This study evaluated the potential distribution of the potato tuber moth. This species severely impacts global potato production, especially in China and India, which have the world’s largest potato production. We developed two indices considering host plant availability and production in addition to climatic suitability, which was simulated using the CLIMEX model. Thus, three different indices were used to project potential distribution of the potato tuber moth under a climate change scenario: (1) climatic suitability (ecoclimatic index (EI)) (EIM), (2) climatic suitability combined with host plant availability (EIN1), and (3) climatic suitability combined with host plant production (EIN2). Under the current climate, EIM was high in southern India and central to southern China, while EIN1 and EIN2 were approximately 38% and 20% lower than EIM, respectively. Under the Special Report on Emissions Scenario A1B, the potato tuber moth would probably not occur in India, but its distribution could be extended to the north, reaching N47°. The areas with the highest climatic suitability by potato tuber moth based on three indices were Sichuan and Karnataka in response to climate change. These areas require adequate pest control, such as prevention of spread through transport of potato seed or by using cold storage facilities.

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Ricania shantungensis is a pest causiong problems in many crops. We tested the possibility of controling Ricania shantungensis using essential oil of Valeriana fauriei which were extracted by three different methods (steam distillation, solvent and supercritical extraction). Steam distillation were showed the most high mortality to adult (1,040 µL/mL) and nymph (2,370 µL/mL) of R. shantungensis. The yield of steam distillation extraction was 0.67%, which was lower than other methods. However, it is determined that steam extraction was showed higher efficiency by considering time and cost. The result of this study showed the possibility of control R. shantungensis by essential oil of V. fauriei.

Chord recognition is an important task since chords are highly abstract and descriptive features of music. For effective chord recognition, it is essential to utilize relevant context in audio sequence. While various machine learning models such as convolutional neural networks (CNNs) and recurrent neural networks (RNNs) have been employed for the task, most of them have limitations in capturing long-term dependency or require training of an additional model.In this work, we utilize a self-attention mechanism for chord recognition to focus on certain regions of chords. Training of the proposed bi-directional Transformer for chord recognition (BTC) consists of a single phase while showing competitive performance. Through an attention map analysis, we have visualized how attention was performed. It turns out that the model was able to divide segments of chords by utilizing adaptive receptive field of the attention mechanism. Furthermore, it was observed that the model was able to effectively capture long-term dependencies, making use of essential information regardless of distance.