Explore the vast range of titles available.

Recently, considerable efforts have been devoted to the estimation of soil properties using optical payloads mounted on drones or satellites. Nevertheless, many studies focus on diverse pretreatments and modeling techniques, while there continues to be a conspicuous absence of research examining the impact of parameters related to optical remote sensing payloads on predictive performance. The main aim of this study is to evaluate how the spectral resolution and signal-to-noise ratio (SNR) of spectrometers affect the precision of predictions for soil organic matter (SOM) content. For this purpose, the initial soil spectral library was partitioned into to two simulated soil spectral libraries, each of which were individually adjusted with respect to the spectral resolutions and SNR levels. To verify the consistency and generality of our results, we employed four multiple regression models to develop multivariate calibration models. Subsequently, in order to determine the minimum spectral resolution and SNR level without significantly affecting the prediction accuracy, we conducted ANOVA tests on the RMSE and R2 obtained from the independent validation dataset. Our results revealed that (i) the factors significantly affecting SOM prediction performance, in descending order of magnitude, were the SNR levels > spectral resolutions > estimation models, (ii) no substantial difference existed in predictive performance when the spectral resolution fell within 100 nm, and (iii) when the SNR levels exceeded 15%, altering them did not notably affect the SOM predictive performance. This study is expected to provide valuable insights for the design of future optical remote sensing payloads aimed at monitoring large-scale SOM dynamics.

The mechanical properties of resin samples in low-force stereolithography additive manufacturing were affected by the printing orientation, and were investigated and optimized to achieve excellent single or comprehensive tensile strength, compressive strength, and flexural modulus. The resin samples were fabricated using a Form3 3D printer based on light curing technology according to the corresponding national standards, and they were detected using a universal testing machine to test their mechanical properties. The influence of the printing orientation was represented by the rotation angle of the resin samples relative to the x–axis, y–axis and z–axis, and the parameters was selected in the range 0°–90° with an interval of 30°. The multiple regression models for the mechanical properties of the prepared resin samples were obtained based on least square estimation, which offered a foundation from which to optimize the parameters of the printing orientation by cuckoo search algorithm. The optimal parameters for the tensile strength, compressive strength and flexural modulus were ‘α = 45°, β = 25°, γ = 90°’, ‘β = 0°, β = 51°, γ = 85°’ and ‘α = 26°, β = 0°, γ = 90°’, respectively, which obtained the improvements of 80.52%, 15.94%, and 48.85%, respectively, relative to the worst conditions. The mechanism was qualitatively discussed based on the force analysis. The achievements obtained in this study proved that optimization of the printing orientation could improve the mechanical properties of the fabricated sample, which provided a reference for all additive manufacturing methods.

Higher mathematics is a term used to describe complex mathematical ideas and subjects that go beyond the fundamentals of algebra, geometry, and number theory. Geometry, linear algebra, discrete mathematics, topology, and analysis are often covered. It entails creating fresh mathematical ideas and solving challenging issues by applying solid mathematical rationalization. Economics, statistics, mathematical modeling, and software for descriptive statistics are all areas covered by mathematics applications. Global concern is being raised by the falling number of students pursuing elevated amounts of mathematics. The underperformance and disinterest of students in mathematics may be attributed to a variety of issues. One cause of the drop is the knowledge gaps that arise when learners do not acquire or comprehend important mathematical ideas. It is essential to provide the best teaching strategy. Blended learning combines online and in-person instruction utilizing a range of tools and communication channels that are accessible to both students and instructors. In the setting of data processing and statistics, multiple regression analysis could serve as a helpful tool for teaching mathematics. Thus, we suggested using a multiple regression model (MRM) in blended higher mathematics instruction. Using performance measures and comparisons to existing methods, we assessed the efficacy of the suggested approach. The study results proved that MRM has provided an implementation cost of 45. According to the results, the proposed approach helps students learn mathematics in a more significant way.

Ridge regression model is a widely used model with many successful applications, especially in managing correlated covariates in a multiple regression model. Multicollinearity represents a serious threat in fuzzy regression models as well. We address this issue by combining ridge regression with the fuzzy regression model. Our proposed algorithm uses the α -level estimation method to evaluate the parameters of the ridge fuzzy regression model. Two examples are given to illustrate the ridge fuzzy regression model with crisp input/fuzzy output and fuzzy coefficients.

Crops grown in ecologically vulnerable oases are increasingly vulnerable to climate change, a trend that poses a severe threat to the sustainability of agricultural production in arid zones. Clarifying the relative contributions of climate change and crop management to crop phenology is critical for designing climate-resilient agricultural practices—yet this remains underexplored for wheat in Xinjiang’s oases, a major arid-region agricultural hub. Using 1981–2021 phenological and meteorological data from 26 agrometeorological stations, we integrated a first-difference multiple regression model, Pearson’s correlation, multiple linear regression, and path analysis to quantify spatiotemporal phenological dynamics; disentangle the distinct impacts of climate and management factors; and identify dominant climatic drivers regulating wheat growth. Temperature was confirmed as the dominant climatic factor regulating wheat growth in arid oasis regions. Results showed that the annual change rates of sowing, emergence, booting, flowering, and maturity dates were 0.261 (−0.027), 0.265 (−0.103), −0.272 (−0.161), −0.269 (−0.226), and −0.216 (−0.127) days/year for winter (spring) wheat, respectively. For phenological durations, the annual change rates of sowing-to-emergence, emergence-to-anthesis, anthesis-to-maturity, vegetative growth period, reproductive growth period, and whole growth period were 0.007 (−0.076), −0.537 (−0.068), 0.096 (0.099), −0.502 (−0.134), 0.068 (0.034), and −0.434 (−0.100) days/year for winter (spring) wheat, respectively. Regarding climatic effects, maximum, minimum, and mean temperatures generally exerted positive impacts on wheat phenological durations; increased precipitation prolonged growth periods; and higher sunshine hours shortened winter wheat growth periods while extending those of spring wheat. Multiple regression and path analysis were employed to clarify the relative importance of climatic and management factors, as well as their direct and indirect effects on wheat phenology and yield. Furthermore, climate change had a substantially weaker impact on wheat phenology and yield compared to crop management, with climatic driver intensity following the order of mean temperature > precipitation > sunshine hours—confirming mean temperature as the key climate-induced driver. Correlation analysis revealed a positive relationship between yield and growth period length. This study provides novel insights into region-specific climate adaptation for wheat production in arid oases, highlighting that planting longer-growth-period varieties is an effective, eco-friendly strategy to enhance climate resilience and ensure sustainable agricultural development in fragile ecosystems.

Conventional slurry wall protection exhibits reduced film performance upon exposure to water in saturated sand layers with high permeability, frequently resulting in hole wall instability. Optimizing the slurry ratio to enhance film performance is thus critical for borehole stability. A multiple regression model was developed to determine the optimal slurry ratio for saturated sand. Slurry permeability tests assessed filtration loss, film formation time, and film morphology changes. Scanning electron microscopy (SEM) further elucidated the film formation mechanism. Bentonite, clay, Na2CO3, and sodium carboxymethyl cellulose (CMC) significantly affected the slurry’s properties: specific gravity and sand content increased with bentonite/clay; viscosity increased with CMC; and pH increased with Na2CO3. The optimized slurry (water–bentonite–Na2CO3–clay–CMC = 1000:220:32:110:1; specific gravity, 1.20 g/cm3; viscosity, 29 s) demonstrated low filtration loss and stable film morphology. SEM revealed that simultaneous CMC and clay addition (ratio of 1:110) improved film surface flatness, reduced porosity and pore size, enhanced formation surface filling, and produced a denser film. The optimized slurry ratio significantly enhanced film performance in saturated sand layers. The findings provide a theoretical and engineering framework for bored pile wall protection slurry design and film formation mechanisms.

There is widespread concern about pollution of freshwater ecosystems caused by heavy metals. The aim of this study was to determine how humic acid affected the bioavailability of Cu at a range of Cu concentrations in water. The concentrations of selected Cu species were characterized by spectroscopic methods and multiple regression models. The results showed that the dissolved Cu concentration decreased by an average of 13.4% for every 5-mg/l increase in the humic acid concentration in the Cu-treated water, indicating that humic acid could reduce the Cu bioavailability. Tests to show how DOM fluorescent components affected the Cu species concentrations showed that the Cu species were significantly correlated with five fluorescent components of DOM (P<0.01, linear goodness of fit: R2min=0.8374). We then examined how the pH and DOM fluorescent components together affected the concentrations of the Cu forms. When 3≤pH≤7, both tryptophan and fulvic acid promoted the transformation of dissolved Cu forms to suspended Cu forms. The Cu forms were significantly correlated with the pH and DOM fluorescence (P<0.01, linear regression goodness of fit: R2min=0.8898). However, tryptophan and fulvic acid had contrasting effects when the pH was less than, or greater than, 7. The influences on the migration ability and bioavailability of Cu therefore varied, depending on the water conditions. We conclude that it may be possible to identify the substances that have most effect on the bioavailability of Cu in water environments from the pH range of the water body, and that the bioavailability of heavy metals in water environments may be controlled by adding substances with specific properties.

A minimally-invasive manipulator characterized by hyper-redundant kinematics and embedded sensing modules is presented in this work. The bending angles (tilt and pan) of the robot tip are controlled through tendon-driven actuation; the transmission of the actuation forces to the tip is based on a Bowden-cable solution integrating some channels for optical fibers. The viability of the real-time measurement of the feedback control variables, through optoelectronic acquisition, is evaluated for automated bending of the flexible endoscope and trajectory tracking of the tip angles. Indeed, unlike conventional catheters and cannulae adopted in neurosurgery, the proposed robot can extend the actuation and control of snake-like kinematic chains with embedded sensing solutions, enabling real-time measurement, robust and accurate control of curvature, and tip bending of continuum robots for the manipulation of cannulae and microsurgical instruments in neurosurgical procedures. A prototype of the manipulator with a length of 43 mm and a diameter of 5.5 mm has been realized via 3D printing. Moreover, a multiple regression model has been estimated through a novel experimental setup to predict the tip angles from measured outputs of the optoelectronic modules. The sensing and control performance has also been evaluated during tasks involving tip rotations.

In this study, descriptive statistics, correlation matrix, multiple regression model, and geostatistical models were used to assess the contamination of groundwater with respect to trace elements in the Lower Tano river basin, Ghana, West Africa. A total number of 48 boreholes drilled across the basin with depths ranging from 18 to 60 m were used as data sources in this study. The results of the descriptive statistics showed that the average lead, iron, and aluminium concentrations exceeded the WHO permissible limits of 0.3 mg/L, 0.01 mg/L, and 0.2 mg/L respectively. Furthermore, copper, chromium, aluminium, zinc, manganese, nickel, iron, arsenic, electrical conductivity, and total dissolved solids were found to be extreme and highly positively skewed. Even though significant correlations exist among some variables, the statistical results showed that the quality of the boreholes drilled across the basin was mainly originating from geogenic and anthropogenic sources. In addition, each pair of correlated physical parameters and trace elements in the drilled boreholes were predicted using multiple regression models. Likewise, geostatistical modelling was used to assess the spatial analysis of each pair of correlated physical parameters and trace elements in the drilled boreholes. The cross-validation results revealed kriging model, as the most precise model for the spatial distribution maps for the correlated physical parameters, and correlated trace elements concentration in the boreholes drilled across the study region. The semivariogram models showed that most of the correlated physical parameters and correlated trace elements were weak moderately and strongly spatially dependent, suggesting fewer agronomic influences. The results of the spatial analysis were consistent with the multiple regression model and the Pearson correlation matrix.

There exists serious heavy metal contamination of agricultural soils in China. It is not only time- and labor-intensive to monitor soil contamination, but it also has limited scope when using conventional chemical methods. However, the method of the heavy metal monitoring of soil based on vegetation hyperspectral technology can break through the vegetation barrier and obtain the heavy metal content quickly over large areas. This paper discusses a highly accurate method for predicting the soil heavy metal content using hyperspectral techniques. We collected leaf hyperspectral data outdoors, and also collected soil samples to obtain heavy metal content data using chemical analysis. The prediction model for heavy metal content was developed using a difference spectral index, which was not highly satisfactory. Subsequently, the five factors that have a strong influence on the content of heavy metals were analyzed to determine multiple regression models for the elements As, Pb, and Cd. The results showed that the multiple regression model could better estimate the heavy metal content with stable fitting that has high prediction accuracy compared with the linear model. The results of this research provide a scientific basis and technical support for the hyperspectral inversion of the soil heavy metal content.