• Environmental and Resource Economics Review
• /
• v.16 no.1
• /
• pp.99-127
• /
• 2007

When the margin between available capacity and demand is thin in a liberalized electricity market, prices rise steeply and system reliability is threatened. The principal response to these circumstances is often an assumption that price spikes and electricity shortages are the result of a failure to build sufficient new supplying facilities. It is, of course, often the case that additional investments in generation and network facilities would improve reliability, and such investments are often needed. But focusing on additional generation and transmission facilities for restoring balance to the grid overlooks the essential fact that reliability is a function of the relationship between supply and demand, imposing unnecessary costs on electric system. When the relationship is out of balance, the search for solutions must consider not only investments supply-side resources but also cost-effective demand-side resources such as accelerated load management, efficiency measures, and price-responsive load programs. Integrating demand resources into electricity markets can add enormous value to the electric system, widening the capacity margin, lowering costs and enhancing system reliability at the same time. This paper studies several challenges now facing electricity markets: demand-side management-especially, economic effects of demand response, potential reliability problems, market and system operation, CBP market improvements and so on. The paper concludes with a series of policy recommendations in five areas: (i) The Effects of efficient improvement to incorporate demand responses and demand-side resources into modem electricity markets, (ii) Fosteing price based demand response and (iii) improving incentive based demand response, (iv) strengthen demand response analysis and valuation, (v) integrating demand response into resource planning and adopting enabling technologies.

• Asian-Australasian Journal of Animal Sciences
• /
• v.15 no.10
• /
• pp.1496-1501
• /
• 2002

A $2{\times}3$ factorial arrangement of treatments was used in a completely randomized design to determine the effects of dietary Zn on performance and immune response of acutely endotoxemic growing pigs (n=96, mean BW=24.9 kg). Factors included 1) intramuscular injection of $10{\mu}g/kg$ BW of Escherichia coli lipopolysaccharide (LPS) or control and 2) supplemental Zn at 10, 50, or 150 ppm. Diets were fed beginning after weaning (initial body weight=7.6 kg) in the nursery and continued for 16 d into the grower phase. The basal corn-soybean meal grower diet contained 1% lysine and 34.3 ppm Zn. Pigs were acclimated for 12 d in the growerfinishing facility before LPS treatment on d 13. Gain, feed intake, and feed efficiency were unaffected by dietary Zn. Feed intake decreased (p<0.10) and gain/feed was greater (p<0.10) from d 13 to d 16 for pigs injected with LPS. Serum Zn and alkaline phosphatase activity increased (p<0.05) with increasing Zn levels. The febrile response to LPS peaked at 6 h post exposure and pigs were afebrile within 12 h. Rectal temperature was greater (p<0.05) in pigs receiving 50 and 150 ppm Zn than in pigs supplemented with 10 ppm Zn. In vivo cellular immune response, measured on d 13 by skin thickness response to phytohemagglutinin (PHA), was greater after 6 h (p<0.05) in pigs fed 10 ppm Zn and exposed to LPS compared to all other treatments, but was not affected at 12, 24 or 48 h. Zinc did not affect mitogen induced lymphocyte proliferation. Zinc supplemented at 50 or 150 ppm resulted in an enhanced febrile response in pigs subjected to iatrogenic endotoxemia, but did not affect pig performance or immune response measurements.

• Journal of Internet Computing and Services
• /
• v.18 no.5
• /
• pp.69-77
• /
• 2017

In this paper, a disaster response scheme based on deep learning and augmented reality technology is proposed and a network resource reservation scheme is presented accordingly. The features of deep learning, augmented reality technology and its relevance to the disaster areas are explained. Deep learning technology can be used to accurately recognize disaster situations and to implement related disaster information as augmented reality, and to enhance disaster response capabilities by providing disaster response On-site disaster response agent, ICS (Incident Command System) and MCS (Multi-agency Coordination Systems). In the case of various disasters, the fire situation is focused on and it is proposed that a plan to strengthen disaster response capability effectively by providing fire situation recognition based on deep learning and augmented reality information. Finally, a scheme to secure network resources to utilize the disaster response method of this paper is proposed.

• Journal of Digital Convergence
• /
• v.12 no.7
• /
• pp.123-132
• /
• 2014

This study examines the influence of the physical environment on tourist's emotional response and behavioral intention in the tourist destination. The research was mainly performed in leading tourist destinations of Busan Metropolitan City (Haeundae, Gwanganri & Songjeong area) by the sampling survey method in April-May 2013. The results demonstrate as follows. Firstly, the tourist's convenience and amenity in physical environments have significant impacts on the negative feeling of emotional response, but attractiveness did not influence in negative feeling. By contrast, the attractiveness and amenity the a of physical environments give significant impacts on the positive feeling of emotional response but convenience did not work in positive response. Secondly, both positive and negative feeling of emotional responses give meaningful impacts on Tourist's behavior intention. According to the meaningful relationship between physical environment and emotional response, the regional characteristics and effective marketing management of tourism resource and preparedness should be considered in setting up tourist facilities.

• The Transactions of The Korean Institute of Electrical Engineers
• /
• v.63 no.8
• /
• pp.1116-1120
• /
• 2014

With the shift of the energy paradigm from supply side management to demand side management, demand resource management and demand response plays an important role in the energy industry. As a consequency, a lot of researches have been done to provide a suitable demand response system. However, most of the demand response systems are based on the propriety products that cannot be modified. In this paper, we are proposing an automated demand response system using an EnerNOC provided open source code. We implemented the demand response server (VTN) and demand response client (VEN), and validated the OpenADR2.0 compliances using the open source code. We also used an Arduino microcontoller to demonstrate the communication schemes to control various devices.

• Journal of Electrical Engineering and Technology
• /
• v.9 no.3
• /
• pp.789-795
• /
• 2014

In a demand response (DR) market run by independent system operators (ISOs), load aggregators are important market participants who aggregate small retail customers through various DR programs. A load aggregator can minimize the allocation cost by efficiently allocating its demand response resources (DRRs) considering retail customers' characteristics. However, the uncertain response behaviors of retail customers can influence the allocation strategy of its DRRs, increasing the economic risk of DRR allocation. This paper presents a risk-based DRR allocation method for the load aggregator that takes into account not only the physical characteristics of retail customers but also the risk due to the associated response uncertainties. In the paper, a conditional value-at-risk (CVaR) is applied to deal with the risk due to response uncertainties. Numerical results are presented to illustrate the effectiveness of the proposed method.

• Journal of Forest and Environmental Science
• /
• v.38 no.2
• /
• pp.110-121
• /
• 2022

Leaf functional traits widely have been used to understand the environmental controls of resource utilization strategy of plants along the environmental gradients. By using key leaf functional traits, we quantified the relationships between leaf traits and local climate throughout the distributional range of Rhododendron caucasicum Pall. in eastern and western Georgian mountains (the South Caucasus). Our results revealed, that all traits showed high levels of intraspecific variability across study locations and confirmed a strong phenotypic differentiation of leaf functional variation along the east-west longitudinal gradient in response to the local climate; out of the explored climatic variables, the moisture factors related to precipitation and number of precipitation and dry days for winter and growth seasons were more strongly related to leaf trait variation than the elevation and air temperature. Among studied leaf traits, the leaf specific area (SLA) showed the highest level of variability indicating the different resource utilization strategies of eastern and western-central Rh. caucasicum individuals. High SLA leaves for western-central Caucasian individuals work in relatively resource-rich environments (more humid in terms of precipitation amount and the number of precipitation days in winter) and could be explained by preferential allocation to photosynthesis and growth, while eastern Caucasian samples work in resource-poor environments (less humid in terms of precipitation amount and the number of precipitation days in winter) and the retention of captured resources is a higher priority appearing in a low SLA leaves. However, more evidence from a broader study of the species throughout its distribution range by including additional environmental factors and molecular markers are needed for firmer conclusions of intraspecific variability of Rh. caucasicum.

• Korean Journal of Food Science and Technology
• /
• v.46 no.5
• /
• pp.588-592
• /
• 2014

Under optimized condition mild pressure in combination with specific temperature for heat treatment transform the 6-gingerol into 6-shogaol. The purpose of this study was to optimize the conditions used for heat treatment under pressure for increasing 6-shogaol content in ginger (Zingiber officinale Roscoe). A central composite experimental design was used to evaluate the effects of application temperature ($70-130^{\circ}C$) and temperature-holding time (95-265 min) on the transformation of 6-shogaol. The experimental values were shown to be in significantly good agreement with the predicted values (adjusted determination coefficient, $R^2{_{Adj}}=0.9857$). 6-Shogaol content increased as the application temperature and temperature-holding time increased. By analyzing the response surface plots, the optimum conditions of heat treatment (temperature and time) for increasing 6-shogaol content were found to be $127^{\circ}C$ and 109 min, respectively. Under these optimal conditions, the predicted 6-shogaol content was 3.98 mg/g dried ginger. The adequacy of the model equation for predicting the optimum response values was effectively verified by the validation data.

• KSII Transactions on Internet and Information Systems (TIIS)
• /
• v.7 no.8
• /
• pp.1825-1842
• /
• 2013

Rapid growth of the IT industry has led to significant energy consumption in the last decade. Data centers swallow an enormous amount of electrical energy and have high operating costs and carbon dioxide excretions. In response to this, the dynamic consolidation of virtual machines (VMs) allows for efficient resource management and reduces power consumption through the live migration of VMs in the hosts. Moreover, each client typically has a service level agreement (SLA), this leads to stipulations in dealing with energy-performance trade-offs, as aggressive consolidation may lead to performance degradation beyond the negotiation. In this paper we propose a heuristic based resource allocation of VM selection and a VM allocation approach that aims to minimize the total energy consumption and operating costs while meeting the client-level SLA. Our experiment results demonstrate significant enhancements in cloud providers' profit and energy savings while improving the SLA at a certain level.

• Journal of Information Processing Systems
• /
• v.18 no.3
• /
• pp.389-401
• /
• 2022

The business of Internet of Vehicles (IoV) is growing rapidly, and the large amount of data exchange has caused problems of large mobile network communication delay and large energy loss. A strategy for resource allocation of IoV communication based on mobile edge computing (MEC) is thus proposed. First, a model of the cloud-side collaborative cache and resource allocation system for the IoV is designed. Vehicles can offload tasks to MEC servers or neighboring vehicles for communication. Then, the communication model and the calculation model of IoV system are comprehensively analyzed. The optimization objective of minimizing delay and energy consumption is constructed. Finally, the on-board computing task is coded, and the optimization problem is transformed into a knapsack problem. The optimal resource allocation strategy is obtained through genetic algorithm. The simulation results based on the MATLAB platform show that: The proposed strategy offloads tasks to the MEC server or neighboring vehicles, making full use of system resources. In different situations, the energy consumption does not exceed 300 J and 180 J, with an average delay of 210 ms, effectively reducing system overhead and improving response speed.