Search Results (47)

Objective: This study investigated the shaping efficiency of four reciprocating systems—WaveOne Gold, Reciproc Blue, Excalibur, and One Reci—in three dimensional (3D) resin models of natural mandibular premolar teeth with two canals. Methods: Forty 3D-printed mandibular premolars (Vertucci configuration type V) were divided into four groups, each of which was assigned one of the reciprocating systems. According to the manufacturer’s protocols, each canal was prepared, with pre- and post-instrumentation micro computed tomography (micro-CT) scans evaluating canal volume, surface area, percentage of unprepared canal walls, and resin reduction in the pericervical area. Instrumentation time and screw-in sensation were recorded as qualitative performance indicators. Statistical analysis was performed using one-way ANOVA and chi-square tests with a significance of (p < 0.05). Results: All systems increased canal volume and surface area, primarily in the apical third, with Reciproc Blue and One Reci achieving the largest volume. WaveOne Gold had the highest percentage of unprepared walls (27.03%) and Reciproc Blue the lowest (19.65%), though these differences were not statistically significant (p > 0.05). Reciproc Blue caused the highest pericervical resin loss (22.24%), significantly higher than Excalibur (15.09%) and One Reci (15.17%) (p = 0.035). Reciproc Blue exhibited the highest incidence of screw-in sensation (70%), while WaveOne Gold achieved the shortest instrumentation time (86.7 s), although neither variable showed statistical significance. Conclusions: All systems effectively shaped complex canal anatomies, with Reciproc Blue demonstrating the highest dentin removal and WaveOne Gold proving the most time efficient. Clinically, these findings suggest that instrument selection should balance shaping efficiency with dentin preservation. Minimizing unprepared areas and preserving pericervical dentin are essential for enhancing disinfection and reducing the risk of root fractures, ultimately contributing to the long-term success of endodontic treatment. Full article

Monitoring dust on plant canopies around open-pit coal mines is crucial to assessing environmental pollution and developing effective dust suppression strategies. This research focuses on the Ha’erwusu open-pit coal mine in Inner Mongolia, China, using measured dust content on plant canopies and UAV-borne VNIR hyperspectral data as the data sources. The study employed five spectral transformation forms—first derivative (FD), second derivative (SD), logarithm transformation (LT), reciprocal transformation (RT), and square root (SR)—alongside the competitive adaptive reweighted sampling (CARS) method to extract characteristic bands associated with canopy dust. Various regression models, including extreme learning machine (ELM), random forest (RF), partial least squares regression (PLSR), and support vector machine (SVM), were utilized to establish dust inversion models. The spatial distribution of canopy dust was then analyzed. The results demonstrate that the geometric and radiometric correction of the UAV-borne VNIR hyperspectral images successfully restored the true spatial information and spectral features. The spectral transformations significantly enhance the feature information for canopy dust. The CARS algorithm extracted characteristic bands representing 20 to 30% of the total spectral bands, evenly spread across the entire range, thereby reducing the estimation model’s computational complexity. Both feature extraction and model selection influence the inversion accuracy, with the LT-CARS and RF combination offering the best predictive performance. Canopy dust content decreases with increasing distance from the dust source. These findings offer valuable insights for canopy dust retention monitoring and offer a solid foundation for dust pollution management and the development of suppression strategies. Full article

The core of saline–alkali soil improvement lies in salt leaching by water and reducing alkalinity by improved materials such as acid material or desulphurized gypsum. This study conducted simulation experiments to clarify the impact of furfural residue combined with desulfurization gypsum on saline–alkali soil water–salt and infiltration characteristics in Ningxia. Based on a consistent leaching water volume of 4500 m³/hm² and a furfural residue application amount of 7.5 t/hm², the experiment established three desulfurization gypsum application amounts of 15 t/hm², 22.5 t/hm², and 30 t/hm², with a control group that received no improved materials. The effects of different application amounts of desulfurization gypsum on water and salt distributions, alkalinity, infiltration rate, cumulative infiltration volume, and wetting front of saline–alkali soil were elucidated, and the Philip infiltration model was employed to fit the variations in cumulative infiltration volume. The results indicated the following: (1) Compared to the control group, the application of furfural residue and desulfurization gypsum resulted in an average reduction of 36.7% in soil alkalinity. The enhanced hydraulic conductivity of saline–alkali soil promoted the infiltration of water into deeper soil layers. The desalination effect in the 0–60 cm soil layer was significant; however, excessive application of desulfurization gypsum could lead to the accumulation of salts in soil layers below 80 cm. (2) The downward movement depth of the wetting front, cumulative infiltration volume, and infiltration rate all demonstrated a power function relationship with the infiltration time, with a coefficient of determination (R²) greater than 0.97. Additionally, the infiltration rate exhibited a linear correlation with the square root of the reciprocal of infiltration time, achieving an R² exceeding 0.99. (3) The Philip infiltration model is suitable for describing the relationship between cumulative infiltration volume and infiltration time. Therefore, the application of 7.5 t/hm² of furfural residue and 22.5 t/hm² of desulfurization gypsum can effectively improve the saline–alkali soils in Ningxia. Full article

Recent advancements in hyperspectral imaging have significantly increased the acquired data volume, creating a need for more efficient compression methods for handling the growing storage and transmission demands. These challenges are particularly critical for onboard satellite systems, where power and computational resources are limited, and real-time processing is essential. In this article, we present a novel FPGA-based hardware acceleration of a near-lossless compression technique for hyperspectral images by leveraging a division-free quadrature-based square rooting method. In this regard, the two division operations inherent in the original approach were replaced with pre-computed reciprocals, multiplications, and a geometric series expansion. Optimized for real-time applications, the synthesis results show that our approach achieves a high throughput of 1611.77 Mega Samples per second (MSps) and a low power requirement of 0.886 Watts on the economical Cyclone V FPGA. This results in an efficiency of 1819.15 MSps/Watt, which, to the best of our knowledge, surpasses recent state-of-the-art hardware implementations in the context of near-lossless compression of hyperspectral images. Full article

Rapidly obtaining information on the content and spatial distribution of soil organic carbon (SOC) in farmland is crucial for evaluating regional soil quality, land degradation, and crop yield. This study focuses on mountain soils in various crop cultivation areas in Shangzhou District, Shangluo City, Southern Shaanxi, utilizing ZY1-02D hyperspectral satellite imagery, field-measured hyperspectral data, and field sampling data to achieve precise inversion and spatial mapping of the SOC content. First, to address spectral bias caused by environmental factors, the Spectral Space Transformation (SST) algorithm was employed to establish a transfer relationship between measured and satellite image spectra, enabling systematic correction of the image spectra. Subsequently, multiple spectral transformation methods, including continuous wavelet transform (CWT), reciprocal, first-order derivative, second-order derivative, and continuum removal, were applied to the corrected spectral data to enhance their spectral response characteristics. For feature band selection, three methods were utilized: Variable Importance Projection (VIP), Competitive Adaptive Reweighted Sampling (CARS), and Stepwise Projection Algorithm (SPA). SOC content prediction was conducted using three models: partial least squares regression (PLSR), stepwise multiple linear regression (Step-MLR), and random forest (RF). Finally, leave-one-out cross-validation was employed to optimize the L4-CARS-RF model, which was selected for SOC spatial distribution mapping. The model achieved a coefficient of determination (R²) of 0.81, a root mean square error of prediction (RMSEP) of 1.54 g kg⁻¹, and a mean absolute error (MAE) of 1.37 g kg⁻¹. The results indicate that (1) the Spectral Space Transformation (SST) algorithm effectively eliminates environmental interference on image spectra, enhancing SOC prediction accuracy; (2) continuous wavelet transform significantly reduces data noise compared to other spectral processing methods, further improving SOC prediction accuracy; and (3) among feature band selection methods, the CARS algorithm demonstrated the best performance, achieving the highest SOC prediction accuracy when combined with the random forest model. These findings provide scientific methods and technical support for SOC monitoring and management in mountainous areas and offer valuable insights for assessing the long-term impacts of different crops on soil ecosystems. Full article

Engineering practice has demonstrated that seal failure can result in severe leakage and wear, reducing the efficiency of hydraulic systems and even leading to major safety risks. Currently, analyses of the thermal aspect of seal interfaces are relatively limited, with most studies focusing on mechanical analysis. However, in actual applications, temperature has a significant impact on sealing performance. In this paper, nonlinear elastomechanics, viscous fluid mechanics, micro-contact mechanics, micro-deformation theory, and thermodynamics are coupled to establish a mixed lubrication model considering the thermal effect. The reliability of the mixed lubrication model is verified through experiments, and the temperature distribution of the oil film in the sealing area and the temperature distribution of the seal ring are simulated. Finally, the effects of the reciprocating speed, root mean square roughness, fluid medium pressure, and seal pre-compression on seal friction force and leakage are investigated. The results show that the heat generated in the sealing area accumulates at the bottom of the V-ring. Under the same conditions, compared with the instroke, the temperature-rise area of the outstroke is biased to the left and the increase in temperature is greater. In addition, the piston rod speed and the preliminary compression of the seal ring have a greater impact on the overall seal friction force and leakage. Under a lower seal pre-compression, the RMS roughness has a great influence on the leakage and friction in the outstroke, while the impact of the internal stroke is limited. Full article

Cation exchange capacity is a crucial metric for managing soil fertility and promoting agricultural sustainability. An alternative technique for the non-destructive assessment of important soil parameters is reflectance spectroscopy. The main focus of this paper is on how to analyze and predict the content of various soil cation exchange capacities (CEC) in arid conditions (Sohag governorate, Egypt) at a low cost using laboratory analysis of CEC, visible near-infrared and shortwave infrared (Vis-NIR) spectroscopy, partial least-squares regression (PLSR), and Ordinary Kriging (OK). Utilizing reflectance spectroscopy with a spectral resolution of 10 nm and laboratory studies with a spectral range of 350 to 2500 nm, 104 surface soil samples were collected to a depth of 30 cm in the Sohag governorate, Egypt (which is part of the dry region of North Africa), in order to accomplish this goal. The association between the spectroradiometer and CEC averaged values was modeled using PLSR in order to map the predicted value using Ordinary Kriging (OK). Thirty-one soil samples were selected for validation. The predictive validity of the cross-validated models was evaluated using the coefficient of determination (R²), root mean square error (RMSE), residual prediction deviation (RPD), and ratio of performance to interquartile distance (RPIQ). The results indicate that ten transformation methods yielded calibration models that met the study’s requirements, with R² > 0.6, RPQ > 2.5, and RIQP > 4.05. For evaluating CEC in Vis-NIR spectra, the most efficient transformation and calibration model was the reciprocal of Log R transformation (R² = 0.98, RMSE = 0.40, RPD = 6.99, and RIQP = 9.22). This implies that combining the reciprocal of Log R with PLSR yields the optimal model for predicting CEC values. The CEC values were best fitted by four models: spherical, exponential, Gaussian, and circular. The methodology used here does offer a “quick”, inexpensive tool that can be broadly and quickly used, and it can be readily implemented again in comparable conditions in arid regions. Full article

This study assessed the surface topography and plastic deformation (PD) of new and used contemporary reciprocating instruments. Twenty-six WaveOne Gold (WOG) and EdgeOne Fire (EO) instruments were photographed under magnification. The instruments were randomly assigned to a control group of new instruments preserved for surface roughness analysis (n = 6 each), or to an experimental group to shape the root canal system of a single molar (n = 20 each), making a total of four groups (WOGnew, EOnew, WOGused, EOused). Used instruments were also photographed after instrumentation. The presence of fractures was registered. Preoperative and postoperative images were randomly ordered for evaluation. Two blinded calibrated examiners evaluated the presence of PD. Inter-observer agreement was calculated with the Kappa coefficient (K = 0.89). 3D profilometry was also used for the surface roughness analysis of six randomly selected instruments from the WOGused and EOused groups. Chi-square and two-way ANOVA tests were used to, respectively, compare PD and changes in surface roughness among the groups. No instruments fractured; however, a significantly greater percentage of EO instruments suffered plastic deformation than WOG instruments (p < 0.001), (OR = 11.09 (CI 95% 2.6–56.3)). The overall surface roughness was higher for most parameters in the EO instruments (p < 0.05). Single uses of EO instruments produced significantly higher chances of PD and increased surface roughness values compared to WOG. Full article

A dense and smooth aluminium nitride thin film grown on a silicon (111) substrates using pulsed metal–organic chemical vapor deposition is presented. The influence of the pulsed cycle numbers on the surface morphology and crystalline quality of the aluminium nitride films are discussed in detail. It was found that 70 cycle numbers produced the most optimized aluminium nitride films. Field emission scanning electron microscopy and atomic force microscopy images show a dense and smooth morphology with a root-mean-square-roughness of 2.13 nm. The narrowest FWHM of the X-ray rocking curve for the AlN 0002 and 10–12 reflections are 2756 arcsec and 3450 arcsec, respectively. Furthermore, reciprocal space mapping reveals an in-plane tensile strain of 0.28%, which was induced by the heteroepitaxial growth on the silicon (111) substrate. This work provides an alternative approach to grow aluminium nitride for possible application in optoelectronic and power devices. Full article

Reciprocating motion expands the lifetime of endodontic instruments during the preparation of severely curved root canals. This study aimed to investigate the time to fracture (TTF) and number of cycles to failure (NCF) of different reciprocating instruments (n = 20 in each group) at body temperature using a dynamic testing model (amplitude = 3 mm). Reciproc Blue (RPB), size 25/.08, WaveOne Gold (WOG) 25/.07, Procodile (Proc) 25/.06, R-Motion (RM_06) 25/.06 and R-Motion (RM_04) 30/.04 instruments were tested in their specific reciprocating motion in artificial matching root canals (size of the instrument ± 0.02 mm; angle of curvature 60°, radius 5.0 mm, and centre of curvature 5.0 mm from apical endpoint). The number of fractured instruments, TTF, NCF, the and lengths of the fractured instruments were recorded and statistically analysed using the Chi-Square or Kruskal–Wallis test. Both TTF (median 720, 643, 562, 406, 254 s) and the NCF (3600, 3215, 2810, 2032, 1482 cycles) decreased in the following order RM_06 > RPB > RM_04 > Proc > WOG with partially significant differences. During testing, only six RM_06 instruments fractured, whereas 16/20 (RPB), 18/20 (Proc), and 20/20 (RM_04, WOG) fractures were recorded (p < 0.05). Within the limitations of the present study, blue-coloured RPB and RM instruments exhibited a significantly superior cyclic fatigue resistance compared to SE-NiTi and Gold-wire instruments. Heat treatment, cross-sectional design and core mass significantly influenced the longevity of reciprocating instruments in cyclic dynamic testing. Full article

The gas contained in coal plays a crucial role in triggering coal and gas outbursts. During an outburst, a large quantity of gas originally absorbed by coal is released from pulverized coal. The role this part of the gas plays in the process of coal and gas outbursts has not been clearly elucidated yet. Therefore, investigating the changes in gas desorption rate from coal particles of different sizes could provide some meaningful insights into the outburst process and improve our understanding of the outburst mechanism. First, combining the diffusivity of coal of different particle sizes and the distribution function of broken coal, we present a gas desorption model for fragmented gas-bearing coal that can quantify gas desorption from coal particles within a certain range of size. Second, the gas desorption rate ratio is defined as the ratio of the gas desorption rate from coal being crushed to that from coal before breaking. The desorption rate ratio is mainly determined by the desorption index (γ) and the granularity distribution index (α). Within the limit range of coal particle sizes, the ratio of effective diffusion coefficient for coal particles with different sizes is directly proportional to the reciprocal of the ratio of particle sizes. Under uniform particle size conditions before and after fragmentation, the gas desorption rate ratio is the square root of the reciprocal of the effective diffusion coefficient. The gas desorption model quantitatively elucidates the accelerated desorption of adsorbed gas in coal during the continuous fragmentation process of coal during an outburst. Full article

Photovoltaic (PV) power generation has brought about enormous economic and environmental benefits, promoting sustainable development. However, due to the intermittency and volatility of PV power, the high penetration rate of PV power generation may pose challenges to the planning and operation of power systems. Accurate PV power forecasting is crucial for the safe and stable operation of the power grid. This paper proposes a short-term PV power forecasting method using K-means clustering, ensemble learning (EL), a feature rise-dimensional (FRD) approach, and quantile regression (QR) to improve the accuracy of deterministic and probabilistic forecasting of PV power. The K-means clustering algorithm was used to construct weather categories. The EL method was used to construct a two-layer ensemble learning (TLEL) model based on the eXtreme gradient boosting (XGBoost), random forest (RF), CatBoost, and long short-term memory (LSTM) models. The FRD approach was used to optimize the TLEL model, construct the FRD-XGBoost-LSTM (R-XGBL), FRD-RF-LSTM (R-RFL), and FRD-CatBoost-LSTM (R-CatBL) models, and combine them with the results of the TLEL model using the reciprocal error method, in order to obtain the deterministic forecasting results of the FRD-TLEL model. The QR was used to obtain probability forecasting results with different confidence intervals. The experiments were conducted with data at a time level of 15 min from the Desert Knowledge Australia Solar Center (DKASC) to forecast the PV power of a certain day. Compared to other models, the proposed FRD-TLEL model has the lowest root mean square error (RMSE) and mean absolute percentage error (MAPE) in different seasons and weather types. In probability interval forecasting, the 95%, 75%, and 50% confidence intervals all have good forecasting intervals. The results indicate that the proposed PV power forecasting method exhibits a superior performance in forecasting accuracy compared to other methods. Full article

Hyperspectral remote sensing technology can provide a rapid and nondestructive method for soil nickel (Ni) content detection. In order to select a high-effective method for estimating the soil Ni content using a hyperspectral remote sensing technique, 88 soil samples were collected in Urumqi, northwest China, to obtain Ni contents and related hyperspectral data. At first, 12 spectral transformations were used for the original spectral data. Then, Pearson’s correlation coefficient analysis (PCC) and the CARS method were used for selecting important wavelengths. Finally, partial least squares regression (PLSR), random forest regression (RFR) and support vector machine regression (SVMR) models were used to establish the hyperspectral inversion models of the Ni content in the soil using the important wavelengths. The coefficient of determination (R²), root mean square error (RMSE), mean absolute error (MAE), and residual prediction deviation (RPD) were selected to evaluate the inversion effects of the models. The results indicated that using the PCC and CARS method for the original and transformed wavebands can effectively improve the correlations between the spectral data and Ni content of the soil in the study area. The random forest regression model, based on the first-order differentiation of the reciprocal (RTFD–RFR), was more stable and had the best inversion effects, with the highest predictive ability (R² = 0.866, RMSE = 1.321, MAE = 0.986, RPD = 2.210) for determining the Ni content in the soil. The RTFD–RFR methods can be used as a means of the inversion of the Ni content in urban soil. The results of the study can provide a technical support for the hyperspectral estimation of the Ni content of urban soil. Full article

In this paper, we present new variants of Newton–Raphson-based protocols for the secure computation of the reciprocal and the (reciprocal) square root. The protocols rely on secure fixed-point arithmetic with arbitrary precision parameterized by the total bit length of the fixed-point numbers and the bit length of the fractional part. We perform a rigorous error analysis aiming for tight accuracy claims while minimizing the overall cost of the protocols. Due to the nature of secure fixed-point arithmetic, we perform the analysis in terms of absolute errors. Whenever possible, we allow for stochastic (or probabilistic) rounding as an efficient alternative to deterministic rounding. We also present a new protocol for secure integer division based on our protocol for secure fixed-point reciprocals. The resulting protocol is parameterized by the bit length of the inputs and yields exact results for the integral quotient and remainder. The protocol is very efficient, minimizing the number of secure comparisons. Similarly, we present a new protocol for integer square roots based on our protocol for secure fixed-point square roots. The quadratic convergence of the Newton–Raphson method implies a logarithmic number of iterations as a function of the required precision (independent of the input value). The standard error analysis of the Newton–Raphson method focuses on the termination condition for attaining the required precision, assuming sufficiently precise floating-point arithmetic. We perform an intricate error analysis assuming fixed-point arithmetic of minimal precision throughout and minimizing the number of iterations in the worst case. Full article

Reciprocating piezoelectric micropumps enable miniaturization in microfluidics for lab-on-a-chip applications such as organs-on-chips (OoC). However, achieving a steady flow when using these micropumps is a significant challenge because of flow ripples in the displaced liquid, especially at low frequencies or low flow rates (<50 µL/min). Although dampers are widely used for reducing ripples in a flow, their efficiency depends on the driving frequency of the pump. Here, we investigated multi-phase rectification as an approach to minimize ripples at low flow rates by connecting piezoelectric micropumps in parallel. The efficiency in ripple reduction was evaluated with an increasing number (n) of pumps connected in parallel, each actuated by an alternating voltage waveform with a phase difference of 2π/n (called multi-phase rectification) at a chosen frequency. We introduce a fluidic ripple factor ($R{F}_{fl.}$), which is the ratio of the root mean square ($RMS$) value of the fluctuations present in the rectified output to the average fluctuation-free value of the discharge flow, as a metric to express the quality of the flow. The fluidic ripple factor was reduced by more than 90% by using three-phase rectification when compared to one-phase rectification in the 2–60 μL/min flow rate range. Analytical equations to estimate the fluidic ripple factor for a chosen number of pumps connected in parallel are presented, and we experimentally confirmed up to four pumps. The analysis shown can be used to design a frequency-independent multi-phase fluid rectifier to the desired ripple level in a flow for reciprocating pumps. Full article