다중혈관 관상동맥 환자에서 y-문합을 이용하여 양쪽 내흉동맥만을 사용한 우회술의 조기 성적

http://ieeexplore.ieee.org/iel5/5610941/5624686/05624745.pdf

Power electronics

The Transmission Control Protocol.

This paper presents a review of the turn of events and points of view of biogas in and its utilization for power, heat and in transport in the European Union (EU) and its Member States. Biogas creation has expanded in the EU, empowered by the sustainable power strategies, notwithstanding monetary, ecological and atmosphere benefits, to arrive at 18 billion m3 methane (654 PJ) in 2015, speaking to half of the worldwide biogas creation. The EU is the world chief in biogas power creation, with more than 10 GW introduced and various 17,400 biogas plants, in contrast with the worldwide biogas limit of 15 GW in 2015. In the EU, biogas conveyed 127 TJ of warmth and 61 TWh of power in 2015; about half of absolute biogas utilization in Europe was bound to warm age. Europe is the world's driving maker of biomethane for the utilization as a vehicle fuel or for infusion into the petroleum gas network, with 459 plants in 2015 creating 1.2 billion m3 and 340 plants taking care of into the gas network, with a limit of 1.5 million m3. Around 697 biomethane filling stations guaranteed the utilization 160 million m3 of biomethane as a transport fuel in 2015.

https://www.longdom.org/open-access/biogas-developments-and-perspectives-in-europe.pdf

Biogas Developments and Perspectives in Europe

A club is a voluntary group of individuals who derive mutual benefit from sharing one or more of the following: production costs, the members' characteristics, or a good characterized by excludable benefits. When production costs are shared and the good is purely private, a private good club is being analyzed (McGuire 1972; Wiseman 1957). If membership characteristics differ and motivate sharing, then membership fees will differ among members (DeSerpa 1977; Scotchmer 1994b; Scotchmer and Wooders 1987). Such fees are nonanonymous , inasmuch as a fee structure is related to the identity and attributes of a member. The focus of our analysis is the sharing of an excludable (rivalrous) public good, which we term a club good . Unless otherwise specified, crowding is assumed to be independent of the individual and hence anonymous. A number of aspects of the club definition deserve highlighting. Privately owned and operated clubs must be voluntary; members choose to belong because they anticipate a net benefit from membership. Thus, the utility jointly derived from membership and from the consumption of other goods must exceed the utility associated with nonmembership status. Furthermore, the net gain in utility from membership must exceed or equal membership fees or toll payments. This voluntarism serves as the first characteristic by which to distinguish between pure public goods and club goods. In the case of a pure public good, voluntarism may be absent, since the good might harm some recipients (e.g., defense to a pacifist, fluoridation to someone who opposes its use).

The theory of externalities, public goods, and club goods: Homogeneous clubs and local public goods

In a typical outdoor environment, the propagation of radio waves is usually random in nature, to the extent that the characterization of the wireless channel often becomes very difficult. Several models have been developed to predict the average Received Signal Strength (RSS) for specified distance ranges. However, the use of deterministic models requires high computational efficiency while the prediction results of empirical models may not be as accurate as required. On machine learning approach, the performances of multi-layered feed-forward network models are limited by slow convergence and local minimum, such that a global optimal solution is not guaranteed. In this paper, Extreme Learning Machine (ELM) algorithm is considered in the development of an optimal path loss prediction model for outdoor propagation scenario. Single Hidden Layer Feed-forward Neural Networks (SHLFNNs) are trained and tested with the path loss data that were computed based on the RSS data of a commercial 1800 MHz base station located along Lagos-Badagry highway in Nigeria. The training speed, learning effectiveness, and the generalization ability of Artificial Neural Network Back-Propagation (ANN-BP) and ELM algorithms are analysed. Experimental results show that ELM models are 140 times faster to train than the ANN-BP models. On prediction accuracy, the outputs of ELM, ANN-BP, Okumura–Hata, and COST-231 models have Root Mean Squared Error (RMSE) values of 2.896, 2.449, 7.456, and 6.116 dB respectively; and regression coefficient (R) values of 0.959, 0.973, 0.935, and 0.935 respectively, when compared to the target variable of the training dataset. When the models were tested with new input data that were excluded from the training process, RMSE values of 4.250, 6.622, 8.732, and 7.087 respectively; and R values of 0.893, 0.876, 0.904, and 0.904 respectively are obtained. In conclusion, the findings of this study confirm that ELM algorithm guarantees an optimal path loss model with fast training convergence, high prediction accuracy, and good generalization ability for radio network planning and optimization in outdoor environments.

Outdoor Path Loss Predictions Based on Extreme Learning Machine

Experimental Investigation of a Vapour Compression Refrigeration System with 15nm TiO2-R600a Nano-Refrigerant as the Working Fluid

The projected rise in wireless communication traffic has necessitated the advancement of energy-efficient (EE) techniques for the design of wireless communication systems, given the high operating costs of conventional wireless cellular networks, and the scarcity of energy resources in low-power applications. The objective of this paper is to examine the paradigm shifts in EE approaches in recent times by reviewing traditional approaches to EE, analyzing recent trends, and identifying future challenges and opportunities. Considering the current energy concerns, nodes in emerging wireless networks range from limited-energy nodes (LENs) to high-energy nodes (HENs) with entirely different constraints in either case. In view of these extremes, this paper examines the principles behind energy-efficient wireless communication network design. We then present a broad taxonomy that tracks the areas of impact of these techniques in the network. We specifically discuss the preponderance of prediction-based energy-efficient techniques and their limits, and then discuss the trends in renewable energy supply systems for future networks. Finally, we recommend more context-specific energy-efficient research efforts and cross-vendor collaborations to push the frontiers of energy efficiency in the design of wireless communication networks.

/pdf/energy-efficient-design-techniques-in-next-generation-spcx7r4fhh.pdf

Aderemi A. Atayero

Papers

Outdoor Path Loss Predictions Based on Extreme Learning Machine

Experimental Investigation of a Vapour Compression Refrigeration System with 15nm TiO2-R600a Nano-Refrigerant as the Working Fluid

Energy Efficient Design Techniques in Next-Generation Wireless Communication Networks: Emerging Trends and Future Directions

Received signal strength and local terrain profile data for radio network planning and optimization at GSM frequency bands.

A framework for selecting the location of very large photovoltaic solar power plants on a global/supergrid