Accurate Measure Research Articles

Impact of Model Knowledge on Acoustic Emission Source Localization Accuracy

Reliable and precise damage localization in mechanical structures is of high importance in the context of structural health monitoring (SHM). The acoustic emission (AE) method has already shown its excellent suitability for damage localization. However, low signal-to-noise ratios (SNRs) are often prevalent in SHM, thus an increase in localization accuracy and robustness is still in demand. The present study faces this task through the integration of various model knowledge for AE source localization. The basis of the presented algorithm is the consideration of the dispersive behavior of elastic waves in thin-walled structures. The continuous wavelet transform (CWT) is used to obtain time-frequency representations of the signals, where frequency-dependent values for time-of-arrival (TOA) are extracted. Furthermore, the algorithm incorporates the knowledge that all sensors receive signals with the same time and location of origin, as well as the slightly different sensitivity of the theoretically equal sensors. The final localization results are achieved by a two parameter grid search optimization. The algorithm is experimentally tested on a large aluminum plate with four piezoelectric wafer active sensors (PWASs) arranged in an array of 100 mm × 100 mm. The mean localization error of pencil lead breaks at ten different positions within the sensor array is used as an accuracy measure. It is shown that as more of the described model knowledge is incorporated into the localization, the accuracy increases. With the final algorithm, the mean localization error is more than halved compared to AE localization based on classical TOA estimation. Although the experiment described is conducted under laboratory conditions, the remarkable increase in accuracy suggests that AE source localization may be successful even at low SNR as typical for operational conditions.

Understanding the SSARC Model of Task Sequencing: Assessing L2 Writing Development

This study aimed to explore the impact of task sequencing on the development of second language (L2) writing and investigate how L2 learners performed on three decision-making writing tasks completed in different orders over nine weeks. 120 advanced-high EFL students were randomly assigned to one of three groups and given different task sequences: 1) a simple-medium-complex (SMC) sequence, 2) a complex-medium-simple sequence (CMS), or 3) a random sequence (RDM). Essays were analyzed using measures of syntactic complexity, accuracy, lexical complexity, and fluency (CALF). Results showed that the CALF of L2 writing demonstrated longitudinal development over time in all three task sequencing groups. CALF development was not immediately apparent in the first six weeks, with most measures displaying a significant increase by the end of the ninth week. Furthermore, different task sequences resulted in varying patterns and magnitudes of CALF growth, but no specific sequence was found to be superior overall.

Development and Evaluation of Pediatric Acute Care Hospital Referral Regions in Eight States

To develop a set of pediatric acute care hospital referral regions for use in studying pediatric acute care delivery, and test their utility relative to other regional systems. We used state-level administrative databases capturing all pediatric acute care in eight states to construct novel referral regions. We first constructed pediatric hospital service areas (PHSAs) based on 5,837,464 pediatric emergency department encounters. We then aggregated these PHSAs to pediatric hospital referral regions (PHRRs) based on 344,440 pediatric hospitalizations. Finally, we used three measures of spatial accuracy (localization index, market share index, and net patient flow) to compare this novel region system with the Dartmouth Atlas, designed originally to study adult specialty care, and the Pittsburgh Atlas, designed originally to study adult acute care. The development procedure resulted in 717 novel PHSAs, which were then aggregated to 55 PHRRs across the included states. Relative to HRRs in the Dartmouth and Pittsburgh Atlases, PHRRs were fewer in number and larger in area and population. PHRRs more accurately captured patterns of pediatric hospitalizations, (eg, mean localization index: 69.1 out of 100, compared with a mean of 58.1 for the Dartmouth Atlas and 62.4 for the Pittsburgh Atlas). The use of regional definitions designed specifically to study pediatric acute care better captures contemporary pediatric acute care delivery than the use of existing regional definitions. Future work should extend these definitions to all US states to enable national analyses of pediatric acute care delivery.

Inference of black-hole mass fraction in Galactic globular clusters

Context. Globular clusters (GCs) are suggested to host many stellar-mass black holes (BHs) at their centers, thus resulting in ideal testbeds for BH formation and retention theories. BHs are expected to play a major role in GC structural and dynamical evolution and their study has attracted a lot of attention. In recent years, several works attempted to constrain the BH mass fraction in GCs typically by comparing a single observable (for example, mass segregation proxies) with scaling relations obtained from numerical simulations. Aims. We aim to uncover the possible intrinsic degeneracies in determining the BH mass fraction from single dynamical parameters and identify the possible parameter combinations that are able to break these degeneracies. Methods. We used a set of 101 Monte Carlo simulations sampling a large grid of initial conditions. In particular, we explored the impact of different BH natal kick prescriptions on widely adopted scaling relations. We then compared the results of our simulations with observations obtained using state-of-the-art HST photometric and astrometric catalogs for a sample of 30 Galactic GCs. Results. We find that using a single observable to infer the present-day BH mass fraction in GCs is degenerate, as similar values could be attained by simulations including different BH mass fractions. We argue that the combination of mass-segregation indicators with GC velocity dispersion ratios could help us to break this degeneracy efficiently. We show that such a combination of parameters can be derived with currently available data. However, the limited sample of stars with accurate kinematic measures and its impact on the overall errors do not allow us to discern fully different scenarios yet.

Predicting Survival Among Colorectal Cancer Patients: Development and Validation of Polygenic Survival Score.

Colorectal cancer is the second leading cause of cancer-related death in the United States. A multi-omics approach has contributed in identifying various cancer-specific mutations, epigenetic alterations, and cells response to chemotherapy. This study aimed to determine the factors associated with colorectal cancer survival and develop and validate a polygenic survival scoring system (PSS) using a multi-omics approach. Data were obtained from the Cancer Genome Atlas (TCGA). Colon Adenocarcinoma (TCGA-COAD) data were used to develop a survival prediction model and PSS, whereas rectal adenocarcinoma (TCGA-READ) data were used to validate the PSS. Cox proportional hazards regression analysis was conducted to examine the association between the demographic characteristics, clinical variables, and mRNA gene expression. Overall accuracy of PSS was also evaluated. The median overall survival for TCGA-COAD patients was 7 years and for TCGA-READ patients was 5 years. The multivariate Cox proportional hazards model identified age, cancer stage, and expression of nine genes as predictors of colon cancer survival. Based on the median PSS of 0.38, 48% of TCGA-COAD patients had high mortality risk. Patients in the low risk group had significantly higher 5-year survival rates than those in the high group (p <0.0001). The PSS demonstrated a high overall accuracy in predicting colorectal cancer survival. This study integrated clinical and transcriptome data to identify survival predictors in patients with colorectal cancer. PSS is an accurate and valid measure for estimating colorectal cancer survival. Thus, it can serve as an important tool for future colorectal cancer research.

The impact of indication for cesarean on blood loss

BackgroundPostpartum hemorrhage is the leading cause of maternal mortality worldwide. Quantitative blood loss assessment at the time of cesarean delivery is a more accurate measure of blood loss than is simple estimation. Risk factors for postpartum hemorrhage are well described; however, contemporary, systematic investigations into the impact of the indication for cesarean delivery on quantitative blood loss are lacking. ObjectiveTo investigate whether there are clinically significant differences in quantitative blood loss and postpartum hemorrhage risk based on the indication for cesarean delivery. Study DesignA cohort of 4,881 cesarean deliveries performed at a large academic hospital between 2020 and 2022 was identified. Primary and repeat cesarean deliveries were analyzed separately and further subdivided into seven indications: elective, labor arrest, fetal heart rate abnormalities, placenta previa, placenta accreta, malpresentation, and other. Quantitative blood loss and rates of postpartum hemorrhage ( > 1000 cc and >1500cc) were compared among the different indications. ResultsMean quantitative blood loss for primary, repeat and total cesarean deliveries was 886 cc, 697 cc and 792 cc, respectively. Excluding cases of placenta accreta, the greatest blood loss in both primary and repeat groups was seen in cesareans performed for labor arrest, with blood loss exceeding 1500 cc in 18% and 13% of all cases. Blood loss exceeding 1500cc was seen in 1% and 2% of elective cesarean deliveries. The mean blood loss for planned repeat cesarean/hysterectomies for placenta accreta was < 400cc greater than primary cesareans performed for labor arrest (1442 cc vs. 1065 cc), despite the addition of an often-complex hysterectomy to the procedure. ConclusionsClinically and statistically significant differences in blood loss exist based on the indication for cesarean delivery. Large differences in rates of serious postpartum hemorrhage (>1500cc) with negligible differences in mean quantitative blood loss suggests the presence of frequent, large clinical outliers not reflected in a statistical mean. The indication for cesarean and the possibility of such outliers, rather than the predicted “average blood loss for cesarean delivery” should be considered in determining risk and the degree of necessary pre-operative blood preparation. These data raise questions about whether current, traditional techniques of non-accreta related cesarean delivery can be justified in non-emergent cases when such procedures could be performed with significantly less blood loss using accreta-specific techniques.

Clustering dynamic networks by discriminating roles of vertices and capturing temporality with subsequent feature projection

Clustering dynamic networks has gained popularity due to the need to analyze complex systems that evolve over time, which cannot be fully characterized by traditional static models. It is highly non-trivial in comparison to clustering static network since it requires simultaneously to balance clustering accuracy and clustering drift, where clustering accuracy measures how clustering reflects structure of graph at current time, and clustering drift quantifies how clustering smoothes historical snapshot(s). In this study, we propose an algorithm clustering dynamic network by discriminating roles of vertices and capturing temporality with subsequent feature projection (CDN-DRCT). Specifically, clustering accuracy is achieved by factorizing high-order matrix of slice at current time, and vertices are divided into static and dynamic ones by the reconstruction errors. Finally, the proposed algorithm measures temporality of networks with a projection matrix, which connects subsequent features at the previous and current time, thereby enhancing clustering drift of clusters. In this case, temporality of dynamic networks is characterized from vertex and global level, providing a better way to balance clustering accuracy and clustering drift. Experimental results on 10 typical dynamic networks demonstrate the proposed algorithm is superior to baselines in terms of accuracy as well efficiency.

Intelligent Prediction of Rate of Penetration Using Mechanism-Data Fusion and Transfer Learning

Rate of penetration (ROP) is crucial for evaluating drilling efficiency, with accurate prediction essential for enhancing performance and optimizing parameters. In practice, complex and variable downhole environments pose significant challenges for mechanistic ROP equations, resulting in prediction difficulties and low accuracy. Recently, data-driven machine learning models have been widely applied to ROP prediction. However, these models often lack mechanistic constraints, limiting their performance to specific conditions and reducing their real-world applicability. Additionally, geological variability across wells further hinders the transferability of conventional intelligent models. Thus, combining mechanistic knowledge with intelligent models and enhancing model stability and transferability are key challenges in ROP prediction research. To address these challenges, this paper proposes a Mechanism-Data Fusion and Transfer Learning method to construct an intelligent prediction model for ROP, achieving accurate ROP predictions. A multilayer perceptron (MLP) was selected as the base model, and training was performed using data from neighboring wells and partial data from the target well. The Two-stage TrAdaBoost.R2 algorithm was employed to enhance model transferability. Additionally, drilling mechanistic knowledge was incorporated into the model’s loss function as a constraint to achieve a fusion of mechanistic knowledge and data-driven approaches. Using MAPE as the measure of accuracy, compared with conventional intelligent models, the proposed ROP prediction model improved prediction accuracy on the target well by 64.51%. The model transfer method proposed in this paper has a field test accuracy of 89.71% in an oilfield in China. These results demonstrate the effectiveness and feasibility of the proposed transfer learning method and mechanistic–data integration approach.

Right visual field advantage in orientation discrimination is influenced by biased suppression

Visual input is not equally processed over space. In recent years, a right visual field advantage during free walking and standing in orientation discrimination and contrast detection task was reported. The current study investigated the underlying mechanism of the previously reported right visual field advantage. It particularly tested if the advantage is driven by a stronger suppression of distracting input from the left visual field or improved processing of targets from the right visual field. Combing behavioural and electrophysiological measurements in a mobile EEG and augmented reality setup, human participants (n = 30) in a standing and a walking condition performed a line orientation discrimination task with stimulus eccentricity and distractor status being manipulated. The right visual field advantage, as demonstrated in accuracy and reaction time, was influenced by the distractor status. Specifically, the right visual field advantage was only observed when the target had an incongruent line orientation with the distractor. Neural data further showed that the right visual field advantage was paralleled by a strong modulation of neural activity in the right hemisphere (i.e. contralateral to the distractor). A significant positive correlation between this right hemispheric event related potential (ERP) and behavioural measures (accuracy and reaction time) was found exclusively for trials in which a target was presented on the right and an incongruent distractor was presented on the left. The right hemispheric ERP component further predicted the strength of the right visual field advantage. Notably, the lateralised brain activity and the right visual field advantage were both independent of stimulus eccentricity and the movement state of participants. Overall, our findings suggest an important role of spatially biased suppression of left distracting input in the right visual field advantage as found in orientation discrimination.

Correlation and regression analysis of unionised ammonia ions, NH3and pH in ornamental fish aquatic environment

Assessing ammonia in water is very important for fish farmers as it is one of the pollutants that may affect the water quality. In water, ammonia can be found either in the form of unionised ammonia NH3 or ionised ammonia NH+4, which contribute to a total ammonia nitrogen (TAN). In assessing the ammonia toxicity, NH3 provides a more accurate measure of toxicity and needs to be monitored consistently, rather than relying solely on TAN. However, the method of measuring NH3 in water can be complex and challenging. Therefore, the ability to determine the correlation between NH3 and other parameter which is easier to be measured (such as pH) is crucial to enable the estimation of NH3. This study was conducted to determine the correlation between NH3 and pH based on the data collected from Redgold Aquatics ornamental fishponds located in Sik, Kedah. The pH was assessed by a commercial measuring device, while the NH3 was calculated using an appropriate mathematical equation that requires the data of pH, TAN, and temperature. The correlation and regression analysis were conducted via the IBM SPSS tool and Microsoft Excel. The result shows that NH3 and pH are highly correlated with regression equation of NH3 = 0.01pH – 0.07. In addition, factors that may affect the total ammonia value have been discussed thoroughly in this work. The analysis of these water quality parameters may assist farmers in estimating the toxic ammonia, NH3 from the known water pH.

Evaluating Volatility Using an ANFIS Model for Financial Time Series Prediction

This paper develops and implements an Autoregressive Integrated Moving Average model with an Adaptive Neuro-Fuzzy Inference System (ARIMA-ANFIS) for BTCUSD price prediction and risk assessment. The goal of these forecasts is to identify patterns from past data and achieve an understanding of the future behavior of the price and its volatility. The proposed ARIMA-ANFIS model is compared with a benchmark ARIMA-GARCH model. To evaluated the adequacy of the models in terms of risk assessment, we compare the confidence intervals of the price and accuracy measures for the testing sample. Additionally, we implement the diebold and Mariano test to compare the accuracy of the two volatility forecasts. The results revealed that each volatility model focuses on different aspects of the data dynamics. The ANFIS model, while effective in certain scenarios, may expose one to unexpected risks due to its underestimation of volatility during turbulent periods. On the other hand, the GARCH(1,1) model, by producing higher volatility estimates, may lead to excessive caution, potentially reducing returns.

Raising the Flag of Vigilance on Mpox Outbreak in Indonesia: Are We Prepared?

In the wake of the COVID-19 pandemic, the world has become acutely aware of the devastating impact of infectious diseases on public health, economies, and societies. As we gradually recover from the aftermath of COVID-19, another potential threat looms on the horizon, mpox, formerly known as monkeypox. The recent surge in mpox cases worldwide has prompted health authorities to sound an alarm, and Indonesia, as a populous nation with a complex healthcare system, must ask itself: Are we prepared for this emerging challenge?. The question "Are we prepared?" may not have a simple yes or no answer. Instead, it should serve as a reminder of the need for vigilance, adaptation, and continuous improvement in public health systems. As we navigate this new health challenge, the true measure of Indonesia's preparedness is its ability to respond swiftly, effectively, and equitably to protect the health and well-being of all citizens.

Nondestructive diagnosis of mineral deficiencies in wheat leaves by one- or two-shot saturation pulse measurement of chlorophyll fluorescence and P700+ absorbance with machine learning

We hydroponically cultivated wheat and subjected them to mineral deficiency by a week-long exposure to Hoagland's solution minus one essential mineral. Chlorophyll fluorescence and P700+ absorbance were measured under a single 300 ms saturating light pulse (one-shot) or the conventional two pulses (two-shot). A machine learning-based multiclass classification model was developed to assess mineral deficiency diagnosis. The results indicated that partial least squares discriminant analysis (PLS-DA) was ineffective. Conversely, linear discriminant analysis (LDA) without principal components analysis (PCA) successfully identified 13 or 11 (with a class combining the control with Mo- and B-deficiencies; and ten other distinct classes for respective mineral deficiencies) classes of mineral deficiencies, achieving over 81 % diagnostic performance across all five macro metrics of precision, recall, specificity, accuracy, and F1 measure. The one-shot method exhibited only a slight decrease in performance compared to the two-shot method, highlighting the advantage of substantially reduced measurement time. Applying the developed LDA models to outdoor soil-grown leaf data revealed their practicality and limitations. In multiple deficiency scenarios, the model predicted one predominant deficiency or incorrectly diagnosed a different deficiency. This study is the first to quantitatively demonstrate the efficacy of ultra-fast nutritional diagnostic technology in identifying root zone mineral deficiencies using chlorophyll fluorescence and P700+ absorbance measurements. It delineates the requirements for constructing multilabel or quantitative calibration models for field deployment.

Early prediction of gestational diabetes mellitus using first trimester maternal serum pregnancy-associated plasma protein-a: A cross-sectional study.

The oral glucose tolerance test with 75 g glucose is commonly regarded as the gold standard (GS) for the detection of gestational diabetes mellitus (GDM). However, one limitation of this test is its administration in the late second trimester of pregnancy in some countries (e.g., Iran). This study aimed to evaluate the accuracy of pregnancy-associated plasma protein-A (PAPP-A) for predicting GDM in the early first trimester of pregnancy using a novel statistical modeling technique. The study population consisted of 344 pregnant women who participated in the first trimester screening program for GDM. Maternal serum PAPP-A levels were measured between 11 and 13 gestational weeks for all participants. A Bayesian latent profile model (LPM) under the skew-t (ST) distribution was employed to estimate the diagnostic accuracy measures of PAPP-A in the absence of GS test outcomes. The mean (standard deviation) age of the participants was 28.87 ± 5.20 years. The median (interquartile range (IQR)) PAPP-A MoM was 0.91 (0.69-1.34). Utilizing the LPM under the ST distribution while adjusting for covariates, the sensitivity, specificity, and area under the receiver operating characteristic curve (AUC) of PAPP-A were 92% (95% credible interval [CrI]: 0.89, 0.98), 81% (95% CrI: 0.76, 0.91), and 0.91 (95% CrI: 0.83, 0.97), respectively. Notably, the pregnant women with GDM had significantly lower PAPP-A values ( -0.52, 95% CrI [-0.61, -0.46]). Generally, our findings confirmed that PAPP-A could serve as a potential screening tool for the identification of GDM in the early stages of pregnancy.

Attentional templates for target features versus locations

Visual search is guided by visual working memory representations (i.e., attentional templates) that are activated prior to search and contain target-defining features (e.g., color). In the present study, we tested whether attentional templates can also contain spatial target properties (knowing where to look for) and whether attentional selection guided by such feature-specific templates is equally efficient than selection that is based on feature-specific templates (knowing what to look for). In every trial, search displays were either preceded by semantic color or location cues, indicating the upcoming target color or location, respectively. Qualitative differences between feature- and location-based template guidance were substantiated in terms of selection efficiency in low-load (one target color/location) versus high-load trials (two target colors/locations). Behavioral and electrophysiological (N2pc) measures of target selection speed and accuracy were combined for converging evidence. In line with previous studies, we found that color search was highly efficient, even under high-low conditions, when multiple attentional templates were activated to guide attentional selection in a spatially global fashion. Importantly, results in the location task almost perfectly mirrored the findings of the color task, suggesting that multiple templates for different target locations were activated concurrently when two possible target locations were task relevant. Our findings align with accounts that assume a common neuronal network during preparation for location and color search, but regard spatial and feature-based selection mechanisms as independent.

CLASSIFICATION OF ACUTE LYMPHOBLASTIC LEUKEMIA CELLS USING ARTIFICIAL INTELLIGENCE

Due to the morphological similarity between immature lymphoblasts (cancerous cells) to lymphocytes (non-cancerous cells), detecting Acute Lymphoblastic Leukemia poses a significant challenge for pathologists. These cells, which exhibit a similar pattern, can lead to various errors during the diagnosis of the disease. In this study, the cancerous and non-cancerous cells were classified using 3 different artificial intelligence approaches. In the first approach, the classification process was carried out by training Convolutional Neural Networks in 4 different architectures. In the second approach, a hybrid approach was proposed by combining the convolution layer of the CNN model as the feature extractor with the Support Vector Machine, Naive Bayes and Random Forest algorithms as the classifier. The classification processes were carried out by training the proposed second approach. In the third approach, the classification process was performed using transfer learning process and ResNet50 and VGG16 networks. In all experiments, the effects of hyper-parameter and dataset changes on model performance were also examined. The results obtained by these three approaches were compared using the Accuracy, Precision, Recall, F-score, and AUC performance measures. It was determined that the most successful results were obtained with the 1st approach using the Dataset3.

Exploring sex differences in cardiac interoceptive accuracy using the phase adjustment task.

Previous evidence suggests males and females differ with respect to interoception-the processing of internal bodily signals-with males typically outperforming females on tasks of interoceptive accuracy. However, interpretation of existing evidence in the cardiac domain is hindered by the limitations of existing tools. In this investigation, we pooled data from several samples to examine sex differences in cardiac interoceptive accuracy on the phase adjustment task, a new measure that overcomes several limitations of the existing tools. In a sample of 266 individuals, we observed that females outperformed males, indicative of better cardiac interoceptive accuracy, but had lower confidence than males. These results held after controlling for sex differences in demographic, physiological and engagement factors. Importantly, these results were specific to the measure of cardiac interoceptive accuracy. No sex differences were observed for individuals who completed the structurally identical screener task, although a similar pattern of results was observed in relation to confidence. These surprising data suggest the presence of a female advantage for cardiac interoceptive accuracy and potential differences in interoceptive awareness (metacognition). Possible reasons for mixed results in the literature, as well as implications for theory and future research, are discussed.

Assessing Biofilm at the Bedside: Exploring Reliable Accessible Biofilm Detection Methods.

Biofilm is linked through a variety of mechanisms to the pathogenesis of chronic wounds. However, accurate biofilm detection is challenging, demanding highly specialized and technically complex methods rendering it unapplicable for most clinical settings. This study evaluated promising methods of bedside biofilm localization, fluorescence imaging of wound bacterial loads, and biofilm blotting by comparing their performance against validation scanning electron microscopy (SEM). In this clinical trial, 40 chronic hard-to-heal wounds underwent the following assessments: (1) clinical signs of biofilm (CSB), (2) biofilm blotting, (3) fluorescence imaging for localizing bacterial loads, wound scraping taken for (4) SEM to confirm matrix encased bacteria (biofilm), and (5) PCR (Polymerase Chain Reaction) and NGS (Next Generation Sequencing) to determine absolute bacterial load and species present. We used a combination of SEM and PCR microbiology to calculate the diagnostic accuracy measures of the CSB, biofilm blotting assay, and fluorescence imaging. Study data demonstrate that 62.5% of wounds were identified as biofilm-positive based on SEM and microbiological assessment. By employing this method to determine the gold truth, and thus calculate accuracy measures for all methods, fluorescence imaging demonstrated superior sensitivity (84%) and accuracy (63%) compared to CSB (sensitivity 44% and accuracy 43%) and biofilm blotting (sensitivity 24% and accuracy 40%). Biofilm blotting exhibited the highest specificity (64%), albeit with lower sensitivity and accuracy. Using SEM alone as the validation method slightly altered the results, but all trends held constant. This trial provides the first comparative assessment of bedside methods for wound biofilm detection. We report the diagnostic accuracy measures of these more feasibly implementable methods versus laboratory-based SEM. Fluorescence imaging showed the greatest number of true positives (highest sensitivity), which is clinically relevant and provides assurance that no pathogenic bacteria will be missed. It effectively alerted regions of biofilm at the point-of-care with greater accuracy than standard clinical assessment (CSB) or biofilm blotting paper, providing actionable information that will likely translate into enhanced therapeutic approaches and better patient outcomes.

Linguistic Features of Different Proficiency Learners’ Oral Performance in a Project-Based Learning Context

Project-Based Learning (PBL) can potentially enhance learners’ oral proficiency. However, little evidence supports how PBL improves learners’ complexity, accuracy, and fluency, particularly for learners with different proficiency levels. This study examined the changes and linguistic features of varying proficiency learners’ complexity, accuracy, and fluency within a PBL context. It involved a homogeneous group of 45 Chinese college learners, categorized into high (N=15), medium (N=15), and low proficiency learners (N=15). Their oral complexity, accuracy, and fluency were assessed using speaking tests before and after PBL. The changes in their oral performance were evaluated using objective measures of complexity, accuracy, and fluency. The transcripts were also used to analyze the linguistic features of different proficiency learners. The results indicate that PBL significantly enhances syntactic complexity as learners’ proficiency levels increase. Significant differences were observed in lexical complexity, pronunciation accuracy, and self-repairs between high and low-proficiency learners. However, no significant differences were found in syntactic accuracy and speech rate across high, medium, and low proficiency learners. The findings also indicate that different proficiency learners reveal various linguistic features of syntactic complexity, syntactic accuracy, and self-repairs in their oral performance. This study provides implications for educators to design project-based activities for different proficiency learners to improve their complexity, accuracy, and fluency.

Neuroanatomical photogrammetric models using smartphones: a comparison of apps

ObjectivesA deep knowledge of the surgical anatomy of the target area is mandatory for a successful operative procedure. For this purpose, over the years, many teaching and learning methods have been described, from the most ancient cadaveric dissection to the most recent virtual reality, each with their respective pros and cons. Photogrammetry, an emergent technique, allows for the creation of three-dimensional (3D) models and reconstructions. Thanks to the spreading of photogrammetry nowadays it is possible to generate these models using professional software or even smartphone apps. This study aims to compare the neuroanatomical photogrammetric models generated by the two most utilized smartphone applications in this domain, Metascan and 3D-Scanner, through quantitative analysis.MethodsTwo human head specimens (four sides) were examined. Anatomical dissection was segmented into five stages to systematically expose well-defined structures. After each stage, a photogrammetric model was generated using two prominent smartphone applications. These models were then subjected to both quantitative and qualitative analysis, with a specific focus on comparing the mesh density as a measure of model resolution and accuracy. Appropriate consent was obtained for the publication of the cadaver's image.ResultsThe quantitative analysis revealed that the models generated by Metascan app consistently demonstrated superior mesh density compared to those from 3D-Scanner, indicating a higher level of detail and potential for precise anatomical representation.ConclusionEnabling depth perception, capturing high-quality images, offering flexibility in viewpoints: photogrammetry provides researchers with unprecedented opportunities to explore and understand the intricate and magnificent structure of the brain. However, it is of paramount importance to develop and apply rigorous quality control systems to ensure data integrity and reliability of findings in neurological research. This study has demonstrated the superiority of Metascan in processing photogrammetric models for neuroanatomical studies.