This study aims to design, develop, and evaluate the traction performance of an electric all-wheel-drive (AWD) tractor based on the power transmission and electric systems. The power transmission system includes the electric motor, helical gear reducer, planetary gear reducer, and tires. The electric system consists of a battery pack and charging system. An engine-generator and charger are installed to supply electric energy in emergency situations. The load measurement system consists of analog (current) and digital (battery voltage and rotational speed of the electric motor) components using a controller area network (CAN) bus. A traction test of the electric AWD tractor was performed towing a test vehicle. The output torques of the tractor motors during the traction test were calculated using the current and torque curves provided by the motor manufacturer. The agricultural work performance is verified by comparing the torque and rpm (T–N) curve of the motor with the reduction ratio applied. The traction is calculated using torque and specifications of the wheel, and traction performance is evaluated using tractive efficiency (TE) and dynamic ratio (DR). The results suggest a direction for the improvement of the electric drive system in agricultural research by comparison with the conventional tractor through the analysis of the agricultural performance and traction performance of the electric AWD tractor.

This research proposes a methodology for the selection of input variables based on eXplainable AI (XAI) for energy consumption prediction. For this purpose, the energy consumption prediction model (R² = 0.871; MAE = 2.176; MSE = 9.870) was selected by collecting the energy data used in the building of a university in Seoul, Republic of Korea. Applying XAI to the results from the prediction model, input variables were divided into three groups by the expectation of the ranking-score ($F{q}^{var}$) (10 ≤ Strong, 5 ≤ Ambiguous < 10, and Weak < 5), according to their influence. As a result, the models considering the input variables of the Strong + Ambiguous group (R² = 0.917; MAE = 1.859; MSE = 6.639) or the Strong group (R² = 0.916; MAE = 1.816; MSE = 6.663) showed higher prediction results than other cases (p < 0.05 or 0.01). There were no statistically significant results between the Strong group and the Strong + Ambiguous group (R²: p = 0.408; MAE: p = 0.488; MSE: p = 0.478). This means that when considering the input variables of the Strong group ($F{q}^{var}$: Year = 14.8; E-Diff = 12.8; Hour = 11.0; Temp = 11.0; Surface-Temp = 10.4) determined by the XAI-based methodology, the energy consumption prediction model showed excellent performance. Therefore, the methodology proposed in this study is expected to determine a model that can accurately and efficiently predict energy consumption.

Machine vision with deep learning is a promising type of automatic visual perception for detecting and segmenting an object effectively; however, the scarcity of labelled datasets in agricultural fields prevents the application of deep learning to agriculture. For this reason, this study proposes weakly supervised crop area segmentation (WSCAS) to identify the uncut crop area efficiently for path guidance. Weakly supervised learning has advantage for training models because it entails less laborious annotation. The proposed method trains the classification model using area-specific images so that the target area can be segmented from the input image based on implicitly learned localization. This way makes the model implementation easy even with a small data scale. The performance of the proposed method was evaluated using recorded video frames that were then compared with previous deep-learning-based segmentation methods. The results showed that the proposed method can be conducted with the lowest inference time and that the crop area can be localized with an intersection over union of approximately 0.94. Additionally, the uncut crop edge could be detected for practical use based on the segmentation results with post-image processing such as with a Canny edge detector and Hough transformation. The proposed method showed the significant ability of using automatic perception in agricultural navigation to infer the crop area with real-time level speed and have localization comparable to existing semantic segmentation methods. It is expected that our method will be used as essential tool for the automatic path guidance system of a combine harvester.

The purpose of this study is to develop and evaluate a self-microemulsifying drug delivery system (SMEDDS) to improve the oral absorption of poorly water-soluble olaparib. Through the solubility test of olaparib in various oils, surfactants and co-surfactants, pharmaceutical excipients were selected. Self-emulsifying regions were identified by mixing the selected materials at various ratios, and a pseudoternary phase diagram was constructed by synthesizing these results. The various physicochemical properties of microemulsion incorporating olaparib were confirmed by investigating the morphology, particle size, zeta potential, drug content and stability. In addition, the improved dissolution and absorption of olaparib were also confirmed through a dissolution test and a pharmacokinetic study. An optimal microemulsion was generated in the formulation of Capmul^® MCM 10%, Labrasol^® 80% and PEG 400 10%. The fabricated microemulsions were well-dispersed in aqueous solutions, and it was also confirmed that they were maintained well without any problems of physical or chemical stability. The dissolution profiles of olaparib were significantly improved compared to the value of powder. Associated with the high dissolutions of olaparib, the pharmacokinetic parameters were also greatly improved. Taken together with the results mentioned above, the microemulsion could be an effective tool as a formulation for olaparib and other similar drugs.

Energy consumption plays a significant role in global warming. In order to achieve carbon neutrality and enhance energy efficiency through a stable energy supply, it is necessary to pursue the development of innovative architectures designed to optimize and analyze time series data. Therefore, this study presents a new architecture that highlights the critical role of preprocessing in improving predictive performance and demonstrates its scalability across various energy domains. The architecture, which discerns patterns indicative of time series characteristics, is founded on three core components: data preparation, process optimization methods, and prediction. The core of this architecture is the identification of patterns within the time series and the determination of optimal data processing techniques, with a strong emphasis on preprocessing methods. The experimental results for heat energy demonstrate the potential for data optimization to achieve performance gains, thereby confirming the critical role of preprocessing. This study also confirms that the proposed architecture consistently enhances predictive outcomes, irrespective of the model employed, through the evaluation of five distinct prediction models. Moreover, experiments extending to electric energy validate the architecture’s scalability and efficacy in predicting various energy types using analogous input variables. Furthermore, this research employs explainable artificial intelligence to elucidate the determinants influencing energy prediction, thereby contributing to the management of low-carbon energy supply and demand.