Nakamoto proposed a new solution to transact value via the internet. And since 2009, blockchain technology has expanded and diversified. It has, however, proven to be inefficient in the way it achieves its outcomes, especially through the proof of work protocol. Other developers are promoting alternative methods but, as yet, none has superseded proof of work. The competing protocols illuminate a key feature of the blockchain community, namely, its inability to create consensus in a decentralized community. Because of this lack of consensus, the formation of standards is particularly difficult to achieve. At best standards are contested sites, and we examine three such sites where some form of agreement over standards will be essential if blockchain is to evolve successfully. These three sites are blockchain governance, smart contracts, and interoperability of blockchains. We argue that because standards’ formation is a contested and contingent process, the blockchain community will persist in creating difficulties and barriers for itself until it is able to resolve internal conflicts.

Aiming at the influence of coupling coefficient variation on the output voltage of a high-power LCC-S topology inductively coupled power transfer (ICPT) system, a synchronous three-phase triple-parallel Buck converter is used as the voltage adjustment unit. The control method for the three-phase current sharing of synchronous three-phase triple-parallel Buck converter and the constant voltage output ICPT system under the coupling coefficient variation is studied. Firstly, the hybrid model consisting of the circuit averaging model of the three-phase triple-parallel Buck converter and the generalized state-space average model for the LCC-S type ICPT system is established. Then, the control methods for three-phase current sharing of the synchronous three-phase triple-parallel Buck converter and constant voltage output of ICPT system are studied to achieve the multi-objective integrated control of the system. Finally, a 3.3 kW wireless charging system platform is built, the experimental results have verified the effectiveness of the proposed modeling and control method, and demonstrated the stability of the ICPT system.

In this article, we investigate using deep neural networks with different word representation techniques for named entity recognition (NER) on Turkish noisy text. We argue that valuable latent features for NER can, in fact, be learned without using any hand-crafted features and/or domain-specific resources such as gazetteers and lexicons. In this regard, we utilize character-level, character n-gram-level, morpheme-level, and orthographic character-level word representations. Since noisy data with NER annotation are scarce for Turkish, we introduce a transfer learning model in order to learn infrequent entity types as an extension to the Bi-LSTM-CRF architecture by incorporating an additional conditional random field (CRF) layer that is trained on a larger (but formal) text and a noisy text simultaneously. This allows us to learn from both formal and informal/noisy text, thus improving the performance of our model further for rarely seen entity types. We experimented on Turkish as a morphologically rich language and English as a relatively morphologically poor language. We obtained an entity-level F1 score of 67.39% on Turkish noisy data and 45.30% on English noisy data, which outperforms the current state-of-art models on noisy text. The English scores are lower compared to Turkish scores because of the intense sparsity in the data introduced by the user writing styles. The results prove that using subword information significantly contributes to learning latent features for morphologically rich languages.

The aim of this paper is to introduce a new stochastic order based on the residual lifetimes of two nonnegative dependent random variables and the stochastic precedence order. We develop some characterizations and preservation properties of this stochastic order. In addition, we study some of its reliability properties and its relation with other existing stochastic orders. One of the possible applications in reliability theory has also been discussed.

Is the current formulation of multiset theory, which is based on sets and multiplicities of their elements, adequate? We exhibit both mathematical and metamathematical reasons which should cause one to rethink the definition. Some problems with multiset theory in its accepted formulation concern even the basic operations of union, intersection, and complement; others, more deeply rooted, concern Cartesian products, relations, or morphisms. We compare current definitions and conclude that the problems of multiset theory need to be resolved at the fundamental level of sets and mappings (or equivalent constructs) with multiplicities introduced only as a secondary concept. As a consequence, we propose to define multisets as families. A mapping establishes the connection to the familiar theory of multisets. Without losing anything, our proposal is simple and provides for an elegant mathematical theory.

The popularity of smart cars is increasing around the world as they offer a wide range of services and conveniences. These smart cars are equipped with a variety of sensors generating a large amount of data, many of which are critical. Besides, there are multiple parties involved in the lifespan of a smart car, such as manufacturers, car owners, government agencies, and third-party service providers who also generate data about the vehicle. In addition to managing and sharing data among these entities in a secure and privacy-friendly way which is a great challenge itself, there exists a trust deficit about some types of data as they remain under the custody of the car owner (e.g. satellite navigation and mileage data) and can easily be manipulated. In this article, we propose a blockchain-assisted architecture enabling the owner of a smart car to create an immutable record of every data, called the autobiography of a car, generated within its lifespan. We also explain how the trust about this record is guaranteed by the immutability characteristic of the blockchain. Furthermore, the article describes how the proposed architecture enables a secure and privacy-preserving mechanism for sharing of smart car data among different parties.