Relative increase in production ratio of small dense low-density lipoprotein in acute coronary syndrome with high coronary plaque burden: an ex-vivo analysis.

The absolute value of small dense low-density lipoprotein (sd-LDL) including small LDL (s-LDL) and very small LDL (vs-LDL) has been shown to be associated with increased incidence of atherosclerosis. However, the impact of short-timeframe increases in sd-LDL on arteriosclerosis has not yet been elucidated. Therefore, we investigated the clinical roles of ex-vivo induced sd-LDL in acute coronary syndrome (ACS) using a novel method. This is a prospective, single-blind, and observational study that screened patients who underwent coronary angiography (CAG) for the treatment of ACS or investigation of heart-failure etiology between June 2020 and April 2022 (n = 247). After excluding patients with known diabetes mellitus and advanced renal disease, the patients were further divided into the ACS (n = 34) and control (non-obstructive coronary artery, n = 34) groups. The proportion of sd-LDL (s-LDL + vs-LDL) in total lipoproteins was observed before and after 2-h incubation at 37 ℃ (to approximate physiologic conditions) using 3% polyacrylamide gel electrophoresis. The coronary plaque burden was quantified upon CAG in the ACS group. There were no significant differences between the ACS and control groups in terms of clinical coronary risk factors. The baseline of large, medium, small, and very small LDL were comparable between the two groups. Following a 2-h incubation period, significant increases were observed in the ratios of s-LDL and vs-LDL in both the ACS and control groups (ACS, p = 0.01*; control, p = 0.01*). Notably, the magnitude of increase in sd-LDL was more pronounced in the ACS group compared to the control group, with s-LDL showing a significant difference (p = 0.03*) and vs-LDL showing a tread toward significance (p = 0.08). In addition, in both groups, there was a decrease in IDL and L-LDL, while M-LDL remained unchanged. The plaque burden index and rate of short-timeframe changes in both s-LDL (p = 0.01*) and vs-LDL (p = 0.04*) before and after incubation were significantly correlated in the ACS group. The enhanced production rate of sd-LDL induced under short-term physiologic culture in an ex-vivo model was greater in patients with ACS than in the control group. The increase in sd-LDL is positively correlated with coronary plaque burden. Short-timeframe changes in sd-LDL may serve as markers for the severity of coronary artery disease.

Histopathological graded liver lesions: what role does the IVIM analysis method have?

PURPOSE: This study aims to investigate three different image processing methods on quantitative parameters of IVIM sequence, as well as apparent diffusion coefficients and simple perfusion fractions, for benign and malignant liver tumors. MATERIALS AND METHODS: IVIM images with 8 b-values (0-1000 s/mm2) and 1.5 T MRI scanner in 16 patients and 3 healthy people were obtained. Next, the regions of interest were selected for malignant, benign, and healthy liver regions (50, 56, and 12, respectively). Then, the bi-exponential equation of the IVIM technique was fitted with two segmented fitting methods as well as one full fitting method (three methods in total). Using the segmented fitting method, diffusion coefficient (D) is fixed with a mono-exponential equation with b-values that are greater than 200 s/mm2. The perfusion fraction (f) can then be calculated by extrapolating, as the first method, or fitting simultaneously with the pseudo-diffusion coefficient (D*) as the second method. In the full fitting method, as the third method, all IVIM parameters were obtained simultaneously. The mean values of parameters from different methods were compared in different grades of lesions. RESULTS: Our results indicate that the image processing method can change statistical comparisons between different groups for each parameter. The D value is the only quantity in this technique that does not depend on the fitting process and can be used as an indicator of comparison between studies (P < 0.05). The most effective method to distinguish liver lesions is the extrapolated f method (first method). This method created a significant difference (P < 0.05) between the perfusion parameters between benign and malignant lesions. CONCLUSION: Using extrapolated f is the most effective method of distinguishing liver lesions using IVIM parameters. The comparison between groups does not depend on the fitting method only for parameter D.

Advanced Fitting Method for the Kinetic Analysis of Thermogravimetric Data.

The article considered the solution of the inverse problem of chemical kinetics of the analysis of experimental data of a thermogravimetric experiment at a constant sample heating rate. The fitting method for identifying the parameters of a kinetic triplet using the integral method for a model of a solid-state reaction based on the modified Arrhenius equation is described. The effectiveness of the proposed approach was confirmed by solving test cases for low, medium, and high rates of material conversion. Unlike other methods, setting the parameters of the reaction mechanism is not required, as they are determined by the solution. Solutions for real data of TGA studies with high and low sample heating rates were compared with the results obtained by other authors and experimental data. A description of the full cycle of calculations used to identify kinetic parameters from thermogravimetric experimental data is given, from the derivation of calculated relationships to the implementation of a short (three to five formulas) program code for MS Excel spreadsheets. The presented code is easy to verify and reproduce and can be modified to solve various problems.

Residual behaviors and metabolic pathway of ethylparaben in Drosophila melanogaster.

OBJECTIVE: Parabens are commonly used as preservatives in foodstuffs, cosmetics, and pharmaceutical products. The widespread use of parabens has led to their leaking into the environment. Concerns about the safety of parabens have recently increased due to their potential endocrine-disrupting effects as an emerging contaminant. Thus, it is necessary to study the metabolism of parabens in vivo. METHODS: In this study, Drosophila melanogaster in males and females were exposed to ethylparaben (EP) concentration group (300 mg/L, 700 mg/L, and 1000 mg/L), and control group (0 mg/L) by the capillary feeding assay (CAFE). We quantified the activity of the detoxification-related carboxylesterase (CarE). The contents of EP metabolites in D. melanogaster, including p-hydroxybenzoic acid (PHBA), methylparaben (MP), and intact EP were carried out by high-performance liquid chromatography (HPLC). The regression model between EP metabolites (PHBA and MP) and CarE was developed using the Fourier series fitting method. RESULTS: The general level of EP metabolites (PHBA, MP, and intact EP) accumulation was accounted for 5.6-11.5% in D. melanogaster. As EP accumulated, the activity of CarE increased, and the activity of CarE in females was higher than males, which is inconsistent with the result of EP intake dose. Additionally, there were significant differences in the proportion of EP metabolites between female and male flies, and the results of sex comparison were different depending on the EP treated groups and EP metabolites. In general, PHBA of EP hydrolytic product and MP of EP transesterification product in D. melanogaster were 41.4-63.9% and 10.4-24.6%, respectively. In terms of the rest of the EP existed in intact form and ranged from 22.4% to 34.0%. Moreover, the EP metabolites in the conjugated form were higher than those in the free form. The regression model between EP metabolites and CarE was established, showing that the CarE activity can be used to estimate the content of PHBA and MP. CONCLUSION: The result indicates that the EP can accumulate in the body through food. Hydrolysis is the main metabolic pathway of EP in D. melanogaster, and transesterification is another metabolic pathway of EP. Additionally, the EP metabolites in flies mainly exist in conjugated form. Furthermore, the Fourier series fitting method model between EP metabolites and CarE, providing theoretical support to study the dose-effect relationship between metabolites of parabens and CarE. This study not only provides a mathematical basis for the safety evaluation of parabens, but also provides support for the further study of the toxicological effects of parabens.

Improving Performance of the Human Pupil Orbit Model (HPOM) Estimation Method for Eye-Gaze Tracking.

Eye-gaze direction-tracking technology is used in fields such as medicine, education, engineering, and gaming. Stability, accuracy, and precision of eye-gaze direction-tracking are demanded with simultaneous upgrades in response speed. In this study, a method is proposed to improve the speed with decreases in the system load and precision in the human pupil orbit model (HPOM) estimation method. The new method was proposed based on the phenomenon that the minor axis of the elliptical-deformed pupil always pointed toward the rotational center presented in various eye-gaze direction detection studies and HPOM estimation methods. Simulation experimental results confirmed that the speed was improved by at least 74 times by consuming less than 7 ms compared to the HPOM estimation. The accuracy of the eye's ocular rotational center point showed a maximum error of approximately 0.2 pixels on the x-axis and approximately 8 pixels on the y-axis. The precision of the proposed method was 0.0 pixels when the number of estimation samples (ES) was 7 or less, which showed results consistent with those of the HPOM estimation studies. However, the proposed method was judged to work conservatively against the allowable angle error (AAE), considering that the experiment was conducted under the worst conditions and the cost used to estimate the final model. Therefore, the proposed method could estimate HPOM with high accuracy and precision through AAE adjustment according to system performance and the usage environment.

Estimating the Characteristic Curve of a Directional Control Valve in a Combined Multibody and Hydraulic System Using an Augmented Discrete Extended Kalman Filter.

The estimation of the parameters of a simulation model such that the model's behaviour matches closely with reality can be a cumbersome task. This is due to the fact that a number of model parameters cannot be directly measured, and such parameters might change during the course of operation in a real system. Friction between different machine components is one example of these parameters. This can be due to a number of reasons, such as wear. Nevertheless, if one is able to accurately define all necessary parameters, essential information about the performance of the system machinery can be acquired. This information can be, in turn, utilised for product-specific tuning or predictive maintenance. To estimate parameters, the augmented discrete extended Kalman filter with a curve fitting method can be used, as demonstrated in this paper. In this study, the proposed estimation algorithm is applied to estimate the characteristic curves of a directional control valve in a four-bar mechanism actuated by a fluid power system. The mechanism is modelled by using the double-step semi-recursive multibody formulation, whereas the fluid power system under study is modelled by employing the lumped fluid theory. In practise, the characteristic curves of a directional control valve is described by three to six data control points of a third-order B-spline curve in the augmented discrete extended Kalman filter. The results demonstrate that the highly non-linear unknown characteristic curves can be estimated by using the proposed parameter estimation algorithm. It is also demonstrated that the root mean square error associated with the estimation of the characteristic curve is 0.08% with respect to the real model. In addition, all the errors in the estimated states and parameters of the system are within the 95% confidence interval. The estimation of the characteristic curve in a hydraulic valve can provide essential information for performance monitoring and maintenance applications.

Detrending Technique for Denoising in CW Radar.

A detrending technique is proposed for continuous-wave (CW) radar to remove the effects of direct current (DC) offset, including DC drift, which is a very slow noise that appears near DC. DC drift is mainly caused by unwanted vibrations (generated by the radar itself, target objects, or surroundings) or characteristic changes in components in the radar owing to internal heating. It reduces the accuracy of the circle fitting method required for I/Q imbalance calibration and DC offset removal. The proposed technique effectively removes DC drift from the time-domain waveform of the baseband signals obtained for a certain time using polynomial fitting. The accuracy improvement in the circle fitting by the proposed technique using a 5.8 GHz CW radar decreases the error in the displacement measurement and increases the signal-to-noise ratio (SNR) in vital signal detection. The measurement results using a 5.8 GHz radar show that the proposed technique using a fifth-order polynomial fitting decreased the displacement error from 1.34 mm to 0.62 mm on average when the target was at a distance of 1 m. For a subject at a distance of 0.8 m, the measured SNR improved by 7.2 dB for respiration and 6.6 dB for heartbeat.

Evaluation of Vegetation Index-Based Curve Fitting Models for Accurate Classification of Salt Marsh Vegetation Using Sentinel-2 Time-Series.

The successful launch of the Sentinel-2 constellation satellite, along with advanced cloud detection algorithms, has enabled the generation of continuous time series at high spatial and temporal resolutions, which is in turn expected to enable the classification of salt marsh vegetation over larger spatiotemporal scales. This study presents a critical comparison of vegetation index (VI) and curve fitting methods-two key factors for time series construction that potentially influence vegetation classification performance. To accomplish this objective, the stability of five different VI time series, namely Normalized Difference Vegetation Index (NDVI), Soil-Adjusted Vegetation Index (SAVI), Enhanced Vegetation Index (EVI), Green Normalized Difference Vegetation Index (GNDVI), and Water-Adjusted Vegetation Index (WAVI), was compared empirically; the suitability between three curve fitting methods, namely Asymmetric Gaussian (AG), Double Logistic (DL), and Two-term Fourier (TF), and VI time series was measured using the coefficient of determination, and the salt marsh vegetation separability among different combinations of VI time series and curve fitting methods (i.e., VI time series-based curve fitting model) was quantified using overall the Jeffries-Matusita distance. Six common types of salt marsh vegetation from three typical coastal sites in China were used to validate these findings, which demonstrate: (1) the SAVI performed best in terms of time series stability, while the EVI exhibited relatively poor time series stability with conspicuous outliers induced by the sensitivity to omitted clouds and shadows; (2) the DL method commonly resulted in the most accurate classification of different salt marsh vegetation types, especially when combined with the EVI time series, followed by the TF method; and (3) the SAVI/NDVI-based DL/TF model demonstrated comparable efficiency for classifying salt marsh vegetation. Notably, the SAVI/NDVI-based DL model performed most strongly for high latitude regions with a continental climate, whilst the SAVI/NDVI-based TF model appears to be better suited to mid- to low latitude regions dominated by a monsoonal climate.

Nanometer-Scale Vibration Measurement Using an Optical Quadrature Interferometer based on 3 × 3 Fiber-Optic Coupler.

We propose a nanometer-scale displacement or vibration measurement system, using an optical quadrature interferometer and the post-processing technique that extracts the parameters necessary for characterizing the interferometric system. Using a 3 × 3 fiber-optic coupler, the entire complex interference signal could be reconstructed with two interference signals measured at two return ports of the coupler. The intrinsic phase difference between the return ports was utilized to obtain the quadratic part of the interference signal, which allowed one to reconstruct the entire complex interference signal. However, the two measured signals were appreciably affected by the unequal detector gains and non-uniform intrinsic phases of the coupler. Fortunately, we could find that the Lissajous curve plotted by the two signals of the interferometric system would form an ellipse. Therefore, by fitting the measured Lissajous curve to an ellipse, we could extract the parameters characterizing the actual system, which allowed the nanometer-scale measurement. Experimental results showed that a 20 kHz sinusoidal vibration with an amplitude of 1.5 nm could be measured with a standard deviation of 0.4 nm.

Kinetic studies on the pyrolysis of plastic waste using a combination of model-fitting and model-free methods.

In this work, the pyrolysis behavior of plastic waste-TV plastic shell-was investigated, based on thermogravimetric analysis and using a combination of model-fitting and model-free methods. The possible reaction mechanism and kinetic compensation effects were also examined. Thermogravimetric analysis indicated that the decomposition of plastic waste in a helium atmosphere can be divided into three stages: the minor loss stage (20-300°C), the major loss stage (300-500°C) and the stable loss stage (500-1000°C). The corresponding weight loss at three different heating rates of 15, 25 and 35 K/min were determined to be 2.80-3.02%, 94.45-95.11% and 0.04-0.16%, respectively. The activation energy (Ea) and correlation coefficient (R2) profiles revealed that the kinetic parameters calculated using the Friedman and Kissinger-Akahira-Sunose method displayed a similar trend. The values from the Flynn-Wall-Ozawa and Starink methods were comparable, although the former gave higher R2 values. The Eα values gradually decreased from 269.75 kJ/mol to 184.18 kJ/mol as the degree of conversion (α) increased from 0.1 to 0.8. Beyond this range, the Eα slightly increased to 211.31 kJ/mol. The model-fitting method of Coats-Redfern was used to predict the possible reaction mechanism, for which the first-order model resulted in higher R2 values than and comparable Eα values to those obtained from the Flynn-Wall-Ozawa method. The pre-exponential factors (lnA) were calculated based on the F1 reaction model and the Flynn-Wall-Ozawa method, and fell in the range 59.34-48.05. The study of the kinetic compensation effect confirmed that a compensation effect existed between Ea and lnA during the plastic waste pyrolysis.

Exploring the spatial relationship between airborne-derived red and far-red sun-induced fluorescence and process-based GPP estimates in a forest ecosystem.

Terrestrial gross primary productivity (GPP) plays an essential role in the global carbon cycle, but the quantification of the spatial and temporal variations in photosynthesis is still largely uncertain. Our work aimed to investigate the potential of remote sensing to provide new insights into plant photosynthesis at a fine spatial resolution. This goal was achieved by exploiting high-resolution images acquired with the FLuorescence EXplorer (FLEX) airborne demonstrator HyPlant. The sensor was flown over a mixed forest, and the images collected were elaborated to obtain two independent indicators of plant photosynthesis. First, maps of sun-induced chlorophyll fluorescence (F), a novel indicator of plant photosynthetic activity, were successfully obtained at both the red and far-red peaks (r2 = 0.89 and p < 0.01, r2 = 0.77 and p < 0.01, respectively, compared to top-of-canopy ground-based measurements acquired synchronously with the overflight) over the forested study area. Second, maps of GPP and absorbed photosynthetically active radiation (APAR) were derived using a customised version of the coupled biophysical model Breathing Earth System Simulator (BESS). The model was driven with airborne-derived maps of key forest traits (i.e., leaf chlorophyll content (LCC) and leaf area index (LAI)) and meteorological data providing a high-resolution snapshot of the variables of interest across the study site. The LCC and LAI were accurately estimated (RMSE = 5.66 µg cm-2 and RMSE = 0.51 m2m-2, respectively) through an optimised Look-Up-Table-based inversion of the PROSPECT-4-INFORM radiative transfer model, ensuring the accurate representation of the spatial variation of these determinants of the ecosystem's functionality. The spatial relationships between the measured F and modelled BESS outputs were then analysed to interpret the variability of ecosystem functioning at a regional scale. The results showed that far-red F is significantly correlated with the GPP (r2 = 0.46, p < 0.001) and APAR (r2 = 0.43, p < 0.001) in the spatial domain and that this relationship is nonlinear. Conversely, no statistically significant relationships were found between the red F and the GPP or APAR (p > 0.05). The spatial relationships found at high resolution provide valuable insight into the critical role of spatial heterogeneity in controlling the relationship between the far-red F and the GPP, indicating the need to consider this heterogeneity at a coarser resolution.

Two-Parameter Elliptical Fitting Method for Short-Cavity Fiber Fabry⁻Perot Sensor Interrogation.

To solve the cavity interrogation problem of short cavity fiber Fabry⁻Perot sensors in white light spectral interrogation with amplified spontaneous emissions (ASEs) as the white light sources, a data processing method, using an improved elliptical fitting equation with only two undetermined coefficients, is proposed. Based on the method, the cavity length of a fiber Fabry⁻Perot sensor without a complete reflection spectrum period in the frequency domain can be interrogated with relatively high resolution. Extrinsic fiber Fabry⁻Perot air-gap sensors with cavity lengths less than 30 µm are used to experimentally verify the method, and are successfully interrogated with an accuracy better than 0.55%.

A new method to estimate photosynthetic parameters through net assimilation rate-intercellular space CO2 concentration (A-Ci ) curve and chlorophyll fluorescence measurements.

Gas exchange (GE) and chlorophyll fluorescence (CF) measurements are widely used to noninvasively study photosynthetic parameters, for example the rates of maximum Rubisco carboxylation (Vcmax ), electron transport rate (J), daytime respiration (Rd ) and mesophyll conductance (gm ). Existing methods for fitting GE data (net assimilation rate-intercellular space CO2 concentration (A-Ci ) curve) are based on two assumptions: gm is unvaried with CO2 concentration in the intercellular space (Ci ); and light absorption (α) and the proportion of quanta absorbed by photosystem II (ß) are constant in the data set. These may result in significant bias in estimating photosynthetic parameters. To avoid the above-mentioned hypotheses, we present a new method for fitting A-Ci curves and CF data simultaneously. This method was applied to a data set obtained from cucumber (Cucumis sativus) leaves of various leaf ages and grown under eight different light conditions. The new method had significantly lower root mean square error and a lower rate of failures compared with previously published methods (6.72% versus 24.1%, respectively) and the effect of light conditions on Vcmax and J was better observed. Furthermore, the new method allows the estimation of a new parameter, the fraction of incoming irradiance harvested by photosystem II, and the dependence of gm on Ci .

A New Method to Estimate Changes in Glacier Surface Elevation Based on Polynomial Fitting of Sparse ICESat-GLAS Footprints.

We present in this paper a polynomial fitting method applicable to segments of footprints measured by the Geoscience Laser Altimeter System (GLAS) to estimate glacier thickness change. Our modification makes the method applicable to complex topography, such as a large mountain glacier. After a full analysis of the planar fitting method to characterize errors of estimates due to complex topography, we developed an improved fitting method by adjusting a binary polynomial surface to local topography. The improved method and the planar fitting method were tested on the accumulation areas of the Naimona'nyi glacier and Yanong glacier on along-track facets with lengths of 1000 m, 1500 m, 2000 m, and 2500 m, respectively. The results show that the improved method gives more reliable estimates of changes in elevation than planar fitting. The improved method was also tested on Guliya glacier with a large and relatively flat area and the Chasku Muba glacier with very complex topography. The results in these test sites demonstrate that the improved method can give estimates of glacier thickness change on glaciers with a large area and a complex topography. Additionally, the improved method based on GLAS Data and Shuttle Radar Topography Mission-Digital Elevation Model (SRTM-DEM) can give estimates of glacier thickness change from 2000 to 2008/2009, since it takes the 2000 SRTM-DEM as a reference, which is a longer period than 2004 to 2008/2009, when using the GLAS data only and the planar fitting method.

Simultaneous estimation of size, radial and angular locations of a malignant tumor in a 3-D human breast - A numerical study.

This article reports a numerical study pertaining to simultaneous estimation of size, radial location and angular location of a malignant tumor in a 3-D human breast. The breast skin surface temperature profile is specific to a tumor of specific size and location. The temperature profiles are always the Gaussian one, though their peak magnitudes and areas differ according to the size and location of the tumor. The temperature profiles are obtained by solving the Pennes bioheat equation using the finite element method based solver COMSOL 4.3a. With temperature profiles known, simultaneous estimation of size, radial location and angular location of the tumor is done using the curve fitting method. Effect of measurement errors is also included in the study. Estimations are accurate, and since in the inverse analysis, the curve fitting method does not require solution of the governing bioheat equation, the estimation is very fast.

Correlation analysis and predicting modeling of pyrolysis gas based on landfill excavated waste pyrolysis characteristics.

The contribution of excavated waste to waste management is multifaceted, including minimization, non-hazardous disposal, access to useable land resources, improved waste management techniques and public environmental awareness, consistent with recent circular economy initiatives. Pyrolysis can be converted into tar, pyrolysis gas and char with recyclable utilization, enriching the application of pyrolysis technology in the field of excavation waste. In this study, the pyrolysis system includes horizontal tube furnace, gas collection device and Micro GC. The excavated waste was pyrolyzed at a temperature of 500∼900 °C with a heating rate of 10 °C/min. Pyrolysis gases include H2, CO, CO2, CH4, C2H4, C2H6 and C3H8. Pyrolysis was divided into four stages, the main decomposition range is 230∼500 °C, with a weight loss rate of 68.49% and a co-pyrolysis behavior. As the temperature increases, the tar and char decreased and the gas production increased significantly, and the pyrolysis gas reached 47.02% at 900 °C. According to Pearson correlation coefficient analysis, the generation of H2 and CO is positively correlated with temperature. Therefore, the target products can be influenced by changing the parameters, when considering the practical utilization of the excavated waste pyrolysis products. On this basis, the prediction models were built by polynomial fitting method. This model can reduce the experimental exploration cycle, reduce the cost, and accurately predict the pyrolysis gas, which has practical guidance for the application of pyrolysis industry, and provides a theoretical basis for the resource recycling and energy recovery of landfill.

On the gamma radiation response of commercially available 3D printing materials for medical dosimetry.

3D printing technology has rapidly spread for decades, allowing the fabrication of medical implants and human phantoms and revolutionizing healthcare. The objective of this study is to evaluate some radiological properties of commercially available 3D printing materials as potential tissue mimicking materials. Among fifteen materials, we compared their properties with nine human tissues. In all materials and tissues, exposure and energy absorption buildup factors were calculated for photon energies between 0.015 and 15 MeV and penetration depths up to 40 mean free path. Furthermore, the Geant4 Monte Carlo toolkit (version 10.5) was used to simulate their percentage depth dose distributions. In addition, equivalent atomic numbers, effective atomic numbers, attenuation coefficients, and CT numbers have been examined. All parameters were considered in calculating the average relative error (σ), which was used as a statistical comparison tool. With σ between 6 and 7, we found that Polylactic Acid (PLA) was capable of simulating eye lenses, blood, soft tissue, lung, muscle, and brain tissues. Moreover, Polymethacrylic Acid (PMAA) material has a σ value of 4 when modeling adipose and breast tissues, respectively. Aside from that, variations in 3D printing materials' infilling percentage can affect their CT numbers. We therefore suggest the PLA for mimicking soft tissue, muscle, brain, eye lens, lung and blood tissues, with an infill of between 92.7 and 94.3 percent. We also suggest an 89 percent infill when simulating breast tissue. Furthermore, with a 96.7 percent infill, the PMAA faithfully replicates adipose tissue. Additionally, we found that a 59 percent infill of Fe-PLA material is comparable to cortical bone. Due to the benefits of creating individualized medical phantoms and equipment, the results might be seen as an added value for both patients and clinicians.

Research on WSN reliable ranging and positioning algorithm for forest environment.

Wireless sensor network (WSN) location is a significant research area. In complex environments like forests, inaccurate signal intensity ranging is a major challenge. To address this issue, this paper presents a reliable WSN distance measurement-positioning algorithm for forest environments. The algorithm divides the positioning area into several sub-regions based on the discrete coefficient of the collected signal strength. Then, using the fitting method based on the signal intensity value of each sub-region, the algorithm derives the reference points of the logarithmic distance path loss model and path loss index. Finally, the algorithm locates target nodes using anchor nodes in different regions. Additionally, to enhance the positioning accuracy, weight values are assigned to the positioning result based on the discrete coefficient of the signal intensity in each sub-region. Experimental results demonstrate that the proposed WSN algorithm has high precision in forest environments.

Identification effect of least square fitting method in archives management.

Archives management plays an important role in the current information age. Solving the problem of identifying and classifying archives is essential for promoting the development of archives management. The Least Squares Support Vector Machine (LS-SVM) is obtained by introducing the least squares fitting method into SVM, which is good at solving nonlinear classification. A new wavelet function is used to improve the classifier. At the same time, the cross-validation method is used to optimize the kernel parameters. Finally, the fuzzy theory and LS-SVM are combined to obtain Fuzzy Least Squares Support Vector Machines (FLS-SVM). This FLS-SVM classifier can use the distance between the data points and the classification hyperplane to classify the data in the non-separable region. The performance of FLS-SVM is verified by simulation experiments. The experimental results show that the classification accuracy of FLS-SVM classifier in archive data sets is 98.7%, and the loss rate is only 0.26%. When the wavelet function is used as the kernel function, the average accuracy of the classifier reaches 98.38%. Experiments show that the proposed method has good classification performance. It verifies the feasibility and effectiveness of the least squares fitting method in file management identification and classification.

Comparison of Three Prediction Models for Predicting Chronic Obstructive Pulmonary Disease in China.

Purpose: To predict the future number of patients with chronic obstructive pulmonary disease (COPD) in China and compare the three prediction models. Methods: A generalized additive model (GAM), autoregressive integrated moving average (ARIMA) model, and curve-fitting method were used to fit and predict the number of patients with COPD in China. Data on the number of patients with COPD in China from 1990 to 2019 were obtained from the Global Burden of Disease (GBD) database. The coefficient of determination (R2), mean absolute error (MAE), mean absolute percentage error (MAPE), root mean squared error (RMSE), relative error of prediction, Akaike Information Criterion (AIC), and Bayesian Information Criterion (BIC) were used to evaluate and compare the fitting effect, prediction effect, and reliability of the three models. Results: The GAM, ARIMA, and curve-fitting methods could predict future trends in COPD in China. The performance of the GAM is the best among the three models, whereas the curve fitting method is the worst, and the ARIMA (0,1,2) model is in between. The prediction results of the three models showed that the number of patients with COPD in China is expected to increase from 2020 to 2025. Conclusion: GAM and AIRMA models are recommended for predicting the future prevalence of COPD in China. The number of patients with COPD in China is expected to increase in the next few years. The prevention and control of COPD in China still needs to be strengthened. Using appropriate models to predict future trends in COPD will provide support for health policymakers.