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When utilizing the Crop Water Stress Index (CWSI), the most critical factor is accurately measuring canopy temperature, which is typically done using infrared sensors and imaging cameras. In this study, however, we aimed to develop a machine learning model capable of predicting leaf temperature based on environmental data, without relying on sensors, for calculating CWSI. The data underwent preprocessing to remove outliers and missing values. The number of training data points for each factor was 307,924. After data preprocessing, a Pearson correlation analysis (bivariate correlation coefficient) was conducted to select the training data for model operation. The relationship between leaf temperature and air temperature showed a strong positive correlation of 0.928 (p < 0.01). Solar radiation and relative humidity were also found to have high correlations. However, wind speed and soil moisture tension showed very low correlations with leaf temperature and were excluded from the model operation. The Decision Tree, Random Forest, and Gradient Boosting models were selected, and each model was evaluated using RMSE (Root Mean Squared Error), MAE (Mean Absolute Error), MSE (Mean Squared Error), and R2 (coefficient of determination). The evaluation results showed that the Gradient Boosting model had a high R2 (0.97) and low RMSE (0.88) and MAE (0.54), making it the most suitable model for predicting leaf temperature. Through the leaf temperature prediction model developed in this study, labor and costs associated with sensors can be reduced, and by applying it to real agricultural settings, it can improve crop quality and enhance the sustainability of agriculture. [ABSTRACT FROM AUTHOR]

Featured Application: This research is useful to understanding generalized electron emission in low-dimensional conducting materials. This research focuses on exploring generalized electron emissions in one-dimensional conducting materials, specifically examining field emission and thermionic emission. Additionally, this study investigates one-dimensional thermal radiation. The current density is derived for both one-dimensional thermionic emission and one-dimensional field emission. A generalized current density in one dimension is devised, taking into account arbitrary accelerating fields and temperature conditions. The formula for the one-dimensional current density is depicted with accelerating field and temperature across various work function materials. The emission current is represented as a function of temperature for various electric fields. The emission current is also represented as a function of the electric field for various temperatures. [ABSTRACT FROM AUTHOR]

Ferromagnetic materials have been attracting great interest in the last two decades due to their application in spintronics devices. One of the hot research areas in magnetism is currently the two-dimensional materials, transition metal dichalcogenides (TMDCs), which have unique physical properties. The origins and mechanisms of transition metal dichalcogenides (TMDCs), especially the correlation between magnetism and defects, have been studied recently. We investigate the changes in magnetic properties with a variation in annealing temperature for the nanoscale compound MoS2. The pristine MoS2 exhibits diamagnetic properties from low-to-room temperature. However, MoS2 compounds annealed at different temperatures showed that the controllable magnetism and the strongest ferromagnetic results were obtained for the 700 °C-annealed sample. These magnetizations are attributed to the unpaired electrons of vacancy defects that are induced by annealing, which are confirmed using Raman spectroscopy and electron paramagnetic resonance spectroscopy (EPR). [ABSTRACT FROM AUTHOR]

We study a way to set the natural frequency of a newly added oscillator in a growing network to enhance synchronization. Population growth is one of the typical features of many oscillator systems for which synchronization is required to perform their functions properly. Despite this significance, little has been known about synchronization in growing systems. We suggest effective growing schemes to enhance synchronization as the network grows under a predetermined rule. Specifically, we find that a method based on a link-wise order parameter outperforms that based on the conventional global order parameter. With simple solvable examples, we verify that the results coincide with intuitive expectations. The numerical results demonstrate that the approximate optimal values from the suggested method show a larger synchronization enhancement in comparison with other naïve strategies. The results also show that our proposed approach outperforms others over a wide range of coupling strengths. [ABSTRACT FROM AUTHOR]

Climate change has been turning into a climate crisis. Thus, we live in an era in which it is important to carry out the promise of 2050 carbon neutrality worldwide. South Korea is a country with a very large private education market. As the online education market has also recently expanded rapidly, interest in the electricity consumed by educational institutions is growing. One way to reduce the power consumption of private educational institutions, which is expected to gradually increase, is to replace the existing power system with a hybrid energy system based on renewable energy. This study aims to investigate an optimized renewable-energy-based hybrid system to supply adequate power to private educational institutions in Korea. We propose an optimal system using the HOMER (Hybrid Optimization Model for Electric Renewables) program. The result is that when private educational institutions adopt a hybrid renewable energy system, the renewable fraction is negligible in the grid-connected type, but it is analyzed that the NPC (Net Present Cost) will greatly increase because it exceeds 70% in the stand-alone type. The difference between on-grid and off-grid is significant, so it must be taken into account when devising renewable energy policies. [ABSTRACT FROM AUTHOR]

The demand for efficient water use and automatic systems has been increasing due to the frequent drought damage to crops as a result of climate change, the shortage of water resources in rural areas, and the aging of farmers. The existing automatic irrigation systems reduce the amount of labor required for irrigation and maintain soil moisture. However, the irrigation threshold criteria are user-determined as opposed to being automated according to input objectives such as improving crop productivity and saving water. In this study, an algorithm that could automatically determine suitable soil moisture according to a database and an automatic irrigation system with intermittent irrigation for efficient water use were developed. An experiment was then conducted on the productivity of crops for protected cultivation according to the application of the system. As the frequency domain reflectometry (FDR) sensor used in this system measured the volumetric water content of the soil, the soil moisture tension corresponding with the set value was converted into the volumetric water content using a regression equation. The process of intermittent irrigation was defined by using the moisture movement modeling of Hydrus 2D to reduce water loss on the soil surface and allow moisture to penetrate the soil unobstructed. An experimental field of a tomato farm was divided into empirical manual and controlled automatic irrigation plots. A total of 97.3% of the soil moisture values in the −33 kPa-controlled automatic irrigation plot and 96.6% of the soil moisture values in the −25 kPa-controlled automatic irrigation plot were within each set range during the first cropping season. During the second cropping season, a total of 94.8% of the soil moisture values in the −33 kPa-controlled automatic irrigation plot was within the set range. Compared with the empirical manual irrigation plot, the water productivity in the first cropping season was 113.9% in the −33 kPa-controlled automatic irrigation plot and 106.3% in the −25 kPa-controlled automatic irrigation plot. In the second cropping season, the water productivity was 117.3% in the −33 kPa-controlled automatic irrigation plot. Therefore, an automatic irrigation system applied with intermittent irrigation could be critical to increasing agricultural production and improving water-use efficiency. [ABSTRACT FROM AUTHOR]

Grain boundaries in graphene are formed by the joining of islands during the initial growth stage, and these boundaries govern transport properties and related device performance (1,2). Although information on [...]

Complex network analyses have provided clues to improve power-grid stability with the help of numerical models. The high computational cost of numerical simulations, however, has inhibited the approach, especially when it deals with the dynamic properties of power grids such as frequency synchronization. In this study, we investigate machine learning techniques to estimate the stability of power-grid synchronization. We test three different machine learning algorithms—random forest, support vector machine, and artificial neural network—training them with two different types of synthetic power grids consisting of homogeneous and heterogeneous input-power distribution, respectively. We find that the three machine learning models better predict the synchronization stability of power-grid nodes when they are trained with the heterogeneous input-power distribution rather than the homogeneous one. With the real-world power grids of Great Britain, Spain, France, and Germany, we also demonstrate that the machine learning algorithms trained on synthetic power grids are transferable to the stability prediction of the real-world power grids, which implies the prospective applicability of machine learning techniques on power-grid studies. [ABSTRACT FROM AUTHOR]

The synchronization stability has been analyzed as one of the important dynamical characteristics of power grids. In this study, we bring the operational perspective to the synchronization stability analysis by counting not only full but also partial synchronization between nodes. To do so, we introduce two distinct measures that estimate the operational resilience of power-grid nodes: functional secureness centrality and functional robustness centrality. We demonstrate the practical applicability of the measures in a model network motif and an IEEE test power grid. As a case study of German power grid, we reveal that the modular structure of a power grid and particular unidirectional current flow determine the distribution of the operational resilience of power-grid nodes. Reproducing our finding on clustered benchmark networks, we validate the modular effect on power grid stability and confirm that our measures can be the insightful tools to understand the power grids' synchronization dynamics. [ABSTRACT FROM AUTHOR]