Acaricidal and oviposition deterring effects of santalol identified in sandalwood oil against two-spotted spider mite, Tetranychus urticae Koch (Acari: Tetranychidae)

Thirty-four plant essential oils were screened for their acaricidal and oviposition deterrent activities against two-spotted spider mite (TSSM), Tetranychus urticae Koch (Acari: Tetranychidae), in the laboratory using a leaf-dip bioassay. From initial trials, sandalwood and common thyme oils were observed to be the most effective against TSSM adult females. Subsequent trials confirmed that only sandalwood oil was significantly active (87.2 ± 2.9% mortality) against TSSM adult females. Sandalwood oil also demonstrated oviposition deterring effects based on a 89.3% reduction of the total number of eggs on leaf disks treated with the oil. GC–MS analysis revealed that the main components of the sandalwood oil were α-santalol (45.8%), β-santalol (20.6%), β-sinensal (9.4%), and epi-β-santalol (3.3%). A mixture of α- and β-santalol (51.0:22.9, respectively) produced significantly higher mortality (85.5 ± 2.9%) and oviposition deterrent effects (94.7% reduction in the number of eggs) than the control. Phytotoxicity was not shown on rose shoots to which a 0.1% solution of sandalwood oil was applied

Sensitivity Analysis of a Regional Nutrient Budget Model for Two Regions with Intensive Livestock Farming in Korea

Nutrient budget is one of the Organization for Economic Co-operation and Development (OECD) agri-environmental indicators. A model was developed for regional nutrient management in Korea. In this study, a sensitivity analysis of parameters of a nutrient budget model was performed for two regions with intensive livestock farming in Korea. In the nitrogen budget, gross nitrogen surplus (GNS) and hydrospheric nitrogen surplus (hNS) were analyzed separately. For GNS, the most influential parameters were excreta production per swine in Hongseong and excreta production per beef cattle in Anseong. For hNS, N content of solid manure in swine and beef cattle were the most influential. For GNS and phosphorus surplus (PS), excreta production per livestock and the N(P) in the excreta of livestock were the predominant parameters. Livestock excreta showed a high sensitivity in both areas because the livestock headcount was high; thus, the excreta accounted for a large share of the input parameters for the model. Therefore, calculating reliable regional nutrient budgets would require further research on excreta production per livestock and the N(P) excretion in livestock. The nutrient budget model could be implemented for agri-environmental policy e.g., environment friendly regional livestock farming and sustainable integrated crop livestock systems

Cyberbullying Detection Based on Emotion

Due to the detrimental consequences caused by cyberbullying, a great deal of research has been undertaken to propose effective techniques to resolve this reoccurring problem. The research presented in this paper is motivated by the fact that negative emotions can be caused by cyberbullying. This paper proposes cyberbullying detection models that are trained based on contextual, emotions and sentiment features. An Emotion Detection Model (EDM) was constructed using Twitter datasets that have been improved in terms of its annotations. Emotions and sentiment were extracted from cyberbullying datasets using EDM and lexicons based. Two cyberbullying datasets from Wikipedia and Twitter respectively were further improved by comprehensive annotation of emotion and sentiment features. The results show that anger, fear and guilt were the major emotions associated with cyberbullying. Subsequently, the extracted emotions were used as features in addition to contextual and sentiment features to train models for cyberbullying detection. The results demonstrate that using emotion features and sentiment has improved the performance of detecting cyberbullying by 0.5 to 0.6 recall. The proposed models also outperformed the state-of-the-art models by a 0.7 f1-score. The main contribution of this work is two-fold, which includes a comprehensive emotion-annotated dataset for cyberbullying detection, and an empirical proof of emotions as effective features for cyberbullying detection

Struvite Precipitation for Sustainable Recovery of Nitrogen and Phosphorus from Anaerobic Digestion Effluents of Swine Manure

In this study, we propose the application of struvite precipitation for the sustainable recovery of nitrogen (N) and phosphorus (P) from anaerobic digestion (AD) effluents derived from swine manure. The optimal conditions for four major factors that affect the recovery of N and P were derived by conducting batch experiments on AD effluents obtained from four AD facilities. The optimal conditions were a pH of 10.0, NH4-N:Mg:PO4-P molar ratio of 1:1.4:1, mixing intensity of 240 s−1, and mixing duration of 2 min. Under these optimal conditions, the removal efficiencies of NH4-N and PO4-P were approximately 74% and 83%, respectively, whereas those of Cu and Zn were approximately 74% and 79%, respectively. Herein, a model for swine manure treatment that incorporates AD, struvite precipitation, and biological treatment processes is proposed. We applied this model to 85 public biological treatment facilities in South Korea and recovered 4722 and 51 tons/yr of NH4-N and PO4-P, respectively. The economic analysis of the proposed model’s performance predicts a lack of profitability due to the high cost of chemicals; however, this analysis does not consider the resulting protection of the hydrological environment. Field-scale studies should be conducted in future to prove the effectiveness of the model