Coefficient Correlation Research Articles

Abstract 4120680: Language AI Chat BOTs as Pioneers in Cardiovascular Diagnosis? A Retrospective Analysis on Real-Time Patients in a Tertiary Care Centre

GPT-3 and GPT-4, show promise in assisting healthcare professionals with clinical questions. Their performance in real-time clinical scenarios, particularly with cardiovascular symptoms, remains underexplored. This study aims to evaluate their precision and reliability compared to physicians. Hypothesis: We hypothesize that board-certified emergency department attendings at Maimonides Medical Center exhibit higher accuracy and reliability than GPT-3.5 and GPT-4 in generating differential diagnoses based on history and physical examination for patients presenting to the emergency department. Methods: Patient data from Maimonides Medical Center's emergency department, collected from January 1, 2023, to April 30, 2023, was analyzed. Demographics, symptoms, medical history, and discharge diagnoses recorded by emergency room attendings were examined. ChatGPT-3.5 and GPT-4 generated differential diagnoses, which were compared with those by attending physicians. Accuracy was determined by comparing each rater's diagnoses with discharge diagnosis, calculating the proportion of correctly identified cases. Precision was assessed using Cohen's kappa coefficient and Intraclass Correlation Coefficient to measure agreement between raters. Results: Patient demographics showed mean age of 49.12 years, with 57.3% males and 42.7% females. Chief complaints included fever/sepsis (24.7%), gastrointestinal issues (17.7%), and cardiovascular problems (16.4%). Diagnostic accuracy against discharge diagnoses was highest for Chat GPT-4 (85.5%), followed by Chat GPT-3.5 (84.6%) and ED attendings (83%). Cohen’s kappa demonstrated moderate agreement (0.7) between AI models, with lower agreement observed for ED attendings. Stratified analysis revealed higher accuracy for gastrointestinal complaints with Chat GPT-4 (87.5%) and cardiovascular complaints with Chat GPT-3.5 (81.34%). Conclusion: Our study demonstrates that Chat GPT-4 and GPT-3.5 exhibit comparable diagnostic accuracy to attendings, highlighting their potential to aid decision-making in dynamic clinical settings. The stratified analysis revealed comparable reliability and precision of the AI chat bots for cardiovascular complaints which represents a significant proportion of the high risk patients presenting to the emergency department. This study contributes to integrating AI models into medical practice, enhancing efficiency and effectiveness in clinical decision-making

Validation of the German version of the SarQoL® questionnaire in sarcopenic and probable sarcopenic patients.

The German version of the SarQoL®, a sarcopenia-specific quality of life (QoL) questionnaire, has not been validated hindering its widespread use. This study aimed to evaluate the psychometric properties of the German SarQoL®. Via a cross-sectional study participants were recruited in two geriatric outpatient facilities and one acute geriatric ward in Munich (Germany). Sarcopenia and probable sarcopenia were diagnosed with the European Working Group on Sarcopenia in Older People (EWGSOP2) algorithm. From 185 participants (age 79.8 ± 6.1), 77 had probable sarcopenia, and 51 had sarcopenia. Participants completed the SarQoL® and the European Quality-of-Life 5-Dimension (EQ-5D) questionnaires. The validation included examination of the discriminative power, construct validity, internal consistency, test-retest reliability, and floor/ceiling effects. Lower SarQoL®scores for sarcopenic (p = 0.002) and probable sarcopenic subjects (p < 0.001) compared to controls indicated good discriminative power. Consistent construct validity was found for sarcopenic subjects: moderate to high correlations with domains capturing similar constructs of the EQ-5D: 'Activities of daily living' (r = -0.58, p < 0.001), 'Mobility' (r = -0.72, p < 0.001) and low correlations with domains related to different constructs like 'Pain' (r = -0.32, p < 0.022). Similar correlations were found for probable sarcopenic subjects. The Cronbach's alpha was 0.8. Test-retest reliability was excellent (intraclass coefficient correlation of = 0.96; 95% CI = 0.91-0.99), and no floor/ceiling effects were observed. QoL was similarly reduced in both patient cohorts compared to controls. The German SarQoL® is a valid and reliable instrument for measuring QoL in patients > 65 years of age with sarcopenia and probable sarcopenia and can now be used in epidemiological studies and clinical trials in a German-speaking population. German Clinical Trials Register (DRKS)-ID: DRKS00020504 (March 12th, 2021) .

The Effect of Ellipsoidal Particle Surface Roughness on Drag and Heat Transfer Coefficients Using Particle-Resolved Direct Numerical Simulation

The purpose of this study is to estimate the effect of roughness layer thickness on the heat transfer and drag coefficients of ellipsoidal particles. Using an OpenFOAM-based particle-resolved direct numerical simulation (PR-DNS) method, we calculated the drag coefficient and Nusselt number for an isolated axisymmetric nonspherical particle with a rough surface in a uniform flow. The PR-DNS results indicate that the drag coefficient varies linearly with the effective roughness Sef at different angles, which can be expressed as CD=kSef−1+CD0. The changes in k are consistent with the Happel and Brenner equation. Furthermore, the influence of roughness on the heat transfer efficiency factor can be represented by Ef=Sef−65. The models for the drag coefficient and Nusselt number are valid within the ranges 1.25≤Ar≤2.5,1≤Sef≤2, and 10≤Rep≤200, thereby extending the applicability of the equations developed for smooth particles. These newly developed correlations for the drag coefficient and Nusselt number can be utilized for non-isothermal flows of particle mixtures containing materials with various rough-surfaced ellipsoids.

Integrated Deep Learning Model for the Detection, Segmentation, and Morphologic Analysis of Intracranial Aneurysms Using CT Angiography.

"Just Accepted" papers have undergone full peer review and have been accepted for publication in Radiology: Artificial Intelligence. This article will undergo copyediting, layout, and proof review before it is published in its final version. Please note that during production of the final copyedited article, errors may be discovered which could affect the content. Purpose To develop a deep learning model for the morphologic measurement of unruptured intracranial aneurysms (UIAs) based on CT angiography (CTA) data and validate its performance using a multicenter dataset. Materials and Methods In this retrospective study, patients with CTA examinations, including those with and without UIAs, in a tertiary referral hospital from February 2018 to February 2021 were included as the training dataset. Patients with UIAs who underwent CTA at multiple centers between April 2021 to December 2022 were included as the multicenter external testing set. An integrated deep-learning (IDL) model was developed for UIA detection, segmentation and morphologic measurement using an nnU-net algorithm. Model performance was evaluated using the Dice similarity coefficient (DSC) and intraclass correlation coefficient (ICC), with measurements by senior radiologists serving as the reference standard. The ability of the IDL model to improve performance of junior radiologists in measuring morphologic UIA features was assessed. Results The study included 1182 patients with UIAs and 578 controls without UIAs as the training dataset (55 years [IQR, 47-62], 1,012 [57.5%] females) and 535 patients with UIAs as the multicenter external testing set (57 years [IQR, 50-63], 353 [66.0%] females). The IDL model achieved 97% accuracy in detecting UIAs and achieved a DSC of 0.90 (95%CI, 0.88-0.92) for UIA segmentation. Model-based morphologic measurements showed good agreement with reference standard measurements (all ICCs > 0.85). Within the multicenter external testing set, the IDL model also showed agreement with reference standard measurements (all ICCs > 0.80). Junior radiologists assisted by the IDL model showed significantly improved performance in measuring UIA size (ICC improved from 0.88 [0.80-0.92] to 0.96 [0.92-0.97], P < .001). Conclusion The developed integrated deep learning model using CTA data showed good performance in UIA detection, segmentation and morphologic measurement and may be used to assist less experienced radiologists in morphologic analysis of UIAs. ©RSNA, 2024.

Estimating an adjusted risk difference in a cluster randomized trial with individual-level analyses.

In cluster randomized trials (CRTs) with a binary outcome, intervention effects are usually reported as odds ratios, but the CONSORT statement advocates reporting both a relative and an absolute intervention effect. With a simulation study, we assessed several methods to estimate a risk difference (RD) in the framework of a CRT with adjustment on both individual- and cluster-level covariates. We considered both a conditional approach (with the generalized linear mixed model [GLMM]) and a marginal approach (with the generalized estimating equation [GEE]). For both approaches, we considered the Gaussian, binomial, and Poisson distributions. When considering the binomial or Poisson distribution, we used the g-computation method to estimate the RD. Convergence problems were observed with the GEE approach, especially with low intra-cluster coefficient correlation values, small number of clusters, small mean cluster size, high number of covariates, and prevalences close to 0. All methods reported no bias. The Gaussian distribution with both approaches and binomial and Poisson distributions with the GEE approach had satisfactory results in estimating the standard error. Results for type I error and coverage rates were better with the GEE than GLMM approach. We recommend using the Gaussian distribution because of its ease of use (the RD is estimated in one step only). The GEE approach should be preferred and replaced with the GLMM approach in cases of convergence problems.

Evaluation of the Predictive Capability of CMA Climate Prediction System Model for Summer Surface Heat Source on the Tibetan Plateau

Surface heat source (SHS) is a crucial factor affecting local weather systems. Particularly SHS on the Tibetan Plateau (TP) significantly influences East Asian atmospheric circulation and global climate. Accurate prediction of summer SHS on the TP is of urgent demand for economic development and local climate change. To evaluate the performance of SHS on the TP, the observed SHS data from the eleven sites on the TP verified against CRA40-land (CRA) is evidenced significantly better than ERA5-land (ERA5), another widely used reanalysis. The predictive capability of the CMA Climate Prediction System Model (CMA-CPS) for SHS on the TP was assessed using multiple scoring methods, including the anomaly correlation coefficient and temporal correlation coefficient, among others. Furthermore, relative variability and trend analysis were conducted. Finally, based on these assessments, the causes of the biases were preliminarily discussed. The CMA-CPS demonstrates a reasonable ability to predict the spatial distribution patterns of SHS, sensible heat (SH), and latent heat (LH) on the TP in summer. Specifically, the prediction results of SHS and LH exhibit an “east-high and west-low” distribution, while the distribution of the predicted SH is opposite. Nevertheless, the predicted values are generally lower than CRA, particularly in interannual variations and trends. Among the predictions, LH exhibits the highest temporal correlation coefficients, consistently above 0.6, followed by SHS, while SH predictions are less accurate. The spatial distribution and skill scores indicate that LH on the TP contributes more significantly to SHS than SH in summer. Furthermore, discrepancies in the predictions of surface temperature gradients, ground wind speed, and humidity on the TP may partly explain the biases in SHS and their components.

Studies on Path Coefficient Analysis and Correlation to Evaluate the Relationships between Yield and Its Components in Fenugreek (Trigonella foenum-graecum L.)

The present investigation was carried out for correlation and path coefficient analysis in Fenugreek (Trigonella foenum-graecum L.) The correlation of different traits with seed yield was examined both at phenotypic and genotypic levels and the study showed that genotypic correlation coefficient values were higher than the phenotypic values. This indicated that how much of phenotypic correlation coefficient are influenced by environment. The seed yield per plant was positive and significant with biological yield per plant, test weight and number of branches per plant at genotypic and phenotypic levels. The maximum direct positive effect on seed yield per plant was observed in biological yield per plant, harvest index, days to 50 per cent flowering, number of seed per pod, number of branches per plant and number of pods per plant. In the present investigation, genotypes viz., AFG-4 and RMT-303 were superior for seed yield per plant along with other traits like biological yield, test weight and number of pods per plant. These diverse genotypes can be used in future breeding programme of fenugreek.

Rational design of some 1,3,4 trisubstituted pyrazole-thiazole derivatives to serve as MtInhA inhibitors using QSAR, ADMET, molecular docking, MM-GBSA, and molecular dynamics simulations approach

Using computational approaches, the potential efficacy and specificity of 1,3,4 trisubstituted pyrazole derivatives as MtInhA inhibitors which will aid in rational drug design for tubercular therapy were forecasted. QSARINS software was used to investigate the ability of compound to inhibit MtInhA. Three noteworthy descriptors with significant correlations and impressive statistical values were identified by the produced QSAR model: Correlation of coefficient(R2)= 0.8789, Cross-validation leave one out correlation coefficient (Q2LOO)= 0.8402, Cross-validation leave-many-out correlation coefficient(Q2LMO)=0.7321, Concordance Correlation Coefficient for cross-validation(CCCtr)=0.9355, CCCext =0.888. The descriptor generated by the QSAR model includes Centered Broto-Moreau autocorrelation weighted by Sanderson electronegativities (ATSC1e), Radial distribution functions at 15.0 and 2.0 Å inter-atomic distances weighted by relative van der Waals volumes (RDF150v), Radial distribution function – 145/weighted by relative I-state (RDF145s). Using these, three descriptor model was developed and the designed compounds were evaluated for their MtInhA inhibitory activity. Further, ADMET prediction and Molecular docking studies were carried out using Schrodinger's Software. ADMET prediction were used to evaluate drug likeliness and molecular docking was used to determine the interactions of designed compounds with the target protein. After the docking studies, the compounds were subjected for MM-GBSA calculations and MD simulation. Among the designed compounds, AP2 had the strongest binding affinity towards the MtInhA enzyme. The result of this work helps to understand the key interactions between 1,3,4 trisubstituted pyrazole derivatives and MtInhA protein that may be necessary to develop new lead compounds against tuberculosis.

Numerical Simulation and Experimental Analysis of Aerosol Deposition: Investigating Nozzle Effects on Particle Dynamics and Deposition

Aerosol deposition (AD), also known as vacuum kinetic spray (VKS), is used to deposit dense ceramic films onto a substrate surface at room temperature. One predominant factor influencing the overall deposition process is the nozzle geometry, which significantly impacts the quality, shape, and thickness of the coating. To evaluate the effect of nozzle geometry in AD, particle trajectory and impact velocity were investigated via computational simulation. A Eulerian-Lagrangian model was used to simulate the gas flow, particle in-flight behavior, as well as particle deposition characteristics on a flat substrate. Three common nozzle geometries in AD were utilized: a converging-diverging nozzle with a slit cross-section (CD-Slit), a converging-barrel nozzle with a slit cross-section (CB-Slit), and a converging-diverging round (CD-Round) nozzle. Moreover, suitable drag coefficient and Nusselt number correlations were used to account for compressibility and rarefaction effects on particle dynamics and heat transfer. The simulation results were compared to the deposition pattern obtained experimentally. The results demonstrate that shape of the nozzle has profound effect on the particle impact velocity and deposition pattern. The CD-Round nozzle provides uniform, thinner coatings ideal for extensive surfaces at a slower deposition rate. In contrast, the CB-Slit nozzle is optimized for maximum coating thickness in narrow, thick linear patterns. The CD-Slit nozzle achieves high deposition rates with uniform coatings and a distinctive cat-ear shaped pattern.

Myocardial perfusion SPECT radiomic features reproducibility assessment: Impact of image reconstruction and harmonization.

Coronary artery disease (CAD) has one of the highest mortality rates in humans worldwide. Single-photon emission computed tomography (SPECT) myocardial perfusion imaging (MPI) provides clinicians with myocardial metabolic information non-invasively. However, there are some limitations to interpreting SPECT images performed by physicians or automatic quantitative approaches. Radiomics analyzes images objectively by extracting quantitative features and can potentially reveal biological characteristics that the human eye cannot detect. However, the reproducibility and repeatability of some radiomic features can be highly susceptible to segmentation and imaging conditions. We aimed to assess the reproducibility of radiomic features extracted from uncorrected MPI-SPECT images reconstructed with 15 different settings before and after ComBat harmonization, along with evaluating the effectiveness of ComBat in realigning feature distributions. A total of 200 patients (50% normal and 50% abnormal) including rest and stress (without attenuation and scatter corrections) MPI-SPECT images were included. Images were reconstructed using 15 combinations of filter cut-off frequencies, filter orders, filter types, reconstruction algorithms, number of iterations and subsets resulting in 6000 images. Image segmentation was performed on the left ventricle in the first reconstruction for each patient and applied to 14 others. A total of 93 radiomic features were extracted from the segmented area, and ComBat was used to harmonize them. The intraclass correlation coefficient (ICC) and overall concordance correlation coefficient (OCCC) tests were performed before and after ComBat to examine the impact of each parameter on feature robustness and to assess harmonization efficiency. The ANOVA and the Kruskal-Wallis tests were performed to evaluate the effectiveness of ComBat in correcting feature distributions. In addition, the Student's t-test, Wilcoxon rank-sum, and signed-rank tests were implemented to assess the significance level of the impacts made by each parameter of different batches and patient groups (normal vs. abnormal) on radiomic features. Before applying ComBat, the majority of features (ICC: 82, OCCC: 61) achieved high reproducibility (ICC/OCCC ≥ 0.900) under every batch except Reconstruction. The largest and smallest number of poor features (ICC/OCCC<0.500) were obtained by IterationSubset and Order batches, respectively. The most reliable features were from the first-order (FO) and gray-level co-occurrence matrix (GLCM) families. Following harmonization, the minimum number of robust features increased (ICC: 84, OCCC: 78). Applying ComBat showed that Order and Reconstruction were the least and the most responsive batches, respectively. The most robust families, in a descending order, were found to be FO, neighborhood gray-tone difference matrix (NGTDM), GLCM, gray-level run length matrix (GLRLM), gray-level size zone matrix (GLSZM), and gray-level dependence matrix (GLDM) under Cut-off, Filter, and Order batches. The Wilcoxon rank-sum test showed that the number of robust features significantly differed under most batches in the Normal and Abnormal groups. The majority of radiomic features show high levels of robustness across different OSEM reconstruction parameters in uncorrected MPI-SPECT. ComBat is effective in realigning feature distributions and enhancing radiomic features reproducibility.

Statistical Analyses of the Effect of Precipitation on Levels of Underground Water in Central Sulaimani Province

In this study, we tried to shade light on effect of precipitation on the underground water level in Central Sulaimani Province, based on collected data of precipitation of different weather station in Sulaimani, Bakrajo, Bazyan. Data of all the wells, in practical fields using electrical sounder for measuring weather levels. GPS was also used to detect the astronomical position of the wells and define their places on map using ArcGis10.8. On two different time periods, the water levels were measured including Autumn, when the underground water position is in its mining level, from 17-9-2021 to 13-11-2021, and on Spring from 4-3-2022 to 17-4-2022 when water is at lightest level. To compare water levels before and after precipitation, another method of quantitative analysis based on statistical results and Pearson Coefficient to realize correction of degree of precipitation and underground water levels. Moreover, effect of precipitation on underground water level has been shown using SPSS-26. The aim of this research is to define the relation and precipitation effects on underground water level, and ultimately propose a scientific plan to preserve the underground water level. As a result, according to Pearson Coefficient Correlation, there is negative relation between precipitation and the depth of underground water level. This means that the depth to reach underground water is reduced when the precipitation increases. Furthermore; precipitation has an estimated effect of 90.2% on static level of underground water, and of 67.6% on dynamic level of underground water.

Cycling coupled to intra-coronary thermodilution to evaluate angina with non-obstructive coronary artery disease

Abstract BACKGOUND Patients with Angina and Non-Obstructive Coronary Artery disease (ANOCA) face misdiagnosis and inadequate treatment. A new continuous thermodilution method enables assessment of absolute coronary blood flow (Q) and microvascular resistance (Rµ). The effectiveness of saline-induced hyperemia in capturing exercise-induced coronary flow regulation remains unknown. OBJECTIVE To describe Q and Rµ adaptation during exercise and to explore the correlation between Coronary Flow Reserve (CFR) derived from saline and exercise-induced hyperemia, as well as Microvascular Resistance Reserve (MRR). Methods Q, Rµ, CFR and MRR were first assessed using continuous thermodilution with saline infusion at 10 mL/min (rest) and 20 mL/min (hyperemia) in the left anterior descending artery, followed by subsequent stress testing using a dedicated supine cycling ergometer. The microvascular parameters were assessed during incremental workload of 30 watts every two minutes. CFRsaline&amp;lt;2.5 was considered the cutoff for coronary microvascular dysfunction. A minimal sample size of 29 participants was required to detect a hypothesized Spearman coefficient correlation r=0.5 between CFRsaline and CFRexercise using a 80% power and 5% significance level test. Results Coronary microvascular dysfunction was observed in 53.3% of the participants with suspected ANOCA (16 out of 30). During cycling, participants with coronary microvascular dysfunction had a lower reduction in Rµ (-109 [32; 286] versus -202 [102; 379] WU per 30 watts, p&amp;lt;0.01) and a lower ability to increase coronary flow (7 [30.5; 103.0] versus 21 [5.8; 45.0] mL/min per 30 watts, p=0.01) compared with those without coronary microvascular dysfunction. In the overall population, average CFRsaline and CFRexercise were 2.70±0.90 and 2.85±1.54, respectively (p=0.46) with an agreement classification of 83.3%. A good correlation between the two techniques was observed for both CFR (Spearman coefficient r=0.73, p&amp;lt;0.0001) and MRR (r=0.76, p&amp;lt;0.0001). Monitoring of CFR and Rµ during exercise unmasked functional coronary microvascular dysfunction priorly labeled as normal by saline induced hyperemia in 13.3% of the study population. These individuals had a moderate and late decrease of Rµ during the early stages of exercise, with a plateau up to 75% of their theoretical workload. Conclusion Participants with coronary microvascular dysfunction demonstrated compromised CFR due to impaired reduction of Rµ during physical workload. Saline-induced hyperemia provided a valid surrogate for exercise physiology independently of the absolute level of CFR and MRR, although exercise provided more granularity to evaluate hemodynamic adaptation among participants with functional dysfunction.

Defining Postinduction Hemodynamic Instability With an Automated Classification Model.

Postinduction hypotension (PIH) may be associated with increased morbidity and mortality. In earlier studies, the definition of PIH is solely based on different absolute or relative thresholds. However, the time-course (eg, how fast blood pressure drops during induction) is rarely incorporated, whereas it might represent the hemodynamic instability of a patient. We propose a comprehensive model to distinguish hemodynamically unstable from stable patients by combining blood pressure thresholds with the magnitude and speed of decline. This prospective study included 375 adult elective noncardiac surgery patients. Noninvasive blood pressure was continuously measured between 5 minutes before up to 15 minutes after the first induction agent had been administered. An expert panel rated whether the patient experienced clinically relevant hemodynamic instability or not. Interrater correlation coefficient and intraclass correlation were computed to check for consistency between experts. Next, an automated classification model for clinically relevant hemodynamic instability was developed using mean, maximum, minimum systolic, mean, diastolic arterial blood pressure (SAP, MAP, and DAP, respectively) and their corresponding time course of decline. The model was trained and tested based on the hemodynamic instability labels provided by the experts. In total 78 patients were classified as having experienced hemodynamic instability and 279 as not. The hemodynamically unstable patients were significantly older (7 years, 95% confidence interval (CI), 4-11, P < .001), with a higher prevalence of chronic obstructive pulmonary disease (COPD) (3% higher, 95% CI, 1-8, P = .036). Before induction, hemodynamically unstable patients had a higher SAP (median (first-third quartile): 161 (145-175) mm Hg vs 150 (134-166) mm Hg, P < .001) compared to hemodynamic stable patients. Interrater agreement between experts was 0.92 (95% CI, 0.89-0.94). The random forest classifier model showed excellent performance with an area under the receiver operating curve (AUROC) of 0.96, a sensitivity of 0.84, and specificity of 0.94. Based on the high sensitivity and specificity, the developed model is able to differentiate between clinically relevant hemodynamic instability and hemodynamic stable patients. This classification model will pave the way for future research concerning hemodynamic instability and its prevention.

An efficient state-of-health estimation method for lithium-ion batteries based on feature-importance ranking strategy and hybrid kernel extreme learning machine algorithm

The safe and reliable operation of battery systems depends critically on accurately and dependably estimating the state of health (SOH) of lithium-ion batteries (LIBs). However, accurate prediction of SOH still needs improvement due to the complex battery aging mechanism. This paper introduces a hybrid kernel extremum learning machine (HKELM) for estimating battery SOH. To improve estimation accuracy, we enhance the standard dung beetle optimization algorithm (DBO) with a new initialized population, adaptive weights method, and improved population diversification. We then use the enhanced IDBO algorithm to optimize the parameters of HKELM. This study examines two distinct anode types of LIBs from NASA and Oxford datasets. Fifty significant features are extracted from charging voltage, current, temperature, and incremental capacity (IC) curves. The importance indices of these features are then computed using statistical analyses, including Pearson's correlation coefficient and Spearman's rank correlation coefficient, followed by optimal ranking. The optimal number of final features is determined by comparing various combinations of high-level features, thus reducing model complexity and improving estimation accuracy. This paper proposes the IDBO-HKELM algorithm for SOH estimation. Compared to other methods, the algorithm shows superior estimation performance and anti-interference capability. Both B0007 and Cell8 estimate SOH with MAE < 0.15, RMSE <0.2, and R2 > 99.9 %. Experimental results demonstrate the robustness of the proposed method, achieving accurate and noise-immune SOH estimation.

Cultural adaptation, validity and reliability of the Turkish version of north star ambulatory assessment.

The North Star Ambulatory Assessment (NSAA) is a functional motor outcome measure originally developed for patients with Duchenne muscular dystrophy (DMD). The aim of this study was to perform the cultural adaptation and investigate the validity and reliability of the Turkish version of the NSAA (T-NSAA) in DMD. After translation process, internal consistency, interrater and test-retest reliability of the NSAA were determined by using the Cronbach Alpha Coefficient and Intraclass Correlation Coefficient (ICC), respectively. Absolute reliability was determined by using the Standard Error of Measurement (SEM) with minimal detectable change at 95% limits of confidence (MDC95). Lower limb functionality of children was evaluated by Vignos Lower Extremity Scale (VLERS). To establish convergent validity, the correlations between T-NSAA and Motor Function Measure (MFM-32), 6-minute walk test (6-MWT), and VLERS were analyzed by using the Spearman's correlation coefficient. The study was completed with 86 patients with DMD whose age were mean 104.56 ± 24.66 months. The internal consistency (Cronbach's α = 0.94), intra-reliability (ICC = 0.977) and inter-reliability (ICC = 0.972) of T-NSAA were excellent. SEM and MDC values were low indicating satisfactory absolute agreement (< %10). The T-NSAA had strong correlations with the MFM-total score, 6-MWT, and VLERS (p < 0.01). T-NSAA is a valid and reliable tool to assess ambulatory status of Turkish-speaking DMD population. NCT05549999, Date of registration: September 19, 2022.

Correlation of Seebeck coefficient and selenization temperature in CuSe thin films grown on glass substrate

Copper selenide is emerging as a promising thermoelectric material that has the ability to harvest electricity from heat. In the present research work, copper selenide thin films were grown on glass substrate using thermal evaporation deposition technique. The phase transition from cubic to hexagonal structure was achieved by the selenization of grown samples at different temperatures (250, 300 and 350 °C) for 2 h. The phase, morphology and thermoelectric properties of the selenized CuSe thin films were studied using different characterization techniques. It was observed that the structural, morphological, and thermoelectric properties of the samples were modulated by varying the selenization temperature. XRD results suggested that as grown sample possessed a cubic phase but it transformed into hexagonal phase by selenization process. It was observed that Seebeck coefficient, electrical conductivity and power factor were modulated with the selenization temperature with maximum value of power factor (3.0 × 10−5±0.5 W m−1C−2) was obtained at optimal selinization temperature.

TG-PGAT: An AIS Data-Driven Dynamic Spatiotemporal Prediction Model for Ship Traffic Flow in the Port

Accurate prediction of ship traffic flow is essential for developing intelligent maritime transportation systems. To address the complexity of ship traffic flow data in the port and the challenges of capturing its dynamic spatiotemporal dependencies, a dynamic spatiotemporal model called Temporal convolutional network-bidirectional Gated recurrent unit-Pearson correlation coefficient-Graph Attention Network (TG-PGAT) is proposed for predicting traffic flow in port waters. This model extracts spatial features of traffic flow by combining the adjacency matrix and spatial dynamic coefficient correlation matrix within the Graph Attention Network (GAT) and captures temporal features through the concatenation of the Temporal Convolutional Network (TCN) and Bidirectional Gated Recurrent Unit (BiGRU). The proposed TG-PGAT model demonstrates higher prediction accuracy and stability than other classic traffic flow prediction methods. The experimental results from multiple angles, such as ablation experiments and robustness tests, further validate the critical role and strong noise resistance of different modules in the TG-PGAT model. The experimental results of visualization demonstrate that this model not only exhibits significant predictive advantages in densely trafficked areas of the port but also outperforms other models in surrounding areas with sparse traffic flow data.

A point cloud segmentation algorithm based on multi-feature training and weighted random forest

Abstract Point cloud segmentation is the process of dividing point cloud data into a series of coherent subsets according to its attributes. It has been widely used in target recognition, digital protection of cultural relics, medical research and other fields. To improve the classification accuracy of point cloud and achieve accurate segmentation of objects or scenes, a point cloud segmentation algorithm based on multi–features training and weighted random forest (RF) is proposed. Firstly, the feature vector composed of 3D coordinate value, RGB value, echo intensity, point cloud density, normal direction and average curvature is used to train the SVM classifier, and the ‘one–to–one’ strategy is adopted to achieve the initial multivariate rough segmentation of point cloud. Then, the maximum information coefficient and sample correlation coefficient (SCC) are used to evaluate the correlation of the decision tree, and the decision tree is weighted accordingly to build a weak correlation weighted RF, so as to achieve further accurate segmentation of the point cloud. The experiment verifies the effectiveness of the proposed algorithm by segmenting the outdoor scene point cloud data model. The results show that the segmentation algorithm based on multi–features training and weighted RF can achieve accurate point cloud segmentation, and is an effective point cloud segmentation method.

Validity and reliability of the scale for determining nursing students' perceptions of stigmatization

IntroductionStigmatization can negatively affect the relationship between the patient and health professional. AimsThe aim of this study was to develop the Scale for Determining Nursing Students' Perceptions of Stigmatization. MethodsThe population of the study consisted of 403 nursing students. A literature review and qualitative interviews with 24 students were conducted to generate the initial items. Confirmatory factor analysis and exploratory factor analysis were applied to examine the construct validity of the scale. The reliability of the scale was evaluated with Cronbach's alpha reliability coefficient and item-total score correlation coefficients. ResultsAfter confirmatory factor analysis, the Scale for Determining Nursing Students' Perceptions of Stigmatization was finalized with 19 items and five sub-dimensions. In the confirmatory factor analysis, the goodness of fit values of the scale was determined as χ2/sd: 2.957; GFI: 0.902; AGFI: 0.869; IFI: 0.906; TLI: 0.937; CFI: 0.905; RMSEA: 0.070; SRMR: 0.063. The reliability of the scale over time was evaluated with the test-retest method, and it was found that the agreement between the responses was very good (ICC: 0.980; p < 0.001). The Cronbach's alpha reliability coefficient of the scale was 0.824. ConclusionsIt was determined that the Scale for Determining Nursing Students' Perceptions of Stigmatization is a short, easy-to-understand, and psychometrically sound measurement tool that can be safely applied to nursing students. Nursing students' perceptions of stigmatization can be determined using this scale. The nursing education curriculum can be restructured in line with the findings obtained.

Inter-Software Consistency on the Estimation of Subcortical Structure Volume in Different Age Groups.

There is still little research on the consistency among the subcortical volume estimates of different software packages. It is also unclear whether there are age-related differences in the inter-software consistency. The current study aimed to examine the consistency of three commonly used automated software packages and the effect of age on inter-software consistency. We analyzed T1-weighted structural images from two public datasets, in which the subjects were divided into four age groups ranging from childhood and adolescence to late adulthood. We chose three mainstream automated software packages including FreeSurfer, CAT, and FSL, to estimate the volumes of seven subcortical structures, including thalamus, caudate, putamen, pallidum, hippocampus, amygdala, and accumbens. We used the intraclass correlation coefficient (ICC) and Pearson correlation coefficient (PCC) to quantify inter-software consistency and compared the consistency measures among the age groups. As a measure of validity, we additionally evaluated the predictive power of each software package's estimates for predicting age. The results showed good inter-software consistency in the thalamus, caudate, putamen, and hippocampus, moderate consistency in the pallidum, and poor consistency in the amygdala and accumbens. Significant differences in the inter-software consistency were not observed among the age groups in most cases. FreeSurfer exhibited higher age prediction accuracy than CAT and FSL. The current study showed that the inter-software consistency on the subcortical volume estimation varies with structures but generally not with age groups, which has important implications for the interpretation and reproducibility of neuroimaging findings.