/L, and reportable range for Hgb was confirmed as 0.1 - 26.0 g/dL using controls (1% maximum deviation and 0.991 slope). Carryover was -5.8 (mean concentration) for G6PD and 0% for Hgb after testing 21 replicates of pooled high and low samples in a defined order. Testing 3 samples in 3 replicates at each defined time interval defined stability as 1 week refrigerated or 72 hours room temperature for G6PD, and 3 days refrigerated or 1 day room temperature for Hgb. Conclusions Regression statistics and mixing-recovery studies demonstrated accuracy for detecting G6PD (U/g Hgb) and Hgb (g/dL) from EDTA cord blood. Additional performance characteristics were transferrable for this specimen type from the FDA-approved method for whole blood. Umbilical cord blood is an acceptable alternate specimen type for quantitative detection of G6PD (U/g Hgb) using Pointe Scientific with Sysmex XN-9100™ and Roche cobas® c502 instrumentation.

Real-Time estimation of internal and solar heat gains in buildings using deep learning

This paper presents real-time monitoring of dynamic internal and solar heat gains using programmable low-cost cameras and deep learning techniques. For monitoring changes in occupancy, equipment, lighting, and window status in real time, a convolutional neural network (CNN)-based multi-head classification model was developed and trained with High Dynamic Range (HDR) images, collected using a low-cost fisheye camera in offices. A Python-based region of interest (ROI) generation program was developed to predefine the target areas for monitoring. The classification heads of the model were trained from scratch using a small office image dataset first; then, they were fine-tuned using another HDR image dataset collected in a larger open-plan office with more complex scenes, to evaluate the transferability of the model. The weighted mean classification precision and recall results showed that the developed model could classify the detailed status of each target heat gain (occupants, equipment, lighting, windows) in the predefined areas of the scene with great performance. Finally, to evaluate the impact of real-time monitoring of heat gains on energy demand, the open plan office space used for the experimental dataset collection was modeled using EnergyPlus software using (i) commonly assumed fixed schedules for occupancy, equipment and lighting and (ii) real-time monitored dynamic schedules for internal and solar gain components under the same weather conditions. The results showed that recommended fixed schedules may lead to significant errors in estimated internal and solar gains. The largest discrepancy was noted for occupancy and equipment usage, but other categories also showed both underestimation and overestimation of thermal load components. Implementing reliable and continuous monitoring of dynamic internal and solar heat gains is important for efficient demand-driven HVAC control.

A-028 Evaluation of the Analytical Performance Iron3, Vitamin B12, and Homocysteine Assays on the Atellica CI Analyzer

Abstract Background The Atellica® CI Analyzer is an automated, mid-throughput integrated chemistry and immunoassay analyzer employing both Atellica® CH and Atellica® IM assays. This study evaluated the analytical performance of the Atellica® CH Iron3, and Atellica® IM Vitamin B12 (VB12) and Homocysteine (HCY) assays on the Atellica CI Analyzer. Methods The Atellica CI assays use the same reagents and calibrators as Atellica CH and Atellica IM assays. Precision studies followed CLSI EP05-A3; human contrived and serum samples were tested in duplicate in 2 runs per day for 20 days. Method comparison (MC) studies used human serum samples according to CLSI EP09c and EP09-A3. MC involved comparing the Atellica CH Iron3 Assay on the Atellica CH and Atellica CI Analyzers. MC also compared Atellica IM VB12 and HCY assays on the Atellica CI with Atellica IM Analyzer. Detection capability and linearity were determined. Results Representative precision and MC results are listed for each assay in the table. Slopes determined using the Deming linear regression model were approximately equal to 1. LoB, LoD, LoQ were 2, 4, 5 µg/dL (0.4, 0.7, 0.9 µmol/L) for Iron3, 45, 90, 90 pg/mL (33, 66, 66 pmol/L) for VB12, and 0.50, 1.00, 1.00 µmol/L for HCY; linearity was 5-1000 µg/dL (0.9-179.0 µmol/L), 45-2000 pg/mL (33-1476 pmol/L), and 0.50-65.00 µmol/L, respectively. Conclusions Atellica IM Iron3, VB12, and HCY Assays on the Atellica CI Analyzer demonstrated good precision and equivalent performance to the Atellica CH Analyzer.

A-342 Precision Evaluation of the pH, PO2 and PCO2 tests in the i-STAT® G3+ Cartridge using the i-STAT 1 System for Venous, Arterial and Capillary Whole Blood Specimens in Clinical Settings

Abstract Background Precise and accurate measurements of blood gases are important for the diagnosis, monitoring and treatment of respiratory, metabolic, and acid-base disturbances. This study demonstrates the precision of the pH, partial pressure of oxygen (PO2), and partial pressure of carbon dioxide (PCO2) tests in the i-STAT G3+ cartridge with the i-STAT 1 System in arterial, venous, and capillary whole blood specimens in point-of-care or clinical laboratory settings. Methods The precision of the i-STAT pH, PO2, and PCO2 tests in the i-STAT G3+ cartridge was assessed at external clinical sites by end-users using whole blood specimens across the measurement range for each test: pH (6.500 - 7.800 pH units), PO2 (5 - 700 mmHg), PCO2 (5.0 - 130.0 mmHg). The study included venous whole blood samples from ∼140 adult subjects (≥ 18 years) and arterial whole blood samples from ∼140 adult subjects at 3 clinical sites. Each whole blood sample was run in duplicate on the i-STAT G3+ cartridge. In addition, ∼140 specimens were collected in capillary tubes using two fingerstick draws from adult subjects and ∼20 capillary heel-stick specimens were collected into two capillary tubes from neonate subjects (birth - 28 days) at two clinical sites. Specimens used in this study were prospectively collected venous, arterial, or capillary specimens under an Institutional Review Board approved informed consent or were de-identified leftover venous or arterial specimens. For each specimen type, duplicates from each subject were grouped into subintervals based on their mean values. The subintervals included the medical decision levels of 7.300, 7.350 and 7.450 pH units for pH, 30, 45 and 60 mmHg for PO2 and 35, 45, 50 and 70 mmHg for PCO2. The estimates of the pooled standard deviation (SD) and pooled coefficient of variation (%CV), as well as their respective 95% confidence intervals were calculated for each subinterval. Results Across all specimen types (excluding outliers), the pooled SD for pH ranged from 0.00591 - 0.01747 pH units for subintervals 6.500 - 7.300, >7.300 - 7.450, >7.450 - 7.800 pH units. For PO2, the pooled SD ranged from 0.71 - 3.76 mmHg for subintervals 10 - 40 mmHg and >40 - 50 mmHg and the pooled %CV ranged from 1.63 - 8.76 %CV for subintervals >50 - 100 mmHg, >100 - 250 mmHg and >250 - 700 mmHg. For PCO2, the pooled SD ranged from 0.000 - 1.709 mmHg for subintervals 5.0 - 35.0 mmHg, >35.0 - 50.0 mmHg and >50.0 - 62.5 mmHg and the pooled %CV ranged from 1.10 - 3.62 %CV for subinterval >62.5 - 130.0 mmHg. Conclusions The precision study demonstrated that the pH, PO2 and PCO2 tests in the i-STAT G3+ cartridge when tested using the i-STAT 1 System deliver precise results within ∼2 minutes for venous, arterial, and capillary whole blood patient specimens when tested by end users in a point-of-care or clinical laboratory setting. These studies were funded by Abbott Laboratories.

A-341 Precision Evaluation of the i-STAT® Glucose Test in the i-STAT G Cartridge using thei-STAT 1 System for Venous, Arterial and Capillary Whole Blood Specimens in Clinical Settings

Abstract Background Precise and accurate measurements of whole blood glucose levels are important for the diagnosis and treatment of patients suffering from diabetes and hypoglycemia. This study demonstrates the precision of the i-STAT G cartridge with the i-STAT 1 System in arterial, venous, or capillary whole blood in point of care or clinical laboratory settings. Methods The precision of the i-STAT G cartridge was assessed at external clinical sites by intended end users with whole blood specimens across the measurement range of 20 - 700 mg/dL. The study included testing venous whole blood samples from 154 adult (≥ 18 years) patients and arterial whole blood samples from 173 adult patients at three clinical sites. Each whole blood sample was run in duplicate on the i-STAT G cartridge. In addition, 139 capillary whole blood specimens from both adult subjects and neonate subjects (birth - 28 days) were collected and tested for capillary whole blood precision. Duplicate fingerstick draws were collected in capillary tubes from 119 adult subjects at two clinical sites and duplicate heel-stick specimens were collected in capillary tubes from 20 neonate subjects at two clinical sites. Specimens used in this study were prospectively collected venous, arterial, or capillary specimens under an Institutional Review Board approved informed consent or were de-identified leftover venous or arterial specimens. For each specimen type, duplicates from each subject were grouped into subintervals based on their mean values. The subintervals included the glucose medical decision levels of 45 mg/dL, 120 mg/dL and 180 mg/dL. The estimates of the pooled standard deviation (SD) and pooled coefficient of variation (%CV), as well as their respective 95% confidence intervals (CI) were calculated for each subinterval (20-90 mg/dL, >90-150 mg/dL, >150-250 mg/dL, >250-700 mg/dL or >250-400 mg/dL and >400-700 mg/dL, as applicable). The precision estimates for each subinterval were compared against an acceptance criteria of the greater of 3 mg/dL or 5% of the mean (mg/dL) of the subinterval. Results The whole blood precision was found to be less than or equal to 5.00 %CV for all subintervals for each specimen type. For venous specimens, the %CV ranged from 0.35% to 0.43%; for arterial specimens the %CV ranged from 0.26% to 0.46% and for capillary specimens (adult and neonate combined), the %CV ranged from 1.38% to 2.71%. Conclusions The study demonstrated that the i-STAT Glucose test in the i-STAT G cartridge when tested using the i-STAT 1 System delivers precise results within ∼2 minutes for venous, arterial, and capillary whole blood patient specimens across a glucose range of 20 - 700 mg/dL when tested by end users in a point of care or clinical laboratory setting. The studies were funded by Abbott Laboratories.

A validated high-performance liquid chromatography-ultraviolet method for the determination of valproic acid derivatives in pharmaceutical formulations with a microbiological suitability evaluation

Valproic acid and its derivatives are common drugs used in the treatment of epilepsy, a result of CNS disorders. Because the molecular structure of valproic acid, as a fatty acid, does not contain any chromophore groups, its peak response demanded special conditions to be detected in HPLC, ensuring the best precision results. Still, the current approach illustrates simple, validated procedures for conduction. The conducted chromatographic system consists of the BDS Hypersil C8, 150 × 4.6 mm, 5 µm column using a mobile phase of acetonitrile: phosphate buffer (4:6) at a detection wavelength of 215 nm at room temperature. A full method validation study was conducted and approved to ensure precise, repeatable, and accurate results by implementing system suitability parameters. In microbiological terms, a suitability test is used to validate microbiological testing methods, ensuring their efficacy despite potential interference from antimicrobial properties in the tested materials. The HPLC-UV developed and validated analytical method was evaluated, and it was found to be sensitive for use in the detection of low concentrations of methylparaben, propylparaben, and valproate with an optimum run time of six minutes. limit of detection (LODs) were statistically estimated and found to be 2.27 µg/mL, 39.77 ng/mL, and 1.84 µg/mL for methylparaben, propylparaben, and valproate, respectively. Additionally, the method demonstrated a high recovery of methylparaben, propylparaben, and valproate with an excellent closed-accuracy range (98.84% - 101.34%). An excellent regression coefficient (r) of 0.99924, 0.99998, and 0.99997 for methylparaben, valproate, and propylparaben, respectively, was achieved. Assay determination of various pharmaceutical dosage forms in the local market was implemented, yielding admirable results.

Analysis of Combustion Characteristics of Can-Type and Annular-Type Combustion Chamber

Abstract High performance is always desired for the propulsion system and the assortment of a better combustion type is also a vital part of the propulsion system. The analysis of different designs of combustion chambers and comparison of their combustion characteristics. In this paper, the assessment of Can-type and Annular-type combustion chambers and the design developed with SOLIDWORKS software and analysis done with ANSYS FLUENT. The systematic and translucent method approaches reduce the design complexity and time. The combustion with turbulence interface was examined to analyze with precision results, and the analysis specified that the nowadays experiment depends on the behavior of gas exit pressure, temperature, and velocity. Parametric analysis is used to acquire the sizing of the combustion chamber.