Search Results (7)

This study introduces a novel dual-battery architecture with intelligent auto-switching control, designed to ensure uninterrupted operation of agricultural sensing systems in environments with unpredictable energy availability. The proposed system integrates Lithium-Sulphur (Li-S) and Lithium-Ion (Li-Ion) batteries with advanced switching algorithms—specifically, the Dynamic Load Balancing–Power Allocation Optimisation (DLB–PAO) and Dynamic Load Balancing–Genetic Algorithm (DLB–GA)—tailored to maximise sensor operational longevity. By optimizing the dual-battery configuration for real-world deployment and conducting comparative evaluations across multiple system designs, this work advances an innovative engineering solution with significant practical implications for sustainable agriculture and remote sensing applications. Unlike conventional single-battery systems or passive redundancy approaches, the architecture introduces active redundancy, adaptive energy management, and fault tolerance, substantially improving operational continuity. A functional prototype was experimentally validated using realistic load profiles, demonstrating seamless battery switching, extended uptime, and enhanced energy reliability. To further assess long-term performance under continuous Internet of Things (IoT) operation, a simulation framework was developed in MATLAB/Simulink, incorporating battery degradation models and empirical sensor load profiles. The experimental results reveal distinct performance improvements. A baseline single-battery system sustains 28 h of operation with 31.2% average reliability, while a conventional dual-battery configuration extends operation to 45 h with 42.6% reliability. Implementing the DLB–PAO algorithm elevates the average reliability to 91.7% over 120 h, whereas the DLB–GA algorithm achieves near-perfect reliability (99.9%) for over 170 h, exhibiting minimal variability (standard deviation: 0.9%). The integration of intelligent auto-switching mechanisms and metaheuristic optimisation algorithms demonstrates a marked enhancement in both reliability and energy efficiency for soil nutrient monitoring systems. This method extends the lifespan of electronic devices while ensuring reliable energy storage over time. It creates a practical foundation for sustainable IoT agricultural systems in areas with limited resources. Full article

This study was conducted to verify the usefulness of the laser diffractometer method for determining the particle size distribution of selected organic soils from the Podkarpacie region in Poland. The soil selected for this research represented three main classification groups, namely, low-organic, medium-organic and high-organic soil, in accordance with the standard criterion. Particle size distribution was determined using two types of laser diffractometers: the Helos laser diffractometer manufactured by Sympatec GmbH (Clausthal-Zellerfeld, Germany) and the laser particle size analyzer Analysette 22 MicroTech plus manufactured by Fritsch GmbH (Idar-Oberstein, Germany). The standard mechanical and sedimentation methods, which are perfect for testing mineral soils, are not applicable to organic soils; therefore, a serious problem was found and examined. A reference method that could verify the test results obtained using the laser diffractometer methods was required. After analyzing the literature, the hydrometric (sedimentation) method was adopted as the reference method. Currently, there are no reliable and fully verified methods for testing soils with such a complex skeleton structure, and the resources, standards and guidelines concerning the issues discussed are extremely limited; therefore, new research methods are being sought to fill this gap, and this work is a step in this direction. The results of the conducted studies and analyses have shown that laser diffractometry methods can be useful for determining the particle size distribution of organic soils, but to a limited extent, depending mainly on the quantity of organic substances. The highest agreement was obtained by comparing the results of the sedimentation method with those obtained using the diffractometer analyzer Analysette 22 in the group of highly organic soils. Full article

Calculation expressions for the electric and magnetic fields produced by a horizontal cloud-to-cloud lightning channel, assuming a perfectly conducted ground, are proposed in this paper. These expressions depend on the current model traveling through the channel and serve as the starting point to calculate the induced fields and potentials at any point in space. The derived expressions for the fields are used to calculate the induced potentials by the channel on metallic structures such as vertically driven rods in the ground and aircraft in flight. The influence of soil with finite conductivity is discussed, and an estimation of the induced potentials in this situation is proposed. Full article

Large-deformation finite element (LDFE) analysis with the coupled Eulerian–Lagrangian (CEL) technique for large-deformation soil functions without twisting or distorting the mesh. However, the model does not consider the strain-softening and strain-rate dependence of clay-based soils. The undrained shear strength of clay is sensitive to the strain rate. In addition, the strain-softening effect of soil strength reduction accompanied by large-scale shear deformation should be considered. In this study, anchor dragging simulations were performed for large-deformation analysis considering strain-softening and strain-rate dependence. Furthermore, a shear strength equation expressing the strain-softening and strain-rate dependence of the Tresca constitutive model was developed based on VUMAT, an ABAQUS/Explicit subroutine. The equation was designed so that it could be linked to the LDFE/CEL model. The model was verified by performing comparative analysis with the Mohr–Coulomb (M–C) perfect-plasticity model. The newly constructed Tresca base strain-softening and strain-rate-dependence VUMAT algorithm in the LDFE/CEL model analysis confirmed the effects of strain-softening and strain-rate dependence. The proposed model enabled a highly realistic simulation of the actual phenomenon than the M–C model. Finally, a parametric study on strain-softening and strain-rate dependence was conducted, and the behavior of clay due to the anchor drag phenomenon was revealed. Full article

The chemical reaction between calcium ions (Ca²⁺) and phosphate in the soil is the main way to maintain the availability of soil phosphorus. Thus, we believe stimulating coal gangue with Ca²⁺ solution would be an effective way to improve its adsorption and desorption capacity toward phosphate. In order to explore the effects of different pH of Ca²⁺ solution on the modified effect of coal gangue, we conducted mechanical grinding (<1 mm), high temperature calcination (800 °C), and the stimulation of Ca²⁺ solution with different pH (2, 7, 13), to prepare acidic calcium-modified coal gangue (Ac-CG) (Ac-CG, acidic calcium-modified coal gangue; Ne-CG, neutral calcium-modified coal gangue; Al-CG, alkali calcium-modified coal gangue; RCG, raw coal gangue), neutral calcium-modified coal gangue (Ne-CG), and alkali calcium-modified coal gangue (Al-CG); raw coal gangue (RCG) was regarded as the control. The results indicated that Al-CG had better phosphate adsorption (3.599 mg g⁻¹); this favorable adsorption performance of Al-CG was related to the formation of hydrated calcium silicate gel and ettringite, which provided more Ca²⁺, Al³⁺, and hydroxyl groups, and a larger specific surface area (9.497 m² g⁻¹). Moreover, Al-CG not only held more phosphate but also maintained its availability longer for plants. It is suggested that stimulating coal gangue with Ca²⁺ solution under alkaline conditions is a perfect way to enhance its adsorption and desorption capacity toward phosphate; the Al-CG we prepared could be used as filling material and soil conditioner in the reclamation area. Full article

Information on soil erosion and related sedimentation processes are very important for natural resource management and sustainable farming. Plenty of models are available for studying soil erosion but only a few are suitable for dynamic soil erosion assessments at the field-scale. To date, there are no field-scale dynamic models available considering complex agricultural systems for the simulation of soil erosion. We conducted a review of 51 different models evaluated based on their representation of the processes of soil erosion by water. Secondly, we consider their suitability for assessing soil erosion for more complex field designs, such as patch cropping, strip cropping and agroforestry (alley-cropping systems) and other land management practices. Several models allow daily soil erosion assessments at the sub-field scale, such as EPIC, PERFECT, GUEST, EPM, TCRP, SLEMSA, APSIM, RillGrow, WaNuLCAS, SCUAF, and CREAMS. However, further model development is needed with respect to the interaction of components, i.e., rainfall intensity, overland flow, crop cover, and their scaling limitations. A particular shortcoming of most of the existing field scale models is their one-dimensional nature. We further suggest that platforms with modular structure, such as SIMPLACE and APSIM, offer the possibility to integrate soil erosion as a separate module/component and link to GIS capabilities, and are more flexible to simulate fluxes of matter in the 2D/3D dimensions. Since models operating at daily scales often do not consider a horizontal transfer of matter, such modeling platforms can link erosion components with other environmental components to provide robust estimations of the three-dimensional fluxes and sedimentation processes occurring during soil erosion events. Full article

State-of-the-art forest models are often complex, analytically intractable, and computationally expensive, due to the explicit representation of detailed biogeochemical and ecological processes. Different models often produce distinct results while predictions from the same model vary with parameter values. In this project, we developed a rigorous quantitative approach for conducting model intercomparisons and assessing model performance. We have applied our original methodology to compare two forest biogeochemistry models, the Perfect Plasticity Approximation with Simple Biogeochemistry (PPA-SiBGC) and Landscape Disturbance and Succession with Net Ecosystem Carbon and Nitrogen (LANDIS-II NECN). We simulated past-decade conditions at flux tower sites located within Harvard Forest, MA, USA (HF-EMS) and Jones Ecological Research Center, GA, USA (JERC-RD). We mined field data available from both sites to perform model parameterization, validation, and intercomparison. We assessed model performance using the following time-series metrics: Net ecosystem exchange, aboveground net primary production, aboveground biomass, C, and N, belowground biomass, C, and N, soil respiration, and species total biomass and relative abundance. We also assessed static observations of soil organic C and N, and concluded with an assessment of general model usability, performance, and transferability. Despite substantial differences in design, both models achieved good accuracy across the range of pool metrics. While LANDIS-II NECN showed better fidelity to interannual NEE fluxes, PPA-SiBGC indicated better overall performance for both sites across the 11 temporal and two static metrics tested (HF-EMS $\overline{R^2}=0.73,+0.07$ , $\overline{RMSE}=4.68,-9.96$ ; JERC-RD $\overline{R^2}=0.73,+0.01$ , $\overline{RMSE}=2.18,-1.64$ ). To facilitate further testing of forest models at the two sites, we provide pre-processed datasets and original software written in the R language of statistical computing. In addition to model intercomparisons, our approach may be employed to test modifications to forest models and their sensitivity to different parameterizations. Full article