2,991 research outputs found

    Effect of quadratic residue diffuser (QRD) microwave energy on root-lesion nematode, Prathlenchus penetrans

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    In this study, quadratic residue diffuser (QRD) microwave energy was used to control nematode Pratylenchus penetrans in soil. Microwave energy is a physical method that has been used to manage nematodes. This approach provides rapid heat transfer to soil with no lingering residual effects. QRD microwave radiation at a frequency of 2450 MHz was used to irradiate sandy clay loam soil containing a nematode layer.The pot dimensions were 17 cm high, 10 cm diameter and exposure times used were 10, 20, 30, 40, 50, 60, and 120 s. The soil water content was set at 0, 10, 20, 30, and 40%, respectively, based on dry mass. Total mortality was calculated at soil depths of 5, 10 and 15 cm. Microwave treatment time and soil water content significantly affected nematode mortality; also, longer exposure time and decreased soil moisture content resulted in an greater total mortality. However, 120 s radiation was demonstrated to be the most effective for killing nematodes at all soil water contents and soil depths.Keywords: Microwave energy, nematodes, pepper, Pratylenchus penetrans, physical control, quadratic residue diffuserAfrican Journal of Biotechnology Vol. 12(18), pp. 2471-247

    COMPARISON OF THE RISK FACTORS OF KOREAN ADOLESCENT SUICIDE RESIDING IN HIGH SUICIDAL REGIONS VERSUS THOSE IN LOW SUICIDAL REGIONS

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    Background: The suicide rate of the youth in South Korea has been increasing, and suicide of the youth still has been the most common cause of death since 2007. We aimed to determine the trends and the regional risk factors of youth suicide in South Korea from 2001 to 2010. Subjects and Methods: We used the data from the National Statistical Office to calculate the standardized suicide rates and various regional data including population census, employment, and labor. To calculate the effect of individual risk factors, we used the data from the fourth Korean Youth Risk Behavior Web-based Survey (KYRBWS-VI). Conditional autoregressive model for regional standardized mortality ratio (SMR) using inter-regional spatial information was fitted. Results: Suicide rates of adolescents aged 12 to 18 was from 3.5 per 100,000 people in 2001 and 5.3 per 100,000 in 2010. There were no significant gender difference in suicide rates, however, the number of suicides among adolescents aged 15-18 accounted for four times than those of adolescents ages 12-14. High proportion of late adolescents, higher number of recipients of national basic livelihood, and higher number of adolescents who treated with depression were related to elevated suicide rate of adolescent. Total sleep time of adolescents and regional unemployment rate were negatively associated with the suicide risk of respective regions. Conclusions: Age distribution, economic status, total sleep time, and the number of adolescent patients with depression were different between those in low and in high adolescent suicidal regions in Korea. Our findings suggest that preferential appliance of adolescent suicide prevention program for regions by considering those factors may be important steps to reduce adolescent suicide in Korea

    Development of KAISTSAT-4 Expanding the Role of Small Satellite for Scientific Research

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    The fourth Korean small satellite, KAISTSAT-4, is under development by Satellite Technology Research Center (SaTReC) of the Korea Advanced Institute of Science and Technology (KAIST). The KAISTSAT-4 program was commenced on October 1998 with multiple mission objectives, which include exploring space science, deploying satellite-based data collection system and development of precision star sensor. Despite severe constraints on mass and size, these advanced science and engineering payloads are expected to deliver various useful results and exhibit the unique role of small satellite. We present an overview of the KAISTSAT-4 mission and describe its current status. Finally the prospect of future small satellite programs is briefly introduced

    Subcutaneous Sacrococcygeal Myxopapillary Ependymoma in Asian Female:A Case Report

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    Subcutaneous sacrococcygeal myxopapillary ependymoma is extremely rare tumor that has a tendency to develop in children and adolescents. There have been several case reports and sporadic reports in the literature. However, no case has been reported in an Asian patient, to the best of our knowledge. We describe a 25-year-old Asian female patient with a subcutaneous sacrococcygeal myxopapillary ependymoma that had been clinically diagnosed as a pilonidal cyst. The tumor was treated successfully by surgical excision and the patient is doing well without evidence of local recurrence or distant metastasis at 2 years after surgery.

    Direct Preference-based Policy Optimization without Reward Modeling

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    Preference-based reinforcement learning (PbRL) is an approach that enables RL agents to learn from preference, which is particularly useful when formulating a reward function is challenging. Existing PbRL methods generally involve a two-step procedure: they first learn a reward model based on given preference data and then employ off-the-shelf reinforcement learning algorithms using the learned reward model. However, obtaining an accurate reward model solely from preference information, especially when the preference is from human teachers, can be difficult. Instead, we propose a PbRL algorithm that directly learns from preference without requiring any reward modeling. To achieve this, we adopt a contrastive learning framework to design a novel policy scoring metric that assigns a high score to policies that align with the given preferences. We apply our algorithm to offline RL tasks with actual human preference labels and show that our algorithm outperforms or is on par with the existing PbRL methods. Notably, on high-dimensional control tasks, our algorithm surpasses offline RL methods that learn with ground-truth reward information. Finally, we show that our algorithm can be successfully applied to fine-tune large language models.Comment: NeurIPS 202
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