Pearson Correlation Coefficient Research Articles

Prediction of resilience based on parenting and coping strategies in patients with psychosomatic disorders.

The present research was conducted aiming at predicting the resilience based on parenting and coping strategies in patients with psychosomatic disorders. The statistical population of the present research consisted of all patients suffering from psychosomatic disorders who had visited medical clinics related to Medical Sciences (University) of Arak County in 2019-20. The statistical sample includes 347 women, aged 18 to 55 years-old, who were selected by available sampling. Data collection tool included Young parenting, Moss and Billings coping strategies questionnaire and Connor and Davidson resilience questionnaire. Pearson's correlation coefficient and multiple regression analysis were used to analyze the data. The findings showed that parenting (dependency, preoccupied/untransformed self) have a negative and significant relationship with resilience. Coping strategies focused on emotion, coping focused on physical restraint or physicalization of problems have a negative and significant relationship with resilience and have a positive and significant relationship with coping strategies focused on problem-solving and coping focused on cognitive evaluation. According to the findings, it can be concluded that it is possible to pave the way for increasing resilience and preventing the development of psychosomatic disorders by creating suitable conditions during the childhood, improving parent-child relationships, and by strengthening coping strategies focused on problem-solving and coping focused on cognitive evaluation.

Repeatability, interocular correlation and agreement of optic nerve head vessel density in healthy eyes: a swept-source optical coherence tomographic angiography study.

To assess the repeatability, interocular correlation, and agreement of quantitative swept-source optical coherence tomography angiography (OCTA) optic nerve head (ONH) parameters in healthy subjects. Thirty-three healthy subjects were enrolled. The ONH of both eyes were imaged four times by a swept-source-OCTA using a 3 mm ×3 mm scanning protocol. Images of the radial peripapillary capillary were analyzed by a customized Matlab program, and the vessel density, fractal dimension, and vessel diameter index were measured. The repeatability of the four scans was determined by the intraclass correlation coefficient (ICC). The most well-centered optic disc from the four repeated scans was then selected for the interocular correlation and agreement analysis using the Pearson correlation coefficient, ICC and Bland-Altman plots. All swept-source-OCTA ONH parameters exhibited certain repeatability, with ICC>0.760 and coefficient of variation (CoV)≤7.301%. The obvious interocular correlation was observed for papillary vessel density (ICC=0.857), vessel diameter index (ICC=0.857) and fractal dimension (ICC=0.906), while circumpapillary vessel density exhibited moderate interocular correlation (ICC=0.687). Bland-Altman plots revealed an agreement range of -5.26% to 6.21% for circumpapillary vessel density. OCTA ONH parameters demonstrate good repeatability in healthy subjects. The interocular correlations of papillary vessel density, fractal dimension and vessel diameter index are high, but the correlation for circumpapillary vessel density is moderate.

Chemometric Analysis and Human Health Implications of Trace and Heavy/Non-Essential Metals through ingestion of Carbonated and Non-Carbonated Beverages.

Elevated levels of trace metals (TMs) and heavy/non-essential metals (HnMs) in commonly consumed beverages concern the public and regulatory agencies. Thus, frequent monitoring of these metals is critically important. The present study intended to assess TMs and HnMs concentrations and associated health risks in beverages. Ten metals, such as Mn, Co, Cr, Cu, and Zn (TMs) and Ni, Cd, Pb, Al, and As (HnMs), were quantified in different beverage brands categorized into two groups such as non-carbonated and carbonated beverages. Chemometric analysis such as hierarchical cluster analysis (HCA), Pearson's correlation coefficient (PCC), and principal component analysis (PCA) were also performed to demonstrate the possible natural and anthropogenic sources of metal contamination. Among the TMs, the mean concentration of Zn (233.3 ± 3.3-291.7 ± 3.2µg/L) followed by Mn (119.0 ± 2.3-146.4 ± 2.2µg/L) was found highest in both carbonated and non-carbonated beverage samples. In the case of HnMs, the lowest mean concentration of Cd (7.4 ± 0.9-18.6 ± 1.2µg/L) followed by Pb (4.1 ± 0.4-4.5 ± 0.4µg/L) was observed in both types of beverage samples. The tolerable dietary intake (TDI) value for Ni and provisional tolerable monthly intake (PTMI) value for Cd were higher than the value established by the WHO and EFSA. The computed values of the hazard index (HI < 1) and the cumulative cancer risk (CCR) indicated a low risk of exposure.

The relationship between job stress and job burnout of preschool teachers during the COVID-19: The moderation of perceived organizational support.

COVID-19 poses great challenges for preschool teachers in China, which will increase the level of job stress and job burnout, and have an impact on the relationship between job stress, job burnout, and perceived organizational support (POS). However, few studies have examined trilateral relationships, especially the role of POS concerning job stress and job burnout of preschool teachers. This study aimed to investigate the relationship among the three variables of job burnout, job stress, and POS, as well as explore the moderating effects of POS between job stress and job burnout. A cross-sectional study was conducted among preschool teachers in six provinces of China. A total of 408 preschool teachers completed a self-report questionnaire, including three scales that measured job burnout, job stress, and POS respectively. The Pearson correlation coefficient and regression analysis were used to examine the relationship among variables. The results showed that job burnout among Chinese preschool teachers was at a medium level during the COVID-19 pandemic. Job stress was positively related to job burnout, and the POS was negatively related to job burnout. Additionally, POS moderated the relationship between job stress and job burnout and alleviated the adverse effects of job stress on the job burnout of preschool teachers. POS can play a moderating role between job stress and job burnout of Chinese preschool teachers during the COVID-19 pandemic.

An Indian classical dance form, Kathak in maintaining handgrip strength symmetry and reducing the risk of hypertension.

The present paper aimed to study handgrip strength (HGS) asymmetry and its association with hypertension in a sample of Kathak dancers and non-dancers of North India. Data on HGS and blood pressure were collected from 206 Kathak dancers and 235 age-matched controls, using standardized protocols. Pearson correlation coefficients assessed the association between HGS and hypertension, and binary logistic regression identified the risk of developing hypertension. HGS asymmetry was higher among non-dancers. In Kathak dancers, systolic blood pressure (SBP) was positively correlated with HGS and negatively correlated with HGS asymmetry whereas diastolic blood pressure (DBP) was negatively correlated with both HGS and HGS asymmetry. Dancers with high HGS, have significantly reduced risk of developing hypertension. Non-dancers with HGS asymmetry were 1.8 times more likely to report hypertension, relative to dancers. Kathak may be used as an alternative method for maintaining HGS symmetry and reducing the risk of developing hypertension.

Residual network improves the prediction accuracy of genomic selection.

Genetic improvement of complex traits in animal and plant breeding depends on the efficient and accurate estimation of breeding values. Deep learning methods have been shown to be not superior over traditional genomic selection (GS) methods, partially due to the degradation problem (i.e. with the increase of the model depth, the performance of the deeper model deteriorates). Since the deep learning method residual network (ResNet) is designed to solve gradient degradation, we examined its performance and factors related to its prediction accuracy in GS. Here we compared the prediction accuracy of conventional genomic best linear unbiased prediction, Bayesian methods (BayesA, BayesB, BayesC, and Bayesian Lasso), and two deep learning methods, convolutional neural network and ResNet, on three datasets (wheat, simulated and real pig data). ResNet outperformed other methods in both Pearson's correlation coefficient (PCC) and mean squared error (MSE) on the wheat and simulated data. For the pig backfat depth trait, ResNet still had the lowest MSE, whereas Bayesian Lasso had the highest PCC. We further clustered the pig data into four groups and, on one separated group, ResNet had the highest prediction accuracy (both PCC and MSE). Transfer learning was adopted and capable of enhancing the performance of both convolutional neural network and ResNet. Taken together, our findings indicate that ResNet could improve GS prediction accuracy, affected potentially by factors such as the genetic architecture of complex traits, data volume, and heterogeneity.

Emotional and motivational intelligence in Ethiopian high school learners' writing performance in relation to gender differences

In recent years, there has been increasing interest in the role of emotional and motivational intelligence in educational settings. Studies have shown that these factors can significantly impact students' academic performance. However, little attention has been given to the influence of emotional and motivational intelligence on writing performance, especially in relation to gender differences. This study aims to examine the relationship between emotional and motivational intelligence and writing performance among Ethiopian high school learners, while considering potential variations based on gender. To achieve this, a sample of 176 Ethiopian high school learners (69 male and 107 female, aged between 17 and 24) was selected using a stratified random sampling technique. The participants completed self-reported questionnaires to assess their emotional and motivational intelligence, and their writing performance was evaluated based on a set of predetermined writing rubrics. The collected data was analyzed using Exploratory Factor Analysis (EFA), Confirmatory Factor Analysis (CFA), Pearson's correlation coefficient, and path coefficients to determine any significant relationships or gender differences. The results showed a statistically significant relationship between emotional intelligence dimensions and writing performance. However, there was no statistically significant difference in writing performance and writing motivation based on gender. These findings provide valuable insights into the influence of emotional and motivational intelligence on writing performance among Ethiopian high school learners. Emotional intelligence, including the ability to understand and manage one's own and others' emotions, positively affected writing performance. This study contributes to the existing literature on the role of emotional and motivational intelligence in educational settings, specifically focusing on writing performance among Ethiopian high school learners. Overall, this research sheds light on the importance of considering emotional and motivational factors in educational practices to foster improved academic outcomes.

Radiomics analysis of ultrasound images to discriminate between benign and malignant adnexal masses with solid ultrasound morphology.

Our primary aim was to identify radiomic ultrasound features that can distinguish benign from malignant adnexal masses with solid ultrasound morphology, and primary invasive from metastatic solid ovarian masses, and to develop ultrasound-based machine learning models that include radiomics features to discriminate between benign and malignant solid adnexal masses. Our secondary aim was to compare the diagnostic performance of our radiomics models with that of the ADNEX model and subjective assessment by an experienced ultrasound examiner. This is a retrospective observational single center study. Patients with a histological diagnosis of an adnexal tumor with solid morphology at preoperative ultrasound examination performed between 2014 and 2021 were included. The patient cohort was split into training and validation sets with a ratio of 70:30 and with the same proportion of benign and malignant (borderline, primary invasive and metastatic) tumors in the two subsets. The extracted radiomic features belonged to two different families: intensity-based statistical features and textural features. Models to predict malignancy were built based on a random forest classifier, fine-tuned using 5-fold cross-validation over the training set, and tested on the held-out validation set. The variables used in model building were patient's age, and those radiomic features that were statistically significantly different between benign and malignant adnexal masses (Wilcoxon-Mann-Whitney Test with Benjamini-Hochberg correction for multiple comparisons) and assessed as not redundant based on the Pearson correlation coefficient. We describe discriminative ability as area under the receiver operating characteristics curve (AUC) and classification performance as sensitivity and specificity. 326 patients were identified and 775 preoperative ultrasound images were analyzed. 68 radiomic features were extracted, 52 differed statistically significantly between benign and malignant tumors in the training set, and 18 features were selected for inclusion in model building. The same 52 radiomic features differed statistically significantly between benign, primary invasive malignant and metastatic tumors. However, the values of the features manifested overlap between primary malignant and metastatic tumors and did not differ statistically significantly between them. In the validation set, 25/98 tumors (25.5%) were benign, 73/98 (74.5%) were malignant (6 borderline, 57 primary invasive, 10 metastases). In the validation set, a model including only radiomics features had an AUC of 0.80, and 78% sensitivity and 76% specificity at its optimal risk of malignancy cutoff (68% based on Youden's index). The corresponding results for a model including age and radiomics features were 0.79, 86% and 56% (cutoff 60% based on Youden's method), while those of the ADNEX model were 0.88, 99% and 64% (at 20% malignancy cutoff). Subjective assessment had sensitivity 99% and specificity 72%. Even though our radiomics models had discriminative ability inferior to that of the ADNEX model, our results are promising enough to justify continued development of radiomics analysis of ultrasound images of adnexal masses. This article is protected by copyright. All rights reserved.

간호대학생의 체질량지수에 따른 신체상 및 자아존중감, 섭식태도가 다이어트 행동에 미치는 영향

Objectives This research aimed to investigate the extent of dieting behaviors among nursing students and to ex-amine the impact of body image, self-esteem, and eating attitudes on dieting behaviors based on BMI. Methods The study included 135 nursing students located in Seoul, Gyeonggi, and Incheon. Data collection was conducted from October 17th to November 3rd, 2023. Data analysis was carried out using SPSS WIN/29.0 and included the calculation of means, standard deviations, Pearson correlation coefficients, and multiple regression analysis. Results The research results revealed that the average dieting behavior score of nursing students was 2.70. Depending on their body mass index (BMI), the scores were 2.00, 2.79, 2.59, and 3.67, respectively. When ana-lyzing the relationship between nursing students' body image, self-esteem, eating attitudes, and dieting behav-ior, significant positive correlations were found with the subjects' gender, BMI, body image, and self-esteem. The factors influencing nursing students' dieting behaviors were identified as gender, BMI, body image, and self-es-teem, with an explanatory power of 14.6%. Conclusions Based on the findings of this study, it is suggested to develop individualized diet behavior inter-ventions to promote positive eating attitudes and self-esteem among nursing students based on their BMI. Subsequent research should focus on evaluating these interventions.

Analyzing meteorological parameters using Pearson correlation coefficient and implementing machine learning models for solar energy prediction in Kuching, Sarawak

Solar energy is one of the clean renewable energy sources that can offset the rising consumption of fossil fuels. However, the meteorological parameters, such as solar irradiance, ambient and solar module temperatures, relative humidity, etc., constantly change, and so does the solar power generation. Such variations cause instability in the power grid operation due to injecting an unpredicted amount of power. Hence, solar energy prediction models capable of learning from past weather data and predicting future energy generation are highly desired for grid operation and planning. The objective of this study is to determine the suitable meteorological parameters for the solar energy prediction model based on the Pearson correlation coefficient and to implement them in different machine learning models. It is found in this study that five meteorological parameters, namely Air temperature, cloud opacity, global tilted irradiance, relative humidity, and zenith angle, correlate highly with solar energy generation. Later, based on the correlations, four machine-learning models were implemented to predict the solar power for Kuching, Sarawak. The accuracy of the models is measured through standard matrices such as root mean square error, mean square error, mean absolute error, and R-squared value.

Relationship between Anxiety, Attitude, Test Motivation, and Exam Success Regarding Exam Question Type: An Investigation of Middle School Learners

The present study examined the relationship between anxiety about exam question types and students’ attitudes, test motivation, and exam success. The sample was 121 middle school pupils in Sakarya, Turkey, aged 11 to 15 years. Data obtained from scales on attitude, anxiety, and test motivation were analyzed using Pearson correlation coefficients and regression analyses. The findings indicate that anxiety about exam question type was significantly negatively related to attitude, test motivation, and exam success. Moreover, attitudes about question type were significantly positively related to test motivation and exam success, and test motivation was also significantly positively related to exam success. Regression analysis results showed that students' anxiety about exam question types was predicted by their attitudes and exam success but was not predicted by test motivation.

간호대학생의 윤리적 가치관, 윤리적 민감성, 간호전문직관이 윤리적 의사결정 자신감에 영향을 미치는 요인

Objectives This study aimed to lay the groundwork for enhancing the ethical decision-making confidence of nurs-ing students by identifying factors influencing this competence. Methods The study participants were 180 second-year nursing students from two universities in J Province. They were surveryed from January 5 to 15, 2024 using questionnaire. The data were analyzed using descriptive sta-tistics in the SPSS/23.0 program, in addition to an Indepent t-test, Pearson correlation coefficients, and stepwise multiple regression. Results The results are as follows. First, nursing students’ ethical decision-making confidence had a significant positive correlation with ethical values (r=.597, p&lt;.001), ethical sensitivity (r=.589, p&lt;.001), and nursing pro-fessionalism (r=.528, p&lt;.001). Second, the factors influencing participants’ ethical decision-making confidence were ethical values (β=.332, p&lt;.001), ethical sensitivity (β=.220, p=.009), nursing professionalism (β=.215, p=.003), and awareness of the Korean Nurses Code of Ethics (known) (β=.177, p=.040), in that order. These varia-bles explained 45.1% of nursing students’ ethical decision-making confidence. Conclusions This study is meaningful as it identified factors that affect the ethical decision-making confidence of lower-grade nursing students. To improve lower-grade nursing students’ confidence in ethical decision-mak-ing, it is necessary to impart appropriate ethical values, improve ethical sensitivity and nursing professional in-tuition, and develop curriculum and programs for accurate recognition of the Korean Nurses Code of Ethics, tail-ored to the level of the target audience. Various learning methods that take these factors into account will be needed.

Exploring the usage of learning resources by medical students in the basic science stage and their effect on academic performance

BackgroundThe United States Medical Licensing Examination (USMLE) step 1 is one of the two examinations written after completion of the first two years (basic science stage) of medical school to be eligible to apply for residency training in the USA. A huge number and types of study materials are available to prepare for the exam which might confuse students choosing a resource.We investigated learning resources being used by the third and fifth-semester medical students and their association with academic performance. We also compared learning resources and exam scores of high-performing and low-performing students.MethodsData collection was done using structured (quantitative study) and semi-structured (qualitative study) questionnaires during a face-to-face interview. This article is about the quantitative part which was designed as a correlational study. Single factor one-way analysis of variance (ANOVA), Pearson correlation coefficient test, T-test, and Fisher’s exact test were used to analyze the data.ResultsAbout half of all students used three or more commercial resources dealing with the same content. A weak negative correlation was observed between the number of commercial resources and the exam scores, especially when the number of these resources was three or more (r = -0.26). The mean exam score of textbook users was statistically significantly higher than the mean score of textbook non-users (p = 0.01). The usage of textbooks was statistically significantly higher in the cohort of top performers in comparison to the rest of the students (p = 0.006). In addition to less usage of textbooks, the mean number of review books was higher in the group of weakest students (2.84 versus 3.7; p = 0.75).ConclusionsMost students did not use professional textbooks and about half used too many commercial review resources. While the former fact was significantly associated with poor academic performance, the later fact had weak negative correlation with exam score.Pedagogical interventions are urgently needed to make the right type of learning resources available by making professional textbooks more USMLE-oriented and helping the students choose the best and right number of resources for optimum academic performance. By fulfilling the observed needs of the students in this way, they might feel empowered because of self-determination which will motivate studies.

Preservation of sentiment in machine translation of low-resource languages: a case study on Slovak movie subtitles

AbstractThis research explores the effectiveness of machine translation from Slovak to English for sentiment analysis, specifically focusing on the translation of movie subtitles. The study employs a parallel corpus of segmented movie subtitles in both languages and utilizes IBM Watson™ Natural Language Understanding service and Google Translate. The research aims to assess the correlation between human-generated text and machine-translated text in sentiment analysis. A comparative analysis was also conducted using OpenAI to evaluate the sentiment of the Slovak text directly, without translation into English. The findings reveal a strong correlation between human text and machine translation, with a Pearson correlation coefficient of 0.86, and a correlation with OpenAI’s GPT model evaluation at 0.72. Despite the relatively high accuracy of the end-to-end solution using OpenAI, the methodology comprising machine translation followed by sentiment analysis in English was found to be significantly more precise. The research further investigates the challenges in translating specific language nuances, such as humor and vulgarism, and their impact on sentiment analysis. The study concludes that machine translation can be effectively used for sentiment analysis in Slovak, a flective language, and highlights the potential of advanced language models in low-resource languages. Future research directions include expanding the study to other text types and comparable languages beyond Slovak.

Adjusting for the effect of IV contrast on automated CT body composition measures during the portal venous phase.

Fully-automated CT-based algorithms for quantifying numerous biomarkers have been validated for unenhanced abdominal scans. There is great interest in optimizing the documentation and reporting of biophysical measures present on all CT scans for the purposes of opportunistic screening and risk profiling. The purpose of this study was to determine and adjust the effect of intravenous (IV) contrast on these automated body composition measures at routine portal venous phase post-contrast imaging. Final study cohort consisted of 1,612 older adults (mean age, 68.0 years; 594 women) all imaged utilizing a uniform CT urothelial protocol consisting of pre-contrast, portal venous, and delayed excretory phases. Fully-automated CT-based algorithms for quantifying numerous biomarkers, including muscle and fat area and density, bone mineral density, and solid organ volume were applied to pre-contrast and portal venous phases. The effect of IV contrast upon these body composition measures was analyzed. Regression analyses, including square of the Pearson correlation coefficient (r2), were performed for each comparison. We found that simple, linear relationships can be derived to determine non-contrast equivalent values from the post-contrast CT biomeasures. Excellent positive linear correlation (r2 = 0.91-0.99) between pre- and post-contrast values was observed for all automated soft tissue measures, whereas moderate positive linear correlation was observed for bone attenuation (r2 = 0.58-0.76). In general, the area- and volume-based measurement require less adjustment than attenuation-based measures, as expected. Fully-automated quantitative CT-biomarker measures at portal venous phase abdominal CT can be adjusted to a non-contrast equivalent using simple, linear relationships.

Estimating the volume of penumbra in rodents using DTI and stack-based ensemble machine learning framework

BackgroundThis study investigates the potential of diffusion tensor imaging (DTI) in identifying penumbral volume (PV) compared to the standard gadolinium-required perfusion–diffusion mismatch (PDM), utilizing a stack-based ensemble machine learning (ML) approach with enhanced explainability.MethodsSixteen male rats were subjected to middle cerebral artery occlusion. The penumbra was identified using PDM at 30 and 90 min after occlusion. We used 11 DTI-derived metrics and 14 distance-based features to train five voxel-wise ML models. The model predictions were integrated using stack-based ensemble techniques. ML-estimated and PDM-defined PVs were compared to evaluate model performance through volume similarity assessment, the Pearson correlation analysis, and Bland–Altman analysis. Feature importance was determined for explainability.ResultsIn the test rats, the ML-estimated median PV was 106.4 mL (interquartile range 44.6–157.3 mL), whereas the PDM-defined median PV was 102.0 mL (52.1–144.9 mL). These PVs had a volume similarity of 0.88 (0.79–0.96), a Pearson correlation coefficient of 0.93 (p < 0.001), and a Bland–Altman bias of 2.5 mL (2.4% of the mean PDM-defined PV), with 95% limits of agreement ranging from -44.9 to 49.9 mL. Among the features used for PV prediction, the mean diffusivity was the most important feature.ConclusionsOur study confirmed that PV can be estimated using DTI metrics with a stack-based ensemble ML approach, yielding results comparable to the volume defined by the standard PDM. The model explainability enhanced its clinical relevance. Human studies are warranted to validate our findings.Relevance statementThe proposed DTI-based ML model can estimate PV without the need for contrast agent administration, offering a valuable option for patients with kidney dysfunction. It also can serve as an alternative if perfusion map interpretation fails in the clinical setting.Key points• Penumbral volume can be estimated by DTI combined with stack-based ensemble ML.• Mean diffusivity was the most important feature used for predicting penumbral volume.• The proposed approach can be beneficial for patients with kidney dysfunction.Graphical

Visual Functions Are Associated with Biomarker Changes in Alzheimer's Disease.

While various biomarkers of Alzheimer's disease (AD) have been associated with general cognitive function, their association to visual-perceptive function across the AD spectrum warrant more attention due to its significant impact on quality of life. Thus, this study explores how AD biomarkers are associated with decline in this cognitive domain. To explore associations between various fluid and imaging biomarkers and visual-based cognitive assessments in participants across the AD spectrum. Data from participants (N = 1,460) in the Alzheimer's Disease Neuroimaging Initiative were analyzed, including fluid and imaging biomarkers. Along with the Mini-Mental State Examination (MMSE), three specific visual-based cognitive tests were investigated: Trail Making Test (TMT) A and TMT B, and the Boston Naming Test (BNT). Locally estimated scatterplot smoothing curves and Pearson correlation coefficients were used to examine associations. MMSE showed the strongest correlations with most biomarkers, followed by TMT-B. The p-tau181/Aβ1-42 ratio, along with the volume of the hippocampus and entorhinal cortex, had the strongest associations among the biomarkers. Several biomarkers are associated with visual processing across the disease spectrum, emphasizing their potential in assessing disease severity and contributing to progression models of visual function and cognition.

ARCnet: A Multi-Feature-Based Auto Radio Check Model

Radio checks serve as the foundation for ground-to-air communication. To integrate machine learning for automated and reliable radio checks, this study introduces an Auto Radio Check network (ARCnet), a novel algorithm for non-intrusive speech quality assessment in civil aviation, addressing the crucial need for dependable ground-to-air communication. By employing a multi-scale feature fusion approach, including the consideration of audio’s frequency domain, comprehensibility, and temporal information within the radio check scoring network, ARCnet integrates manually designed features with self-supervised features and utilizes a transformer network to enhance speech segment analysis. Utilizing the NISQA open-source dataset and the proprietary RadioCheckSpeech dataset, ARCnet demonstrates superior performance in predicting speech quality, showing a 12% improvement in both the Pearson correlation coefficient and root mean square error (RMSE) compared to existing models. This research not only highlights the significance of applying multi-scale attributes and deep neural network parameters in speech quality assessment but also emphasizes the crucial role of the temporal network in capturing the nuances of voice data. Through a comprehensive comparison of the ARCnet approach to traditional methods, this study underscores its innovative contribution to enhancing communication efficiency and safety in civil aviation.

Non-Contact Blood Pressure Estimation from Radar Signals by a Stacked Deformable Convolution Network.

This study introduces a contactless blood pressure monitoring approach that combines conventional radar signal processing with novel deep learning architectures. During the preprocessing phase, datasets suitable for synchronization are created by integrating Kalman filtering, multiscale bandpass filters, and a periodic extraction method in the time domain. These data comprise data on chest micro variations, encapsulating a complex array of physiological and biomedical information reflective of cardiac micromotions. The Radar-based Stacked Deformable convolution Network (RSD-Net) integrates channel and spatial self attention mechanisms within a deformable convolutional framework to enhance feature extraction from radar signals. The network architecture systematically employs deformable convolutions for initial deep feature extraction from individual signals. Subsequently, continuous blood pressure estimation is conducted using self attention mechanisms on feature map from single source coupled with multi-feature map channel attention. The performance of model is corroborated via the open-source dataset procured using a non-invasive 24GHz six-port continuous wave radar system. The dataset, encompassing readings from 30 healthy individuals subjected to diverse conditions including rest, the Valsalva maneuver, apnea, and tilt-table examinations. It serves to substantiate the validity and resilience of the proposed method in the non-contact assessment of continuous blood pressure. Evaluation metrics reveal Pearson correlation coefficients of 0.838 for systolic and 0.797 for diastolic blood pressure predictions. The Mean Error (ME) and Standard Deviation (SD) for systolic and diastolic blood pressure measurements are -0.32 ±6.14mmHg and -0.20 ±5.50mmHg, respectively. The ablation study assesses the contribution of different structural components of the RSD-Net, validating their significance in the overall of model performance.

POP-Q Versus Upright MRI Distance Measurements: A Prospective Study in Patients with POP.

The gold standard for quantifying pelvic organ prolapse is the pelvic organ prolapse quantification (POP-Q) system; however, upright magnetic resonance imaging (MRI) is a promising new method. The objective of this study was to determine the correlation between POP-Q and MRI measurements of the bladder and cervix. This prospective study included patients with prolapse in whom POP-Q points Aa or Ba and C were measured as standard care. MRI scans were performed in an upright position, and the distances of the lowest points of the bladder and cervix to the Pelvic Inclination Correction System (PICS) were calculated. Correlations between POP-Q and MRI-PICS measurements were determined using the Pearson correlation coefficient for normally distributed data and the Spearman's rank correlation coefficient for non-normally distributed data. A total of 63 patients were suitable for analysis. There was a moderate positive correlation between the POP-Q and MRI-PICS measurements for bladder (r(61) = 0.480, r < 0.001) and uterus (r(61) = 0.527, p < 0.001). Measurement differences between POP-Q and MRI-PICS of the bladder and uterus vary from -3.2 cm to 7.1 cm, and from -2.1 cm to 8.5 cm respectively. In 71.4% of patients more descent was seen on upright MRI than with POP-Q measurement for both bladder and uterus. For patients with similar POP-Q measurements, a high variation in MRI measurements of the bladder and uterus was found. Despite a moderate positive correlation, upright MRI shows a larger POP extent in 71.4% of the patients than POP-Q. A high variation in MRI measurements for patients with the same POP-Q measurement was seen.