Test Data Research Articles

Design, simulation and energy dissipation evaluating of a new lead shear damper

This study presents a novel design for a lead shear damper (LSD) that features lead blocks encased in laminated steel and rubber plates, intersected by X-shaped steel sticks. It combines the excellent energy dissipation and fatigue resistance of lead with the uniform yielding capabilities of X-shaped steel sticks along their height. Cyclic loading testing and fatigue tests are conducted in order to determine the mechanical properties of the LSD. The test results reveal that the LSD has a variable yielding force, and exhibits stiffness hardening when the damper's deformation direction deviates from the origin. However, when the deformation direction returns to the origin, there is no stiffness hardening observed, and the resistant force remains constant. Furthermore, this LSD can work effectively despite the failure of energy dissipation parts, which improves hysteretic energy dissipation under large deformation. In order to replicate the hysteretic behaviour, a uniaxial constitutive model is formulated and its parameters are determined by the utilization of the differential evolution approach, based on test data. The observed strong concurrence between the experimental and simulated hysteresis curves serves as compelling evidence for the efficacy of the differential evolution method. Meanwhile, a three-story building from the third-generation benchmark problem is employed as the testbed structure to examine the energy dissipation capacity of the developed dampers. Nonlinear seismic analysis on the benchmark model equipped with the proposed dampers is also conducted, and the results are compared to those fitted with conventional dampers, such as mild dampers and friction dampers. The findings of this study indicate that the performance of the LSD in reducing structural displacements is superior to that of mild dampers and friction dampers.

SYSTEM DESIGN FOR CALCUALTING THE NUMBER AND DENSITY OF MOTORCYCLES IN PARKING AREA BASED ON BACKGROUND SUBTRACTION METHOD

There are still issues with a number of students parking their vehicles improperly in the parking spaces provided. This causes suboptimal parking capacity due to a lack of information about parking capacity. In recent years, at a certain library, vehicle detection has been implemented using a Gaussian mixture model algorithm using a Raspberry Pi. However, this library does not provide information about the density status of the parking area. Therefore, an information system was created to determine the level of density of the parking area based on the ratio of vehicles entering and exiting compared to the maximum capacity of the parking area. The system uses the Gaussian mixture model algorithm with the machine learning method of background subtraction MOG2, which can calculate the number of vehicles based on the difference between objects and the background of objects, using test data in the form of videos recorded using a camera positioned horizontally to the entrance and exit lanes of the parking area. This research resulted in an accuracy of 89.7% for Video1TA, precision of 93.2%, a crowded parking area density level, and a value of 129.12. Video2TA had a value of 101.08, precision of 100%, and accuracy of 90%, while Video3TA had an accuracy of 35%, precision of 56.7%, and a value of 49.48. The density levels of videos 2 and 3 are the same, indicating that the parking area is still empty. The test results show that the value can affect the system in detecting an object.

Impacts of socioeconomic and environmental factors on neoplasms incidence rates using machine learning and GIS: a cross-sectional study in Iran

Neoplasm is an umbrella term used to describe either benign or malignant conditions. The correlations between socioeconomic and environmental factors and the occurrence of new-onset of neoplasms have already been demonstrated in a body of research. Nevertheless, few studies have specifically dealt with the nature of relationship, significance of risk factors, and geographic variation of them, particularly in low- and middle-income communities. This study, thus, set out to (1) analyze spatiotemporal variations of the age-adjusted incidence rate (AAIR) of neoplasms in Iran throughout five time periods, (2) investigate relationships between a collection of environmental and socioeconomic indicators and the AAIR of neoplasms all over the country, and (3) evaluate geographical alterations in their relative importance. Our cross-sectional study design was based on county-level data from 2010 to 2020. AAIR of neoplasms data was acquired from the Institute for Health Metrics and Evaluation (IHME). HotSpot analyses and Anselin Local Moran's I indices were deployed to precisely identify AAIR of neoplasms high- and low-risk clusters. Multi-scale geographically weight regression (MGWR) analysis was worked out to evaluate the association between each explanatory variable and the AAIR of neoplasms. Utilizing random forests (RF), we also examined the relationships between environmental (e.g., UV index and PM2.5 concentration) and socioeconomic (e.g., Gini coefficient and literacy rate) factors and AAIR of neoplasms. AAIR of neoplasms displayed a significant increasing trend over the study period. According to the MGWR, the only factor that significantly varied spatially and was associated with the AAIR of neoplasms in Iran was the UV index. A good accuracy RF model was confirmed for both training and testing data with correlation coefficients R2 greater than 0.91 and 0.92, respectively. UV index and Gini coefficient ranked the highest variables in the prediction of AAIR of neoplasms, based on the relative influence of each variable. More research using machine learning approaches taking the advantages of considering all possible determinants is required to assess health strategies outcomes and properly formulate policy planning.

An attempt to augment performance of machine learning models in a pilot-scale urban wastewater treatment system

The challenge we face is striking a balance between ensuring the standards for wastewater treatment processes and minimizing energy consumption. As such, accurate predictive models become critical. With machine learning algorithms having the ability to discern relationships between inputs and outputs, broad interest has been shown in their applications in wastewater treatment. A key research focus is on effectively adjusting these generic machine learning algorithms to adapt to frequent fluctuations of water quality, inflow volume, and environmental variations. This study developed a Nonlinear Autoregressive neural network (NARX) model having an adaptive real-time updating module to predict the ammonium concentration in the treated effluent. The model has exhibited high predictive accuracy on the testing data, achieving an R2 value of 0.997. By incorporating an adaptive real-time updating module, the model's capability to predict effluent ammonium concentration on novel data sets has been significantly improved, with the R2 value for these predictions increasing from an initial 0.918 to a more refined range of 0.966 to 0.984. Our findings confirm that integrating real-time updating capabilities with machine learning algorithms substantially improves the stability and predictive performance of the models. These outcomes will prompt a broader integration of artificial intelligence in the domain of environmental engineering, which shall foster the implementation of more accurate and eco-friendly wastewater treatment strategies.

Predictive Analysis and Discerning of Polycystic Ovary Syndrome in Women using Machine Learning Computations

This paper focuses on the data-driven diagnosis of polycystic ovary syndrome (PCOS) in women. For this, machine learning algorithms are applied to a dataset freely available in Kaggle repository. This dataset has 43 attributes of 541 women, among which 177 are patients of PCOS disease. Firstly, univariate feature selection algorithm is applied to find the best features that can predict PCOS. The ranking of the attributes is computed and it is found that the most important attribute is the ratio of Follicle stimulating hormone (FSH) and Luteinizing hormone (LH), Next, holdout and cross validation methods are applied to the dataset to separate the training and testing data. A number of classifiers such as gradient boosting, random forest, logistic regression, and hybrid random forest and logistic regression (RFLR) are applied to the attributed are good enough to predict the PCOS disease

Computational Study on Influence Factors and Vortical Structures in Static Drift Tests

This paper conducted a computational study on the KCS benchmark model at static drift conditions. At the first instance, the roles played by the grid size, turbulence model, and time step are qualitatively and quantitatively analyzed with the orthogonal experimental method (OEM). After the verification of simulated results compared with experimental data in a Static Oblique Towing Test (OTT), hydrodynamic performance is obtained with the employment of the SST κ-ω turbulence model. The grid size is set as 0.07 m while the time step as 0.01 s. The characteristics of the wake field are illustrated in different forms, such as contours of the free surface, distribution of pressure and hydrodynamic forces, variation of turbulent kinetic energy (TKE), and so on. For a deep insight into the physical mechanisms of the asymmetrical flow field, the Detached Eddy Simulation (DES) method is also utilized to capture vortical structures occurring around the hull, in comparison with results obtained through the Reynolds Averaged Navier Stokes (RANS) model. With the aim of a hydrodynamic derivative estimation or detailed flow characteristics analysis, corresponding selections of the computational method are disparate.

Tensile characteristics of ultra-high-performance fibre-reinforced concrete with and without longitudinal steel rebars

Ultra-high-performance fibre-reinforced concrete (UHPFRC) specimens with and without longitudinal reinforcement were experimentally tested under direct tensile loading. The variables considered were the volume fraction of fibres (1.0% and 2.0%), the type of steel fibres (straight and hooked end) and the longitudinal steel reinforcement ratio (none and 1.2%). All of the specimens were tested using a servo-controlled fatigue testing machine in displacement control mode. The changes in displacement were monitored using linear variable displacement transducers and the digital image correlation technique. The strain profiles at different loading stages were used to identify the crack evolution process. The average localised strain was found to be 0.2–0.36%, with corresponding crack widths of 0.3–0.6 mm. A uniaxial tensile stress–strain model was developed based on the test results and data from the literature. Longitudinally steel-reinforced specimens showed both stiffening and strengthening effects. Tension-stiffened specimens with 1.0% fibres failed at a higher strain due to the formation of multiple macrocracks. In the specimens with 2.0% fibres, the rebar fractured in a brittle manner due to crack localisation. It is concluded that a higher longitudinal reinforcement ratio is needed to utilise UHPFRC effectively under tension-dominant loads.

A Machine Learning-Based Approach for Predicting Installation Torque of Helical Piles from SPT Data

Helical piles are advantageous alternatives in constructions subjected to high tractions in their foundations, like transmission towers. Installation torque is a key parameter to define installation equipment and the final depth of the helical pile. This work applies machine learning (ML) techniques to predict helical pile installation torque based on information from 707 installation reports, including Standard Penetration Test (SPT) data. It uses this information to build three datasets to train and test eight machine-learning techniques. Decision tree (DT) was the worst technique for comparing performances, and cubist (CUB) was the best. Pile length was the most important variable, while soil type had little relevance for predictions. Predictions become more accurate for torque values greater than 8 kNm. Results show that CUB predictions are within 0.71,1.59 times the real value with a 95% confidence. Thus, CUB successfully predicted the pile length using SPT data in a case study. One can conclude that the proposed methodology has the potential to aid in the helical pile design and the equipment specification for installation.

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.

Development of a Mobile-Based Application for Classifying Caladium Plants Using the CNN Algorithm

Caladium is a popular ornamental plant and has business potential. However, difficulties in recognizing the type of Caladium often occur because of the similarities in shape, pattern, and color of the leaves between the different kinds of Caladium. To overcome this problem, research will use machine learning with the Convolutional Neural Network (CNN) algorithm to build a mobile application that can accurately classify four types of Caladiums. The data set used is 1200 data with four classes; each class has 300 data. The best model is found with the parameter epoch 100, learning rate 0.001, and batch size 64. The model is then implemented in a mobile application with two menus, "Take a photo" and "Choose an image," which will display the classification output and confidence values of the four types of Caladiums. Testing with 30 test data per class achieves 0.975 accuracy on both menus. On the “Take a photo” menu, precision is 0.974, recall is 0.9725, and f1-score is 0.965. Meanwhile, on the “Choose an image” menu a precision and recall value is 0.975, and f1-score value of 0.97.

Physical fitness decline and career paths: a longitudinal study of medical undergraduates

IntroductionExercise enhances one's health and competitiveness. A strong physical fitness status can pave the way for a promising future. This study presents the time-based trends in physical fitness indicators—including height, weight, BMI, lung capacity, dash, long-distance running, and standing long jump—among medical undergraduates during their university years. Additionally, we analyzed the impact of students' physical fitness on their career paths.MethodWe conducted a retrospective database study by collecting physical fitness test data and career paths information for 634 medical students from a university in southwestern China. These students graduated in 2022. The career paths included pursuits in further studies, employment, and unemployment. To detect differences in these aspects, we used the t-test and Chi-square test.ResultsOur study indicates a significant declining trend in the physical fitness of medical students during their university years. The changes observed between the first and fourth tests are as follows:Weight (kg): 58.52 ± 10.48 to 60.73 ± 12.07, P < 0.00BMI (kg/m^2): 20.79 ± 2.74 to 21.24 ± 3.06, P < 0.0050-m dash (s): 8.91 ± 0.99 to 9.25 ± 1.11, P < 0.00Standing long jump (cm): 187.74 ± 30.98 to 182.59 ± 32.25, P < 0.00800-m run for females (min): 3.84 ± 0.47 to 4.48 ± 0.85, P < 0.001000-m run for males (min): 3.98 ± 0.63 to 4.62 ± 0.87, P < 0.00Sit-ups for females (count): 30.39 ± 7.5 to 29.03 ± 8.82, P < 0.00Upon analyzing the correlation between changes in physical fitness and career paths, students with stable or decreased BMI had better post-graduation outcomes compared to students with increased BMI.ConclusionsMedical students show a declining trend in physical fitness during their undergraduate years. A good physical health status is beneficial for achieving better career paths. Medical students should place greater emphasis on physical exercise during their time in school.

Heavy metals removal from wastewater using nanoporous adsorbent: Separation analysis via machine learning model

This paper presents a comprehensive analysis of three predictive models, namely Multi-Layer Perceptron (MLP), LASSO, and Extreme Gradient Boosting (XGB) for estimating concentration (C) of a substance in a given dataset. Adsorption separation was considered for water treatment, and the models were employed for tracking the concentration variations of solute in the process. With x(m) and y(m) as input variables which are the location, and concentration (C) measured in mol/m³ as output, the dataset comprises more than 19,000 data points. The Fireworks Algorithm (FWA) was employed to perform hyper-parameter optimization for the models. Different metrics were utilized to gauge the proficiency of each model, such as the R2 (coefficient of determination) for both the training and testing datasets, RMSE, and MAE. Results indicate that the MLP model obtained the highest score in terms of R2, with values of 0.99751 for the training data and 0.99756 for the testing data, suggesting excellent predictive accuracy. The MLP model also demonstrated the lowest error rates, with an RMSE of 1.3937 E+00 and an MAE of 8.81875E-01. The results revealed that the employed machine learning models are well capable of predicting adsorption process when combined with mass transfer modeling and great accuracy can be obtained.

ECG-based estimation of respiration-induced autonomic modulation of AV nodal conduction during atrial fibrillation

Introduction: Information about autonomic nervous system (ANS) activity may offer insights about atrial fibrillation (AF) progression and support personalized AF treatment but is not easily accessible from the ECG. In this study, we propose a new approach for ECG-based assessment of respiratory modulation in atrioventricular (AV) nodal refractory period and conduction delay.Methods: A 1-dimensional convolutional neural network (1D-CNN) was trained to estimate respiratory modulation of AV nodal conduction properties from 1-minute segments of RR series, respiration signals, and atrial fibrillatory rates (AFR) using synthetic data that replicates clinical ECG-derived data. The synthetic data were generated using a network model of the AV node and 4 million unique model parameter sets. The 1D-CNN was then used to analyze respiratory modulation in clinical deep breathing test data of 28 patients in AF, where an ECG-derived respiration signal was extracted using a novel approach based on periodic component analysis.Results: We demonstrated using synthetic data that the 1D-CNN can estimate the respiratory modulation from RR series alone with a Pearson sample correlation of r = 0.805 and that the addition of either respiration signal (r = 0.830), AFR (r = 0.837), or both (r = 0.855) improves the estimation.Discussion: Initial results from analysis of ECG data suggest that our proposed estimate of respiration-induced autonomic modulation, aresp, is reproducible and sufficiently sensitive to monitor changes and detect individual differences. However, further studies are needed to verify the reproducibility, sensitivity, and clinical significance of aresp.

Critical Risk Assessment, Diagnosis, and Survival Analysis of Breast Cancer

Breast cancer is the most prevalent type of cancer in women. Risk factor assessment can aid in directing counseling regarding risk reduction and breast cancer surveillance. This research aims to (1) investigate the relationship between various risk factors and breast cancer incidence using the BCSC (Breast Cancer Surveillance Consortium) Risk Factor Dataset and create a prediction model for assessing the risk of developing breast cancer; (2) diagnose breast cancer using the Breast Cancer Wisconsin diagnostic dataset; and (3) analyze breast cancer survivability using the SEER (Surveillance, Epidemiology, and End Results) Breast Cancer Dataset. Applying resampling techniques on the training dataset before using various machine learning techniques can affect the performance of the classifiers. The three breast cancer datasets were examined using a variety of pre-processing approaches and classification models to assess their performance in terms of accuracy, precision, F-1 scores, etc. The PCA (principal component analysis) and resampling strategies produced remarkable results. For the BCSC Dataset, the Random Forest algorithm exhibited the best performance out of the applied classifiers, with an accuracy of 87.53%. Out of the different resampling techniques applied to the training dataset for training the Random Forest classifier, the Tomek Link exhibited the best test accuracy, at 87.47%. We compared all the models used with previously used techniques. After applying the resampling techniques, the accuracy scores of the test data decreased even if the training data accuracy increased. For the Breast Cancer Wisconsin diagnostic dataset, the K-Nearest Neighbor algorithm had the best accuracy with the original dataset test set, at 94.71%, and the PCA dataset test set exhibited 95.29% accuracy for detecting breast cancer. Using the SEER Dataset, this study also explores survival analysis, employing supervised and unsupervised learning approaches to offer insights into the variables affecting breast cancer survivability. This study emphasizes the significance of individualized approaches in the management and treatment of breast cancer by incorporating phenotypic variations and recognizing the heterogeneity of the disease. Through data-driven insights and advanced machine learning, this study contributes significantly to the ongoing efforts in breast cancer research, diagnostics, and personalized medicine.

Behavior of steel liner tray walls under external blast: Experimental testing and numerical simulations

Explosions produced in urban or industrial areas due to accidental or intentional detonation of explosives are low-probability but high-impact events. If standoff distance from buildings or other constructions is small, explosions can cause structural failures and serious injuries or deaths of occupants. Although heavy/stiff enclosure systems were long considered appropriate to protect buildings against explosive threats, lighter and flexible steel-based systems, which are increasingly used for modern buildings, may also provide satisfactory performance. The study presents the results of full-scale blast tests on liner tray walls attached to a steel frame building. Walls were tested against increasing blast charges until failure. Due to very high peak pressures and very short durations, the loading regime could be classified as impulsive. The ultimate strength is given by the failure of the end fasteners, after large bolt hole elongations coupled with the pull-through of the end fasteners. If the insulation and outer cladding are lost, the liner trays are directly exposed to blast and the seaming fasteners at overlapping sheets are prematurely lost. As a result, the wall panels work mostly independently, as one-way elements. On the contrary, if the outer cladding protects the seaming fasteners, the overlapping provides additional capacity due to membrane effect (two-way behavior). Numerical models were calibrated using test data and were further used to obtain more information about liner tray behavior under blast.

Identification of genetically plastic forms among Belarusian ancient flax (Linum usitatissimum convar. elongatum Vav. et Ell.) varieties using the Linum Insertion Sequence LIS-1

The Linum Insertion Sequence 1 (LIS-1) occurs in the genetically plastic flax genotypes in response to the lack or excess of mineral and water nutrition, but also naturally, and can be transmitted to the progeny. We have analyzed 21 ancient Belarusian varieties of flax Linum usitatissimum convar. elongatum Vav. et Ell. The LIS-1 presence or absence was checked for individual plants in at minimum two generations with primer-specific polymerase chain reaction (PCR) and agarose gel electrophoresis. The studied flax varieties formed four groups: non-responsive varieties (LIS-1 was not found, group NR); responsive, which formed and completely lost the insertion (group R0); responsive, which formed and retained LIS-1 (group R1); and responsive unstable (group R2). A statistically significant difference was found in ‘plant height’ (p &lt; 0.05), ‘technical length of the stem’ (p &lt; 0.05) between R0 and NR, and R2 and NR LIS-1 groups. The machine learning algorithm random forest classifier was used to predict the presence, absence or heterozygosity of LIS-1 in flax plants based on their growth and reproductive characteristics. As a result, the accuracy of the prediction was 98% on test data. In terms of sources for the selection of fibre flax varieties adaptive to environmental challenges, the most promising group consists of responsive varieties that have formed LIS-1 insertion (R0, R1 and R2 groups).

Quality-of-Life Assessment in Children with Mild to Moderate Bronchial Asthma

Background and Objectives: Assess the quality of life of children aged 2–10 with mild to moderate bronchial asthma. To evaluate the general health condition of children with mild and moderate severity bronchial asthma. To determine health changes in children with mild- and moderate-severity bronchial asthma as they grow older. To evaluate the impact of mild- and moderate-severity bronchial asthma on children’s daily and social activities, physical health, emotional state, and general well-being. Materials and Methods: A comparative cross-sectional study was conducted in March–June 2020. Parents or guardians of 2–10-year-old children without bronchial asthma and children with mild to moderate bronchial asthma were interviewed after receiving their written informed consent. The questionnaire was based on the standardized quality-of-life quiz SF-36. A total of 248 questionnaires were collected—106 from the parents or guardians of children with bronchial asthma and 142 from parents/guardians of children without bronchial asthma. For further analysis, 106 children without bronchial asthma and with no chronic conditions were selected. Quantitative variables were compared using the Mann–Whitney U test and qualitative data using the chi-square (χ2) criteria. Quantitative data were described by giving means, medians, and standard deviations (SD); qualitative features by giving relative frequencies. Statistical data were analyzed using SPSS and Excel 2020. Results: Children with mild and moderate asthma exhibit poorer health compared to their healthy counterparts. Only 20.7% of respondents with asthma reported excellent or very good health, contrasting with 64.1% of healthy children (p &lt; 0.001). As children with asthma age, their general condition improves, with 46.2% showing improvement in the past year, while 42.5% of healthy children had a stable condition (p &lt; 0.05). In various activities, children with asthma face more constraints than healthy children (p &lt; 0.05), including energetic activities (sick—59.5%; healthy—10.3%), moderate activities (sick—24.5%; healthy—4.7%), climbing stairs (sick—22.7%; healthy—3.8%), and walking over 100 m (sick—9.4%; healthy—0%). Children with asthma are more likely to experience exhaustion, anxiety, tiredness, lack of energy, and restraint in public activities (p &lt; 0.05). Conclusions: Parents/caregivers of children with mild to moderate bronchial asthma rate their health worse than those of healthy children do. As children with mild to moderate bronchial asthma grow, the disease impact on their overall well-being decreases. Children with mild to moderate bronchial asthma, compared to healthy children, experience more limitations in vigorous or moderate activities; face more difficulties climbing stairs or walking more than 100 m; frequently feel exhaustion, anxiety, fatigue, or lack of energy; and encounter restrictions in social activities.

Distributed acoustic sensing for seismic surface wave data acquisition in an intertidal environment

The application of distributed acoustic sensing (DAS) for shallow marine seismic investigations is assessed, in particular with respect to the collection of seismic surface wave data in an intertidal setting. Appropriate selection and directional sensitivity of fiber-optic cables is considered and the measured data is validated with respect to conventional seismic data acquisition approaches, using geophones and hydrophones, along with independent borehole and seismic cone penetration test (SCPT) data. In terms of cable selection, a reduction in amplitude and frequency response of an armored cable is observed, when compared with an unarmored cable. For seismic surface wave surveys in an offshore environment where the cable would need to withstand significant stresses, the use of the armored variant with limited loss in frequency response may be acceptable from a practical perspective. The DAS approach also has indicated good consistency with conventional means of surface wave data acquisition, and the inverted [Formula: see text] also is very consistent with downhole SCPT data. Observed differences in phase velocity between high tide (Scholte wave propagation) and low tide (Rayleigh wave propagation) are not thought to be related to the particular type of interface wave due to shallow water depth. These differences are more likely to be related to the development of capillary forces in the partially saturated granular medium at low tide. Overall, this study demonstrates that our novel approach of DAS using seabed fiber-optic cables in the intertidal environment is capable of rapidly providing near-surface S-wave velocity data across considerable spatial scales (multikilometer) at high resolution, which is beneficial for the design of subsea cables routes and landfall locations. The associated reduction in deployment and survey duration, when compared with conventional approaches, is particularly important when working in the marine environment due to potentially short weather windows and expensive downtime.

EPortfolios: Enhancing confidence in student radiographers' communication of radiographic anatomy and pathology. Across-sectional study.

In 2020, the Medical Radiation Practice Board of Australia made several revisions to its professional capabilities. To address this, medical radiation practitioners, including diagnostic radiographers, are required to escalate urgent findings in all radiographic settings. However, the confidence of radiographers in articulating descriptions of radiographic findings varies despite this requirement. This cross-sectional study explores how the implementation of eportfolio affects student self-perceived confidence in identifying and describing radiographic findings in both an academic and a clinical setting. A Qualtrics survey was distributed to second-year radiography students who had used eportfolios. The survey comprised of four questions using a Likert-scale and one open-ended question. Quantitative data were analysed using the Wilcoxon signed-rank test and qualitative data was thematically assessed. Overall, 55 of 65 radiographic students (85%) completed the survey. Confidence (strongly agree and agree) decreased from 89% to 74% between academic and clinical environments when identifying abnormalities, and 89% to 73% when describing findings. This finding highlights the challenges students face when in the clinical environment. Wilcoxon signed rank test analysed a statistically significant relation between the two environments (P < 0.05). However, the relationship between identifying and describing skills was not statistically significant (P > 0.05). Following a review of the qualitative data, three recurring themes were identified among responses. ePortfolios assist in improving confidence in identification and description of radiographic abnormalities, particularly in an academic setting. The clinical environment presents unique challenges which may limit student clinical performance; however, this requires further investigation.

Optimization and Application of XGBoost Logging Prediction Model for Porosity and Permeability Based on K-means Method

The prediction and distribution of reservoir porosity and permeability are of paramount importance for the exploration and development of regional oil and gas resources. In order to optimize the prediction methods of porosity and permeability and better guide gas field development, it is necessary to identify the most effective approaches. Therefore, based on the extreme gradient boosting (XGBoost) algorithm, laboratory test data of the porosity and permeability of cores from the southern margin of the Ordos Basin were selected as the target labels, conventional logging curves were used as the input feature variables, and the mean absolute error (MAE) and the coefficient of determination (R2) were used as the evaluation indicators. Following the selection of the optimal feature variables and optimization of the hyper-parameters, an XGBoost porosity and permeability prediction model was established. Subsequently, the innovative application of homogeneous clustering (K-means) data preprocessing was applied to enhance the XGBoost model’s performance. The results show that logarithmically preprocessed (LOG(PERM)) target labels enhanced the performance of the XGBoost permeability prediction model, with an increase of 0.26 in its test set R2. Furthermore, the application of K-means improved the performance of the XGBoost prediction model, with an increase of 0.15 in the R2 of the model and a decrease of 0.017 in the MAE. Finally, the POR_0/POR_1 grouped porosity model was selected as the final predictive model for porosity in the study area, and the Arctan(PERM)_0/Arctan(PER0M)_1 grouped model was selected as the final predictive model for permeability, which has better prediction accuracy than logging curves. The combination of K-means and the XGBoost modeling method provides a new approach and reference for the efficient and relatively accurate evaluation of porosity and permeability in the study area.