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

The threat of plastic waste pollution in African countries is increasing exponentially since the World Health Organisation declared the coronavirus infection as a pandemic. Fundamental to this growing threat are multiple factors, including the increased public consumption for single-use plastics, limited or non-existence of adequate plastic waste management infrastructures, and urbanisation. Plastics-based personal protective equipment including millions of surgical masks, medical gowns, face shields, safety glasses, protective aprons, sanitiser containers, plastics shoes, and gloves have been widely used for the reduction of exposure risk to Severe Acute Respiratory Syndrome (SARS) Coronavirus 2 (SARS-CoV-2). This paper estimates and elucidates the growing plethora of plastic wastes in African countries in the context of the current SARS-CoV-2 pandemic. A Fourier transform infrared (FTIR) spectral fingerprint indicates that face masks were characterised by natural and artificial fibres including polyester fibres, polypropylene, natural latex resin. Our estimate suggests that over 12 billion medical and fabric face masks are discarded monthly, giving the likelihood that an equivalent of about 105,000 tonnes of face masks per month could be disposed into the environment by Africans. In general, 15 out of 57 African countries are significant plastic waste contributors with Nigeria (15%), Ethiopia (8.6%), Egypt (7.6%), DR Congo (6.7%), Tanzania (4.5%), and South Africa (4.4%) topping the list. Therefore, this expert insight is an attempt to draw the attention of governments, healthcare agencies, and the public to the potential risks of SARS-CoV-2-generated plastics (COVID plastic wastes), and the environmental impacts that could exacerbate the existing plastic pollution epidemic after the COVID-19 pandemic.

COVID-19 Pandemic and Emerging Plastic-based Personal Protective Equipment Waste Pollution and Management in Africa.

The prominence of the place of cloud computing in future converged networks is incontestable. This is due to the obvious advantages of the cloud as a medium of storage with ubiquity of access platforms and minimal hardware requirements on
the user end. Secure delivery of data to and from the cloud is however a serious issue that needs to be addressed. We present in this paper the security issues affecting cloud computing and propose the use of homomorphic encryption as a panacea for dealing with these serious security concerns vis-a-vis the access to cloud data.

Security Issues in Cloud Computing: The Potentials of Homomorphic Encryption

It is very important to understand the input features and the neural network parameters required for optimal path loss prediction in wireless communication channels. In this paper, an extensive investigation was conducted to determine the most appropriate neural network parameters for path loss prediction in Very High Frequency (VHF) band. Field measurements were conducted in an urban propagation environment to obtain relevant geographical and network information about the receiving mobile equipment and quantify the path losses of radio signals transmitted at 189.25 MHz and 479.25 MHz. Different neural network architectures were trained with varying kinds of input parameters, number of hidden neurons, activation functions, and learning algorithms to accurately predict corresponding path loss values. At the end of the experimentations, the performance of the developed Artificial Neural Network (ANN) models are evaluated using the following statistical metrics: Mean Absolute Error (MAE), Mean Squared Error (MSE), Root Mean Squared Error (RMSE), Standard Deviation (SD) and Regression coefficient (R). Results obtained show that the ANN model that yielded the best performance employed four input variables (latitude, longitude, elevation, and distance), nine hidden neurons, hyperbolic tangent sigmoid (tansig) activation function, and the Levenberg-Marquardt (LM) learning algorithm with MAE, MSE, RMSE, SD and R values of 0.58 dB, 0.66 dB, 0.81 dB, 0.56 dB and 0.99 respectively. Finally, a comparative analysis of the developed model with Hata, COST 231, ECC-33 and Egli models showed that ANN-based path loss model has better prediction accuracy and generalization ability than the empirical models.

/pdf/determination-of-neural-network-parameters-for-path-loss-3zgw4egfwu.pdf

Determination of Neural Network Parameters for Path Loss Prediction in Very High Frequency Wireless Channel

In this paper, an optimal model is developed for path loss predictions using the Feed-Forward Neural Network (FFNN) algorithm. Drive test measurements were carried out in Canaanland Ota, Nigeria an...

https://www.tandfonline.com/doi/pdf/10.1080/23311916.2018.1444345

Optimal model for path loss predictions using feed-forward neural networks

Embedded systems technology is undergoing a phase of transformation owing to the novel advancements in computer architecture and the breakthroughs in machine learning applications. The areas of applications of embedded machine learning (EML) include accurate computer vision schemes, reliable speech recognition, innovative healthcare, robotics, and more. However, there exists a critical drawback in the efficient implementation of ML algorithms targeting embedded applications. Machine learning algorithms are generally computationally and memory intensive, making them unsuitable for resource-constrained environments such as embedded and mobile devices. In order to efficiently implement these compute and memory-intensive algorithms within the embedded and mobile computing space, innovative optimization techniques are required at the algorithm and hardware levels. To this end, this survey aims at exploring current research trends within this circumference. First, we present a brief overview of compute intensive machine learning algorithms such as hidden Markov models (HMM), k-nearest neighbors (k-NNs), support vector machines (SVMs), Gaussian mixture models (GMMs), and deep neural networks (DNNs). Furthermore, we consider different optimization techniques currently adopted to squeeze these computational and memory-intensive algorithms within resource-limited embedded and mobile environments. Additionally, we discuss the implementation of these algorithms in microcontroller units, mobile devices, and hardware accelerators. Conclusively, we give a comprehensive overview of key application areas of EML technology, point out key research directions and highlight key take-away lessons for future research exploration in the embedded machine learning domain.

https://www.mdpi.com/1424-8220/21/13/4412/pdf

Aderemi A. Atayero

Papers

COVID-19 Pandemic and Emerging Plastic-based Personal Protective Equipment Waste Pollution and Management in Africa.

Security Issues in Cloud Computing: The Potentials of Homomorphic Encryption

Determination of Neural Network Parameters for Path Loss Prediction in Very High Frequency Wireless Channel

Optimal model for path loss predictions using feed-forward neural networks

An Overview of Machine Learning within Embedded and Mobile Devices-Optimizations and Applications.