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The mechanical properties of wheat grains were measured and analyzed using discrete element software, which provided crucial data for their processing in a mill. The foundational Hertz–Mindlin model was used as a theoretical framework to evaluate the accumulation angle of wheat grains. The Plackett–Burman, steepest ascent, and Box–Behnken methods were utilized in a series of experiments, with the accumulation angle serving as the dependent variable. Targeting the actual angle of repose in the response surface, the optimal parameters were derived using the regression equations. These included a static‐friction coefficient of 0.3 between individual wheat grains, rolling‐friction coefficient of 0.04 for wheat–wheat interactions, static‐friction coefficient of 0.554 for wheat–tooth roller interactions, collision recovery coefficient of 0.5 for wheat–wheat collisions, collision recovery coefficient of 0.45 for wheat–tooth roller collisions, and rolling‐friction coefficient of 0.05 for wheat–roller interactions. Relying on the bonding contact model of Hertz–Mindlin, virtual uniaxial compression tests were conducted to calibrate the wheat grain bonding parameters. A regression equation for the critical load was subsequently generated using the critical load of the wheat grain bonding model as the response variable. The optimal parameters were calculated and incorporated into the EDEM model for computation, which resulted in a relative error of 1.6% between the calculated and observed values, confirming the accuracy and feasibility of the calibration method, suggesting that the calibrated parameters were accurate. [ABSTRACT FROM AUTHOR]

Hydrogen production modules (HPMs) play a crucial role in harnessing abundant photovoltaic power by producing and supplying hydrogen to factories, resulting in significant operational cost reductions and efficient utilization of the photovoltaic panel output. However, the output of photovoltaic power is stochastic, which will affect the revenue of investing in an HPM. This paper presents a comprehensive analysis of HPMs, starting with the modeling of their operational process and investigating their influence on distribution system operations. Building upon these discussions, a deterministic optimization model is established to address the corresponding challenges. Furthermore, a two-stage stochastic planning model is proposed to determine optimal locations and sizes of HPMs in distribution systems, accounting for uncertainties. The objective of the two-stage stochastic planning model is to minimize the distribution system's operational costs plus the investment costs of the HPM subject to power flow constraints. To tackle the stochastic nature of photovoltaic power, a data-driven algorithm is introduced to cluster historical data into representative scenarios, effectively reducing the planning model's scale. To ensure an efficient solution, a Benders' decomposition-based algorithm is proposed, which is an iterative method with a fast convergence speed. The proposed model and algorithms are validated using a widely utilized IEEE 33-bus system through numerical experiments, demonstrating the optimality of the HPM plan generated by the algorithm. The proposed model and algorithms offer an effective approach for decision-makers in managing uncertainties and optimizing HPM deployment, paving the way for sustainable and efficient energy solutions in distribution systems. Sensitivity analysis verifies the optimality of the HPM's siting and sizing obtained by the proposed algorithm, which also reveals immense economic and environmental benefits. [ABSTRACT FROM AUTHOR]

This study investigates the impact of different conductive buffer electrode layers on the electrical properties, fatigue behavior and phase transition of epitaxial KNN-based ferroelectric films. Two types of buffer electrode layers, p-type La0.67Ca0.33MnO3/La0.67Ba0.33MnO3 and n-type LaNiO3/Nb-SrTiO3, are selected for analysis. The results demonstrate that the electrical properties and fatigue behavior of the KNN-based films are significantly influenced by the buffer electrode layers. Specifically, the KNN-based films buffered with p-type electrodes exhibit superior ferroelectric/dielectric properties and fatigue resistance compared to those buffered with n-type electrodes. The ferroelectric hysteresis loops of p-type electrode buffered KNN-based films demonstrate higher saturation, larger twice remnant polarization (2Pr) and higher dielectric constant (ε) (2Pr = 58.5/46.9 μC cm−2 and ε = 1290/1003 for La0.67Ca0.33MnO3/La0.67Ba0.33MnO3-buffered KNNLT-M films) compared to the films grown on n-type electrodes (2Pr = 29.5/24.5 μC cm−2 and ε = 802/668 for LaNiO3/Nb-SrTiO3-buffered KNNLT-M films). Moreover, the KNN-based films grown on p-type buffer electrode layers exhibit high fatigue resistance, maintaining consistent ferroelectric hysteresis loops even after 108 bipolar switching cycles, while those on n-type buffer electrode layers experience dramatic fatigue after only 106 cycles. Temperature-dependent dielectric analysis reveals that all studied KNN-based films exhibit relaxor ferroelectric behavior, with the p-type electrode buffered films exhibiting higher phase transition temperatures than the n-type electrode buffered films. These findings demonstrate that the conductive type of the buffer electrode layer can effectively tune the performance of KNN-based films, indicating the high application potential in lead-free electronic device. [ABSTRACT FROM AUTHOR]

In this work, a total of 19 novel naphthalene hybrids with chimonanthine scaffolds were efficiently synthesised from indole-3-acetonitrile in good yields. The prepared compounds were evaluated for biological activity against Cryptococcus neoformans, Escherichia coli, Shigella spp, Candida albicans, Salmonella spp, and Staphylococcus aureus. The preliminary bioassays showed that most of the synthesised compounds exhibited significant antibacterial or antifungal activity. Notably, compound 8 showed potent activity against Cryptococcus neofonmans, Escherichia coli, Shigella spp, and Candida albicans than the positive control, all with the same MIC value of 3.53 µM. Compound 8 had a broad spectrum of antibacterial or antifungal activity, and will be studied further. [ABSTRACT FROM AUTHOR]

In the study of mineral resources evaluation model, the problem that the number of positive samples must be specified in the TSVM in the unlabeled samples.Then this article applied the improved algorithm to the evaluation of mineral resources, and the algorithm is TSVM based on BP neural network and BP-TSVM for short.The SVM is applied to studying the discernment and classification of labeling samples.We will annotate the plus or minus of unlabeled data within the boundary of already classified by utilizing BP neural network.The labeling samples as learned by SVM in the next iteration.The new algorithm will avoid decreased classified ability.TSVM can have more accurate estimates of number of plus of labeled data.Also, the number of training samples can reduce training times.The experimental result proves that compared with conventional SVM, TSVM and BP neural network the BP-TSVM is more accurate in evaluation of mineral resources.

China's coal production has formed a large number of goaf, which contains a large amount of coal and coalbed methane resources. If these resources are stored in the goaf, it will not only cause a waste of resources, but also cause environmental pollution if the coalbed methane in the goaf leaks into the atmosphere. Therefore, the utilization of goaf resources is imminent. In order to achieve the purpose of efficient extraction, it is necessary to evaluate the amount of goaf resources. As an important part of goaf resources, the desorption law of abandoned coal is affected by many factors, among which water is an important factor affecting the desorption law of abandoned coal. At present, there are few studies on the desorption law of block coal under different watered-out volumes.