Modelling Clock Synchronization in the Chess gMAC WSN Protocol

We present a detailled timed automata model of the clock synchronization algorithm that is currently being used in a wireless sensor network (WSN) that has been developed by the Dutch company Chess. Using the Uppaal model checker, we establish that in certain cases a static, fully synchronized network may eventually become unsynchronized if the current algorithm is used, even in a setting with infinitesimal clock drifts

Evaluation of classical precipitation descriptions for γ′′(Ni3Nb−D022) in Ni-base superalloys

The growth/coarsening kinetics of γ′′(Ni3Nb−D022) precipitates have been found by numerous researchers to show an apparent correspondence with the classical (Ostwald ripening) equation outlined by Lifshitz, Slyozov and (separately) Wagner for a diffusion controlled regime. Nevertheless, a significant disparity between the actual precipitate size distribution shape and that predicted by LSW is frequently observed in the interpretation of these results, the origin of which is unclear. Analysis of the literature indicates one likely cause for this deviation from LSW for γ′′ precipitates is the “encounter” phenomenon described by Davies et al. (Acta Metall 28(2):179–189, 1980) that is associated with secondary phases comprising a high volume fraction. Consequently, the distributions of both γ′′ precipitates described in the literature (Alloy 718) and measured in this research in Alloy 625 are analysed through employing the Lifshitz–Slyozov-Encounter-Modified (LSEM) formulation (created by Davies et al.). The results of the LSEM analysis show good far better agreement than LSW with experimental distributions after the application of a necessary correction for what is termed in this research as “directional encounter”. Moreover, the activation energy for γ′′ coarsening in Alloy 625 shows conformity with literature data once the effect of heterogeneous (on dislocations) precipitate nucleation at higher temperatures is accounted for

Lead-related quantum emitters in diamond

We report on quantum emission from Pb-related color centers in diamond following ion implantation and high-temperature vacuum annealing. First-principles calculations predict a negatively charged Pb-vacancy (PbV) center in a split-vacancy configuration, with a zero-phonon transition around 2.4 eV. Cryogenic photoluminescence measurements performed on emitters in nanofabricated pillars reveal several transitions, including a prominent doublet near 520 nm. The splitting of this doublet, 5.7 THz, exceeds that reported for other group-IV centers. These observations are consistent with the PbV center, which is expected to have a combination of narrow optical transitions and stable spin states, making it a promising system for quantum network nodes.U.S. Army Research Laboratory. Center for Distributed Quantum InformationNational Science Foundation (U.S.). Graduate Research Fellowship ProgramNational Science Foundation (U.S.) (Grant DMR-1231319)United States. National Aeronautics and Space Administration (Space Technology Research Fellowship)MIT-Harvard Center for Ultracold Atoms MIT International Science and Technology Initiativ

DeepPep: Deep proteome inference from peptide profiles

Protein inference, the identification of the protein set that is the origin of a given peptide profile, is a fundamental challenge in proteomics. We present DeepPep, a deep-convolutional neural network framework that predicts the protein set from a proteomics mixture, given the sequence universe of possible proteins and a target peptide profile. In its core, DeepPep quantifies the change in probabilistic score of peptide-spectrum matches in the presence or absence of a specific protein, hence selecting as candidate proteins with the largest impact to the peptide profile. Application of the method across datasets argues for its competitive predictive ability (AUC of 0.80±0.18, AUPR of 0.84±0.28) in inferring proteins without need of peptide detectability on which the most competitive methods rely. We find that the convolutional neural network architecture outperforms the traditional artificial neural network architectures without convolution layers in protein inference. We expect that similar deep learning architectures that allow learning nonlinear patterns can be further extended to problems in metagenome profiling and cell type inference. The source code of DeepPep and the benchmark datasets used in this study are available at https://deeppep.github.io/DeepPep/

Towards applying FCM with DBSCAN for Detecting DDoS Attack in Cloud Infrastructure to Improve Data Transmission Rate

Cloud is a pay-to-use technology which can be used to offer IT resources instead of buying computer hardware. It is time saving and cheaper technology. This paper analyzes the DDoS attack on cloud infrastructure and can be detected by using FCM with DBSCAN hybrid algorithm that classifies the clusters of data packets and detects the outlier in that particular data packet. The experimental outcome shows that the enhanced hybrid approach has better results in detecting the DDoS attack. The DDoS attack targets the main host of the cloud infrastructure by sending unwanted packets. This attack is a major threat to the network security. The FCM with DBSCAN hybrid approach detects outliers and also assigns one specific data point in clusters to detect DDoS attack in cloud infrastructure. By using this hybrid approach the data can be grouped as clusters and the data beyond the noise level can also be detected. This algorithm helps in identifying the data that are vulnerable to DDoS attack. This detection helps in improving the data transmission rate

Distinct and shared functions of ALS-associated proteins TDP-43, FUS and TAF15 revealed by multisystem analyses

The RNA-binding protein (RBP) TAF15 is implicated in amyotrophic lateral sclerosis (ALS). To compare TAF15 function to that of two ALS-associated RBPs, FUS and TDP-43, we integrate CLIP-seq and RNA Bind-N-Seq technologies, and show that TAF15 binds to ∼4,900 RNAs enriched for GGUA motifs in adult mouse brains. TAF15 and FUS exhibit similar binding patterns in introns, are enriched in 3′ untranslated regions and alter genes distinct from TDP-43. However, unlike FUS and TDP-43, TAF15 has a minimal role in alternative splicing. In human neural progenitors, TAF15 and FUS affect turnover of their RNA targets. In human stem cell-derived motor neurons, the RNA profile associated with concomitant loss of both TAF15 and FUS resembles that observed in the presence of the ALS-associated mutation FUS R521G, but contrasts with late-stage sporadic ALS patients. Taken together, our findings reveal convergent and divergent roles for FUS, TAF15 and TDP-43 in RNA metabolism.National Institutes of Health (U.S.) (Grant HG007005

Distinguishing Inner and Outer-Sphere Hot Electron Transfer in Au/p-GaN Photocathodes

Exploring nonequilibrium hot carriers from plasmonic metal nanostructures is a dynamic field in optoelectronics, driving photochemical reactions such as solar fuel generation. The hot carrier injection mechanism and the reaction rate are highly impacted by the metal/molecule interaction. However, determining the primary type of the reaction and thus the injection mechanism of the hot carriers has remained elusive. In this work, we reveal an electron injection mechanism deviating from a purely outersphere process for the reduction of ferricyanide redox molecule in a gold/p-type gallium nitride (Au/p- GaN) photocathode system. Combining our experimental approach with ab initio simulations, we discover that the efficient inner-sphere transfer of low-energy electrons leads to a continuous enhancement in the photocathode device performance in the interband regime. These findings provide important mechanistic insights, showing our methodology as a powerful tool for analyzing and engineering hot-carrier-driven processes in plasmonic photocatalytic systems and optoelectronic devices

Design and implementation of high frequency induction heating with LLC resonant load matching using ELTA

Induction heating is a non-contact method of producing heat which can be used to perform various processes like hardening, annealing, tempering, welding, brazing, melting, forging, etc. This paper discusses the design and implementation of induction heating on a given work-piece, using an LLC resonant circuit and a transformer for impedance matching, so as to transfer a maximum power of 5KW to the load. The load parameters are found out using ELTA software which calculates the values based on the dimensions of the work piece, operating frequency and temperature. The inverter used is based on SiC MOSFETs which minimizes the losses at high frequencies and high temperatures. The theoretical and simulated results from MATLAB are analysed and verified.  The hardware is implemented for the LLC circuit with transformer and the results are presented

Cellular Automata with Synthetic Image A Secure Image Communication with Transform Domain

    Image encryption has attained a great attention due to the necessity to safeguard confidential images. Digital documents, site images, battlefield photographs, etc. need a secure approach for sharing in an open channel. Hardware – software co-design is a better option for exploiting unique features to cipher the confidential images. Cellular automata (CA) and synthetic image influenced transform domain approach for image encryption is proposed in this paper. The digital image is initially divided into four subsections by applying integer wavelet transform. Confusion is accomplished on low – low section of the transformed image using CA rules 90 and 150. The first level of diffusion with consecutive XORing operation of image pixels is initiated by CA rule 42. A synthetic random key image is developed by extracting true random bits generated by Cyclone V field programmable gate array 5CSEMA5F31C6. This random image plays an important role in second level of diffusion. The proposed confusion and two level diffusion assisted image encryption approach has been validated through the entropy, correlation, histogram, number of pixels change rate, unified average change intensity, contrast and encryption quality analyses

Interfacial Hot Carrier Collection Controls Plasmonic Chemistry

Harnessing non-equilibrium hot carriers from plasmonic metal nanostructures constitutes a vibrant research field. It promises to enable control of activity and selectivity of photochemical reactions, especially for solar fuel generation. However, a comprehensive understanding of the interplay of plasmonic hot carrier-driven processes in metal/semiconducting heterostructures has remained elusive. In this work, we reveal the complex interdependence between plasmon excitation, hot carrier generation, transport and interfacial collection in plasmonic photocatalytic devices, uniquely determining the charge injection efficiencies at the solid/solid and solid/liquid interfaces. Interestingly, by measuring the internal quantum efficiency of ultrathin (14 to 33 nm) single-crystalline plasmonic gold (Au) nanoantenna arrays on titanium dioxide substrates, we find that the performance of the device is governed by hot hole collection at the metal/electrolyte interface. In particular, by combining a solid- and liquid-state experimental approach with ab initio simulations, we show a more efficient collection of high-energy d-band holes traveling in [111] orientation, resulting in a stronger oxidation reaction at the {111} surfaces of the nanoantenna. These results thus establish new guidelines for the design and optimization of plasmonic photocatalytic systems and optoelectronic devices