Search Results (7,085)

Assessing and mapping the geographical variation of soil properties is essential for precision agriculture to maintain the sustainability of the soil and plants. This study was conducted in El-Ismaillia Governorate in Egypt (arid zones), to establish site-specific management zones utilizing certain soil parameters in the study area. The goal of the study is to map out the variability of some soil properties. One hundred georeferenced soil profiles were gathered from the study area using a standard grid pattern of 400 × 400 m. Soil parameters such as pH, soil salinity (EC), soil organic carbon (SOC), calcium carbonate (CaCO₃), gravel, and soil-available micronutrients (Cu, Zn, Mn, and Fe) were determined. After the data were normalized, the soil characteristics were described and their geographical variability distribution was shown using classical and geostatistical statistics. The geographic variation of soil properties was analyzed using semivariogram models, and the associated maps were generated using the ordinary co-Kriging technique. The findings showed notable differences in soil properties across the study area. Statistical analysis of soil chemical properties showed that soil EC and pH have the highest and lowest coefficient of variation (CV), with a CV of 110.05 and 4.80%, respectively. At the same time Cu and Fe had the highest and lowest CV among the soil micronutrients, with a CV of 171.43 and 71.43%, respectively. Regarding the physical properties, clay and sand were the highest and lowest CV, with a CV of 177.01 and 9.97%, respectively. Moreover, the finest models for the examined soil attributes were determined to be exponential, spherical, K-Bessel, and Gaussian semivariogram models. The selected semivariogram models are the most suitable for mapping and estimating the spatial distribution surfaces of the investigated soil parameters, as indicated by the cross-validation findings. The results demonstrated that while Fe, Cu, Zn, gravel, silt, and sand suggested a weak spatial dependence, the soil variables under investigation had a moderate spatial dependence. The findings showed that there are three site- specific management zones in the investigated area. SSMZs were classified into three zones, namely high management zone (I) with an area 123.32 ha (7.09%), moderate management zone (II) with an area 1365.61ha (78.49%), and low management zone (III) with an area 250.8162 ha (14.42%). The majority of the researched area is included in the second site zone, which represents regions with low productivity. Decision-makers can identify locations with the finest, moderate, and poorest soil quality by using the spatial distribution maps that are produced, which can also help in understanding how each feature influences plant development. The results showed that geostatistical analysis is a reliable method for evaluating and forecasting the spatial correlations between soil properties. Full article

The Yellow River Basin (YRB) is an important grain production base, and exploring the spatiotemporal heterogeneity and driving factors of soil erosion in the YRB is of great significance to the ecological environment and sustainable agricultural development. In this study, we employed the Revised Universal Soil Loss Equation (RUSLE) in conjunction with Transport-Limited Sediment Delivery (TLSD) to explore a modified RUSLE-TLSD for use assessing net water erosion. This modification was performed using sediment data, and the explanatory power of driving factors was assessed utilizing an optimal parameters-based geographical detector (OPGD). The results demonstrated that the modified RUSLE-TLSD can accurately simulate the spatiotemporal distribution of net water erosion (NSE = 0.5766; R² = 0.6708). From 2000 to 2020, the net water erosion modulus in the YRB ranged between 1.62 and 5.33 t/(ha·a). Specifically, the net water erosion modulus decreased in the YRB and the middle reaches of the YRB (MYRB), but it increased in the upper reaches of the YRB (UYRB). The erosion occurred mainly in the Loess Plateau region, while the deposition occurred mainly in the Hetao Plain and Guanzhong Plain. The Normalized Difference Vegetation Index (NDVI) and slope emerged as significant driving factors, and their interaction explained 31.36% of YRB net water erosion. In addition, the redistribution of precipitation by vegetation and the slope weakened the impact of precipitation on the spatial pattern of net water erosion. This study provides a reference, offering insights to aid in the development of soil erosion control strategies within the YRB. Full article

In this paper, we introduce the skew-symmetric generalized normal and the skew-symmetric generalized t distributions, which are skewed extensions of symmetric special cases of generalized skew-normal and generalized skew-t distributions, respectively. We derive key distributional properties for these new distributions, including a recurrence relation and an explicit form for the cumulative distribution function (cdf) of the skew-symmetric generalized t distribution. Numerical examples including a simulation study and a real data analysis are presented to illustrate the practical applicability of these distributions. Full article

To investigate the causes of water leakage in the waterproof hammer air valve and its impact on sustainable water resource management, the DN100 waterproof hammer air valve was taken as the research object. By using the overset grid solution method of ANSYS Fluent 2021 R1 software, the flow field simulation of the waterproof hammer air valve was carried out. The transient action during the ascent phase of the key structural component floating ball, and the velocity and pressure distribution of the flow field inside the air valve are analyzed. The results showed that by giving different inlet flow velocities, the normal flow velocity range for the floating ball to float up was below 35 m/s and above 50 m/s. When the inlet flow velocity was between 35 m/s and 50 m/s, the growth rate of the pressure difference above and below the floating ball increased from 1.48% to 5.79% and then decreased to 0.4%. The floating ball would not be able to float up due to excessive outlet pressure above, which would cause the DN100 waterproof hammer air valve to leak water and fail to provide water hammer protection. When the inlet flow rate is 5 m/s, the velocity and pressure inside the valve body increase with time during the upward movement of the floating ball inside the waterproof hammer air valve and tend to stabilize at 400 ms. Through the generated pressure and velocity cloud maps, it can be observed that the location of maximum pressure is at the bottom of the buoy, directly below the floating ball, and at the narrow channels on both sides of the outflow domain. The location of the maximum velocity is at the small inlet of the bottom of the buoy. When the inlet speed of the valve is constant, a large amount of water flow is blocked by the floating ball, reducing the flow velocity and forming partial backflow below the floating ball, with an obvious vortex phenomenon. A small portion of the water flow passes through the air valve at a high velocity from both ends of the channel, and the water flow below the floating ball is in an extremely unstable state under the impact of high-speed water flow, resulting in a large gradient of water flow velocity passing through the valve. The research results not only help to improve the operational efficiency of water resource management systems but also reduce unnecessary water resource waste, thereby supporting the goal of sustainable water resource management. Full article

This study investigates the impact of tightening torque (preload) and the friction coefficient on stress generation and fatigue resistance of a Ti-6Al-4V abutment screw with an internal hexagonal connection under dynamic multi-axial masticatory loads in high-cycle fatigue (HCF) conditions. A three-dimensional model of the implant–abutment assembly was simulated using ANSYS Workbench 16.2 computer aided engineering software with chewing forces ranging from 300 N to 1000 N, evaluated over 1.35 × 10⁷ cycles, simulating 15 years of service. Results indicate that the healthy range of normal to maximal mastication forces (300–550 N) preserved the screw’s structural integrity, while higher loads (≥800 N) exceeded the Ti-6Al-4V alloy’s yield strength, indicating a risk of plastic deformation under extreme conditions. Stress peaked near the end of the occluding phase (206.5 ms), marking a critical temporal point for fatigue accumulation. Optimizing the friction coefficient (0.5 µ) and preload management improved stress distribution, minimized fatigue damage, and ensured joint stability. Masticatory forces up to 550 N were well within the abutment screw’s capacity to sustain extended service life and maintain its elastic behavior. Full article

Ultraviolet shortwave (UV-C) is a technology for postharvest fruit disinfection. This study aimed to use computational fluid dynamics (CFD) based on the discrete ordinate (DO) radiation model to analyze the distribution of UV-C intensity on whole and minimally processed mangoes within a disinfection chamber and to predict treatments against foodborne pathogens. The mango spears were oriented both parallel and perpendicular to the lamp and positioned at varying distances from the radiation source (50, 75, and 100 mm for spears and 100 mm for whole fruit). CFD simulations integrated with in vitro kinetic parameters enabled predictions of the time and doses needed to inactivate one to three logarithmic units of pathogens on the fruit surface. The highest average radiation intensity values were recorded for the whole mango oriented parallel to the lamp at 100 mm and the spears oriented normally to the lamp at 50 mm. The estimated times to achieve inactivation of one to three logarithmic units of microorganisms ranged from approximately 15 to 6540 s, while the doses necessary for this inactivation were, on average, 1.854, 5.291, and 10.656 kJ/m², respectively. CFD simulations are valuable for optimizing UV-C treatments in large-scale designing from both microbicide and sustainable perspectives. Full article

The energy storage adjustment strategy of source and load storage in a DC microgrid is very important to the economic benefits of a power grid. Therefore, a multi-timescale energy storage optimization method for direct current (DC) microgrid source-load storage based on a virtual bus voltage control is studied. It uses a virtual damping compensation strategy to control the stability of virtual bus voltage and establishes a virtual energy storage model by combining different types of distributed capability units. The design of an optimization process for upper-level daily energy storage has the objective function of maximizing the economic benefits of microgrids to cope with unplanned fluctuations in power. A real-time energy-adjustment scheme for the lower level is introduced, and a real-time energy-adjustment scheme based on virtual energy storage for the short-term partition of the source-load storage is designed to improve the reliability of microgrid operations. The experiment shows that, in response to the constant amplitude oscillation of the power grid after a sudden increase in power, this method introduces a virtual damping compensation strategy at 20 s, which can stabilize the virtual bus voltage. From 00:00 to 09:00, the battery power remains at around 4 MW, and from 12:00 to 21:00, the battery exits the discharge state. The economic benefits from applying this method are significantly higher than before. This method can effectively adjust the source-load energy storage in real time. During peak electricity price periods, the SOC value of supercapacitors is below 0.4, and during normal electricity price periods, the SOC value of supercapacitors can reach up to 1.0, which can make the state of the charge value of supercapacitors meet economic requirements. Full article

When using point cloud technology to measure the dimension and geometric error of aircraft landing gear components, the point cloud data obtained after scanning may have certain differences because of the sophistication and diversity of the components that make up the landing gear. However, when using traditional point cloud registration algorithms, if the initial pose between point clouds is poor, it can lead to significant errors in the final registration results or even registration failure. Furthermore, the significant difference in registration results between point clouds can affect the final measurement results. Adopting Teaching-Learning-Based Optimization (TLBO) to solve some optimization problems has unique advantages such as high accuracy and good stability. This study integrates TLBO with point cloud registration. To increase the probability of using TLBO for point cloud registration to search for the global optimal solution, adaptive learning weights are first introduced during the learner phase of the basic TLBO. Secondly, an additional tutoring phase has been designed based on the symmetry and unimodality of the normal distribution to improve the accuracy of the solution results. In order to evaluate the performance of the proposed algorithm, it was first used to solve the CEC2017 test function. The comparison results with other metaheuristics showed that the improved TLBO has excellent comprehensive performance. Then, registration experiments were conducted using the open point cloud dataset and the landing gear point cloud dataset, respectively. The registration results showed that the point cloud registration method proposed in this paper has strong competitiveness. Full article

The upcoming Landsat Next will provide more frequent land surface observations at higher spatial and spectral resolutions that will greatly benefit the agricultural sector. Early modeling of the upcoming Landsat Next products for soybean yield prediction is essential for long-term satellite monitoring strategies. In this context, this article evaluates the contribution of Landsat Next’s improved spectral resolution for soybean yield prediction under varying levels of water availability. Ground-based hyperspectral data collected over five cropping seasons at the Brazilian Agricultural Research Corporation were resampled to Landsat Next spectral resolution. The spectral dataset (n = 384) was divided into calibration and external validation datasets and investigated using three strategies for soybean yield prediction: (1) using the reflectance from each spectral band; (2) using existing and new vegetation indices developed based on three general equations: Normalized Difference Vegetation Index (NDVI-like), Band Ratio Vegetation Index (RVI-like), and Band Difference Vegetation Index (DVI-like), replacing the traditional spectral bands by all possible combinations between two bands for index calculation; and (3) using a partial least squares regression (PLSR) model composed of all Landsat Next spectral bands, in comparison to PLSR models using Landsat OLI and Sentienel-2 MSI bands. The results show the distribution of the new spectral bands over the most prominent changes in leaf reflectance due to water deficit, particularly in the visible and shortwave infrared spectrum. (1) Band 18 (centered at 1610 nm) had the highest correlation with yield (R² = 0.34). (2) A new vegetation index, called Normalized Difference Shortwave Vegetation Index (NDSWVI), is proposed and calculated from bands 19 and 20 (centered at 2028 and 2108 nm). NDSWVI showed the best performance (R² = 0.37) compared to traditional existing and new vegetation indices. (3) The PLSR model gave the best results (R² = 0.65), outperforming the Landsat OLI and Sentinel-2 MSI sensors. The improved spectral resolution of Landsat Next is expected to contribute to improved crop monitoring, especially for soybean crops in Brazil, increasing the sustainability of the production systems and strengthening food security in Brazil and globally. Full article

Aiming at the problem of multiple unmanned aerial vehicles (UAVs) cooperatively intercepting a maneuvering target, this paper proposes a cooperative guidance law with less energy consumption and a newly accurate time-to-go estimation algorithm in the two-dimensional (2D) plane. Firstly, based on the relative motion equations between UAVs and the target on the 2D plane, the line-of-sight (LOS) direction and the LOS normal direction models are established. Then, based on the distributed model predictive control (DMPC) theory, DMPC cooperative guidance laws are designed in two directions. This guidance law can ensure that all UAVs intercept the maneuvering target at the expected LOS angle at the same time and reduce the energy consumption during the guidance process. Then, a new time-to-go estimation algorithm is designed, which can reduce the time-to-go estimation error and improve the cooperative accuracy. Finally, the simulation results show that the DMPC cooperative guidance law reduces energy consumption by more than 50% compared to other guidance laws and the proposed time-to-go estimation algorithm improves the accuracy by 200% compared to traditional methods. Full article

In a real-world implementation, machine learning models frequently experience concept drift when forecasting the electricity load. This is due to seasonal changes influencing the scale, mean, and median values found in the input data, changing their distribution. Several methods have been proposed to solve this, such as implementing automated model retraining, feature engineering, and ensemble learning. The biggest drawback, however, is that they are too complex for simple implementation in existing projects. Since the drifted data follow the same pattern as the training dataset in terms of having different scale, mean, and median values, radian scaling was proposed as a new way to scale without relying on these values. It works by converting the difference between the two sequential values into a radian for the model to compute, removing the bounding, and allowing the model to forecast beyond the training dataset scale. In the experiment, not only does the constrained gated recurrent unit model with radian scaling have shorter average training epochs, but it also lowers the average root mean square error from 158.63 to 43.375, outperforming the best existing normalization method by 72.657%. Full article

To enhance the performance of entropy algorithms in analyzing complex time series, generalized Gaussian distribution improved permutation entropy (GGDIPE) and its multiscale variant (MGGDIPE) are proposed in this paper. First, the generalized Gaussian distribution cumulative distribution function is employed for data normalization to enhance the algorithm’s applicability across time series with diverse distributions. The algorithm further processes the normalized data using improved permutation entropy, which maintains both the absolute magnitude and temporal correlations of the signals, overcoming the equal value issue found in traditional permutation entropy (PE). Simulation results indicate that GGDIPE is less sensitive to parameter variations, exhibits strong noise resistance, accurately reveals the dynamic behavior of chaotic systems, and operates significantly faster than PE. Real-world data analysis shows that MGGDIPE provides markedly better separability for RR interval signals, EEG signals, bearing fault signals, and underwater acoustic signals compared to multiscale PE (MPE) and multiscale dispersion entropy (MDE). Notably, in underwater target recognition tasks, MGGDIPE achieves a classification accuracy of 97.5% across four types of acoustic signals, substantially surpassing the performance of MDE (70.5%) and MPE (62.5%). Thus, the proposed method demonstrates exceptional capability in processing complex time series. Full article

Compared with high spectrum efficiency faster-than-Nyquist (FTN) backbone network, an enhanced carrier phase recovery based on dual pilot tones is more sensitive to capital cost in FTN metropolitan areas as well as inter-datacenter optical networks. The use of distributed feedback (DFB) lasers is a way to effectively reduce the cost. However, under high symbol rate FTN systems, equalization-enhanced phase noise (EEPN) induced by a DFB laser with large linewidth will significantly deteriorate the system performance. What is worse, in FTN systems, tight filtering introduces inter-symbol interference so severe that the carrier phase estimation (CPE) algorithm of the FTN systems is more sensitive to EEPN, thus it will lead to a more serious cycle slip problem. In this paper, an enhanced carrier phase recovery based on dual pilot tones is proposed to mitigate EEPN and suppress cycle slip, in which the chromatic dispersion (CD)-aware Tx and LO laser phase noise is estimated, respectively. Offline experiments results under 40 Gbaud polarization multiplexing (PM) 16-quadrature amplitude modulation (QAM) FTN wavelength division multiplexing (FTN-WDM) systems at 0.9 acceleration factor, 5 MHz laser linewidth, and 500 km transmission demonstrate that the proposed algorithm could bring about 0.65 dB improvement of the required SNR for the normalized generalized mutual information of 0.9 compared with the training sequence-based cycle slip suppression carrier phase estimation (TS-CSS) algorithm. Full article

This study presents new results on the practical adhesion behavior of a boride layer formed on Monel 400 alloy, developed using the powder-pack boriding (PPBP) at 1223 K for 2, 4, and 6 h of exposure times, obtaining layer thicknesses from approximately 7.9 to 23.8 µm. The nickel boride layers were characterized using optical microscopy, Berkovich nanoindentation, X-ray diffraction (XRD), and scanning electron microscopy (SEM) to determine microstructure, hardness distribution, and failure mechanisms over the worn tracks. Scratch tests were conducted on the borided Monel 400 alloy according to the ASTM C-1624 standard, applying a progressively increasing normal load from 1 to 85 N using a Rockwell-C diamond indenter, revealing that critical loads (L_C1, L_C2, and L_C3) increased with layer thickness. The tests monitored the coefficient of friction and residual stress in real time. Critical loads were determined based on the correlation between the normal force and visual inspection of the worn surface, identifying cracks (cohesive failure) or detachment (adhesive failure). The results exposed those cohesive failures that appeared as Hertzian cracks, while adhesive failures were chipping and delamination, with critical loads reaching up to 49.0 N for the 6 h borided samples. Also, the results indicated that critical loads increased with greater layer thickness. The boride layer hardness was approximately 12 ± 0.3 GPa, ~4.0 times greater than the substrate, and Young’s modulus reached 268 ± 15 GPa. These findings underscore that PPBP significantly enhances surface mechanical properties, demonstrating the potential for applications demanding high wear resistance and strong layer adhesion. Full article

For reservoir structural models with obvious nonstationary and heterogeneous characteristics, traditional geostatistical simulation methods tend to produce suboptimal results. Additionally, these methods are computationally resource-intensive in consecutive simulation processes. Thanks to the feature extraction capability of deep learning, the generative adversarial network-based method can overcome the limitations of geostatistical simulation and effectively portray the structural attributes of the reservoir models. However, the fixed receptive fields may restrict the extraction of local geospatial multiscale features, while the gradient anomalies and mode collapse during the training process can cause poor reconstruction. Moreover, the sparsely distributed conditioning data lead to possible noise and artifacts in the simulation results due to its weak constraint ability. Therefore, this paper proposes an improved conditioning spectral normalization generation adversarial network framework (CSNGAN-ASPP) to achieve efficient and automatic reconstruction of reservoir geological bodies under sparse hard data constraints. Specifically, CSNGAN-ASPP features an encoder-decoder type generator with an atrous spatial pyramid pooling (ASPP) structure, which effectively identifies and extracts multi-scale geological features. A spectral normalization strategy is integrated into the discriminator to enhance the network stability. Attention mechanisms are incorporated to focus on the critical features. In addition, a joint loss function is defined to optimize the network parameters and thereby ensure the realism and accuracy of the simulation results. Three types of reservoir model were introduced to validate the reconstruction performance of CSNGAN-ASPP. The results show that they not only accurately conform to conditioning data constraints but also closely match the reference model in terms of spatial variance, channel connectivity, and facies attribute distribution. For the trained CSNGAN-ASPP, multiple corresponding simulation results can be obtained quickly through inputting conditioning data, thus achieving efficient and automatic reservoir geological model reconstruction. Full article