Efficient Machine-type Communication using Multi-metric Context-awareness for Cars used as Mobile Sensors in Upcoming 5G Networks

Upcoming 5G-based communication networks will be confronted with huge increases in the amount of transmitted sensor data related to massive deployments of static and mobile Internet of Things (IoT) systems. Cars acting as mobile sensors will become important data sources for cloud-based applications like predictive maintenance and dynamic traffic forecast. Due to the limitation of available communication resources, it is expected that the grows in Machine-Type Communication (MTC) will cause severe interference with Human-to-human (H2H) communication. Consequently, more efficient transmission methods are highly required. In this paper, we present a probabilistic scheme for efficient transmission of vehicular sensor data which leverages favorable channel conditions and avoids transmissions when they are expected to be highly resource-consuming. Multiple variants of the proposed scheme are evaluated in comprehensive realworld experiments. Through machine learning based combination of multiple context metrics, the proposed scheme is able to achieve up to 164% higher average data rate values for sensor applications with soft deadline requirements compared to regular periodic transmission.Comment: Best Student Paper Awar

Machine learning based context-predictive car-to-cloud communication using multi-layer connectivity maps for upcoming 5G networks

While cars were only considered as means of personal transportation for a long time, they are currently transcending to mobile sensor nodes that gather highly up-to-date information for crowdsensing-enabled big data services in a smart city context. Consequently, upcoming 5G communication networks will be confronted with massive increases in Machine-type Communication (MTC) and require resource-efficient transmission methods in order to optimize the overall system performance and provide interference-free coexistence with human data traffic that is using the same public cellular network. In this paper, we bring together mobility prediction and machine learning based channel quality estimation in order to improve the resource-efficiency of car-to-cloud data transfer by scheduling the transmission time of the sensor data with respect to the anticipated behavior of the communication context. In a comprehensive field evaluation campaign, we evaluate the proposed context-predictive approach in a public cellular network scenario where it is able to increase the average data rate by up to 194% while simultaneously reducing the mean uplink power consumption by up to 54%

Phylogenetic survey of the subtilase family and a data-mining-based search for new subtilisins from Bacillacea

The subtilase family (S8), a member of the clan SB of serine proteases are ubiquitous in all kingdoms of life and fulfil different physiological functions. Subtilases are divided in several groups and especially subtilisins are of interest as they are used in various industrial sectors. Therefore, we searched for new subtilisin sequences of the family Bacillaceae using a data mining approach. The obtained 1,400 sequences were phylogenetically classified in the context of the subtilase family. This required an updated comprehensive overview of the different groups within this family. To fill this gap, we conducted a phylogenetic survey of the S8 family with characterised holotypes derived from the MEROPS database. The analysis revealed the presence of eight previously uncharacterised groups and 13 subgroups within the S8 family. The sequences that emerged from the data mining with the set filter parameters were mainly assigned to the subtilisin subgroups of true subtilisins, high-alkaline subtilisins, and phylogenetically intermediate subtilisins and represent an excellent source for new subtilisin candidates.</p

Biochemical characterisation of a novel broad pH spectrum subtilisin from Fictibacillus arsenicus DSM 15822T

Subtilisins from microbial sources, especially from the Bacillaceae family, are of particular interest for biotechnological applications and serve the currently growing enzyme market as efficient and novel biocatalysts. Biotechnological applications include use in detergents, cosmetics, leather processing, wastewater treatment and pharmaceuticals. To identify a possible candidate for the enzyme market, here we cloned the gene of the subtilisin SPFA from Fictibacillus arsenicus DSM 15822T (obtained through a data mining‐based search) and expressed it in Bacillus subtilis DB104. After production and purification, the protease showed a molecular mass of 27.57 kDa and a pI of 5.8. SPFA displayed hydrolytic activity at a temperature optimum of 80 °C and a very broad pH optimum between 8.5 and 11.5, with high activity up to pH 12.5. SPFA displayed no NaCl dependence but a high NaCl tolerance, with decreasing activity up to concentrations of 5 m NaCl. The stability enhanced with increasing NaCl concentration. Based on its substrate preference for 10 synthetic peptide 4‐nitroanilide substrates with three or four amino acids and its phylogenetic classification, SPFA can be assigned to the subgroup of true subtilisins. Moreover, SPFA exhibited high tolerance to 5% (w/v) SDS and 5% H2O2 (v/v). The biochemical properties of SPFA, especially its tolerance of remarkably high pH, SDS and H2O2, suggest it has potential for biotechnological applications

Resource-Efficient Transmission of Vehicular Sensor Data Using Context-Aware Communication

Upcoming Intelligent Traffic Control Systems (ITSCs) will base their optimization processes on crowdsensing data obtained for cars that are used as mobile sensor nodes. In conclusion, public cellular networks will be confronted with massive increases in Machine-Type Communication (MTC) and will require efficient communication schemes to minimize the interference of Internet of Things (IoT) data traffic with human communication. In this demonstration, we present an Open Source framework for context-aware transmission of vehicular sensor data that exploits knowledge about the characteristics of the transmission channel for leveraging connectivity hotspots, where data transmissions can be performed with a high grade if resource efficiency. At the conference, we will present the measurement application for acquisition and live-visualization of the required network quality indicators and show how the transmission scheme performs in real-world vehicular scenarios based on measurement data obtained from field experiments

Data_Sheet_1_Phylogenetic survey of the subtilase family and a data-mining-based search for new subtilisins from Bacillaceae.pdf

The subtilase family (S8), a member of the clan SB of serine proteases are ubiquitous in all kingdoms of life and fulfil different physiological functions. Subtilases are divided in several groups and especially subtilisins are of interest as they are used in various industrial sectors. Therefore, we searched for new subtilisin sequences of the family Bacillaceae using a data mining approach. The obtained 1,400 sequences were phylogenetically classified in the context of the subtilase family. This required an updated comprehensive overview of the different groups within this family. To fill this gap, we conducted a phylogenetic survey of the S8 family with characterised holotypes derived from the MEROPS database. The analysis revealed the presence of eight previously uncharacterised groups and 13 subgroups within the S8 family. The sequences that emerged from the data mining with the set filter parameters were mainly assigned to the subtilisin subgroups of true subtilisins, high-alkaline subtilisins, and phylogenetically intermediate subtilisins and represent an excellent source for new subtilisin candidates.</p

Biochemical characterization of a novel oxidatively stable, halotolerant, and high‐alkaline subtilisin from Alkalihalobacillus okhensis Kh10‐101 T

Halophilic and halotolerant microorganisms represent a promising source of salt-tolerant enzymes suitable for various biotechnological applications where high salt concentrations would otherwise limit enzymatic activity. Considering the current growing enzyme market and the need for more efficient and new biocatalysts, the present study aimed at the characterization of a high-alkaline subtilisin from Alkalihalobacillus okhensis Kh10-101T. The protease gene was cloned and expressed in Bacillus subtilis DB104. The recombinant protease SPAO with 269 amino acids belongs to the subfamily of high-alkaline subtilisins. The biochemical characteristics of purified SPAO were analyzed in comparison with subtilisin Carlsberg, Savinase, and BPN'. SPAO, a monomer with a molecular mass of 27.1 kDa, was active over a wide range of pH 6.0–12.0 and temperature 20–80 °C, optimally at pH 9.0–9.5 and 55 °C. The protease is highly oxidatively stable to hydrogen peroxide and retained 58% of residual activity when incubated at 10 °C with 5% (v/v) H2O2 for 1 h while stimulated at 1% (v/v) H2O2. Furthermore, SPAO was very stable and active at NaCl concentrations up to 5.0 m. This study demonstrates the potential of SPAO for biotechnological applications in the future

Sample Environment for Operando Hard X-ray Tomography—An Enabling Technology for Multimodal Characterization in Heterogeneous Catalysis

Structure–activity relations in heterogeneous catalysis can be revealed through in situ and operando measurements of catalysts in their active state. While hard X-ray tomography is an ideal method for non-invasive, multimodal 3D structural characterization on the micron to nm scale, performing tomography under controlled gas and temperature conditions is challenging. Here, we present a flexible sample environment for operando hard X-ray tomography at synchrotron radiation sources. The setup features are discussed, with demonstrations of operando powder X-ray diffraction tomography (XRD-CT) and energy-dispersive tomographic X-ray absorption spectroscopy (ED-XAS-CT). Catalysts for CO$_2$ methanation and partial oxidation of methane are shown as case studies. The setup can be adapted for different hard X-ray microscopy, spectroscopy, or scattering synchrotron radiation beamlines, is compatible with absorption, diffraction, fluorescence, and phase-contrast imaging, and can operate with scanning focused beam or full-field acquisition mode. We present an accessible methodology for operando hard X-ray tomography studies, which offer a unique source of 3D spatially resolved characterization data unavailable to contemporary methods

Comparison of XBIC and LBIC measurements of a fully encapsulated c-Si solar cell

A fully encapsulated c-Si solar cell was evaluatedusing focused X-ray and laser beams to probe the microscopicelectrical performance and composition. Particular emphasiswas placed on the influence of the silver fingers on the laser(LBIC) and X-ray beam induced current (XBIC). Therefore,an uncommonly high X-ray energy of 28 keV was utilized formaximum sensitivity to the Ag distribution measured by X-rayfluorescence through the back sheet. The direct comparison ofLBIC and XBIC measurements yields a comprehensive pictureof these techniques, illustrating the advantages and challenges ofboth approaches. Specifically, the effect of heavy elements actingas a secondary photon source that increase the XBIC signal isdiscussed and supported by Monte-Carlo simulations