Precise Results Research Articles

ACCEPTANCE OF SIMULATION TECHNOLOGY AND THE INFLUENCES FOR USING SIMULATION TECHNOLOGY AMONGST TEACHERS IN HIGHER EDUCATION

Acceptance of simulation technology and the influences for using simulation technology amongst teachers in higher education. The research study was conducted to study the effect of technology support for simulation education based on teachers involvement with and usage of simulation. Previous literature reviews discuss challenges teachers face with using simulation technology such as teachers lack of training, intentions, attitudes, and behavior such as resentment about using simulation technology. The research study conducted can help one identify factors and learning challenges that teachers face when it comes to learning technology. Simulation technology is a new way of learning and is used more frequently for learning purposes. Therefore, to study in depth factors concerning teachers challenges and influences for using simulation technology research interviews were conducted. Results indicate teachers are influenced and motivated to use simulation technology if they are trained properly and have adequate support. When not supported or trained, one may be resistant to use simulation technology. Teachers admit they will use simulation technology as long as they know how to use the technology properly. Teachers evaluated display common interest in using the simulation technology and are adamant about using technology if it adds value and enhanced learning to their courses. Teachers admit that simulation technology seminars and conferences will be utilized for further growth and learning initiatives. Limitations of research are time constraints. Further recommendations for studying the research study are to use a form of triangulation to generalize results for transferability and transparency. Future research needs to be conducted to strengthen the results for precision, reliability, and validity of information. Further research should also be conducted to help close the existing gap in literature about simulation education in the higher education sector.

Surrogate-based shape optimization and sensitivity analysis on the aerodynamic performance of HCW configuration

With an additional wing upon the fuselage, the novel high-pressure capturing wing (HCW) configuration exhibits remarkable aerodynamic characteristics at hypersonic speeds under beneficial aerodynamic interference. This bi-wing structure can also enhance the lift at subsonic speeds, positioning HCW configuration as an excellent aerodynamic layout under wide-speed range conditions. In this paper, a single-wing principle HCW configuration is carried out to analyze the influence of the variations in the geometric parameters of the HCW on the aerodynamic performance. Drawing on extant research, four key geometric parameters of the HCW are chosen as design variables, and the hypersonic as well as supersonic conditions are selected for surrogate-based shape optimization. Utilizing polynomial response surface method and method of moving asymptotes, the single-objective optimization studies are carried out with the objectives of maximum lift-to-drag ratio at Ma = 6 and minimum drag coefficient at Ma = 3. Subsequently, the sensitivity analysis is performed for each design parameter. The above methods exhibit notable precision and favorable results. The results show that except for the leading-edge sweep angle, the other three optimization results exhibit divergent trends in their variations. The setting angle has the most significant influence on the aerodynamic forces, owing to the influences of its variation on the shock wave intensity and reflection angle. Based on the stronger intensity of shock wave, this sensitivity indices are higher at Ma = 6. The other three parameters (half-span, leading-edge sweep angle, and trailing-edge sweep angle) modify the aerodynamic forces by adjusting the area of high-pressure region on HCW. Due to the different flow field structures, optimum parameters exhibit diverse effects on lift coefficient and lift-to-drag ratio.

Improved convolutional neural network model for leukemia classification using EfficientNetV2M and bayesian optimization

Leukemia is a health condition in which the body produces too many abnormal white blood cells or leukocytes. Leukemia can affect both children and adults. Early diagnosis of leukemia faces significant challenges, as diagnostic methods are time consuming, require experienced medical experts, and are expensive. Previous studies have been conducted using deep learning approaches, but it is still rare to find a model that shows the best performance and uses optimization methods to classify leukemia diseases. Therefore, a Convolutional Neural Network (CNN) model with EfficientNetV2M architecture and Bayesian Optimization is proposed as the main method assisted by ImageDataGenerator in preprocessing. This study shows a significant impact of Bayesian optimization with good Accuracy, Precision, Recall and F1-Score results of 91.37%, 93.00%, 87.00%, 89.00%, respectively, which are expected to improve the performance of the model in previous studies in classifying leukemia diseases.

Adaptive Weighted Data Fusion for Line Structured Light and Photometric Stereo Measurement System.

Line structured light (LSL) measurement systems can obtain high accuracy profiles, but the overall clarity relies greatly on the sampling interval of the scanning process. Photometric stereo (PS), on the other hand, is sensitive to tiny features but has poor geometrical accuracy. Cooperative measurement with these two methods is an effective way to ensure precision and clarity results. In this paper, an LSL-PS cooperative measurement system is brought out. The calibration methods used in the LSL and PS measurement system are given. Then, a data fusion algorithm with adaptive weights is proposed, where an error function that contains the 3D point cloud matching error and normal vector error is established. The weights, which are based on the angles of adjacent normal vectors, are also added to the error function. Afterward, the fusion results can be obtained by solving linear equations. From the experimental results, it can be seen that the proposed method has the advantages of both the LSL and PS methods. The 3D reconstruction results have the merits of high accuracy and high clarity.

Development of an unsupervised pseudo-deep approach for brain tumor detection in magnetic resonance images

Brain tumor detection has long been a significant and challenging issue in medical research. In this study, we propose an unsupervised pseudo-deep method for detecting brain tumors in Magnetic Resonance (MR) images. Our approach utilizes iterative spectral Co-Clustering and Fuzzy C-means techniques to achieve precise segmentation results. In each iteration, the algorithm recognizes the tumor block from its input image using spectral Co-Clustering. The selected block from the previous iteration serves as the input for the subsequent iteration. In the final iteration, Fuzzy C-Means is applied to the last selected block to extract the tumor, and a scheme is proposed to determine the location of the tumor in the original image. Through this iterative pseudo-deep method, our approach exhibits a layered-like structure, resembling deep learning architectures, within the context of unsupervised methods. We evaluated our approach using the BraTS2020 and BraTS2021 datasets and assessed its performance using key metrics. Our method achieved values of 99.12% and 81.42% for accuracy and dice coefficient on BraTS2020 and also 99.21% and 82.03% on the BraTS2021 dataset, surpassing the performance of existing unsupervised methods reported in the literature. Moreover, our approach demonstrates notably superior performance when dealing with complex images. The proposed method offers a unique perspective in the application of unsupervised techniques for brain tumor detection using a pseudo-deep structure.

Robust well-balanced method with flow resistance terms for accurate wetting and drying modeling in shallow water simulations

Hydrodynamic simulations in shallow water environments require careful consideration of the Wetting and Drying (WD) processes, which poses challenges to accurately modeling moving boundaries. This study introduces a novel method called the flow resistance method (FRM), which builds upon the foundation of the Negative-Depth Method (NDM) to tackle the intricacies of the moving boundary problem. Inspired by the Navier-Stokes/Brinkman (NSB) model from porous media theory, FRM incorporates a continuous function related to additional flow resistance that is proportional to the flow velocity. This approach facilitates a seamless transition between the exposed bed and fluid area wherein the additional flow resistance becomes 0 within the fluid area and approaches infinity in the exposed bed. Consequently, FRM adeptly and implicitly manages the moving boundary problem, causing a rapid decay of flow velocity to 0 in the exposed bed. In order to test the performance of FRM, four typical numerical experiments were conducted, along with an examination of a real-life case. Accuracy, robustness, and computational efficiency were assessed as key performance indicators. The simulations demonstrate that FRM adeptly tracks the moving water front, yielding precise results. Furthermore, when compared to established methods such as the Element Removal Method (ERM) and NDM, FRM exhibits broader applicability and achieves significant enhancements in the key performance indicators. These findings underscore the promising potential and broad applications of FRM in the field.

Bio-analytical Methodology for Exploring Ripretinib’s Pharmacokinetic Profile in Rat Plasma Employing Bosutinib as an Internal Standard Utilizing LC-MS/MS

A robust, consistent, straightforward, swift, and precise liquid chromatography-tandem mass spectrometry (LC-MS/MS) method was tailored for the bioanalytical analysis of ripretinib, with bosutinib serving as the comparative standard. This article provides a synopsis of recent developments in bioanalytical LC-MS/MS techniques utilizing a phenyl column (150x4.6 mm, 3.5 μ) and an eluting agent composed of ammonium formate at a pH of 2.5, which was altered in a 50:50 ratio with formic acid and acetonitrile. The chromatogram was utilized to analyse ripretinib, and the MS detector was equipped with electrospray ionization (ESI) method functioning in positive ionization conditions with multiple reactions monitoring (MRM). This allowed for the recording of transition m/z values of 531.4465→170.9638 for ripretinib and 511.4230 → 160.7133 for BTB. In the chromatographic analysis, ripretinib was retained for approximately 2.433 minutes, whereas BTB was retained for around 3.608 minutes. The validity of the approach extends across a linearity scale of 5 to 100 ng/mL for ripretinib, boasting a correlation index of 0.9999. In the realm of biological matrices, the extraction procedure employing the protein precipitation technique yields recovery rates of 99.6, 99.2, and 98% at the lower, middle, and higher quality control levels, respectively. The obtained results for accuracy, precision, matrix effect, and stability were determined to be within the acceptable range. To observe the investigated analyte in bodily fluids, pharmacokinetic studies yielded key parameters, including a half-life of 10 hours and a time to reach a maximum drug level of 4 hours. This thorough assessment affirms that the method satisfies rigorous criteria for system suitability and specificity well within predefined acceptance limits. Such performance positions it as an indispensable asset in the realm of bioanalysis, significantly broadening its clinical applicability.

Study on UPLC–MS/MS Method for Four Compositions of Cangzhu

Background: The legal quality control of Cangzhu is used the single component as an evaluation indicator, which would not be enough to reflect the authenticity of quality. It is necessary to select several typical chemical components as the basis for quality control of medicinal materials and decoction pieces to reflect the content of effective ingredients. Purpose: Based on UPLC-MS/MS, the study aimed to build the content determination method of atractyloidin, atractylidin A, atractylenolide I, and atractylenolide III in Cangzhu from different sources. Materials and Methods: Agilent SB-C18 (2.1 × 100 µm, 1.8 µm) was used to be the stationary phase. The mobile phase system was formed by water-formic acid (1000:1) and acetonitrile-formic acid (1000:1) in gradient elution. The column temperature set at 40℃, and the flow rate was 0.3 mL min–1. Results: The contents of atractyloidin, atractylidin A, atractylenolide I, and atractylenolide III had good linearity in a certain range (r2 ≥ 0. 9990). The RSD of the precision, stability and repetitive test results were less than 3.00%. In addition, the content of the four components were some different among different patches of Cangzhu. Conclusion: The method was simple, rapid and highly sensitive, which was suitable for the determination of atractyloidin, atractylidin A, atractylenolide I, and atractylenolide III in Cangzhu.

Quality by Design Approach for the Stability Indicating Method Development and Validation of Selpercatinib Drug Formulation by using RP-HPLC

In method development quality design approach is useful for method goal identification evaluation, method selection and risk assessment. Selpercatinib is a drug used in the treatment of certain types of cancer. The method development includes selecting appropriate chromatographic conditions such as the mobile phase composition, column, and selection wave length. The aim is to achieve optimum separation and quantification of selpercatinib in the dosage form. Optimizing chromatographic conditions by using design expert software. The mobile phase methanol and 0.1% Acetic acid are used. The mobile phase range is 80:20 v/v. The range of this mobile phase is 80:20 .0.7 mL/min found flow rate. The retention time of selpercatinib is 3.20 minutes. The assay was found to be 99.01% for selpercatinib. Optimization was performed. Factorial design performed, i.e., flow rate and percentages of methanol. Analysis of variance confirmed that method parameters. UV detection at 220 nm. The International Council of Harmonization (ICH) guidelines ensure the accurate, precise and reliable results to validate the parameters is an acceptable range. Stress studies reveal that drugs were degraded acidic conditions, alkaline conditions, neutral conditions, oxidative conditions, and photolytic conditions. Hence the proposed method was stability, indicating using quality by design (QbD) approach all the method parameters were better understood. This systematic approach ensures a thorough understanding of the assay, facilitates process optimization, and enables effective control strategies to reduce variability and enhance product quality.

Research on Safety Risk Assessment of Xi'an Metro Operation Based on Structural Equation Model (SEM)-Matter-Element Extension Model (MEA)

To ensure the safety of Xi'an Metro operation and improve the level of operational safety risk management, a comprehensive evaluation model of operational safety risk of Xi'an Metro based on structural equation model (SEM)-matter-element extension model (MEA) was established based on 4M theory. Firstly, by analyzing both domestic and international Metro accident cases and literature, four risk factor perspectives were identified as personnel, equipment, management, and environment. Secondly, to accurately assess the safety risk of Xi'an Metro operation, a risk evaluation index system consisting of four primary indicators, eleven secondary indicators and forty-four observation points was established. Finally, the index weights were determined using structural equation modeling, and a comprehensive evaluation utilizing the Matter-element extension model was conducted to obtain precise safety evaluation results. The model was applied to Xi'an Metro Line 2, and the results indicated that the operational safety level of Xi'an Metro Line 2 is relatively secure. According to the evaluation results above, the findings align with the current conditions. Xi'an Metro operating company can utilize these results as a point of reference to propose measures that correspond to varying risks. This will effectively reduce safety risks and ensure the safe operation of Xi'an Metro.

CFD approach for determining the losses in two-phase flows through the last stage of condensing steam turbine

Analyzing and simulating wet steam flows within steam turbines holds significant importance, especially due to the presence of a liquid phase in the low-pressure stages of the turbine, resulting in consequential losses that demand careful consideration. Considering these conditions, this study employs advanced predictive CFD models to simulate the wet steam flow within the actual 3D geometry of the final stage in a 200 MW steam turbine, which is observed in the least research due to the complexity of modeling and analysis of three-dimensional flows. This advancement promises more precise and reliable results. Employing the ANSYS CFX software, this study engages in numerical simulations of steady-state compressible two-phase flow. Furthermore, the study offers practical equations to quantify the losses within the entire stage, which serves as a valuable metric for assessing loss levels and offers a benchmark for design improvements aimed at loss reduction. Based on the results, 10 % of losses occur along the stator and 1 % along the rotor, and a total of 11 % of losses occur along the entire stage. Finally, the efficiency of the stage is calculated to be 89 %. Steam condensation strengthens the shock waves, changes their location, diminishes Mach number and the flow velocity, and alters the flow angle. It reduces the efficiency of the turbine by 1.27 % and also reduces the work done by the turbine by 13.97 %. This emphasizes the significant influence of condensation on flow characteristics, emphasizing the necessity of conducting comprehensive investigations into this phenomenon.

Hybrid Tiki Taka and Mean Differential Evolution based Weibull distribution: A comprehensive approach for solar PV modules parameter extraction with Newton-Raphson optimization

Effective parameter extraction is crucial in modeling and analyzing complex systems, particularly photovoltaic (PV) cells/modules. Therefore, it is essential to develop a robust optimization model that can effectively determine the optimal parameters of PV models. This study introduces a novel metaheuristic algorithm, Tiki Taka Algorithm (TTA), inspired by sports strategies, particularly the renowned tiki-taka style. The innovation lies in the hybridization of TTA with the Mean Differential Evolution based on Weibull distribution (MDEW). The resultant optimal parameters obtained from this hybridization serve as the initial guess for Newton-Raphson (NR), a critical step that ensures the estimated current closely aligns with the measured current, leading to a minimized Root Mean Square Error (RMSE). The hybrid approach leverages the excellent exploration capabilities of Weibull distribution and the exploitation strength of the Mean Differential Evolution (MeanDE) mutation, offering a comprehensive solution for navigating complex optimization landscapes. The proposed TTA-MDEW effectively identifies various parameters in PV models, including single diodes, double diodes, and PV modules. The findings show that TTA-MDEW holds its ground against the most sophisticated optimization methods in terms of reliability, accuracy, and speed of convergence. Additionally, data from real-world manufacturer datasheets reveal that our method delivers exceptionally precise results under varying irradiance and temperature conditions. Therefore, these outcomes establish the TTA-MDEW as an effective tool for extracting precise parameters from various PV models, enhancing the modeling of PV systems.

Enhancing the interpretability of sentiment analysis with signatures

Sentiment analysis is a significant research area within the field of natural language processing, with applications in various social contexts. Recent advances in related research have led to notable improvements in accuracy, yet existing text sentiment analysis models continue to face challenges in terms of interpretability. This paper proposes a text sentiment classification method integrating BERT and BiLstm, comprising three stages: training, signature generation, and testing. Our primary contribution lies in the signature generation and testing stages. In the training stage, BERT is employed to generate word embeddings, while BiLstm is utilized for learning to construct the text sentiment classification model. In the signature generation stage, the training and validation sets are passed through the aforementioned classification model, the feature vectors are extracted following their passage through the fully-connected layer, and the feature vectors are utilized to generate the signature generation stage. The signature is then stored in the database. In the Testing Stage, the aforementioned operation is repeated on the test set in order to obtain the signatures. The signatures are then matched in the constructed signature database in order to obtain the final classification results. The matching degree is subsequently used as a threshold in order to be employed for subsequent evaluation of the results. The method enhances the interpretability of the classification task by matching through signatures. Two datasets, SST-2 and IMDB, were subjected to experimentation. Four quantitative metrics were employed for the evaluation of the results: accuracy, recall, precision, and F1-score. With a threshold of 0.99, the models optimal accuracy, recall, precision, and F1-score results reached 0.896, 1, 0.896, 0.945 and 0.847, 0.999, 0.848, 0.917, respectively.

Micro-mechanical properties characterization of shot peening strengthened layer with nanoindentation

In this work, the influences of shot peening (SP) on ultra-high strength steel were investigated. For the characterization of the strengthened layer, an inclined specimen was proposed for X-ray diffraction and nanoindentation, the micro-mechanical properties in the strengthened layer were studied with both experimental and crystal plasticity finite element (CPFE) analysis. It's noted that SP introduced grain refinement and compressive residual stress were highly concentrated in the surface layer due to the high strength and hardness, and the maximum depth influenced was only 0.1 mm. Based on simulation results, it's found that the differences in nanoindentation loading curves were introduced by the work done by residual stresses. To estimate the residual stress in the strengthened layer, a modified model was proposed with nanoindentation. The newly proposed model gives improved estimation accuracy compared with Wang's model, and it could give more precise results in contrast to the X-ray diffraction method for strengthened layers.

Effect of saponins in Panax notoginseng (Burkill) F. H. Chen on the steroid hormone levels in the chronic unpredictable mild stress model of depression in rats

Recent research has indicated that Panax notoginseng saponins (PNS) extracted from the radix of Panax notoginseng (Burkill) F. H. Chen exert antidepressant effects. This study aimed to assess the antidepressive effects of ginsenoside Rg1 and PNS in a depression model induced by chronic unpredictable mild stress (CUMS). Over a period of three weeks, rats were administered ginsenoside Rg1 at a dose of 30 mg/kg and PNS at dosages ranging from 100 to 200 mg/kg body weight per day. To assess how ginsenoside Rg1 and PNS influence depression-like behaviours in rats, various assessments were conducted, including coat state evaluation, forced swim test, and elevated plus maze test. The levels of cortisol and testosterone in serum samples were analysed using the liquid chromatography-electrospray ionisation tandem mass spectrometry (LC-ESI-MS/MS) method. LC-ESI-MS/MS method provides precise and accurate results. The lower limit of quantification values for cortisol and testosterone were determined as 100 and 2 pg/mL, respectively. Our data demonstrated that both ginsenoside Rg1 and PNS significantly reversed depression-like behaviour in rats by improving coat condition, reducing immobility time in the forced swim test, and increasing time spent in the open arms of the elevated plus maze test. Furthermore, ginsenoside Rg1 and PNS exhibited a regulatory effect on cortisol and testosterone levels in plasma. These findings suggest that ginsenoside Rg1 and PNS may be potential antidepressants in clinical treatment.

Advancing Skin Cancer Prediction Using Ensemble Models

There are many different kinds of skin cancer, and an early and precise diagnosis is crucial because skin cancer is both frequent and deadly. The key to effective treatment is accurately classifying the various skin cancers, which have unique traits. Dermoscopy and other advanced imaging techniques have enhanced early detection by providing detailed images of lesions. However, accurately interpreting these images to distinguish between benign and malignant tumors remains a difficult task. Improved predictive modeling techniques are necessary due to the frequent occurrence of erroneous and inconsistent outcomes in the present diagnostic processes. Machine learning (ML) models have become essential in the field of dermatology for the automated identification and categorization of skin cancer lesions using image data. The aim of this work is to develop improved skin cancer predictions by using ensemble models, which combine numerous machine learning approaches to maximize their combined strengths and reduce their individual shortcomings. This paper proposes a fresh and special approach for ensemble model optimization for skin cancer classification: the Max Voting method. We trained and assessed five different ensemble models using the ISIC 2018 and HAM10000 datasets: AdaBoost, CatBoost, Random Forest, Gradient Boosting, and Extra Trees. Their combined predictions enhance the overall performance with the Max Voting method. Moreover, the ensemble models were fed with feature vectors that were optimally generated from the image data by a genetic algorithm (GA). We show that, with an accuracy of 95.80%, the Max Voting approach significantly improves the predictive performance when compared to the five ensemble models individually. Obtaining the best results for F1-measure, recall, and precision, the Max Voting method turned out to be the most dependable and robust. The novel aspect of this work is that skin cancer lesions are more robustly and reliably classified using the Max Voting technique. Several pre-trained machine learning models’ benefits are combined in this approach.

Environmental information-assisted intelligent fusion localization for vehicles in urban area

In this paper, we propose a localization methodology aimed at improving accuracy through two primary aspects: environment information assisting fusion algorithm and the elaborate localization errors prediction. First, the environment-assisted particle filter (EA-PF) is proposed by enhancing the particle filter algorithm with perceived environmental feature information, which is reflected by the rasterized map constructed based on LiDAR sensors. Then, an elaborate localization error prediction model is designed to accurately forecast Inertial Navigation System (INS) errors. The model comprises the noise characteristic feature extraction and encoding network and multi-feature error prediction network. The localization errors in the EA-PF output can be further compensated. The proposed localization methodology can be divided into two operational modes based on the availability of Global Navigation Satellite System (GNSS) signal: GNSS-available and GNSS-unavailable modes. In the GNSS-available mode, the EA-PF algorithm yields precise localization results, while in the GNSS-unavailable modes, the elaborate localization error prediction model can effectively predict INS errors and provide the compensation for the EA-PF. To validate the effectiveness of the proposed methodology, relevant test experiments were performed using the KITTI dataset. The experimental validation results demonstrate the feasibility and effectiveness of the proposed methodology. The experimental results demonstrate that the EA-PF outperforms traditional PF methods by improving the root mean square error (RMS.) by 32.70 % on average during 90 s GNSS outages. Moreover, the proposed localization methodology achieves 2.30 m RMS error on average during 90 s GNSS outage.

Improved sports image classification using deep neural network and novel tuna swarm optimization

Sports image classification is a complex undertaking that necessitates the utilization of precise and robust techniques to differentiate between various sports activities. This study introduces a novel approach that combines the deep neural network (DNN) with a modified metaheuristic algorithm known as novel tuna swarm optimization (NTSO) for the purpose of sports image classification. The DNN is a potent technique capable of extracting high-level features from raw images, while the NTSO algorithm optimizes the hyperparameters of the DNN, including the number of layers, neurons, and activation functions. Through the application of NTSO to the DNN, a finely-tuned network is developed, exhibiting exceptional performance in sports image classification. Rigorous experiments have been conducted on an extensive dataset of sports images, and the obtained results have been compared against other state-of-the-art methods, including Attention-based graph convolution-guided third-order hourglass network (AGTH-Net), particle swarm optimization algorithm (PSO), YOLOv5 backbone and SPD-Conv, and Depth Learning (DL). According to a fivefold cross-validation technique, the DNN/NTSO model provided remarkable precision, recall, and F1-score results: 97.665 ± 0.352%, 95.400 ± 0.374%, and 0.8787 ± 0.0031, respectively. Detailed comparisons reveal the DNN/NTSO model's superiority toward various performance metrics, solidifying its standing as a top choice for sports image classification tasks. Based on the practical dataset, the DNN/NTSO model has been successfully evaluated in real-world scenarios, showcasing its resilience and flexibility in various sports categories. Its capacity to uphold precision in dynamic settings, where elements like lighting, backdrop, and motion blur are prominent, highlights its utility. The model's scalability and efficiency in analyzing images from live sports competitions additionally validate its suitability for integration into real-time sports analytics and media platforms. This research not only confirms the theoretical superiority of the DNN/NTSO model but also its pragmatic effectiveness in a wide array of demanding sports image classification assignments.

Sensitive Detection and Quantification of Oxygenated Compounds in Complex Samples Using GC-Combustion-MS.

This work introduces a new element-selective gas chromatography detector for the accurate quantification of traces of volatile oxygen-containing compounds in complex samples without the need for specific standards. The key to this approach is the use of oxygen highly enriched in 18O as the oxidizing gas in a combustion unit (800 °C) that allows us to directly and unambiguously detect the natural oxygen present in the GC-separated compounds through its incorporation into the volatile species formed after their combustion and their subsequent degradation to 16O in the ion source. The unspecific signal due to the low 16O abundance in the oxidizing gas could be compensated by measuring the m/z 12 that comes as well from the CO2 degradation. Equimolarity was proved with several O-containing compounds with different sizes and functionalities. A detection limit of 28 pg of injected O was achieved, which is the lowest ever reported for any GC detector, which barely worsened to 55 and 214 pg of O when the oxygenate partially or completely coeluted with a very abundant matrix compound. Validation was attained by the analysis of a SRM to obtain accurate (99-103%) and precise (1-4% RSD) results. Robustness was tested after spiking a hydrotreated diesel with 10 O-compounds at the ppm level, which could be discriminated from the matrix crowd and quantified (mean recovery of 102 ± 9%) with a single generic standard. Finally, it was also successfully applied to easily spot and quantify the 33 oxygenates naturally present in a complex wood bio-oil sample.

Elastic Electron Scattering from Be, Mg, and Ca

We present a comprehensive set of theoretical results for differential, integrated, and momentum transfer cross sections for the elastic scattering of electrons by beryllium, magnesium, and calcium at energies below 1 keV. In addition, we provide Sherman function values for elastic electron scattering from calcium in the same energy range. This study extends the application of our method of calculations, already employed for barium and strontium, to all stable alkaline-earth-metal atoms. Our semi-empirical approach to treating target polarization has produced in our earlier work a satisfactory agreement with experimental values and precise theoretical results such as convergent close-coupling calculations for barium. The present data are expected to be of similar high accuracy, based on our previous success in similar calculations for barium and all inert gases.