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Please wait a minute Author Center. Current Issue , Vol. DOI: It is believed that the blockchain that creates the bitcoin miracle is with a much broader application prospect. The blockchain provides anonymity, decentralization, tampering, trust-free consensus mechanisms that removes constraints of various types of system applications and technical possibilities for the realization of many ideas. The blockchain is developed rapidly in the industrial sectors such as virtual currency, financial technology and initial coin offering, however, the research on underlying technology and basic theory is still relatively backward.
Starting from the overview of blockchain platform, the article describes the main research content and progress from the point of view of peer to peer protocol, consensus algorithm and intelligent contract respectively.
From the perspective of blockchain application, several application scenarios are discussed as well. Extreme learning machine ELM achieves faster training speed and higher classification accuracy, compared with other widely used classifiers, such as back propagation BP , support vector machine SVM , spectral clustering SC , and so forth.
However, ELM suffers from some drawbacks:1 ELM utilizes the calculation of inverse matrix for training, which cannot be adopted in the embedded system; 2 the training time of ELM increases dramatically for large-scale applications. Therefore, SELM can be adopted in the embedded system. It is proven that SELM achieves lower complexity than other widely used algorithms.
Furthermore, simulations based on practical datasets indicate that the classification accuracy of SELM is higher than traditional ELM and other widely used classifiers with shorter training time.
A production planning method of supply chain based on cloud manufacturing platform was presented. The purposes of this method were to minimize the makespan, total service cost and total transportation cost. In order to effectively solve the presented model, a genetic algorithm and an improved genetic-annealing algorithm were designed.
Meanwhile, a model for selecting the efficient algorithms was established based on the makespan and total cost. The results of two algorithms in different situations were compared by simulation example, and then a selection criteria of the algorithms was given. The simulation results shown that the proposed method can provide a reasonable production plan for the supply chain based on the cloud manufacturing platform.
Focusing on the nodes authentication problem of clustered wireless sensor network, the paper proposes a lightweight authentication and key negotiation scheme using hash function and exclusive or XOR operation. The anonymity mechanism is introduced into the authentication process, which can thwart the traffic analysis attacks, and protect the privacy of nodes.
In addition, by analyzing and comparing the anti-attack performance and the resource overhead of the authentication scheme, the results show that the proposed scheme can provide good authentication and also have a lower overhead at the cost of computation, storage and communication.
Because of anonymity of Bitcoin accounts, Bitcoin may be popular in some illegal trades and black market, such as the Silk Road. The author proposed an improved heuristic approaches-based method to cluster Bitcoin addresses and identify different addresses controlled by the same user.
Three heuristic evidences were employed jointly. Through an elaborately designed experimental analysis, the precision and recall of the proposed method was verified. Furthermore, the efficiency with different iterations was analyzed, which can provide guidance in designing efficient clustering algorithm. A new adaptive user scheduling was proposed to solve the multi-user scheduling problem of millimeter wave multi-user multiple-input single-output MU-MISO downlink system, in which users are located in the finite areas.
Firstly, the area's user will be grouped based on the access information of user, and then the radio frequency chain will be properly allocated for each area based on the user density.
Secondly, based on the RF's number and the range of angle of departure of each area, the communication beams will be designed. Finally, the best matched user will be selected to each pre-designed random beam for minimum inter-interference of beams in the base station, based on the channel quality indicator and the channel state information CSI feedback only from the matched users. It is shown that, the proposed scheme can achieve good rate performance.
Although there is a certain gap compared with the semi-orthogonal user selection with zero-forcing beamforming that requires full CSI feedback from all users, it reduces feedback overhead to some degree. Moreover, the allocation of RF resources guarantees the fairness of user communication to a certain extent. Densely deployed small cells suffer from the problem of severe co-channel interference among the neighbouring cells and user equipments UEs in the network are confronted with a challenge of intensive energy consumption in inter-frequency scanning IFS.
The proposed method achieves the purpose of energy saving for UE by tremendously reducing the number of invalid inter-frequency detection. Simulation shows that the proposed method can effectively improve the probability of detecting small cells and the energy efficiency of UEs in the network.
In order to predict the link quality accurately, a link quality prediction model was proposed to predict link quality for sensor networks based on improved least square support vector regression machine LS-SVR.
The rough set RS was introduced to reduce the link quality metrics so as to extract the effective characteristic metrics of the link quality. Experiments show that compared with the experts advice-based prediction model, the proposed prediction model achieves better accuracy. To solve the performance overhead and resource consumption brought by an antivirus software when performing operations of virus scanning and virus database updating, an antivirus framework named HyperAV based on virtualization was proposed.
HyperAV was able to provide antivirus capability for virtual machine files with low performance overhead, a mechanism of access control and isolation at the granularity of sector level was also provided. The process of virus scanning was optimized by monitoring the sector change information of a running virtual machine, which had a significant acceleration effect to the virus scanning process of virtual machines.
HyperAV was constructed by a front and a rear end with each used as a controller and an antivirus worker, the data needed by antivirus software was redirected to server clusters so that duplications of virus database updating could be avoided, and performance overload brought by antivirus software running inside virtual machines was avoided.
A prototype system based on kernel-based virtual machine virtualization platform was realized, the results showed that HyperAV was able to provide antivirus capability with low performance overload for virtual machines. A new vocal effort detection method based on model fusion was presented. By analyzing the ability to discriminate the vocal effort modes, the spectral information entropy feature which contains more salient information regarding the vocal effort level was proposed.
Then, the complementary models were presented to achieve the fusion of the spectrum features, Mel-frequency cepstral coefficients and spectral information entropy feature. Experiments are conducted on isolated words test set, and the proposed method achieves The results show the spectral information entropy has the best performance among the three kinds of features and the complementary models can integrate the three kinds of features effectively.
In order to solve the problem that the distribution network topology is difficult to monitor, a novel data driven algorithm is proposed to generate the distribution network topology. Firstly, the least absolute shrinkage and selection operator LASSO algorithm is adopted to obtain the correlation coefficient matrix of all buses in the network.
Then, the "and" logic rule and the criteria based on the voltage correlation analysis model are utilized to correct the matrix. Finally, the topology is reconstructed based on the modified matrix. Simulation results show that the proposed algorithm can efficiently and accurately generate the un-loopy and loopy distribution network topology based on timing voltage data without using any prior knowledge of distribution network.
The algorithm could be employed as an auxiliary decision-making method to generate the operational distribution network topology. S -parameters of Sub-Miniature-A SMA coaxial connector was calculated based on the equivalent circuit method, the high frequency characteristics of the connector effectively was simulated thereafter.
Especially for the case of connector contact degradation, the parameters in the equivalent circuit model were calculated, and the relationship between the S -parameter and degraded level was established. The developed model could be used as a fault feature for the life prediction, reliability evaluation and fault diagnosis of the connector. In order to improve node localization accuracy, a node localization algorithm based on adaptive random-walk module was presented for wireless sensor networks.
First, a novel metric for relative distance among node sensors was modeled by applying the idea of random walk to the connectivity of system topology. Then an adaptive approach was designed to increase the validity of the metric.
At last, node positions were finally obtained by embedding the metric in the classical localization algorithm distance vector-hop DV-Hop. The relationship between the Fermat number and the T function generated by single cycle T-function's maximal periodic sequence were found.
The 2-adic complexity of the k th coordinate sequence and the state output sequence were studied. Values and bounds of the 2-adic complexity were obtained. It is shown that the two sequences generated by the single cycle T-function cannot form l -sequences. Node energy efficiency and energy consumption balance are two important aspects in wireless sensor networks WSN , which have a direct impact on network life.
In view of the fact that the underground sensor network cannot take account of both of them, an adaptive data collection method of node energy efficiency and energy consumption balance AEBADA was proposed on the basis of the hybrid communication mode.
The ring width and the minimum communication radius of the node in the detection area are determined by comparative analysis. In selecting the relay node, not only the distance between nodes and the residual energy, but also the reliability of the link and the number of nodes adjacent to the candidate nodes are considered. Simulation shows that, compared with other similar hybrid communication scheme and some typical data collection methods, the AEBADA approach has obvious advantages in terms of node efficiency, energy consumption balance and routing reliability.
It is not affected by the density and distribution of nodes and suitable for underground communication environment. In order to reduce the feedback bits of channel state information CSI in multi-cell cooperative multiple input multiple output MIMO system, a multi-cell partial cooperative MIMO precoding algorithm is proposed. Firstly, a joint channel error model including channel estimation error, quantization error and delay error, is established.
Then, by using zero forcing and signal to leakage and interference ratio, a double-layer MIMO robust precoding algorithm is proposed. Numerical analysis and simulation results show that, compared with the traditional multi-cell MIMO system precoding algorithms, the bit error rate and the sum rate capacity are slightly worse, but the proposed algorithm has great advantage in CSI feedback bits.
In the heterogeneous network based on multi-source power supply, two user selection algorithms based on energy cost were proposed considering the influences of supply rate of renewable energy, peak-valley time-of-use tariff strategy of the traditional energy and the fairness among users.
The processes of user selection can be divided into two stages which are allocating the users on the cell edge and determining the users who can get service, and a balanced load distribution was obtained. According to the relationships among power consumption, energy cost and the fairness among users, two exponential utility functions were constructed in the direct and the indirect method, respectively.
Simulations show that the direct and indirect method can significantly reduce the power consumption and energy cost of base station system. Compared with the indirect method, the direct method employs an adaptive price factor, which changes with the electricity price of traditional price and the supply rate of renewable energy.
Thus, it can reduce the power consumption at a lower level of energy cost. Two important data sources in social networks, i. When building topic models, the topic distributions of contents for each user at each time are obtained.
And features used for prediction are extracted by summarizing the topical information based on the social network structure. Finally, these prediction features are exploited to dynamically predict user interest via several classification methods, such as logistic regression and support vector machine.
The effectiveness of the proposed method is illustrated based on the Sina Weibo dataset. In order to improve the accuracy of evidence conflict measure and evidence fusion, a local conflict information redistribution algorithm based on evidence ranking fusion is proposed.
The evidence conflict is firstly measured based on the evidence distance and the conflict coefficient in the new algorithm, and on this basis, the order of evidence fusion is optimized and the conflict measure algorithm of different focal elements in different pieces of evidence is improved.
Further, during the sequential fusion of evidence, the new conflict measure results of the evidence and the focal element are applied to the redistribution of local conflict information. The performances of the new algorithm and the related algorithms are comparatively analyzed in theory and application, and the results show that the evidence fusion effect of the new algorithm is more stable and reliable.
Aiming at the problem of communication signal service type identification, the author proposes a polynomial fitting factor that extracts the power spectrum of the signal using the linear regression algorithm and the polynomial fitting model in machine learning field as a unified feature of the signal to construct the training set. A multi-layer fully connected neural network classifier model was built on depth learning platform.
Compared with the traditional ones, this method has advantages of unifying the radio signal without the need of individual service to extract the personalized features. In view of high computational complexity, low compression efficiency and data recovery rate of current data compression methods of wireless sensor network, a wireless sensor networks WSN data compression method based on a cluster head base station separation structure is proposed.
Based on the monolayer cluster structure of WSN, the method requests the terminal sensor node to collect data at equal intervals and transmit data in segments at first. Then the spatial correlation data compression method is performed to eliminate the space noise and space redundancy of data in cluster head node.
Resolve IPv4 Fragmentation, MTU, MSS, and PMTUD Issues with GRE and IPsec
Please wait a minute Author Center. Current Issue , Vol. DOI: It is believed that the blockchain that creates the bitcoin miracle is with a much broader application prospect. The blockchain provides anonymity, decentralization, tampering, trust-free consensus mechanisms that removes constraints of various types of system applications and technical possibilities for the realization of many ideas.
Clere," Bank Underground November Monetary Policy Made in China? Grexit deja vu: is Europe really ready to let its Grecians go? Draghi," Handelsblatt , 12 September The rules of central banking are made to be broken , Financial Times , 22 August Rose, VoxEU , 5 June Losing Interest , Project Syndicate ,11 April Yuan Dive? Bernanke's "Tapering Crisis"-Made in Shanghai?
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Pulmonary Disease and Critical Care Medicine Fellowship
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Bitcoin and Future of Cryptocurrency
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The latter one has emphasized the necessity of reshaping the very structure of the economy, reformulating business models, financial systems, and economic policies. Dissemination and widespread use of new technologies has become crucial, especially in the face of multifaceted problems of economic social, ecological, and political nature that governments, regulators, entrepreneurs, and individuals have to face in the post-pandemic world. At the same time the very technologies — especially those connected with broadly understood digitalization — changes significantly.
Terrorist financing is the economic basis of terrorist activities and the lifeline of terrorist organizations. In recent years, terrorist organizations have gradually come to use cryptocurrency to finance their activities based on traditional ways of raising funds. The anonymity of cryptocurrency is attractive to terrorist organizations, but its use remains at a low level. To explore the future development ability of cryptocurrency in terrorist financing, we study its internal characteristics and development status, as well as the supervisory systems of international organizations. This study hopes to enhance our understanding of the potential risks of cryptocurrency and serve as a reference for the fight against terrorist financing in the international community. Terrorism creates social panic, impacts the social order, and destroys economic development through extreme means, such as violence, intimidation, and kidnapping, to achieve political or ideological purposes.
The fellowship is accredited by ACGME as a three year combined program that provides state-of-the-art training in both pulmonary disease and critical care medicine. We are incredibly proud of our fellows and our program, and are fortunate to be part of an amazing Department of Internal Medicine and outstanding medical campus at VCU. Both campuses have advanced simulation facilities that supplement bedside training in bronchoscopy, airway management, bedside ultrasound, and critical care procedures. Our fellows receive outstanding clinical and scholarly training in both pulmonary and critical care medicine, benefiting from direct experience, training, and mentorship in our comprehensive and advanced subspecialty programs including the Interventional Pulmonary Program, Pulmonary Hypertension Center of Excellence, Adult Cystic Fibrosis Program, Interstitial Lung Disease Clinic, and Multisystem Sarcoid Clinic, and Severe Asthma Clinic. Our division has been expanding the last few years under the leadership of Dr. Patrick Nana-Sinkam, adding new faculty in advanced ILD, interventional pulmonary, critical care medicine, ethics, pulmonary hypertension, lung cancer, and advanced asthma.
Each Carnegie Mellon course number begins with a two-digit prefix that designates the department offering the course i. Although each department maintains its own course numbering practices, typically, the first digit after the prefix indicates the class level: xx-1xx courses are freshmen-level, xx-2xx courses are sophomore level, etc. Depending on the department, xx-6xx courses may be either undergraduate senior-level or graduate-level, and xx-7xx courses and higher are graduate-level.