DTER: Schedule Optimal RF Energy Request and Harvest for Internet of Things

We propose a new energy harvesting strategy that uses a dedicated energy source (ES) to optimally replenish energy for radio frequency (RF) energy harvesting powered Internet of Things. Specifically, we develop a two-step dual tunnel energy requesting (DTER) strategy that minimizes the energy consumption on both the energy harvesting device and the ES. Besides the causality and capacity constraints that are investigated in the existing approaches, DTER also takes into account the overhead issue and the nonlinear charge characteristics of an energy storage component to make the proposed strategy practical. Both offline and online scenarios are considered in the second step of DTER. To solve the nonlinear optimization problem of the offline scenario, we convert the design of offline optimal energy requesting problem into a classic shortest path problem and thus a global optimal solution can be obtained through dynamic programming (DP) algorithms. The online suboptimal transmission strategy is developed as well. Simulation study verifies that the online strategy can achieve almost the same energy efficiency as the global optimal solution in the long term

Developing a Hybrid Dictionary-based Bio-entity Recognition Technique

Background: Bio-entity extraction is a pivotal component for information extraction from biomedical literature. The dictionary-based bio-entity extraction is the first generation of Named Entity Recognition (NER) techniques. Methods: This paper presents a hybrid dictionary-based bio-entity extraction technique. The approach expands the bio-entity dictionary by combining different data sources and improves the recall rate through the shortest path edit distance algorithm. In addition, the proposed technique adopts text mining techniques in the merging stage of similar entities such as Part of Speech (POS) expansion, stemming, and the exploitation of the contextual cues to further improve the performance. Results: The experimental results show that the proposed technique achieves the best or at least equivalent performance among compared techniques, GENIA, MESH, UMLS, and combinations of these three resources in F-measure. Conclusions: The results imply that the performance of dictionary-based extraction techniques is largely influenced by information resources used to build the dictionary. In addition, the edit distance algorithm shows steady performance with three different dictionaries in precision whereas the context-only technique achieves a high-end performance with three difference dictionaries in recall.X1133Ysciescopu

Clinical applicability of quantitative nailfold capillaroscopy in differential diagnosis of connective tissue diseases with Raynaud's phenomenon

Background/PurposeNailfold capillaroscopy is a useful tool to distinguish primary from secondary Raynaud's phenomenon (RP) by examining the morphology of nailfold capillaries but its role in disease diagnosis is not clearly established. The purpose of this study was to evaluate the roles of quantitative nailfold capillaroscopy in differential diagnosis of connective tissue diseases (CTDs) with RP.MethodsThe data between the year 2005 and 2009 were retrieved from the nailfold capillaroscopic database of National Taiwan University Hospital (NTUH). Only the data from the patients with RP were analyzed. The criteria for interpretation of capillaroscopic findings were predefined. The final diagnoses of the patients were based on the American College of Rheumatology classification criteria for individual diseases, independent of nailfold capillaroscopic findings. The sensitivity and the specificity of each capillaroscopic pattern to the diseases were determined.ResultsThe data from a total of 67 patients were qualified for the current study. We found the sensitivity and specificity of scleroderma pattern for systemic sclerosis (SSc) were 89.47% and 80%, and the specificity of the early, active, and late scleroderma patterns for SSc reached 87.5%, 97.5%, and 95%, respectively. The sensitivity/specificity of systemic lupus erythematosus (SLE) pattern for SLE and polymyositis/dermatomyositis (PM/DM) pattern for PM/DM were 33.33%/95.45% and 60%/96.3%, respectively. The sensitivity/specificity of mixed connective tissue disease (MCTD) pattern for MCTD were 20%/100%.ConclusionThe nailfold capillaroscopic (NC) patterns may be useful in the differential diagnosis of CTDs with RP. The NC patterns for SSc and PM/DM are both sensitive and specific to the diseases, while the SLE and MCTD patterns exhibit high specificity but relatively low sensitivity

Improvement on PDP Evaluation Performance Based on Neural Networks and SGDK-means Algorithm

With the purpose of improving the PDP (policy decision point) evaluation performance, a novel and efficient evaluation engine, namely XDNNEngine, based on neural networks and an SGDK-means (stochastic gradient descent K-means) algorithm is proposed. We divide a policy set into different clusters, distinguish different rules based on their own features and label them for the training of neural networks by using the K-means algorithm and an asynchronous SGDK-means algorithm. Then, we utilize neural networks to search for the applicable rule. A quantitative neural network is introduced to reduce a server’s computational cost. By simulating the arrival of requests, XDNNEngine is compared with the Sun PDP, XEngine and SBA-XACML. Experimental results show that 1) if the number of requests reaches 10,000, the evaluation time of XDNNEngine on the large-scale policy set with 10,000 rules is approximately 2.5 ms, and 2) in the same condition as 1), the evaluation time of XDNNEngine is reduced by 98.27%, 90.36% and 84.69%, respectively, over that of the Sun PDP, XEngine and SBA-XACML

Dense Polarized Positrons from Laser-Irradiated Foil Targets in the QED Regime

Dense positrons are shown to be effectively generated from laser-solid interactions in the strong-field quantum electrodynamics (QED) regime. Whether these positrons are polarized has not yet been reported, limiting their potential applications. Here, by QED particle-in-cell simulations including electron-positron spin and photon polarization effects, we investigate a typical laser-solid setup that an ultraintense linearly polarized laser irradiates a foil target with $\mu$m-scale-length preplasma. We find that once the positron yield becomes appreciable with the laser intensity exceeding $10^{24}~\rm W/\rm cm^2$, the positrons are obviously polarized. The polarized positrons can acquire $>30\%$ polarization degree and $>30$ nC charge with a flux of $10^{12}\,{\rm sr}^{-1}$. The polarization relies on the deflected angles and can reach 60\% at some angles and energies. The angularly-dependent polarization is attributed to the asymmetrical laser fields positrons undergo in the skin layer of overdense plasma, where the radiative spin-flip and radiation reaction play significant roles. The positron polarization is robust and could generally appear in future 100-PW-class laser-solid experiments for various applications.Comment: 6 pages, 5 figures, with Supplemental Materia