Search Results (20,543)

This study aims to develop systematic multiscale models to accurately predict the compressive strength of cement mortar for tile adhesive applications, specifically tailored for applications in the construction industry. Drawing on data from 200 cement mortar tests conducted in previous studies, various factors such as cement/water ratios, curing times, cement/sand ratios, and chemical compositions were analyzed through static modeling techniques. The model selection involved utilizing various approaches, including linear regression, pure quadratic, interaction, M5P tree, and artificial neural network models to identify the most influential parameters affecting mortar strength. The analysis considered the water/cement ratio, testing ages, cement/sand ratio, and chemical compositions, such as silicon dioxide, calcium dioxide, iron (III) oxide, aluminum oxide, and the pH value. Evaluation metrics, such as the determination coefficient, mean absolute error, root-mean-square error, objective function, scatter index, and a-20 index, were employed to ensure the accuracy of the compressive strength estimates. Additionally, empirical equations were utilized to predict flexural and tensile strengths based on the compressive strength of the cement mortar for tile adhesive applications. Full article

Seasonally Dry Tropical Forests (SDTFs) are situated in regions prone to significant water deficits. This study aimed to evaluate and quantify the dynamics and spatial patterns of vegetation and water bodies through the analysis of physical–hydrological indices for a remnant of FTSD between 2013 and 2021. Basal area, biomass, and tree number were monitored in 80 permanent plots located in two areas of an SDTF remnant with different usage histories. To assess vegetation and water resource conditions, geospatial parameters NDVI, NDWIveg, NDWI, and MNDWI were estimated for the period from 2013 to 2021. The observed patterns were evaluated by simple linear regression, principal component analysis (PCA), and principal component regression (PCR). Area 2 presented higher values of basal area, biomass, and number of trees. In area 1, there was an annual increase in basal area and biomass, even during drought years. The NDVI and NDWIveg indicated the vulnerability of vegetation to the effects of droughts, with higher values recorded in 2020. NDWI and MNDWI detected the water availability pattern in the study area. Physical–hydrological indices in the dynamics of tree vegetation in dry forests are influenced by various factors, including disturbances, soil characteristics, and precipitation patterns. However, their predictive capacity for basal area, biomass, and tree number is limited, highlighting the importance of future research incorporating seasonal variability and specific local conditions into their analyses. Full article

Analysis of turbulent heat fluxes in urban forests is crucial for understanding structural variations in the urban sub-surface boundary layer. This study used data captured by an unmanned aerial vehicle (UAV) and an improved semi-empirical triangle method to estimate small-scale turbulent heat fluxes in the sub-surface of an urban forest. To improve the estimation accuracy, the surface temperature (T_S) of the UAV-based remote sensing inversion was corrected using the hot and cold spot correction method, and the process of calculating ${\varphi }_{max}$ using the traditional semi-empirical triangle method was improved to simplify the calculation process and reduce the number of parameters in the model. Based on this method, latent heat fluxes (LE) and sensible heat fluxes (H) were obtained with a horizontal resolution of 0.13 m at different time points in the study area. A comparison and validation with the measured values of the eddy covariance (EC) system showed that the absolute error of the LE estimates ranged from 4.43 to 23.11 W/m², the relative error ranged from 4.57% to 25.33%, the correlation coefficient (r) with the measured values was 0.95, and the root mean square error (RMSE) was 35.96 W/m², while the absolute error of the H estimates ranged from 3.42 to 15.45 W/m², the relative error ranged from 7.51% to 28.65%, r was 0.91, and RMSE was 9.77 W/m². Compared to the traditional triangle method, the r of LE was improved by 0.04, while that of H was improved by 0.06, and the improved triangle method was more accurate in estimating the heat fluxes of urban mixed forest ecosystems in the region. Using this method, it was possible to accurately track the LE and H of individual trees at the leaf level. Full article

It is well known that the first stage of mine-to-mill optimization is rock fragmentation by blasting. The degree of rock fragmentation can be expressed in terms of average grain (X₅₀) size and size distribution. There are approaches in which exponential functions are used to estimate the size distribution of the pile that will be formed before blasting. The most common of these exponential functions used to estimate the average grain size is the Kuz–Ram and KCO functions. The exponential functions provide a curve from 0% to 100% using the mean grain size (X₅₀), characteristic size (X_C), and uniformity index (n) parameters. This distribution curve can make predictions in the range of fine grains and coarse grains outside the acceptable error limits in some cases. In this article, the usability of the hyperbolic tangent function, which is symmetrical at origin, in the estimation of the size distribution as an alternative to the exponential distribution functions used in almost all estimation models is investigated. As with exponential functions, the hyperbolic tangent function can express the aggregated size distribution as a percentage with reference to the variables X₅₀ and X_C. It has been shown that the hyperbolic tangent function provides 99% accuracy to the distribution of fine grains and coarse grains of the pile formed as a result of blasting data for the characteristic size (X_C) parameter and the uniformity index (n). Full article

Induction motors dominate industrial applications due to their unwavering reliability. However, optimal vector control, critical for maximizing dynamic performance, hinges on accurate parameter estimation. This control strategy necessitates precise knowledge of the motor’s parameters, obtainable through experimentation or calculation based on its design specifications. Numerous methods, ranging from traditional to computational, have been proposed by various researchers, often relying on specific assumptions that might compromise the performance of modern motor control techniques. This paper meticulously reviews the most frequently utilized methods and presents experimental results from a single motor. We rigorously compare these results against established benchmark methods, including IEEE Standard 112-2017, and subsequently identify the superior approach, boasting a maximum error of only 6.5% compared to 19.65% for competing methods. Our study investigates the parameter estimation of induction motor. The methodology primarily utilizes RMS values for measurement tasks. Moreover, the impact of harmonics, particularly when an induction motor is supplied by an inverter is briefly addressed. The pioneering contribution of this work lies in pinpointing a more accurate parameter estimation method for enhanced vector control performance. These findings pave the way for exceptional vector control, particularly at lower speeds, ultimately elevating both vector control and drive performance. Full article

We present our results on two Z Cam stars: Z Cam and AT Cnc. We apply observational data for the periods that cover the states of outbursts and standstills, which are typical for this type of object. We report an appearance of periodic oscillations in brightness during the standstill in AT Cnc, with small-amplitude variations of 0.03–0.04 mag and periodicity of ≈20–30 min. Based on the estimated dereddened color index (B − V)₀, we calculate the color temperature for both states of the two objects. During the transition from the outburst to the standstill state, Z Cam varies from bluer to redder, while AT Cnc stays redder in both states. We calculate some of the stars’ parameters as the radii of the primary and secondary components and the orbital separation for both objects. We construct the profiles of the effective temperature in the discs of the two objects. Comparing the parameters of both systems, we see that Z Cam is definitely the hotter object and we conclude that it has a more active accretion disc. Full article

Large-scale yield estimation in the field or plot during wheat grain filling can contribute to high-throughput plant phenotyping and precision agriculture. To overcome the challenges of poor yield estimation at a large scale and for multiple species, this study employed a combination of multispectral and RGB drones to capture images and generation of time-series data on vegetation indices and canopy structure information during the wheat grubbing period. Five machine learning methods, partial least squares, random forest, support vector regression machine, BP neural networks, and long and short-term memory networks were used. The yield estimation of wheat grain filling period data was executed using a long and short-term memory network based on the preferred machine learning model, with a particular focus on distinguishing different heat-tolerant genotypes of wheat. The results unveiled a declining trend in the spectral reflectance characteristics of vegetation indices as the filling period progressed. Among the time-series data of the wheat filling period, the long and short-term memory network exhibited the highest estimation effectiveness, surpassing the BP neural network, which displayed the weakest estimation performance, by an impressive improvement in R² of 0.21. The three genotypes of wheat were categorized into heat-tolerant genotype, moderate heat-tolerant genotype, and heat-sensitive genotype. Subsequently, the long and short-term memory network, which exhibited the most accurate yield estimation effect, was selected for regression prediction. The results indicate that the yield estimation effect was notably better than that achieved without distinguishing genotypes. Among the wheat genotypes, the heat-sensitive genotype demonstrated the most accurate prediction with an R² of 0.91 and RMSE% of 3.25%. Moreover, by fusing the vegetation index with canopy structure information, the yield prediction accuracy (R²) witnessed an overall enhancement of about 0.07 compared to using the vegetation index alone. This approach also displayed enhanced adaptability to spatial variation. In conclusion, this study successfully utilized a cost-effective UAV for data fusion, enabling the extraction of canopy parameters and the application of a long and short-term memory network for yield estimation in wheat with different heat-tolerant genotypes. These findings have significant implications for informed crop management decisions, including harvesting and contingency forecasting, particularly for vast wheat areas. Full article

Background: The World Health Organization recommends health initiatives focused on the early detection of frailty and falls. Objectives: 1—To compare clinical characteristics, functional performance and gait parameters (estimated with the G-STRIDE inertial sensor) between different frailty groups in older adults with and without falls. 2—To identify variables that stratify participants according to frailty status and falls. 3—To verify the sensitivity, specificity and accuracy of the model that stratifies participants according to frailty status and falls. Methods: Observational, multicenter case-control study. Participants, adults over 70 years with and without falls were recruited from two outpatient clinics and three nursing homes from September 2021 to March 2022. Clinical variables and gait parameters were gathered using the G-STRIDE inertial sensor. Random Forest regression was applied to stratify participants. Results: 163 participants with a mean age of 82.6 ± 6.2 years, of which 118 (72%) were women, were included. Significant differences were found in all gait parameters (both conventional assessment and G-STRIDE evaluation). A hierarchy of factors contributed to the risk of frailty and falls. The confusion matrix and the performance metrics demonstrated high accuracy in classifying participants. Conclusions: Gait parameters, particularly those assessed by G-STRIDE, are effective in stratifying individuals by frailty status and falls. These findings underscore the importance of gait analysis in early intervention strategies. Full article

We propose a new methodology to transform a time series into an ordered sequence of any entropic and information functionals, providing a novel tool for data analysis. To achieve this, a new algorithm has been designed to optimize the Probability Density Function (PDF) associated with a time signal in the context of non-parametric Kernel Density Estimation (KDE). We illustrate the applicability of this method for anomaly detection in time signals. Specifically, our approach combines a non-parametric kernel density estimator with overlapping windows of various scales. Regarding the parameters involved in the KDE, it is well-known that bandwidth tuning is crucial for the kernel density estimator. To optimize it for time-series data, we introduce an adaptive solution based on Jensen–Shannon divergence, which adjusts the bandwidth for each window length to balance overfitting and underfitting. This solution selects unique bandwidth parameters for each window scale. Furthermore, it is implemented offline, eliminating the need for online optimization for each time-series window. To validate our methodology, we designed a synthetic experiment using a non-stationary signal generated by the composition of two stationary signals and a modulation function that controls the transitions between a normal and an abnormal state, allowing for the arbitrary design of various anomaly transitions. Additionally, we tested the methodology on real scalp-EEG data to detect epileptic crises. The results show our approach effectively detects and characterizes anomaly transitions. The use of overlapping windows at various scales significantly enhances detection ability, allowing for the simultaneous analysis of phenomena at different scales. Full article

This paper proposes a novel physics-based equivalent circuit model of the lithium-ion battery for electric vehicle applications that has comprehensive electrochemical significance and an acceptable level of complexity. Initially, the physics-based extended single particle (ESP) model is improved by adding a correction term to mitigate its voltage bias. Then, the equivalent circuit model based on the improved extended single particle (ECMIESP) model is derived. In this model, the surface state of charge (SOC) of solid particles is approximated using a capacity and multi first-order resistance-capacity equivalent circuits with only two lumped parameters. The overpotential of electrolyte diffusion is approximated using a first-order resistance-capacitance equivalent circuit. The electrochemical reaction overpotential is characterized by a nonlinear resistance. The voltage accuracies of ECMIESP and conventional 2RC equivalent circuit model (ECM2RC) are compared across the entire SOC range under various load profiles. The results demonstrate that the ECMIESP model outperforms ECM2RC model, particularly at low SOC or when the electrochemical reaction overpotential exceeds 50 mV. For instance, the ECMIESP model shows an 820.4 mV reduction in voltage error compared to the ECM2RC model at the endpoint during a 2C constant current discharge test. Lastly, the ECMIESP model was used for SOC estimation with extended Kalman filter, resulting in significantly improved accuracy compared to the conventional ECM2RC model. Therefore, the ECMIESP model has great potential for real-time applications in enhancing voltage and SOC estimation precision. Full article

In a pressurized water reactor (PWR) during a loss of coolant accident (LOCA) or a station blackout (SBO) accident, water and steam are released into the containment building. The water vapor mixes with the atmosphere, partially condensing into droplets or condensing on the containment walls. Although a significant amount of water vapor condenses, it coexists with hydrogen generated by the reactor core oxidation. As water vapor condenses, the volume fraction of hydrogen increases, raising the risk of explosion or flame acceleration. As such, water vapor’s behavior directly affects hydrogen distribution. To conservatively evaluate hydrogen safety in a PWR during a severe accident, lumped-parameter codes have been heavily used. As a best-estimate approach for hydrogen safety analysis in a PWR containment, a turbulence-resolved CFD code called contain3D has been developed. This paper presents the validation results of the code and simulation results of hydrogen behavior affected by water vapor condensation and hydrogen removal by passive autocatalytic recombiners (PARs) in the APR1400 containment. The results provide insight into the three-dimensional behaviors of the hydrogen in the containment. Full article

In detecting hypersonic vehicles, the radar echo signal is coupled with an intra-pulse Doppler frequency (I-D frequency) component caused by relative motion of a plasma sheath (PSh) and the vehicle, which can induce the phenomenon of a ghost target in a one-dimensional range profile. In order to investigate the I-D frequency generated by the relative motion of a PSh, this study transforms a linear frequency modulated (LFM) signal into a single carrier frequency signal based on echo signal equivalent time delay-dechirp processing and realizes high resolution and fast extraction of the I-D frequency coupled with the frequency-domain echo signal. Furthermore, by relying on the computation of the surface flow field of the RAMC-II Blunt Cone Reentry Vehicle, the coupled I-D frequency in the radar echo signal of a PSh-enveloped target under circumstances of typical altitudes and carrier frequencies is extracted and further investigated, revealing the variation law of I-D frequency. The key findings of this study provide a novel approach for suppressing anomalies in radar detection of PSh-enveloped targets as well as effective detecting and as robust target tracking. Full article

Traditional leaf chlorophyll estimation using Soil Plant Analysis Development (SPAD) devices and spectrophotometers is a high-cost mechanism in agriculture. Recently, research on chlorophyll estimation using leaf camera images and machine learning has been seen. However, these techniques use self-defined image color combinations where the system performance varies, and the potential utility has not been well explored. This paper proposes a new method that combines an improved contact imaging technique, the images’ original color parameters, and a 1-D Convolutional Neural Network (CNN) specifically for tea leaves’ chlorophyll estimation. This method utilizes a smartphone and flashlight to capture tea leaf contact images at multiple locations on the front and backside of the leaves. It extracts 12 different original color features, such as the mean of RGB, the standard deviation of RGB and HSV, kurtosis, skewness, and variance from images for 1-D CNN input. We captured 15,000 contact images of tea leaves, collected from different tea gardens across Assam, India to create a dataset. SPAD chlorophyll measurements of the leaves are included as true values. Other models based on Linear Regression (LR), Artificial Neural Networks (ANN), Support Vector Regression (SVR), and K-Nearest Neighbor (KNN) were also trained, evaluated, and tested. The 1-D CNN outperformed them with a Mean Absolute Error (MAE) of 2.96, Mean Square Error (MSE) of 15.4, Root Mean Square Error (RMSE) of 3.92, and Coefficient of Regression ($R^2$) of 0.82. These results show that the method is a digital replication of the traditional method, while also being non-destructive, affordable, less prone to performance variations, and simple to utilize for sustainable agriculture. Full article

In the current paper, we introduce new subclasses of analytic and bi-univalent functions involving Caputo-type fractional derivatives subordinating to the Lucas polynomial. Furthermore, we find non-sharp estimates on the first two Taylor–Maclaurin coefficients $\left|a_2\right|$ and $\left|a_3\right|$ for functions in these subclasses. Using the values of $\left|a_2\right|$ and $\left|a_3\right|$, we determined Fekete–Szegő inequality for functions in these subclasses. Moreover, we pointed out some more subclasses by fixing the parameters involved in Lucas polynomial and stated the estimates and Fekete–Szegő inequality results without proof. Full article

In this paper, the tribological characteristics of polyethersulfone-based composites reinforced with short carbon fibers (SCFs) at aspect ratios of 14–250 and contents of 10–30 wt.% are reported for linear metal–polymer and ceramic–polymer tribological contacts. The results showed that the wear resistance could be greatly improved through tribological layer formation. Loading PES with 30 wt.% SCFs (2 mm) provided a minimum WR value of 0.77 × 10⁻⁶ mm³/N m. The tribological layer thicknesses were estimated to be equal to 2–7 µm. Several conditions were proposed, which contributed to the formation of a tribological layer from debris, including the three-stage pattern of the changing kinetics of the time dependence of the friction coefficient. The kinetics had to sharply increase up to ~0.4–0.5 in the first (running-in) stage and gradually decrease down to ~0.1–0.2 in the second stage. Then, if these levels did not change, it could be argued that any tribological layer had formed, become fixed and fulfilled its functional role. The PES-based composites loaded with SCFs 2 mm long were characterized by possessing the minimum CoF levels, for which their three-stage changing pattern corresponded to one of the conditions for tribological layer formation. This work provides valuable insight for studying the process parameters of tribological layer formation for SCF-reinforced thermoplastic PES composites and revealing their impact on tribological properties. Full article