Validation Tests Research Articles

Signal enhancement ratio of multi-phase contrast-enhanced MRI: an imaging biomarker for survival in pancreatic adenocarcinoma.

To evaluate signal enhancement ratio (SER) for tissue characterization and prognosis stratification in pancreatic adenocarcinoma (PDAC), with quantitative histopathological analysis (QHA) as the reference standard. This retrospective study included 277 PDAC patients who underwent multi-phase contrast-enhanced (CE) MRI and whole-slide imaging (WSI) from three centers (2015-2021). SER is defined as (SIlt - SIpre)/(SIea - SIpre), where SIpre, SIea, and SIlt represent the signal intensity of the tumor in pre-contrast, early-, and late post-contrast images, respectively. Deep-learning algorithms were implemented to quantify the stroma, epithelium, and lumen of PDAC on WSIs. Correlation, regression, and Bland-Altman analyses were utilized to investigate the associations between SER and QHA. The prognostic significance of SER on overall survival (OS) was evaluated using Cox regression analysis and Kaplan-Meier curves. The internal dataset comprised 159 patients, which was further divided into training, validation, and internal test datasets (n = 60, 41, and 58, respectively). Sixty-five and 53 patients were included in two external test datasets. Excluding lumen, SER demonstrated significant correlations with stroma (r = 0.29-0.74, all p < 0.001) and epithelium (r = -0.23 to -0.71, all p < 0.001) across a wide post-injection time window (range, 25-300 s). Bland-Altman analysis revealed a small bias between SER and QHA for quantifying stroma/epithelium in individual training, validation (all within ± 2%), and three test datasets (all within ± 4%). Moreover, SER-predicted low stromal proportion was independently associated with worse OS (HR = 1.84 (1.17-2.91), p = 0.009) in training and validation datasets, which remained significant across three combined test datasets (HR = 1.73 (1.25-2.41), p = 0.001). SER of multi-phase CE-MRI allows for tissue characterization and prognosis stratification in PDAC. The signal enhancement ratio of multi-phase CE-MRI can serve as a novel imaging biomarker for characterizing tissue composition and holds the potential for improving patient stratification and therapy in PDAC. Imaging biomarkers are needed to better characterize tumor tissue in pancreatic adenocarcinoma. Signal enhancement ratio (SER)-predicted stromal/epithelial proportion showed good agreement with histopathology measurements across three distinct centers. Signal enhancement ratio (SER)-predicted stromal proportion was demonstrated to be an independent prognostic factor for OS in PDAC.

DEVELOPMENT OF INTERACTIVE MULTIMEDIA LEARNING MATHEMATICS LEARNING MEDIA USING ADOBE FLASH CS6

Ongoing advancements have profoundly reshaped the development of educational media, with validity being a pivotal factor in creating high-quality products, representing a critical phase in their development. In essence, validity holds a key position in the production of superior educational materials. The primary objective of this study is to design and assess the validity of interactive multimedia-based mathematics learning tools using Adobe Flash CS6. This research belongs to the category of Research and Development (R&amp;D) or developmental research. The methodology adopted for this developmental research follows a modified Plomp procedure, involving three of its five stages: the initial investigation phase, the development phase or prototyping, and the assessment phase. To gauge the validity of the media, the research employs a validation sheet as the data collection instrument. The analysis process entails scoring based on assessments made by the validator. The outcomes of the validation testing indicate that the media is deemed highly valid, achieving an impressive validation rate of 90.59%. The media is now deemed ready for use with only minor revisions required. One notable advantage of this learning media is its compact size, ensuring compatibility with all computer types, including those with low specifications.

Accuracy of methods to estimate central aortic SBP via upper arm cuff: a systematic review and meta-analysis.

Central aortic BP may predict cardiovascular outcomes better than upper arm brachial BP. In recent years, technology has enabled central BP estimation by recording a peripheral BP waveform from a standard upper arm cuff. The accuracy of these devices is not well documented, and this study aimed to address this issue. This study was a systematic review, conducted according to PRISMA guidelines, of observational studies published between 2008 and 2023 that reported accuracy testing of cuff-based central BP devices, compared with reference invasive aortic BP. The primary analysis was stratified according to each commercially available device. Pooled estimates were calculated using random-effects models based on mean differences and standard errors. Six thousand four hundred and fifteen studies were screened, and 27 studies met inclusion criteria (plus one unpublished study). This generated data for seven devices that are commercially available, which were tested among 2125 adult participants. There was very high heterogeneity when all devices were pooled (I2 = 97.5%), and, when stratified by device, the accuracy of estimated central BP was highly device-dependent (range of accuracy across different devices -12.4 mmHg (-16.3 to -8.5) to 3.2 mmHg (0.2-6.1). Two of the seven commercially available devices had not undergone external validation testing. The accuracy of commercially available cuff-based central BP devices is highly device-specific and not all are accurate for the estimation of central SBP. These findings have major implications for the appropriate interpretation of studies that use cuff-based estimated central BP.

Laboratory Considerations for Releasing Next-Generation Sequencing Data to Patients.

Title 45, section 164.524 of the Code of Federal Regulations states that health care systems must provide patient health records upon that patient's request. For complex testing, such as next-generation sequencing (NGS), this raises questions related to what data should be released and the laboratory considerations regarding the release of this data. To describe the laboratory implications of releasing different NGS data files and the limitations for the clinical use of different NGS data files. The College of American Pathologists workgroup, composed of laboratorians with expertise regarding NGS testing, reviewed pertinent literature, including title 45, section 164.524, and the Health and Human Services "Guidance on Individuals' Right to Access Health Information." From an accreditation standpoint, validation of NGS includes both the wet bench and data processing (bioinformatics) portions, and appropriately validated laboratory testing is required to ensure quality patient results. NGS testing generates intermediate data files that have not completed the fully validated process but are often kept by the laboratory. These files may be requested by patients, but most patients will not be aware of the test validation process and the limitations of data that have not gone through a fully validated process. Laboratories should encourage patients to receive their health data and to help individuals understand the content, uses, and limitations of laboratory data they have requested or received. NGS data used in a nonvalidated manner should not be used for clinical purposes without confirmation by a clinically validated method.

Onboard Data Prioritization Using Multi-Class Image Segmentation for Nanosatellites

Nanosatellites are proliferating as low-cost, dedicated remote sensing opportunities for small nations. However, nanosatellites’ performance as remote sensing platforms is impaired by low downlink speeds, which typically range from 1200 to 9600 bps. Additionally, an estimated 67% of downloaded data are unusable for further applications due to excess cloud cover. To alleviate this issue, we propose an image segmentation and prioritization algorithm to classify and segment the contents of captured images onboard the nanosatellite. This algorithm prioritizes images with clear captures of water bodies and vegetated areas with high downlink priority. This in-orbit organization of images will aid ground station operators with downlinking images suitable for further ground-based remote sensing analysis. The proposed algorithm uses Convolutional Neural Network (CNN) models to classify and segment captured image data. In this study, we compare various model architectures and backbone designs for segmentation and assess their performance. The models are trained on a dataset that simulates captured data from nanosatellites and transferred to the satellite hardware to conduct inferences. Ground testing for the satellite has achieved a peak Mean IoU of 75% and an F1 Score of 0.85 for multi-class segmentation. The proposed algorithm is expected to improve data budget downlink efficiency by up to 42% based on validation testing.

MRI-based intratumoral and peritumoral radiomics for preoperative prediction of glioma grade: a multicenter study

BackgroundAccurate preoperative prediction of glioma is crucial for developing individualized treatment decisions and assessing prognosis. In this study, we aimed to establish and evaluate the value of integrated models by incorporating the intratumoral and peritumoral features from conventional MRI and clinical characteristics in the prediction of glioma grade.MethodsA total of 213 glioma patients from two centers were included in the retrospective analysis, among which, 132 patients were classified as the training cohort and internal validation set, and the remaining 81 patients were zoned as the independent external testing cohort. A total of 7728 features were extracted from MRI sequences and various volumes of interest (VOIs). After feature selection, 30 radiomic models depended on five sets of machine learning classifiers, different MRI sequences, and four different combinations of predictive feature sources, including features from the intratumoral region only, features from the peritumoral edema region only, features from the fusion area including intratumoral and peritumoral edema region (VOI-fusion), and features from the intratumoral region with the addition of features from peritumoral edema region (feature-fusion), were established to select the optimal model. A nomogram based on the clinical parameter and optimal radiomic model was constructed for predicting glioma grade in clinical practice.ResultsThe intratumoral radiomic models based on contrast-enhanced T1-weighted and T2-flair sequences outperformed those based on a single MRI sequence. Moreover, the internal validation and independent external test underscored that the XGBoost machine learning classifier, incorporating features extracted from VOI-fusion, showed superior predictive efficiency in differentiating between low-grade gliomas (LGG) and high-grade gliomas (HGG), with an AUC of 0.805 in the external test. The radiomic models of VOI-fusion yielded higher prediction efficiency than those of feature-fusion. Additionally, the developed nomogram presented an optimal predictive efficacy with an AUC of 0.825 in the testing cohort.ConclusionThis study systematically investigated the effect of intratumoral and peritumoral radiomics to predict glioma grading with conventional MRI. The optimal model was the XGBoost classifier coupled radiomic model based on VOI-fusion. The radiomic models that depended on VOI-fusion outperformed those that depended on feature-fusion, suggesting that peritumoral features should be rationally utilized in radiomic studies.

Influence of High-Frequency Operation on the Efficiency of a PMSM Drive with SiC-MOSFET Inverter

This paper investigates the effects of high-frequency switching and a high fundamental frequency on the parameters and efficiency of a high-speed permanent magnet synchronous machine (PMSM) drive. We discuss the design and modeling of the PMSM, taking into account these high-frequency effects. The impact of high frequencies is analyzed across three different inverters (IGBT, Fast IGBT, and SiC-MOSFET) and the motor, and we employ theoretical analysis, computer simulations, and experimental tests for validation. Our goal is to enhance our understanding of how these high-frequency factors affect the performance of the motor drive.

Using satellite image data to identify rice varieties through linear spectral unmixing method (case study: Karangjati Sub District, Ngawi Regency)

&lt;p&gt;Remote sensing technology has increasingly emerged as a potent tool for precision agriculture, particularly in facilitating the mapping and monitoring of crops on a large scale. An application of this technology is the identification of different types of rice by analyzing the pixels acquired in satellite images. Regrettably, the pixels in the image have been mixed from different recorded items. Therefore, they have the potential to influence the outcome of the identification. An effective approach to address this problem is to employ the linear spectral unmixing (LSU) technique. The LSU approach quantifies the ratio of pure objects in every pixel of an image by utilizing the spectral value associated with the endmember of the rice variety. The investigation was carried out in the Karangjati District during the generative stage (70 ± DAP) of the rice planting season. The data indicates that the dominant variety is Inpari 32 HDB. The data validation tests, which involved the use of a confusion matrix and Kappa analysis, resulted in an overall accuracy rate of 85.48% and a Kappa analysis score of 70.6%.&lt;strong&gt;&lt;/strong&gt;&lt;/p&gt;

Development and Validation of an Explainable Deep Learning Model to Predict In-Hospital Mortality for Patients With Acute Myocardial Infarction: Algorithm Development and Validation Study.

Acute myocardial infarction (AMI) is one of the most severe cardiovascular diseases and is associated with a high risk of in-hospital mortality. However, the current deep learning models for in-hospital mortality prediction lack interpretability. This study aims to establish an explainable deep learning model to provide individualized in-hospital mortality prediction and risk factor assessment for patients with AMI. In this retrospective multicenter study, we used data for consecutive patients hospitalized with AMI from the Chongqing University Central Hospital between July 2016 and December 2022 and the Electronic Intensive Care Unit Collaborative Research Database. These patients were randomly divided into training (7668/10,955, 70%) and internal test (3287/10,955, 30%) data sets. In addition, data of patients with AMI from the Medical Information Mart for Intensive Care database were used for external validation. Deep learning models were used to predict in-hospital mortality in patients with AMI, and they were compared with linear and tree-based models. The Shapley Additive Explanations method was used to explain the model with the highest area under the receiver operating characteristic curve in both the internal test and external validation data sets to quantify and visualize the features that drive predictions. A total of 10,955 patients with AMI who were admitted to Chongqing University Central Hospital or included in the Electronic Intensive Care Unit Collaborative Research Database were randomly divided into a training data set of 7668 (70%) patients and an internal test data set of 3287 (30%) patients. A total of 9355 patients from the Medical Information Mart for Intensive Care database were included for independent external validation. In-hospital mortality occurred in 8.74% (670/7668), 8.73% (287/3287), and 9.12% (853/9355) of the patients in the training, internal test, and external validation cohorts, respectively. The Self-Attention and Intersample Attention Transformer model performed best in both the internal test data set and the external validation data set among the 9 prediction models, with the highest area under the receiver operating characteristic curve of 0.86 (95% CI 0.84-0.88) and 0.85 (95% CI 0.84-0.87), respectively. Older age, high heart rate, and low body temperature were the 3 most important predictors of increased mortality, according to the explanations of the Self-Attention and Intersample Attention Transformer model. The explainable deep learning model that we developed could provide estimates of mortality and visual contribution of the features to the prediction for a patient with AMI. The explanations suggested that older age, unstable vital signs, and metabolic disorders may increase the risk of mortality in patients with AMI.

Automatic segmentation of dura for quantitative analysis of lumbar stenosis: A deep learning study with 518 CT myelograms.

The diagnosis of lumbar spinal stenosis (LSS) can be challenging because radicular pain is not often present in the culprit-level localization. Accurate segmentation and quantitative analysis of the lumbar dura on radiographic images are key to the accurate differential diagnosis of LSS. The aim of this study is to develop an automatic dura-contouring tool for radiographic quantification on computed tomography myelogram (CTM) for patients with LSS. A total of 518 CTM cases with or without lumbar stenosis were included in this study. A deep learning (DL) segmentation algorithm 3-dimensional (3D) U-Net was deployed. A total of 210 labeled cases were used to develop the dura-contouring tool, with the ratio of the training, independent testing, and external validation datasets being 150:30:30. The Dice score (DCS) was the primary measure to evaluate the segmentation performance of the 3D U-Net, which was subsequently developed as the dura-contouring tool to segment another unlabeled 308 CTM cases with LSS. Automatic masks of 446 slices on the stenotic levels were then meticulously reviewed and revised by human experts, and the cross-sectional area (CSA) of the dura was compared. The mean DCS of the 3D U-Net were 0.905±0.080, 0.933±0.018, and 0.928±0.034 in the five-fold cross-validation, the independent testing, and the external validation datasets, respectively. The segmentation performance of the dura-contouring tool was also comparable to that of the second observer (the human expert). With the dura-contouring tool, only 59.0% (263/446) of the automatic masks of the stenotic slices needed to be revised. In the revised cases, there were no significant differences in the dura CSA between automatic masks and corresponding revised masks (p=0.652). Additionally, a strong correlation of dura CSA was found between the automatic masks and corresponding revised masks (r=0.805). A dura-contouring tool was developed that could automatically segment the dural sac on CTM, and it demonstrated high accuracy and generalization ability. Additionally, the dura-contouring tool has the potential to be applied in patients with LSS because it facilitates the quantification of the dural CSA on stenotic slices.

Design and Validation of a Computational Thinking Test for Children in the First Grades of Elementary Education

Computational thinking (CT) has garnered significant interest in both computer science and education sciences as it delineates a set of skills that emerge during the problem-solving process. Consequently, numerous assessment instruments aimed at measuring CT have been developed in the recent years. However, a scarce part of the existing CT measurement instruments has been dedicated to early school ages, and few have undergone rigorous validation or reliability testing. Therefore, this work introduces a new instrument for measuring CT in the early grades of elementary education: the Computational Thinking Test for Children (CTTC). To this end, in this work, we provide the design and validation of the CTTC, which is constructed around spatial, sequential, and logical thinking and encompasses abstraction, decomposition, pattern recognition, and coding items organized in five question blocks. The validation and standardization process employs the Kuder–Richardson statistic (KR-20) and expert judgment using V-Aiken for consistency. Additionally, item difficulty indices were utilized to gauge the difficulty level of each question in the CTTC. The study concludes that the CTTC demonstrates consistency and suitability for children in the first cycle of primary education (encompassing the first to third grades).

MRI-based deep learning and radiomics for prediction of occult cervical lymph node metastasis and prognosis in early-stage oral and oropharyngeal squamous cell carcinoma: a diagnostic study.

The incidence of occult cervical lymph node metastases (OCLNM) is reported to be 20%-30% in early-stage oral cancer and oropharyngeal cancer. There is a lack of an accurate diagnostic method to predict occult lymph node metastasis and to help surgeons make precise treatment decisions. To construct and evaluate a preoperative diagnostic method to predict occult lymph node metastasis (OCLNM) in early-stage oral and oropharyngeal squamous cell carcinoma (OC and OP SCC) based on deep learning features (DLFs) and radiomics features. A total of 319 patients diagnosed with early-stage OC or OP SCC were retrospectively enrolled and divided into training, test and external validation sets. Traditional radiomics features and DLFs were extracted from their MRI images. The least absolute shrinkage and selection operator (LASSO) analysis was employed to identify the most valuable features. Prediction models for OCLNM were developed using radiomics features and DLFs. The effectiveness of the models and their clinical applicability were evaluated using the area under the curve (AUC), decision curve analysis (DCA) and survival analysis. Seventeen prediction models were constructed. The Resnet50 deep learning (DL) model based on the combination of radiomics and DL features achieves the optimal performance, with AUC values of 0.928 (95% CI: 0.881-0.975), 0.878 (95% CI: 0.766-0.990), 0.796 (95% CI: 0.666-0.927) and 0.834 (95% CI: 0.721-0.947) in the training, test, external validation set1 and external validation set2, respectively. Moreover, the Resnet50 model has great prediction value of prognosis in patients with early-stage OC and OP SCC. The proposed MRI-based Resnet50 deep learning model demonstrated high capability in diagnosis of OCLNM and prognosis prediction in the early-stage OC and OP SCC. The Resnet50 model could help refine the clinical diagnosis and treatment of the early-stage OC and OP SCC.

Combining deep learning with physical parameters in POC and PIC inversion from spaceborne lidar CALIOP

POC and PIC are indispensable components in the global ocean carbon cycle, their transport and space distribution being driven by the biological carbon pump and the carbonate pump. However, passive ocean color remote sensing, usually employed for POC and PIC research, experiences serious shortcomings in polar winter conditions due to its reliance on sunlight, leading to scarce data coverage in the polar regions. In contrast, CALIOP has shown considerable promise in high-latitude ocean observing. Past approaches to estimate POC from CALIOP data relied on bbp measurements obtained through the application of algorithms that presume an empirical linear correlation between bbp and the backscatter coefficient measured at 180°. This method does not account for any spatiotemporal variability in the conversion coefficient. Furthermore, the potential of CALIOP to provide estimates of PIC has not been explored yet. Here, we developed an innovative Two-Branch-Two-Step (TBTS) model to estimate POC and PIC from CALIOP data, which effectively expands the spatial coverage of the MODIS products. This method exploits the strength of deep learning while encapsulating the generalizability of physical parameters. This method consists of two branches: (1) a deep learning branch based on lidar attenuated backscatter waveform and (2) a branch focusing on physical parameters, including the total column-integrated depolarization ratio and the subsurface cross-polarized component of column-integrated backscatter. The model’s generalizability and accuracy are confirmed through the evaluation of a test dataset and validation using in-situ measurements. Our model outperforms several other prevalent machine learning models. We also dissected the importance of different parts of the input data using SHAP tools, thereby providing insights into the black-box nature of deep learning models. Using CALIOP products, we put forth the inaugural estimation of interannually resolved PIC and POC standing stocks in polar regions. The implementation of CALIOP in polar regions bridges the gap inherent in passive ocean color measurements. Lidar-derived total global PIC standing stock is estimated to be 8% higher than that from MODIS, while the POC standing stock is boosted by 17.2%. The carbon standing stock in polar regions exhibits significant inter-annual variability and apparent seasonal periodicity. Hence, results from this research effort clearly reveal that the exploitation of the CALIOP-derived POC and PIC measurements, in combination with the application of new approaches and algorithms to future space lidar data, will undeniably enhance our comprehension of the polar ocean carbon cycle. However, it is important to acknowledge that the CALIOP products may inherit biases from the MODIS data used for training. Hence, where MODIS data is available, it’s still the “better” product to use, but where there isn’t MODIS data, the CALIOP product is extremely useful, especially in the polar regions.

Development of PBL-Based GLOWASEA (Global Warming on the Sea) Educational Media to Train Critical Thinking Skills on the Topic of Global Warming

The low critical thinking skills of Indonesian students in the 21st century indicate the necessity of training these skills to prepare for a future replete with challenges. Teachers need to innovate learning materials that foster critical thinking skills. This study aims to develop a PBL-based GLOWASEA Edukit media product that is valid, effective, and practical for enhancing students’ critical thinking skills. This developmental research was conducted following the ADDIE model, which comprises five stages: analysis, design, development, implementation, and evaluation. Qualitative data were obtained through comments and suggestions for improvement in questionnaires and interviews. Quantitative data were obtained from the scores on the validation test, practicality test, questions’ validity and reliability test, and pretest and post-test score data. Data were analyzed using percentage analysis and qualitative description. Successively, the results of media and material validation and practicality were 82.61%, 91.17%, and 90.62%. The effectiveness of Edukit media is calculated using the N-Gain formula, resulting in an average score of 0.33, which meets moderate criteria. It can be concluded that the GLOWASEA Edukit media produced is valid, effective, and practical for training students’ critical thinking skills.

Optimization of university management based on reptile search algorithm combined with short-duration memory neural network

Due to the lack of accuracy and difficulty to meet the actual requirements of the poor students' financial assistance in the management of smart colleges and universities. Therefore, a precise funding model for poor students is constructed on the basis of improved reptile search algorithm and short-term memory neural network. The performance of the algorithm is evaluated by test function, rank sum test and combinatorial model validation. In test function 5, the algorithm is 2.90E+01±6.04E-03, which is lower than the comparison algorithm, and begins to converge after about 10 iterations, and the convergence speed is significantly higher than that of the comparison algorithm. In the rank sum test, the experimental results of the comparison algorithm on most test functions are less than 5 %. In the combined model verification, the fitness result of the maximum convergence times was 0.2203 %, and the classification accuracy reached 98.7 %, which was better than the comparison model. The precise funding model of poor students proposed in this study has important application value in the management of smart colleges and universities, which can effectively improve the accuracy of poor students' funding and meet the actual needs. At the same time, the high accuracy and fast convergence of the model provide a new idea and method for smart university management.

Preservation of Tolaki Mekongga Language Through Merdeka Curriculum-Based Local Subject Teaching Modules

To expedite the implementation of the "Merdeka Curriculum," the government of North Kolaka District has established a policy regarding the subject of local language to show the identity of the Tolaki Mekongga community. However, this policy has not yet been realized in the form of teaching modules, particularly at the elementary school level. This research aims to create a teaching module based on the "Merdeka Curriculum" that accommodates the learning of the Tolaki Mekongga language at the elementary school level in North Kolaka District, Province of Southeast Sulawesi, Indonesia. This study employs a developmental research approach using the ASSURE model. This study's trial subjects (users) are teachers and students from SDN 3 Lambai, SDN 5 Kodeoha, and SDN 8 Kodeoha in North Kolaka District. Data were collected through expert validation and user trial questionnaires (teachers and students). The data of the research were processed and analyzed descriptively. The validation test results scored 83%, meeting the validity and suitability criteria. The user trial results (teachers and students) gained a score of 85%, meeting the practicality criteria. Therefore, the module resulting from this research is considered appropriate and practical for use in teaching the local subject of the Tolaki Mekongga language at the elementary school level in North Kolaka District.

Study and Experiment on Screen Surface Homogenization Technology of Dislodged Material Based on Longitudinal Flow Threshing

Aiming at the problems of uneven distribution of dislodged material on the screen surface of longitudinal axial flow grain combine harvester, a large difference in material clearing time, and large clearing loss, a dislodged material homogenizing device that can realize dislodged material return and homogenization at the rear of longitudinal axial flow was developed. (1) The structure and motion parameters of the reflux plate were determined, and simulation tests were carried out to verify them; (2) A test bench was set up, and the Box-Behnken test method was adopted to determine the influence law of each factor on the operating effect and the optimal parameter combination, and the results showed that the tilt angle of the return plate, motor speed, and amplitude had a significant influence on the distribution uniformity of the material on the screen surface; it was determined that the optimal combination of the angle of the return plate configuration was 28.7°, the speed of the motor was 247 r/min, the amplitude of the return plate was 18.3 mm, and the seed contamination rate was 0.48%. The optimum combination was determined to be 28.7°, 247 r/min, 18.3 mm, and 0.48% impurity rate; (3) under the conditions of the field test validation, the validation error is less than 5%, proving that it can effectively improve the performance of the clearing and reduce the rate of impurity content.

High-order accurate transient and free-vibration analysis of plates and shells

The limited availability of analytical solutions and the high cost associated with experimental testing motivate the use of computational tools to assess the dynamic behavior of load-bearing components, especially when a wide design space must be explored, as is often the case with composite structures. In this context, a novel high-order accurate discontinuous Galerkin formulation for transient and free-vibration analysis of multilayered plates and shells is presented and numerical validated. The starting point of the formulation is a generalized structural theory for multilayered shells with arbitrary curvature based on the expansion of the displacement covariant components throughout the shell thickness. The variational statement of three-dimensional elastodynamics allows deriving the strong form of the governing differential equations, which form the basis to obtain the corresponding discontinuous Galerkin weak statements. As the order of the through-the-thickness expansion and the order of the discontinuous Galerkin basis functions are free parameters, the proposed approach allows tuning the order of accuracy of the computed solution throughout both the shell thickness and the shell modeling domain. Numerical results are reported and discussed for several validation test cases in terms of h- and p-convergence analyses, demonstrating the high-order accuracy, robustness, and computational savings of the formulation.

Maize kernel damage dynamic prediction in threshing through PSO-LSTM and discrete element modelling

This study presents the development of a maize ear model and a predictive approach for kernel damage in maize threshing, integrating physical simulation and predictive analytics to understand better and forecast threshing-related damage. First, a maize ear model was developed to analyse kernel damage during threshing. Through the angle of repose experiments, the optimal number of spheres for the kernel model was established as 65. Tensile tests were conducted to evaluate the kernel-cob bond strength, revealing an average relative error in the bonding force of 8.71%. Vogel impact energy modelling was applied to the kernel threshing process to determine kernel damage. The correlation between the speed of seed grain movement and the occurrence of damage was analysed by post-processing to identify locations with frequent kernel damage in the drum. In-depth data analysis of kernel damage in the threshing drum further elucidates the inherent relationship between kernel velocity and damage extent. The study then focused on applying neural networks to predict damage rates. The comparative evaluation shows that the PSO-LSTM model has better prediction accuracy than LSTM and RNN models, with the PSO-LSTM network achieving an RMSE of 0.096, a R2 of 99.96%, and a final damage rate of 2.41% in validation tests. Threshing experiments were conducted to verify the model, showing a 1.4% discrepancy between predicted and actual damage rates. This study proposes a kernel damage prediction model and provides new insights and directions for the structural design of threshing drums.

Isolation and evaluation of probiotics from traditional Chinese foods for aflatoxin B1 detoxification: Geotrichum candidum XG1 (yeast) and mechanistic insights

Identifying aflatoxin-detoxifying probiotics remains a significant challenge in mitigating the risks associated with aflatoxin contamination in crops. Biological detoxification is a popular technique that reduces mycotoxin hazards and garners consumer acceptance. Through multiple rounds of screening and validation tests, Geotrichum candidum XG1 demonstrated the ability to degrade aflatoxin B1 (AFB1) by 99–100%, exceeding the capabilities of mere adsorption mechanisms. Notably, the degradation efficiency was demonstrably influenced by the presence of copper and iron ions in the liquid medium, suggesting a potential role for proteases in the degradation process. Subsequent validation experiments with red pepper revealed an 83% reduction in AFB1 levels following fermentation with G. candidum XG1. Furthermore, mass spectrometry analysis confirmed the disruption of the AFB1 furan ring structure, leading to a subsequent reduction in its toxicity. Collectively, these findings establish G. candidum XG1 as a promising candidate for effective aflatoxin degradation, with potential applications within the food industry.