Root Mean Square Research Articles

Design and performance analysis of portable solar powered cooler for vaccine storage

AbstractThe efficacy of vaccine storage is significantly impacted by temperature fluctuations within the cooler, often exacerbated by using phase change materials in existing cooler designs for remote areas. These materials can undergo uneven melting and phase separation, leading to temperature instability and vaccine potency loss. In response to this challenge, the present study introduces a novel design of a portable, locally‐made solar‐powered cooler optimized for longer storage periods. The cooler's performance in terms of temperature distribution, airflow dynamics, and the coefficient of performance (COP) is meticulously examined through computational fluid dynamics (CFD) simulations. The simulated results were validated using experimental data from the open literature, ensuring accuracy and reliability. The findings indicate that the developed cooler achieves significant improvements over traditional models. For instance, the current model reaches a temperature of +12°C in just 84 min, compared to 208 min, as reported in the literature results. Moreover, the current model reaches a temperature of −12°C in 195 min and it has energy efficient with a COP of 4.5. Statistical analysis further confirms the reliability of the simulation results, with root mean square and mean absolute percentage errors of 6.587 and 24.2%, respectively. Additionally, a comparative study of five insulative materials highlights polyurethane (Po) as the top performer, with a heat transfer performance of 14.3%, followed by feather fiber (Fe) (18.7%), fly ash (Fl) (19.8%), fiberglass (Fi) (21.9%), and coconut fiber (Co) (25.9%). Notably, net present value (NPV) of $689.336 and $448.01 was obtained for economic analysis of the current model over the existing model, showing the feasibility of the study. Hence, the cooler's effectiveness in storing vaccines in isolated regions exceeds that of conventional models, providing a hopeful solution to tackle vital challenges in vaccine distribution and preservation.

Estimation of Forest Growing Stock Volume with Synthetic Aperture Radar: A Comparison of Model-Fitting Methods

Satellite-based estimation of forest variables including forest biomass relies on model-based approaches since forest biomass cannot be directly measured from space. Such models require ground reference data to adapt to the local forest structure and acquired satellite data. For wide-area mapping, such reference data are too sparse to train the biomass retrieval model and approaches for calibrating that are independent from training data are sought. In this study, we compare the performance of one such calibration approach with the traditional regression modelling using reference measurements. The performance was evaluated at four sites representative of the major forest biomes in Europe focusing on growing stock volume (GSV) prediction from time series of C-band Sentinel-1 and Advanced Land Observing Satellite Phased Array L-band Synthetic Aperture Radar (ALOS-2 PALSAR-2) backscatter measurements. The retrieval model was based on a Water Cloud Model (WCM) and integrated two forest structural functions. The WCM trained with plot inventory GSV values or calibrated with the aid of auxiliary data products correctly reproduced the trend between SAR backscatter and GSV measurements across all sites. The WCM-predicted backscatter was within the range of measurements for a given GSV level with average model residuals being smaller than the range of the observations. The accuracy of the GSV estimated with the calibrated WCM was close to the accuracy obtained with the trained WCM. The difference in terms of root mean square error (RMSE) was less than 5% units. This study demonstrates that it is possible to predict biomass without providing reference measurements for model training provided that the modelling scheme is physically based and the calibration is well set and understood.

Plasmonic properties of silicon nitride encapsulated copper

We investigated the optical properties (i.e. plasmonic loss) of thin-film copper that was deposited and processed in a 300mm CMOS pilot line. The optical properties at 1550 nm (QSPP,Cu = 3921 ± 350) were evaluated by measuring the propagation loss of dielectric loaded plasmonic waveguides on samples with high and low root mean square surface roughness (Rq) with and without a silicon nitride diffusion barrier and at room temperature and cryogenic temperatures. In our fabrication result, the average grain size is 2.08±0.05μm, which is much larger than the mean free path of free electrons in copper, so the surface roughness becomes the main cause of the waveguide propagation loss from the fabrication constrains. Further, we show that copper can be encapsulated by a 15 nm silicon nitride diffusion and oxidation barrier without degrading the optical properties.

Detection of organic carbon in shale by laser induced breakdown spectroscopy and Raman spectroscopy combined with partial least squares methods

In this study, data fusion of laser-induced breakdown spectroscopy (LIBS) and Raman spectroscopy was used to characterize the total organic carbon content (TOC) of shale samples in Ordos Basin. The powder samples are pressed into pellets for LIBS and Raman analysis. Partial least squares calibration method was used for quantitative analysis. By comparing the quantitative results of the two techniques and the quantitative results of different spectral fusion strategies, it is found that the fusion strategy of LIBS and Raman spectral data can improve the prediction ability of the model. The model based on PLS latent variable fusion strategy has better fitting effect, its coefficient of determination (R2) for the calibration sets is 0.9915, the coefficient of determination for the prediction sets is 0.9574 and the root mean square error of the prediction set is 0.5580 wt%. The model has better generalization ability, and the root-mean-square-error-of-cross-validation (RMSECV) of the model is 0.5252 wt%. This study shows that data fusion of LIBS and Raman spectra can achieve rapid analysis of total organic carbon content in shale samples, and may be beneficial to carbon storage research in depleted gas shale.

Deep learning models for hepatitis E incidence prediction leveraging Baidu index.

Infectious diseases are major medical and social challenges of the 21st century. Accurately predicting incidence is of great significance for public health organizations to prevent the spread of diseases. Internet search engine data, like Baidu search index, may be useful for analyzing epidemics and improving prediction. We collected data on hepatitis E incidence and cases in Shandong province from January 2009 to December 2022 are extracted. Baidu index is available from January 2009 to December 2022. Employing Pearson correlation analysis, we validated the relationship between the Baidu index and hepatitis E incidence. We utilized various LSTM architectures, including LSTM, stacked LSTM, attention-based LSTM, and attention-based stacked LSTM, to forecast hepatitis E incidence both with and without incorporating the Baidu index. Meanwhile, we introduce KAN to LSTM models for improving nonlinear learning capability. The performance of models are evaluated by three standard quality metrics, including root mean square error(RMSE), mean absolute percentage error(MAPE) and mean absolute error(MAE). Adjusting for the Baidu index altered the correlation between hepatitis E incidence and the Baidu index from -0.1654 to 0.1733. Without Baidu index, we obtained 17.04±0.13%, 17.19±0.57%, in terms of MAPE, by LSTM and attention based stacked LSTM, respectively. With the Baidu index, we obtained 15.36±0.16%, 15.15±0.07%, in term of MAPE, by the same methods. The prediction accuracy increased by 2%. The methods with KAN can improve the performance by 0.3%. More detailed results are shown in results section of this paper. Our experiments reveal a weak correlation and similar trends between the Baidu index and hepatitis E incidence. Baidu index proves to be valuable for predicting hepatitis E incidence. Furthermore, stack layers and KAN can also improve the representational ability of LSTM models.

Effects of robot-assisted hand function therapy on brain functional mechanisms: a synchronized study using fNIRS and sEMG.

Robot-assisted hand function therapy is pivotal in the rehabilitation of patients with stroke; however, its therapeutic mechanism remains elusive. Currently, research examining the impact of robot-assisted hand function therapy on brain function in patients with stroke is scarce, and there is a lack of studies investigating the correlation between muscle activity and alterations in brain function. This study aimed to investigate the correlation between forearm muscle movement and brain functional activation by employing the synchronized use of functional near-infrared spectroscopy and surface electromyography methods. Moreover, it sought to compare neural activity patterns during different rehabilitation tasks and refine the mechanism of robot-assisted hand function therapy for post-stroke hand function impairments. Stroke patients with hand dysfunction underwent three sessions of robot-assisted hand function therapy within 2 weeks to 3 months of onset. The fNIRS-sEMG synchronous technique was used to observe brain function and forearm muscle activation. Ten participants were randomly assigned to receive mirror, resistance, or passive rehabilitation training. During the intervention, cortical and muscle activation information was obtained using fNIRS and electromyographic signals. The primary outcomes included changes in oxyhemoglobin concentration and root mean square of surface electromyography. Compared to the resting state, the Oxy-Hb concentration in the brain regions involved in three rehabilitation tasks with robot-assisted hand function therapy significantly increased (p < 0.05). Mirror therapy significantly enhanced the prefrontal cortex and the superior frontal cortex activation levels. In contrast, resistance therapy significantly promoted the activation of the supplementary motor area and the premotor cortex. Passive rehabilitation tasks showed some activation in the target brain area premotor cortex region. Robot-assisted hand function therapy has shown that forearm muscle movement is closely related to oxygenated hemoglobin concentration activity in specific brain regions during different rehabilitation tasks. The simultaneous sEMG-fNIRS study found a significant correlation between muscle movement and brain activity after stroke, which provides an important basis for understanding the treatment mechanism of hand function impairment.

Optimizing prediction of stainless steel mechanical properties with random forest: a comparison of feature selection methods

In machine learning, predicting the mechanical properties of stainless steel, such as Yield Strength (YS), Ultimate Tensile Strength (UTS), and Elongation (EL), requires many input variables, such as chemical composition, type of heat treatment, heating duration, and cooling method. However, the complexity and number of these variables can increase processing time and reduce model accuracy. This study aims to explore the impact of selecting the most influential input variables to improve prediction accuracy. We compared two feature selection techniques: Recursive Feature Elimination (RFE), which systematically removes less important features, and Information Gain (IG), which measures the contribution of each variable to the target prediction. Both techniques were implemented using the random forest algorithm, chosen for its robustness in handling large datasets and its ability to capture complex interactions between variables. Parameter optimization was performed using a grid search. The analysis showed that the RFE-based model outperformed both the IG-based model and the model without feature selection. In predicting YS, RFE identified 13 out of 21 influential variables, achieving a Mean Absolute Error (MAE) of 9.91, Root Mean Square Error (RMSE) of 14.20, and R-squared value of 0.89. For UTS, RFE identified 8 out of 21 variables, with an MAE of 12.89, RMSE of 16.97, and R-squared of 0.97. In predicting EL, RFE identified 14 out of 21 variables, with an MAE of 3.82, RMSE of 6.10, and an R-squared value of 0.85. The high R-squared values (0.85) across all properties indicate the model’s strong predictive capabilities, making it suitable for practical applications in predicting the mechanical properties of stainless steel.

Comparative Study of Semi-Active Control Effectiveness of Structures on Soft Soil Using MR Damper: Shaking Table Investigation

The effect of soil-structure interaction (SSI) cannot be neglected in semi-active vibration control of structures located on soft soil. To investigate the mitigating effect of magnetorheological (MR) damper on the semi-active control of structures considering the SSI, shaking table tests were conducted to evaluate the performance of the MR damper-based semi-active control system for structures on soft soil. In addition to a novel fuzzy control method based on online learning deep neural network (OL-DFNN), various control strategies, including passive OFF, passive ON, ON–OFF and deep neural network fuzzy control (DFNN) were systematically evaluated. The test results showed that the OL-DFNN control method exhibited superior adaptability to varying ground motion and structural dynamic characteristics compared to the other control methods. In particular, the OL-DFNN controller effectively mitigated the peak displacement and root mean square displacement, outperforming the passive OFF, passive ON, ON–OFF and DFNN control strategies. The test results highlight the effectiveness of the OL-DFNN control method in addressing the challenges posed by dynamic and time-varying characteristics in structural vibration control systems considering SSI.

Innovative xanthan gum-based nanocomposites for asphaltene precipitation prevention in shale and carbonate rocks

Asphaltene deposition in porous media creates many challenges in porous media. This study synthesizes ZnO/SiO2/xanthan nanocomposites (NCs) to adsorb asphaltene and reduce its effect on the shale and carbonate rocks. NCs structure is analyzed using SEM, EDX, BET, and FTIR tests. Also, the rocks' surface is analyzed by an atomic force microscopy (AFM) test after 48 and 96 h of contact with 20 ppm NCs and 20 mg asphaltene. Core flooding tests are performed on shale rocks using 20 ppm NCs at 5500, 4000, and 2500 psi at 48 h. Using AFM in calcite and dolomite formations and selecting core flooding tests based on that are new scenarios that followed in this paper. FTIR results confirm asphaltene adsorption on NCs's surface by changing 854 and 962 cm−1 peaks. AFM tests confirmed asphaltene adsorption on NCs surface, too. Average roughness, root mean square roughness, peak to valley roughness, and average size of the shale were higher than the carbonate sheets. At 20 ppm NCs in shale reservoirs, permeability reduction in porous media was increased up to 39.5 %, and asphaltene precipitation decreased from 8.95 and 20.06 wt% to 2.25 and 10.25 wt%, which shows our suggested scenarios were efficient.

A Multi-Spatial Scale Ocean Sound Speed Prediction Method Based on Deep Learning

As sound speed is a fundamental parameter of ocean acoustic characteristics, its prediction is a central focus of underwater acoustics research. Traditional numerical and statistical forecasting methods often exhibit suboptimal performance under complex conditions, whereas deep learning approaches demonstrate promising results. However, these methodologies fall short in adequately addressing multi-spatial coupling effects and spatiotemporal weighting, particularly in scenarios characterized by limited data availability. To investigate the interactions across multiple spatial scales and to achieve accurate predictions, we propose the STA-ConvLSTM framework that integrates spatiotemporal attention mechanisms with convolutional long short-term memory neural networks (ConvLSTM). The core concept involves accounting for the coupling effects among various spatial scales while extracting temporal and spatial information from the data and assigning appropriate weights to different spatiotemporal entities. Furthermore, we introduce an interpolation method for ocean temperature and salinity data based on the KNN algorithm to enhance dataset resolution. Experimental results indicate that STA-ConvLSTM provides precise predictions of sound speed. Specifically, relative to the measured data, it achieved a root mean square error (RMSE) of approximately 0.57 m/s and a mean absolute error (MAE) of about 0.29 m/s. Additionally, when compared to single-dimensional spatial analysis, incorporating multi-spatial scale considerations yielded superior predictive performance.

Power Signal Histograms—A Method of Power Grid Data Compression on the Edge for Real-Time Incipient Fault Forensics

Across the power grid infrastructure, deployed power transmission systems are susceptible to incipient faults that interrupt standard operations. These incipient faults can range from being benign in impact to causing massive hardware damage and even loss of life. The power grid is continuously monitored, and incipient faults are recorded by Digital Fault Recorders (DFRs) to mitigate such outcomes. DFR-recorded data allow for power quality forensics and event analysis, but this ability comes at the cost of high data storage and data transmission requirements. It is common for data older than two weeks to be overwritten due to storage limitations, without being analyzed. This inhibits the creation of long-term data libraries that would enable incipient fault forensics and the characterization of behavior that precedes them, which limits the development and implementation of preventive measures; thus, there is a critical need to reduce DFR-recorded data’s storage requirements. This work addresses this critical need by leveraging the cyclic and residual histograms and introducing the frequency and Root Means Squared (RMS) histograms, which alleviate the current high data storage requirements and provide effective Incipient Fault Prediction (IFP). The residual, frequency, and RMS histograms are an extension of the cyclic histogram, reduce the data storage requirement by up to 99.58%, can be generated on the DFR without interrupting its normal operations, and are capable of predicting voltage arcing six hours before it is strong enough to trigger a DFR-recorded event.

Analyzing Discrepancies and Correlations in Soft and Hard Tissue Asymmetry: A Focused Study on Hemifacial Microsomia and Isolated Microtia.

This study aims to thoroughly investigate the distinctions and relationships between facial hard and soft tissue asymmetry, as well as their variations within different conditions and age groups. This cross-sectional study analyzed pre-treatment computed tomography (CT) images from 120 male patients aged 5 to 12 years with unilateral HFM (Pruzansky-Kaban types I and IIA) or isolated microtia. The 120 patients were categorized into four groups by condition (HFM or isolated microtia) and age (5-7, 8-12 years). Segmentation and extraction of anatomical landmarks were performed using Mimics software, followed by generating root-mean-square (RMS) values for facial asymmetry. Statistical analysis, including the Wilcoxon signed-rank test and Spearman correlation coefficient, was conducted to assess differences and correlations in asymmetry. The study found that soft tissue asymmetry was generally greater than hard tissue asymmetry across all facial regions. The complexity of regional asymmetry correlations increased with age in HFM patients but decreased in isolated microtia patients. Significant correlations between hard and soft tissues were observed within the same anatomical regions, particularly in HFM patients aged 8-12 years. The intricate interplay between hard and soft tissues was observed in HFM and isolated microtia patients. Personalized analysis of the causes of asymmetry in different regions is crucial for the formulation of effective treatment plans. This journal requires that authors assign a level of evidence to each article. For a full description of these Evidence-Based Medicine ratings, please refer to the Table of Contents or the online Instructions to Authors www.springer.com/00266 .

Optimizing Precipitation Forecasting and Agricultural Water Resource Allocation Using the Gaussian-Stacked-LSTM Model

Our study investigates the use of machine learning models for daily precipitation prediction using data from 56 meteorological stations in Jilin Province, China. We evaluate Stacked Long Short-Term Memory (LSTM), Transformer, and Support Vector Regression (SVR) models, with Stacked-LSTM showing the best performance in terms of accuracy and stability, as measured by the Root Mean Square Error (RMSE). To improve robustness, Gaussian noise was introduced, particularly enhancing predictions for zero-precipitation days. Key predictors identified through variable attribution analysis include temperature, dew point, prior precipitation, and air pressure. Additionally, we demonstrate the practical benefits of precipitation forecasts in optimizing water resource allocation. A prediction-based strategy outperforms equal distribution in managing resources efficiently, as shown in a case study using 2022 Beidahu data. Overall, our research advances precipitation forecasting through deep learning and offers valuable insights for water resource management.

Whole body vibration and rider comfort determination of an electric two-wheeler test rig

Background Two-wheeled vehicles are the major mode of transportation in India. Such vehicles are exposed to excessive vibration on the road when compared to four-wheeled vehicles. However, the research on the reduction of whole body vibration in the case of two-wheelers is not explored in detail. The present study predicts rider comfort in the case of an electric two-wheeler as per ISO 2631-1, by obtaining the finding the weighted acceleration at the strategic locations of vibration at the test rig. Methods An electric two-wheeler test rig is used in the study. The values of acceleration from the test rig in running conditions are obtained by using NI LabVIEW 2019. The drive cycle of the electric vehicle (EV) test rig is controlled by Sync sols’ EV lab software. Obtaining the weighted root mean square (RMS) acceleration from running the test setup, it is compared with the ISO 2631-1 standard to obtain the rider comfort. Results Loading area, traction motor, base mount, and suspension were found to be the strategic points of vibration. Frequency weighted RMS acceleration of 0.3 to 0.4 m/s2 obtained at these points are prone to cause discomfort for the rider. Vehicle speed, road profile, and duration of exposure were found to be important parameters affecting the rider’s comfort. A maximum of 4.6 m/s2 amplitude was observed. The loading area, which corresponds to a rider’s seat in actual vehicle, is important and reduction of these vibrations make the ride comfortable for the rider. Suspension and base mount of the test rig are found to be uncomfortable observing the weighted RMS acceleration. Conclusions A suitable damping technique design is very much essential in reducing these vibrations and improve the rider comfort, as many more non-deterministic vibrations are prone to cause dis-comfort in case of actual on road riding conditions.

The ground states of hidden-charm tetraquarks and their radial excitations

Inspired by the great progress in the observations of charmonium-like states in recent years, we perform a systematic analysis about the ground states and the first radially excited states of qcq¯c¯\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$qc\\bar{q}\\bar{c}$$\\end{document} (q = u/d and s) tetraquark systems. Their mass spectra, root mean square (r.m.s.) radii and radial density distributions are predicted within the framework of relativized quark model. By comparing with experimental data, some potential candidates for hidden-charm tetraquark states are suggested. For qcq¯c¯\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$qc\\bar{q}\\bar{c}$$\\end{document} (q = u/d) system, if Zc(3900)\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$Z_{c}(3900)$$\\end{document} is supposed to be a compact tetraquark state with JPC=1+-\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$J^{PC}=1^{+-}$$\\end{document}, Z(4430) can be interpreted as the first radially excited states of Zc(3900)\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$Z_{c}(3900)$$\\end{document}. Another broad structure Zc(4200)\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$Z_{c}(4200)$$\\end{document} can also be explained as a partner of Zc(3900)\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$Z_{c}(3900)$$\\end{document}, and it arise from a higher state with JPC=1+-\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$J^{PC}=1^{+-}$$\\end{document}. In addition, theoretical predictions indicate that the possible assignments for X(3930), X(4050) and X(4250) are low lying 0++\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$0^{++}$$\\end{document} tetraquark states. As for the scs¯c¯\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$sc\\bar{s}\\bar{c}$$\\end{document} system, X(4140) and X(4274) structures can be interpreted as this type of tetraquark states with JPC=1++\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$J^{PC}=1^{++}$$\\end{document}, and X(4350) can be described as a scs¯c¯\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$sc\\bar{s}\\bar{c}$$\\end{document} tetraquark with JPC=0++\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$J^{PC}=0^{++}$$\\end{document}. With regard to qcs¯c¯\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$qc\\bar{s}\\bar{c}$$\\end{document} (q = u/d) system, we find two potential candidates for this type of tetraquark, which are Zcs(4000)\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$Z_{cs}(4000)$$\\end{document} and Zcs(4220)\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$Z_{cs}(4220)$$\\end{document} structures. The measured masses of these two structures are in agreement with theoretical predictions for the 1+\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$1^{+}$$\\end{document} state.

Accuracy of intraoral versus desktop scanners used in digitizing polyvinyl siloxane impression for fabricating a CAD-CAM customized post and core.

This in vitro study aimed to compare the 3-dimensional (3D) accuracy of intraoral and desktop scanners when digitizing the post and core polyvinyl siloxane impressions. Ten extracted human teeth were prepared to build a post space in the root canal. Each tooth was scanned using a micro-computed tomography device (MCT), and the slice data were reconstructed as controls. A conventional quadrant polyvinyl siloxane impression was made and scanned thrice using an intraoral scanner (IOS) and a desktop blue-light scanner (IMS), with one of the scans randomly selected as the definitive cast. Precision was assessed by comparing the deviations among repeated scans. Trueness was evaluated by analyzing the deviations between the MCT and each definitive cast (MCT-IOS and MCT-IMS). The clinically acceptable root mean square (RMS) value was set to 50 µm. The mean RMS values of the repeated scans of IOS and IMS were 18.3 ± 3.9 µm and 13.9 ± 6.2 µm (P < 0.05), and the difference between them was 4.4 ± 5.9 µm. The mean RMS values of MCT-IOS and MCT-IMS were 28.3 ±3.0 µm and 31.3 ± 4.2 µm (P < 0.05), with a difference of 3.0 ± 2.9 µm. The RMS values were significantly lower than 50 µm (P < 0.05). The IOS showed slightly better trueness than the IMS but slightly lower precision. The precision and trueness of the two scanners for digitizing post and core impressions were acceptable for clinical application.

Intelligent Sentinel satellite image processing technology for land cover mapping

Purpose. This article proposes to develop an intelligent Sentinel satellite image processing technology for land cover mapping using convolutional neural networks. The result will be an image with improved spatial resolution. Methodology. The paper presents a technology using a combination of biquadratic interpolation, histogram alignment, PCA transform, as well as a parallel residual architecture of convolutional neural networks. The technology increases the information content of Sentinel-2 optical images by combining 10 and 20-meter resolution data, resulting in primary 20-meter images with improved spatial resolution. Findings. The root mean square error (RMSE = 3.64) indicates a high accuracy in reproducing the spectral properties of the images. The correlation coefficient (CC = 0.997) confirms a high linear relationship between the estimated and observed images. The low value of Spectral Angle Mapper (SAM = 0.52) with the high Universal Image Quality Index (UIQI = 0.999) indicates high quality and structural similarity between the synthesized and reference images. These results confirm the proposed technology’s effectiveness in enhancing the spatial resolution of Sentinel satellite images. Originality. Traditional pansharpening methods of multispectral images developed for satellite images with panchromatic channels cannot be directly applied to Sentinel multispectral data, because these images do not contain a panchromatic channel. In addition, atmospheric conditions and the presence of clouds affect the quality of optical images, complicating their further thematic processing. The proposed technology, using biquadratic interpolation, histogram alignment, convolutional neural networks, and PCA transformation, removes clouds and enhances the spatial resolution of the primary 20-meter optical satellite image channels of Sentinel-2. This technology reduces color distortion and increases the detail of digital optical images, which allows for more accurate analysis of the state of the earth’s surface. Practical value. The results obtained can be used to improve the methods for processing Sentinel satellite images, which provide high spatial resolution and accurate preservation of spectral characteristics. It provides the foundation for the development of new geographic information systems for land cover monitoring.

Using intrahost single nucleotide variant data to predict SARS-CoV-2 detection cycle threshold values.

Over the last four years, each successive wave of the COVID-19 pandemic has been caused by variants with mutations that improve the transmissibility of the virus. Despite this, we still lack tools for predicting clinically important features of the virus. In this study, we show that it is possible to predict the PCR cycle threshold (Ct) values from clinical detection assays using sequence data. Ct values often correspond with patient viral load and the epidemiological trajectory of the pandemic. Using a collection of 36,335 high quality genomes, we built models from SARS-CoV-2 intrahost single nucleotide variant (iSNV) data, computing XGBoost models from the frequencies of A, T, G, C, insertions, and deletions at each position relative to the Wuhan-Hu-1 reference genome. Our best model had an R2 of 0.604 [0.593-0.616, 95% confidence interval] and a Root Mean Square Error (RMSE) of 5.247 [5.156-5.337], demonstrating modest predictive power. Overall, we show that the results are stable relative to an external holdout set of genomes selected from SRA and are robust to patient status and the detection instruments that were used. This study highlights the importance of developing modeling strategies that can be applied to publicly available genome sequence data for use in disease prevention and control.

Prediction of single cell mechanical properties in microchannels based on deep learning

Traditional methods for measuring single-cell mechanical characteristics face several challenges, including lengthy measurement times, low throughput, and a requirement for advanced technical skills. To overcome these challenges, a novel machine learning (ML) approach is implemented based on the convolutional neural networks (CNNs), aiming at predicting cells’ elastic modulus and constitutive equations from their deformations while passing through micro-constriction channels. In the present study, the computational fluid dynamics technology is used to generate a dataset within the range of the cell elastic modulus, incorporating three widely-used constitutive models that characterize the cellular mechanical behavior, i.e., the Mooney-Rivlin (M-R), Neo-Hookean (N-H), and Kelvin-Voigt (K-V) models. Utilizing this dataset, a multi-input convolutional neural network (MI-CNN) algorithm is developed by incorporating cellular deformation data as well as the time and positional information. This approach accurately predicts the cell elastic modulus, with a coefficient of determination R2 of 0.999, a root mean square error of 0.218, and a mean absolute percentage error of 1.089%. The model consistently achieves high-precision predictions of the cellular elastic modulus with a maximum R2 of 0.99, even when the stochastic noise is added to the simulated data. One significant feature of the present model is that it has the ability to effectively classify the three types of constitutive equations we applied. The model accurately and reliably predicts single-cell mechanical properties, showcasing a robust ability to generalize. We demonstrate that incorporating deformation features at multiple time points can enhance the algorithm’s accuracy and generalization. This algorithm presents a possibility for high-throughput, highly automated, real-time, and precise characterization of single-cell mechanical properties.

Data protection enhancement in smart grid communication: An efficient multi-layer encrypting approach based on chaotic techniques and steganography

In this study, a multi-layer encryption system is proposed through which sensitive and important data in smart grids can be secured from unauthorized persons as well as cyber threats. The suggested system is analyzed using fundamental performance assessment metrics say as average processing times across ten runs, peak signal-to-noise ratio (PSNR), and root mean square error (RMSE). The encryption process involves hiding the data encrypted by a digital dictionary and the advanced encryption standard (AES) algorithm in an image. In this way, AES encryption suggests rapid processing with a short average time per each block; similarly, the decryption technique, which includes the dictionary and AES decryption also renders the results in a justified computational time nominating it as a practical approach. It is worth noting that the small key era and dictionary construction instances provoke innovations in the general efficiency of the encryption mechanism. The conducted study, based on recorded values for PSNR, RMSE, UACI, and NPCR, demonstrates that the proposed multi-layer encryption system, which incorporates dictionary encryption and AES, is a robust and a fast method to shield important information and attaining a balance between protection and computational time.