THE effect of Srandom DOPANT fluctuation on threshold voltage and drain current variation in junctionless nanotransistors

We investigate the effect of dopant random fluctuation on threshold voltage and drain current variation in a two-gate nanoscale transistor. We used a quantum-corrected technology computer aided design simulation to run the simulation (10000 randomizations). With this simulation, we could study the effects of varying the dimensions (length and width), and thicknesses of oxide and dopant factors of a transistor on the threshold voltage and drain current in subthreshold region (off) and overthreshold (on). It was found that in the subthreshold region the variability of the drain current and threshold voltage is relatively fixed while in the overthreshold region the variability of the threshold voltage and drain current decreases remarkably, despite the slight reduction of gate voltage diffusion (compared with that of the subthreshold). These results have been interpreted by using previously reported models for threshold current variability, load displacement, and simple analytical calculations. Scaling analysis shows that the variability of the characteristics of this semiconductor increases as the effects of the short channel increases. Therefore, with a slight increase of length and a reduction of width, oxide thickness, and dopant factor, we could correct the effect of the short channel.     

Low complexity quantum private queries protocol

Private information retrieval(PIR) is an important privacy protection issue of secure multi-party computation, but the PIR protocols based on classical cryptography are vulnerable because of new technologies,such as quantum computing and cloud computing. The quantum private queries(QPQ) protocols available, however, has a high complexity and is inefficient in the face of large database. This paper, based on the QKD technology which is mature now, proposes a novel QPQ protocol utilizing the key dilution and auxiliary parameter. Only N quits are required to be sent in the quantum channel to generate the raw key, then the straight k bits in the raw key are added bitwise to dilute the raw key, and a final key is consequently obtained to encrypt the database. By flexible adjusting of auxiliary parameters θ and k, privacy is secured and the query success ratio is improved. Feasibility and performance analyses indicate that the protocol has a high success ratio in first-trial query and is easy to implement, and that the communication complexity of O(N) is achieved.     

Nash equilibrium cooperative transmission strategy considering competition

In cooperative communications, employing the idle relay node for forwarding data can effectively improve the transmission rate and performance of the wireless networks. Therefore, various relay node selection algorithms are proposed. However, most of the existing algorithms just focus on the benefit caused by relay nodes, while ignoring the negative effect of the backoff duration time which does not exist in direct transmission, when more than one flow compete for the same relay node. To this end, considering both advantage and adverse impact caused by the relay node, we adopt the game-theoretic approach and propose a cooperative transmission strategy based on the Nash equilibrium, named the Nash Equilibrium Cooperative Transmission Strategy (NECTS). The NECTS can not only guarantee the gain of each flow, but also maximize the system performance, and improve the system throughput effectively. Simulation results show that the NECTS can take the advantage of cooperative communications. Meanwhile, it can avoid the unnecessary competition, and its performance outperforms that of the existing methods.     

A pseudo-likelihood approach for estimating diagnostic accuracy of multiple binary medical tests

Latent class models with crossed subject-specific and test(rater)-specific random effects have been proposed to estimate the diagnostic accuracy (sensitivity and specificity) of a group of binary tests or binary ratings. However, the computation of these models are hindered by their complicated Monte Carlo Expectation–Maximization (MCEM) algorithm. In this article, a class of pseudo-likelihood functions is developed for conducting statistical inference with crossed random-effects latent class models in diagnostic medicine. Theoretically, the maximum pseudo-likelihood estimation is still consistent and has asymptotic normality. Numerically, our results show that not only the pseudo-likelihood approach significantly reduces the computational time, but it has comparable efficiency relative to the MCEM algorithm. In addition, dimension-wise likelihood, one of the proposed pseudo-likelihoods, demonstrates its superior performance in estimating sensitivity and specificity.     

Assessing the impact of geometric design intent annotations on parametric model alteration activities

The effective representation and communication of design intent plays a crucial role in CAD model alteration activities. In history-based parametric modeling systems, design intent information is usually expressed implicitly within the model. However, there is evidence that suggests that an explicit representation can increase productivity and quality and facilitate the transferring of design knowledge throughout the different stages of the product lifecycle. In this paper, we assess the effectiveness of 3D annotations as mechanisms for explicit design intent representation and examine their impact in model alteration processes that require a direct interaction with the model's geometry. We present the results of a series of studies aimed at measuring user performance and model quality in two scenarios. First, we hypothesized that annotations are valuable tools to provide design information when inadequate modeling assumptions can be made by designers. Second, we evaluated annotations as tools to communicate design decisions when multiple options are available. In both cases, results show statistically significant benefits of annotated models, suggesting the use of this technique as a valuable approach to improve design intent communication.     

Two-agent two-machine flowshop scheduling with learning effects to minimize the total completion time

We study a two-agent scheduling problem in a two-machine permutation flowshop with learning effects. The objective is to minimize the total completion time of the jobs from one agent, given that the maximum tardiness of the jobs from the other agent cannot exceed a bound. We provide a branch-and-bound algorithm for the problem. In addition, we present several genetic algorithms to obtain near-optimal solutions. Computational results indicate that the algorithms perform well in either solving the problem or efficiently generating near-optimal solutions.     

Weak Delocalization in Graphene on a Ferromagnetic Insulating Film

Graphene has been predicted to develop a magnetic moment by proximity effect when placed on a ferromagnetic film, a promise that could open exciting possibilities in the fields of spintronics and magnetic data recording. In this work, the interplay between the magnetoresistance of graphene and the magnetization of an underlying ferromagnetic insulating film is studied in detail. A clear correlation between both magnitudes is observed but through a careful modeling of the magnetization and the weak localization measurements, that such correspondence can be explained by the effects of the magnetic stray fields arising from the ferromagnetic insulator is found. The results emphasize the complexity arising at the interface between magnetic and 2D materials.     

The co-learning process in healthcare professionals: Assessing user satisfaction in virtual communities of practice

Communities of practice are nowadays an important concept in the healthcare sector. Particularly, the intensive use of ICT has allowed their creation into a virtual environment – Virtual Communities of Practice (VCoPs) developing optimal conditions to make possible the collaborative learning process. The VCoPs antecedents can be situated on social network phenomenon, where individuals with different traits but a common interest/objective are linked, use ICT potency (especially social media) to interchange information, experiences and contents among them. And as a result, people create and share knowledge, and learn collaboratively. VCoP users have a higher satisfaction level in the collaborative learning process when they can: (1) Achieve benefits related to patient diagnosis and treatment (cost reductions, faster management, quality and accuracy of diagnosis, etc.); (2) Increase the share capital of participants and creating networks of trusted individuals. Given the interest in this topic, the objective of this work is to identify the factors that determine user satisfaction in relation to Community Practice (CoP) and the process of building shared knowledge. For this, a sample of 130 Spanish health professionals participating in an online community, and developed in a virtual community of practice, is discussed. The results obtained from an analysis of logistic regression show evidence of the perception of efficiency and effectiveness in collaboration with the members of the VCoP as positively influencing the perceived satisfaction with the CoP. Also, the degree of individual participation in the community affects the degree of perceived satisfaction. The conclusions provide interesting strategic recommendations in the management process of the CoP.     

Predictors of job seekers’ self-disclosure on social media

Social media-based screening is a well-known practice to both recruiters and job seekers. Little is known, however, about how job seekers present themselves on social media, i.e. ‘self-disclosure’, for employment purposes. This study builds on the theories of hyperpersonal computer-mediated communication, self-efficacy and social exchange to examine job seekers’ professional online image concerns, social media self-efficacy, and perceptions of social media effectiveness in the job search as predictors of inappropriate and career-oriented self-disclosures on these media. Findings from a sample of 3374 Italian respondents showed that career-oriented self-disclosure was predicted by all three factors, whereas inappropriate self-disclosure was only predicted by social media self-efficacy. Furthermore, the relationship between professional online image concerns and inappropriate self-disclosure was moderated by age, education and work experience, but not by gender. Theoretical and practical implications are discussed, and directions for future research are suggested.