Neo-sentimentalism and the bodily attitudinal theory of emotions

Section 1 of this thesis investigates one issue in meta-ethics, namely, the nature of moral judgments. What are moral judgments? What does it mean by wrong when we assert Killing is wrong? Neo-sentimentalism is a meta-ethical theory which holds that the judgment that killing wrong is the judgment that it is appropriate to have a particular negative emotion towards the action. In other words, to judge that murder is wrong is to judge that we have a right reason for having a negative emotion towards the behavior. In the framework of neo-sentimentalism, the concepts of wrongness consist of negative emotions. If the moral judgment is the judgment that it is appropriate to have a negative emotion towards the action, and the concept of wrongness contains a negative emotion, then the following question is what emotions are. In section 2, I endorse the bodily attitudinal theory of emotions, a view which holds that a conscious physiological reaction which induces behavior disposition and the change of facial expression and internal organs is necessary for having an emotion. This section also articulates and replies to three major objections towards the bodily attitudinal theory of emotion

Enhancing household-level load forecasts using daily load profile clustering

Forecasting the electricity demand for individual households is important for both consumers and utilities due to the increasing decentralized nature of the electricity system. Particularly, utilities often have very little information about their consumers except for aggregate building level loads, without knowledge of interior details about the household appliance sets or occupants. In this paper, we explore the possibility of enhancing the day-ahead load forecasts for hundreds of individual households by clustering their daily load profile history to obtain each consumer's specific typical consumption patterns. The clustering method is based on load profile shape using the Earth Mover's Distance metric to calculate similarity between load profiles. The forecasting methods then predict the next day shape from the empirical probability of previous cluster transitions in the consumer's load history and estimate the magnitude either by using historical load relationships with temperature and forecast temperatures or previous day consumption levels. The generated forecasts are compared to a benchmark Multiple Linear Regression (MLR) day-ahead forecast and persistence forecasts for all individuals. While at the aggregate level the MLR method represents a significant improvement over persistence forecasts, on an individual level we find that the best forecasting model is specific to the individual. In particular, we find that the MLR model produces lower errors when consumers have a consistent daily temperature response and the cluster model with previous day magnitude produces lower errors for consumers whose consumption changes abruptly in magnitude for several days at a time. Our work adds to the state of knowledge surrounding individual household load forecasting and demonstrates the potential for cluster-based methodologies to enhance short term load forecasts

DyMo: Dynamic Monitoring of Large Scale LTE-Multicast Systems

LTE evolved Multimedia Broadcast/Multicast Service (eMBMS) is an attractive solution for video delivery to very large groups in crowded venues. However, deployment and management of eMBMS systems is challenging, due to the lack of realtime feedback from the User Equipment (UEs). Therefore, we present the Dynamic Monitoring (DyMo) system for low-overhead feedback collection. DyMo leverages eMBMS for broadcasting Stochastic Group Instructions to all UEs. These instructions indicate the reporting rates as a function of the observed Quality of Service (QoS). This simple feedback mechanism collects very limited QoS reports from the UEs. The reports are used for network optimization, thereby ensuring high QoS to the UEs. We present the design aspects of DyMo and evaluate its performance analytically and via extensive simulations. Specifically, we show that DyMo infers the optimal eMBMS settings with extremely low overhead, while meeting strict QoS requirements under different UE mobility patterns and presence of network component failures. For instance, DyMo can detect the eMBMS Signal-to-Noise Ratio (SNR) experienced by the 0.1% percentile of the UEs with Root Mean Square Error (RMSE) of 0.05% with only 5 to 10 reports per second regardless of the number of UEs

the case of Korea tourism organization

Thesis(Master) --KDI School:Master of Business Administration,2006masterpublishedby Nam-Chun Kim

Quantitative agreement of Dzyaloshinskii-Moriya interactions for domain-wall motion and spin-wave propagation

The magnetic exchange interaction is the one of the key factors governing the basic characteristics of magnetic systems. Unlike the symmetric nature of the Heisenberg exchange interaction, the interfacial Dzyaloshinskii-Moriya interaction (DMI) generates an antisymmetric exchange interaction which offers challenging opportunities in spintronics with intriguing antisymmetric phenomena. The role of the DMI, however, is still being debated, largely because distinct strengths of DMI have been measured for different magnetic objects, particularly chiral magnetic domain walls (DWs) and non-reciprocal spin waves (SWs). In this paper, we show that, after careful data analysis, both the DWs and SWs experience the same strength of DMI. This was confirmed by spin-torque efficiency measurement for the DWs, and Brillouin light scattering measurement for the SWs. This observation, therefore, indicates the unique role of the DMI on the magnetic DW and SW dynamics and also guarantees the compatibility of several DMI-measurement schemes recently proposed.Comment: 24 pages, 5 figure

Production of Reducing Sugars from Laminaria japonica by Subcritical Water Hydrolysis

AbstractThis study was to investigate the production of reducing sugars in hydrolysates from raw and deoiled Laminaria japonica produced by subcritical water hydrolysis. Deoiled Laminaria japonica was collected by supercritical carbon dioxide (SCO2) extraction process. Experiments were performed in a batch-type reactor with stirring. It investigated that the effects of reaction temperature and acetic acid as catalyst on content of reducing sugar production. The addition of acetic acid led to an increase in content of reducing sugar. But Removal of oil in Laminaria japonica by SCO2 and increasing of temperature led to decrease in content of reducing sugar production. The highest content of reducing sugar was 814.10mg/100g raw dried sample at 200°C, adding of 1% acetic acid as catalyst