Root Mean Square Research Articles

Detecting psychometric and diagnostic performance of the RU_SATED v2.0 multidimensional sleep health scale in community-dwelling adults combining exploratory graph analysis and ROC analysis

ObjectiveThe RU_SATED scale is increasingly used across the globe to measure sleep health. However, there is a lack of consensus around its psychometric and diagnostic performance. We conducted an empirical investigation into the psychometrics of the Chinese version of the RU_SATED (RU_SATED-C) scale, with a focus on structural validity and diagnostic performance. Methods1171 adults were enrolled from three communities in Hangzhou, China in July 2022. The dataset was spilt in half, and we ran a bootstrapped exploratory graph analysis (bootEGA) in one half and a confirmatory factor analysis (CFA) in the other half to assess structural validity. Correlations with insomnia, wellness, anxiety, and depression symptoms were examined in order to assess concurrent validity; and Cronbach's α and McDonald's ω were calculated to assess internal consistency. Additionally, a Receiver Operating Characteristic (ROC) analysis established and externally validated the optimal score for identifying insomnia symptoms. ResultsA one-dimensional structure, as identified by bootEGA, was corroborated in the CFA [comparative fit index = 0.934, root mean square error of approximation = 0.088, standardized root mean square residual = 0.051]. A moderate correlation was shown with insomnia symptoms, while weak correlations were observed with wellness, anxiety, and depression symptoms. The RU_SATED-C scale displayed sub-optimal internal consistency where coefficients dropped if any item was removed. A recommended cutoff score of ≤13 was derived for probable insomnia with a satisfactory diagnostic performance. ConclusionThe RU_SATED-C scale displayed a one-dimensional model, along with adequate concurrent validity, internal consistency, and diagnostic performance. Further work necessitates multi-scenario testing and additional validation using objective sleep assessments.

Neuro-enhanced fractional hysteresis modeling and identification by modified Newton-Raphson optimizer

The modeling and parameter identification of a system with hysteresis remains a difficult task. This paper aims to develop a hysteretic model by combining with a fractional Backlash-like model and cascade forward neural network, and proposed a modified Newton-Raphson-based optimizer parameter identification method to precisely capture the nonlinear behavior of piezoelectric platform. There are three contributions in this paper. Firstly, leveraging the definition of fractional calculus, a fractional Backlash-like model is proposed, whose rate dependence is proved theoretically. Secondly, a hybrid fractional Backlash-like model is established by combining fractional Backlash-like model with cascade forward neural network to enhance the fitting and generalization ability of the model. Thirdly, a modified Newton-Raphson-based optimizer enhances the original Newton-Raphson-based optimizer by incorporating a dynamic reverse learning strategy, a Q-learning strategy, a new adaptive coefficient, and local exploitation based on Levy flight, with theoretical analysis demonstrating improvements in convergence, diversity, and accuracy. The ablation test results show that the accuracy of the proposed modeling method is more than 40% less than that of the root mean square error obtained by the classic Backlash-like model. In addition, compared with the traditional Newton-Raphson-based optimizer, dandelion optimizer, particle swarm optimization and african vultures optimization algorithm, the modified algorithm shows significant advantages in accuracy, convergence speed and robustness under all test frequencies. The root mean square error obtained was reduced by more than 20%. These results show that the proposed method provides a powerful research idea for efficient and accurate hysteresis system modeling and parameter identification.

Klotho protein: a new insight into the pathogenesis of essential hypertension

BackgroundEssential hypertension is a most prevalent global health concern. Despite extensive research, the exact mechanisms contributing to essential hypertension remain unclear. Several factors contribute to the pathogenesis of essential hypertension. Klotho, a membrane-bound and soluble protein, has been found to modulate hypertension through physiological processes like vascular function and sodium balance. This study aimed to determine the association of klotho protein with essential hypertension.MethodsThe study included 164 hypertensive cases and 164 normotensive controls, after imposing certain inclusion and exclusion criteria with written consent from all subjects. Subject’s details were obtained using structured proforma to account for potential confounding variables. To estimate klotho protein activity using sandwich enzyme-linked immunosorbent assay, 2 mL blood was collected in a plain vial. All data were tested at a 5% significance level.ResultsThe analysis revealed a significant decrease in klotho protein levels in cases compared to controls (1.52 ± 0.87 vs. 2.45 ± 0.90, P < 0.001), suggesting an inverse relationship of klotho protein with risk of essential hypertension. All indices in the structural equation model have suggested that the final model fitted the data reasonably (chi-square to df ratio, 1.153; goodness of fit index, 0.990; adjusted goodness of fit index, 0.945; normed fit index, 0.936; standardized root mean square residual, 0.953; root mean square error of approximation, 0.031). Also, klotho was negatively associated with blood pressure. The area under the receiver operating characteristic curve for klotho and blood pressure was 0.765 (95% confidence interval, 0.716–0.815; P < 0.001).ConclusionsKlotho levels were significantly reduced in essential hypertension cases compared to controls, Also, klotho had a negative direct association with essential hypertension indicating a potential role for klotho as a prognostic and predictive marker for essential hypertension. This suggests that klotho may have a role in the pathogenesis of essential hypertension. Understanding klotho’s role in essential hypertension may lead to the development of novel therapeutic strategies for this disease.Graphical

Quantification of Amlodipine Maleate Content in Amorphous Solid Dispersions Produced by Fluidized Bed Granulation Using Process Analytical Technology Tools

Background: Active pharmaceutical ingredient (API) content is a critical quality attribute (CQA) of amorphous solid dispersions (ASDs) prepared by spraying a solution of APIs and polymers onto the excipients in fluid bed granulator. This study presents four methods for quantifying API content during ASD preparation. Methods: Raman and three near-infrared (NIR) process analysers were utilized to develop methods for API quantification. Four partial least squares (PLS) models were developed using measurements from three granulation batches, with an additional batch used to evaluate model predictability. Models performance was assessed using metrics such as root mean square error of prediction (RMSEP), root mean square error of cross-validation (RMSECV), residual prediction deviation (RPD), and others. Results: Off-line and at-line NIR models were identified as suitable for process control applications. Additionally, at-line Raman measurements effectively predicted the endpoint of the spraying phase. Conclusions: To the best of authors’ knowledge, this is the first study focused on monitoring API content during fluidized bed granulation (FBG) used for ASD preparation. The findings provide novel insights into the application of Raman and NIR process analysers with PLS modelling for monitoring and controlling ASD preparation processes.

An in-silico approach to target multiple proteins involved in anti-microbial resistance using natural compounds produced by wild mushrooms

Bacterial resistance to antibiotics and the number of patients infected by multi-drug-resistant bacteria have increased significantly over the past decade. This study follows a computational approach to identify potential antibacterial compounds from wild mushrooms. Twenty-six known compounds produced by wild mushrooms were docked to assess their affinity with drug targets of antibiotics such as penicillin-binding protein-1a (PBP1a), DNA gyrase, and isoleucyl-tRNA synthetase (ILERS). Docking scores were further validated by multiple receptor conformer (MRC)-based docking studies. Based on the MRC-based docking results, eight molecules were shortlisted for ADMET analysis. Molecular dynamics (MD) simulations were further performed to evaluate the conformational stability of the ligand-protein complexes. Binding energies were computed by the gmx_MMPBSA method. The data were obtained in terms of root-mean square deviation, and root-mean square fluctuation justified the stability of Austrocortilutein A, Austrocortirubin, and Confluentin in complex with several proteins under physiological conditions. Among these, Austrocortilutein A displayed better binding affinity with PBP1a and ILERS when compared with their respective reference ligands. This study is preliminary and aims to help drive the search for compounds that have the capacity to overcome the anti-microbial resistance of prevalent bacteria, using natural compounds produced by wild mushrooms. Further experimental validation is required to justify the clinical use of the studied compounds.

Efficient standardization of clinical T2-weighted images: Phase-conjugacy e-CAMP with projected gradient descent.

To standardize T -weighted images from clinical Turbo Spin Echo (TSE) scans by generating corresponding T maps with the goal of removing scanner- and/or protocol-specific heterogeneity. The T map is estimated by minimizing an objective function containing a data fidelity term in a Virtual Conjugate Coils (VCC) framework, where the signal evolution model is expressed as a linear constraint. The objective function is minimized by Projected Gradient Descent (PGD). The algorithm achieves accuracy comparable to methods with customized sampling schemes for accelerated T mapping. The results are insensitive to the tunable parameters, and the relaxed background phase prior produces better T maps compared to the strict real-value enforcement. It is worth noting that the algorithm works well with challenging T w-TSE data using typical clinical parameters. The observed normalized root mean square error ranges from 6.8% to 12.3% over grey and white matter, a clinically common level of quantitative map error. The novel methodological development creates an efficient algorithm that allows for T map generated from TSE data with typical clinical parameters, such as high resolution, long echo train length, and low echo spacing. Reconstruction of T maps from TSE data with typical clinical parameters has not been previously reported.

A novel multimodal image feature fusion mechanism:Application to rabbit liveweight estimation in commercial farms

In the realm of commercial rabbit farming, assessment of liveweight for each rabbit is crucial for production management. Traditional manual weighing methods are labor-intensive, time-consuming, and can induce stress and disease transmission. To solve this problem, this paper introduced a liveweight estimation method for rabbit utilizing near-infrared and depth image, including a segmentation stage and a weight estimation stage. Specifically, a dual-stream feature fusion mechanism was proposed to effectively integrate information from both near-infrared and depth imaging. The network was trained and validated on a dataset of 1,957 overhead images from 300 rabbits collected from a commercial farm, achieving superior performance with an R-Square (R2) of 0.95, Root Mean Square Error (RMSE) of 194.95 g, Mean Absolute Deviation (MAD) of 155.10 g, Mean Absolute Percentage Error (MAPE) of 4.08%, and Mean Coefficient of Variation (MCV) of 3.03%. Ablation experiments revealed that employing the proposed dual-stream feature fusion mechanism led to reductions of 52.87g, 38.19g, 1.01%, and 0.75% in RMSE, MAD, MAPE, and MCV, respectively, compared to not employing the mechanism. Compared to machine learning method, our proposed approach demonstrates superior performance in RMSE, MAD, MAPE, and MCV, achieving more accurate liveweight estimation for large-scale commercial farms.

Leveraging transformer models for enhanced temperature forecasting: a comparative analysis in the Beni Mellal region

The remarkable impact of transformers in artificial intelligence, exemplified by applications like GPT-3 in language processing, has sparked interest in their potential for time series analysis. This study aims to explore whether transformers, specifically temporal fusion transformers (TFT), can outperform conventional methods in this domain. The research question is whether TFT exhibits superior performance compared to conventional recurrent neural network (RNN) methods, specifically gated recurrent unit (GRU), and traditional machine learning approaches, notably autoregressive integrated moving average (ARIMA), in the context of time series analysis and temperature prediction. A comparative analysis is conducted among three models: ARIMA, GRU, and TFT. The study utilizes time series data spanning from 1984 to the end of 2022. The models’ performances are evaluated using multiple metrics: mean absolute error (MAE), root mean square error (RMSE), mean absolute percentage error (MAPE), and the coefficient of determination (R2). The TFT model achieves the lowest MAE, indicating high accuracy in its predictions. It outperforms both the RNN and traditional machine learning in temperature prediction tasks. Integrating the TFT model with the FAO penman-monteith method could improve irrigation scheduling due to more accurate temperature predictions, potentially enhancing water efficiency and crop yields.

Correlation Study on the Autonomic Nervous System Balance Using HRV According to Preference for Spicy Taste

Objectives: People generally seek out spicy taste when stressed. Eating spicy food activates the autonomic nervous system, causing the body to sweat and the heart to beat faster. The objective of the study is to investigate the correlation with the autonomic nervous system balance according to the preference for spicy taste using HRV.Method: This study measured the changes in heart rate using SA-3000P in patients who visited the local clinic between January and May 2023. To minimize any fluctuations in the autonomic nervous system before measurement due to external factors, subjects were asked to sit in a chair or lie down to rest for approximately 10 minutes before the test and any jewelry were asked to be removed. The test involved attaching ECG electrodes on both wrists and left ankle to measure the autonomic nervous system indicators by measuring, detecting and recording data for 5 minutes. For HRV measurements, SDNN (standard deviation of all normal R-R intervals) was obtained using time domain analysis, and TP (total power), VLF (very low frequency, 0.003∼0.04 Hz), LF (low frequency, 0.04∼0.15 Hz), HF (high frequency, 0.15∼0.4 Hz), and LF/HF ratio were calculated using frequency domain analysis. Additionally, using the measurements, indicators such as RMSSD (root mean square of the successive difference), autonomic nerve activation, and fatigue were produced.Results: The group that preferred spicy taste showed significantly lower TP, LF, HF, RMSSD, and autonomic nerve activation compared to the control group; VLF was lower but there was no statistically significant difference. Fatigue was higher in the group that preferred spicy taste compared to the control group with statistical significance; LF/HF Ratio was higher but with no statistically significant difference.Conclusion: The preference for spicy taste may cause autonomic nervous system abnormality; thus, it may be advisable to avoid the intake of spicy foods.

Machine learning-based performance prediction for energy storage medium-deep borehole ground source heat pump systems

With the rapid development of artificial intelligence, data-driven prediction models play an important role in energy demand forecasting and performance prediction. This study established performance prediction models for medium-deep borehole ground source heat pump (MDB-GSHP) systems to predict the coefficient of performance (COP) of GSHP, utilizing back propagation neural network (BPNN), extreme learning machine (ELM), support vector machine (SVM), and particle swarm optimization-support vector machine (PSO-SVM) method that optimizes the penalty parameter (C), insensitive loss parameter (ε), and kernel parameter (γ) of SVM using PSO. Operational data were collected through field experiments, and typical parameters were selected as features to establish the feature set. The feature set was then optimized using Pearson correlation analysis and recursive feature elimination (RFE), resulting in the creation of five distinct feature sets. Error analysis and trend evaluation results indicate that the combined feature set (FS5), which incorporates the advantages of multiple feature selection methods, demonstrates the best performance. The PSO-SVM model exhibits outstanding performance under various input conditions, achieving an accuracy of over 90% within the error range. When using FS5, the PSO-SVM model achieves a mean absolute percentage error (MAPE) of 0.037, mean absolute error (MAE) of 0.103, root mean square error (RMSE) of 0.125, coefficient of multiple determinations (R2) of 0.970, and explained variance score (EVS) of 0.967, showcasing excellent predictive performance. The research results can provide a basis for the formulation of storage-related operational parameters and the optimization of system operation for energy storage in MDB-GSHP heating systems.

Integrating infiltration processes in hybrid downscaling methods to estimate sub-surface soil moisture

Soil moisture is a key variable in the water, energy, and carbon cycles. Mapping sub-surface soil moisture with fine spatial resolution requires integrating downscaling approaches and process-based models. However, the effectiveness of hybrid methods, such as regression kriging (RK), in enhancing soil moisture estimates through process-based parameter predictions remains inconclusive. This study aims to integrate infiltration processes into downscaling models to predict 1-km multi-layer soil moisture, while comparing performance of nonlinear and linear models, and evaluating RK improvements. Random forests (RF) and generalized linear model (GLM) were used to downscale surface soil moisture (0–5 cm) from 36-km Soil Moisture Active Passive satellite products to 1 km across the Qinghai-Tibet Plateau. Next, the soil moisture analytical relationship (SMAR) model was applied to simulate infiltration processes and obtain site-scale parameters. RK variants (RFRK and GLMRK) were applied to jointly predict the spatial distribution of multiple infiltration parameters, which were used in SMAR at 1-km grids to estimate sub-surface soil moisture (5–40 cm). The results showed that parameter calibration significantly enhanced sub-surface soil moisture simulation, reducing root mean square error (RMSE) by 61.2 % to 69.8 %, from 0.09 to 0.03. RF outperformed GLM across all depth intervals, providing higher prediction accuracy (average RMSE, RF: 0.07; GLM: 0.09). Moreover, RK enhanced the Nash-Sutcliffe efficiency coefficient (RFRK: 0.34; GLMRK: 0.28) and coefficient of determination (RFRK: 0.5; GLMRK: 0.38) by 7.7 %–13.3 % and 2.2 %–2.4 %. This study provides a reference for mapping multi-layer soil moisture through the integration of data-driven and knowledge-driven approaches in regional-scale study areas.

Deep learning assisting construction of heat transfer constitutive relationships for porous media

Predicting heat transfer in porous materials is challenging due to their complex microstructure, and traditional experimental and theoretical methods often fall short. To address this, we present a machine learning-based approach using a single Descriptor-to-Property Network (D2P-Net) to identify the relationships between the effective thermal conductivity (ETC) of porous media with the structural descriptors. The D2P-Net, an ensemble of decision trees, uses eleven structural descriptors, such as porosity, pore size, and connectivity, derived from four distinct types of three-dimensional porous structures. These structures are computationally generated, and their ETC is computed using the Lattice Boltzmann Method (LBM) to create a robust dataset for training. The model is trained using mean squared error (MSE) employed as the loss function, and achieves an R2 value of 0.994 and a root mean square error (RMSE) of 0.229, indicating high predictive accuracy. Additionally, SHapley Additive exPlanations (SHAP) values are employed to analyze the importance of each structural descriptor, offering valuable insights into their contributions to ETC predictions. This approach provides a reliable and interpretable method for understanding and optimizing the thermal properties of porous materials, with potential applications in areas requiring efficient thermal management, such as hypersonic vehicles.

Objective-based survival individual enhancement in the chimp optimization algorithm for the profit prediction using financial accounting information system

This paper develops an innovative Objective-based Survival Individual Enhancement approach for the Chimp Optimization Algorithm (OSIE-CHOA) designed to enhance financial accounting profit prediction using information systems. The OSIE-CHOA focuses on improving the search process by simultaneously elevating the fitness of under-performing individuals within a population and strengthening the diversity among the top-performing ones. Within the OSIE-CHOA, we identify the four most promising chimps during each iteration. Subsequently, half of the highest-performing chimps are selected for elimination and repositioning around these fortunate individuals, with an equal probability assigned to each chimp. According to the experimental findings, it is clearly seen that OSIE-CHOA considerably enhances prediction accuracy, allowing a decrease in the root mean square error (RMSE) by 15% and the mean absolute error (MAE) by 18% compared to the traditional CHOA. Moreover, OSIE-CHOA shows a convergence rate that is 20% higher, which makes it a good and efficient tool for financial analysts who require accurate and reliable profit forecasting. By facilitating the optimization of profit prediction models, OSIE-CHOA leads to the improvement of decision-making within the context of financial accounting information systems.

Detection of foreign materials on Semen Ziziphi Spinosae using hyperspectral imaging technology coupled with convolutional neural networks

The efficacy of Semen Ziziphi Spinosae, a valued medicinal material, can be compromised by the presence of foreign materials, which may also worsen patient conditions and lead to complications. Traditional machine vision techniques often struggle to identify foreign entities that closely resemble Semen Ziziphi Spinosae in color and shape. This study investigates the application of hyperspectral imaging technology for the detection and differentiation of four types of foreign materials (moldy Semen Ziziphi Spinosae shell, wood, and stone) in processing industries. High-resolution hyperspectral imagery was subjected to image segmentation and background noise reduction to separate the contours of the samples within the images. The average spectral signatures of the regions of interest were extracted to construct models such as Convolutional Neural Networks (CNN), Support Vector Machines (SVMs), Mahalanobis distance, Deep Belief Networks, and Generative Adversarial Networks. These models were employed to extract spectral information indicative of foreign matter within the hyperspectral data to identify foreign materialss in Semen Ziziphi Spinosae. Subsequently, the classification outcomes were mapped onto the original hyperspectral images, enabling a visual representation of foreign matter within the seeds. The classification performance of alternative models was compared with that of the CNN model. The results demonstrate that a CNN trained with spectral information from hyperspectral data can accurately identify foreign materialss in Semen Ziziphi Spinosae. The model underwent 30 iterations, achieving an accuracy rate of 99% with a root mean square error (RMSE) of 0.62 for the validation detection, and an accuracy rate of 98% with an RMSE of 0.62 for the validation set.

Prediction of Summer Precipitation via Machine Learning with Key Climate Variables：A Case Study in Xinjiang, China

Study region: Xinjiang is located in the mid-latitude region of Eurasia in northwestern China. Precipitation is predominantly concentrated in northern Xinjiang, while southern Xinjiang remains comparatively arid. Summer precipitation accounts for 54.4% of the annual total. Study focus: This study aims to develop a machine learning model to predict summer precipitation (June–August) in XJ and explore the key variables contributing to summer precipitation in this region. The SHapley Additive exPlanations method was integrated with an extreme tree model to quantify the contributions of variables towards precipitation. Artificial neural networks, support vector machines, and extreme gradient boosting were considered to predict summer precipitation. To train the ML model, we used precipitation data from 1961 to 2012, whilst the forecast results from 2013 to 2017 were used for validation.New hydrological insights for the regions: The results demonstrated that the ANN model achieved robust performance during both the training and validation periods. For Northern and Southern XJ, the Mean Absolute Error and Root Mean Square Error of the ANN model were 15.34 (20.40) and 23.21 (30.01), respectively. The SHAP analysis showed that in the context of Northern Xinjiang, the Niño B Sea Surface Temperature Anomaly, Western Pacific Subtropical High Intensity, Pacific Subtropical High Intensity, and Multivariate ENSO Index play crucial roles in the prediction of summer precipitation. In Southern Xinjiang, the South China Sea Subtropical High Intensity, South China Sea Subtropical High Area, Western Pacific Warm Pool Strength, and Atlantic multidecadal oscillation have emerged as key variables affecting summer precipitation forecasting.

Eficácia da ferramenta de progressão de arquivos climáticos CCWorldWeatherGen

Abstract This paper aimed to analyze the effectiveness of the CCWorldWeatherGen tool, focusing on climate change in São Paulo, São Paulo State, Brazil. For this, dry-bulb temperature, relative humidity, global solar radiation, and wind speed data from the test reference year weather file (1954) and the CCWorldWeatherGen file for the 2020 period (representing the 2011-2040 period) were compared with observational data collected between 2011 and 2023 by the Meteorological Station of the Institute of Astronomy, Geophysics, and Atmospheric Sciences of the University of São Paulo. The accuracy of variables predicted using weather files was evaluated using five statistical measures of error. Annual relative root mean square error (RRMSE) values for dry-bulb temperature, relative humidity, global solar radiation, and wind speed in the morphed weather file were 17.04% (good), 17.95% (good), 31.57% (poor), and 224.44% (poor), respectively. It is concluded that CCWorldWeatherGen is suitable for generating future weather files with complete information, mainly for its practicality. However, this approach requires caution, as sequences depend on the consistency of the weather file used as a basis.

Prediction model for earthquake death toll based on PCA-BAS-ELM in mainland China

In recent years, China has experienced frequent catastrophic earthquakes, causing huge casualties. If the death toll can be quickly predicted after a disaster, then relief supplies can be delivered in a timely and reasonable manner, and the death toll and property losses can be minimized. Therefore, rapid and effective prediction of earthquake deaths plays a key role in guiding post-earthquake emergency rescue. However, there are many factors affecting the number of deaths in an earthquake. Aimed at this issue, a prediction model for earthquake deaths based on extreme learning machine (ELM) optimized by principal component analysis (PCA) and beetle antennae search (BAS) algorithm has been proposed in this study. Firstly, this study selected sample data of destructive earthquakes in mainland China in the past 50 years, then PCA was used to reduce the dimensionality of the factors affecting earthquake deaths, the principal components with lower contribution rates were removed, and the principal components with higher contribution rates were used as the input variables of ELM. Meanwhile, the earthquake deaths were used as the output variable, and the connection weights and thresholds of ELM was optimized using BAS. Finally, the prediction model for earthquake deaths based on PCA-BAS-ELM was established. The established model was used to predict the test samples. The results showed that the prediction results of PCA-BAS-ELM model had a higher fit with the actual values, and its mean square error, mean absolute percentage error and root mean square error were 2.433, 2.756% and 5.443, respectively, which suggested higher prediction accuracy.

Capacity estimation of lithium-ion battery through interpretation of electrochemical impedance spectroscopy combined with machine learning

Lithium-ion batteries inevitably undergo degradation over extended use, making precise capacity estimation essential for reliable state monitoring and health prognostics. However, conventional capacity estimation methods, which rely predominantly on time-domain information, offer limited insights due to the complex degradation mechanisms intrinsic to lithium-ion batteries. Accordingly, a capacity estimation approach leveraging partial frequency electrochemical impedance spectroscopy data integrated with machine learning is proposed. A reconstructed simplified fractional-order model characterized by a minimal set of parameters and superior fitting performance is introduced to extra health indicators from EIS measurement. Detailed equivalent circuit parameter extraction is performed, and subsequently incorporated into an enhanced Gaussian process regression model for capacity estimation. The proposed approach is validated using datasets derived from real-driving profiles including comprehensive comparisons, demonstrating its feasibility and effectiveness. Experimental results confirm that the framework achieves high estimation accuracy, strong generalization, and robust performance, even in scenarios with limited data availability and training noise presence, with a root mean square error not exceeding 0.81 %, a mean absolute error below 0.62 %, and a maximum absolute error of no more than 0.0653 Ah.

Impact of patient habitus and acquisition protocol on iodine quantification in dual-source photon-counting computed tomography.

Evaluation of iodine quantification accuracy with varying iterative reconstruction level, patient habitus, and acquisition mode on a first-generation dual-source photon-counting computed tomography (PCCT) system. A multi-energy CT phantom with and without its extension ring equipped with various iodine inserts (0.2 to 15.0mg/ml) was scanned over a range of radiation dose levels ( 0.5 to 15.0mGy) using two tube voltages (120, 140kVp) and two different source modes (single-, dual-source). To assess the agreement between nominal and measured iodine concentrations, iodine density maps at different iterative reconstruction levels were utilized to calculate root mean square error (RMSE) and generate Bland-Altman plots by grouping radiation dose levels (ultra-low: ; low: 1.5 to 5; medium: 5 to 15mGy) and iodine concentrations (low: ; high: 5 to 15mg/mL). Overall, quantification of iodine concentrations was accurate and reliable even at ultra-low radiation dose levels. RMSE ranged from 0.25 to 0.37, 0.20 to 0.38, and 0.25 to 0.37mg/ml for ultra-low, low, and medium radiation dose levels, respectively. Similarly, RMSE was stable at 0.31, 0.28, 0.33, and 0.30mg/ml for tube voltage and source mode combinations. Ultimately, the accuracy of iodine quantification was higher for the phantom without an extension ring (RMSE 0.21mg/mL) and did not vary across different levels of iterative reconstruction. The first-generation PCCT allows for accurate iodine quantification over a wide range of iodine concentrations and radiation dose levels. Stable accuracy across iterative reconstruction levels may allow further radiation exposure reductions without affecting quantitative results.

Immediate effects of structured and natural deep breathing on heart rate variability and blood pressure in community-dwelling older adults

BackgroundThis study investigated the immediate effects of structured deep breathing (SDB) and natural deep breathing (NDB) on heart rate variability (HRV) and blood pressure (BP) in community-dwelling older adults. MethodsTwenty-six participants were randomly assigned to SDB (n = 14) or NDB (n = 12) groups. HRV parameters (time domain: standard deviation of normal-to-normal intervals [SDNN], root mean square of successive differences [RMSSD]; frequency domain: low frequency [LF], high frequency [HF], LF/HF ratio, total power [TP], normalized low frequency [LFnu], normalized high frequency [HFnu]) and BP were assessed during spontaneous breathing, DB, and post DB. ResultsBoth groups showed significant increases during DB in SDNN (p < 0.001), RMSSD (p = 0.021), LF power (p < 0.001), LFnu (p < 0.001), TP (p < 0.001), and LF/HF ratio (p < 0.001). HFnu decreased significantly during DB (p < 0.001) with no group differences. HF power showed no significant effect in group and time. BP remained stable throughout the protocol, with no significant changes in either systolic or diastolic BP across time points or between groups. ConclusionsThis first direct comparison of structured versus natural DB in healthy older adults demonstrates that both approaches effectively enhance parasympathetic activity. These findings support DB as a cost-effective, accessible intervention for promoting autonomic balance in healthy aging, without requiring specialized equipment or instruction.