This paper presents the design of a read-optimized relational DBMS that contrasts sharply with most current systems, which are write-optimized. Among the many differences in its design are: storage of data by column rather than by row, careful coding and packing of objects into storage including main memory during query processing, storing an overlapping collection of columnoriented projections, rather than the current fare of tables and indexes, a non-traditional implementation of transactions which includes high availability and snapshot isolation for read-only transactions, and the extensive use of bitmap indexes to complement B-tree structures. We present preliminary performance data on a subset of TPC-H and show that the system we are building, C-Store, is substantially faster than popular commercial products. Hence, the architecture looks very encouraging. EMP1 (name, age) EMP2 (dept, age, DEPT.floor) EMP3 (name, salary) DEPT1(dname, floor) Example 1: Possible projections for EMP and DEPT Name Age Dept Salary Bob 25 Math 10K Bill 27 EECS 50K Jill 24 Biology 80K

Objective Human voluntary movement is associated with two changes in electroencephalography (EEG) that can be observed as early as 1.5 s prior to movement: slow DC potentials and frequency power shifts in the alpha and beta bands. Our goal was to determine whether and when we can reliably predict human natural movement BEFORE it occurs from EEG signals ONLINE IN REAL-TIME. Methods We developed a computational algorithm to support online prediction. Seven healthy volunteers participated in this study and performed wrist extensions at their own pace. Results The average online prediction time was 0.62 ± 0.25 s before actual movement monitored by EMG signals. There were also predictions that occurred without subsequent actual movements, where subjects often reported that they were thinking about making a movement. Conclusion Human voluntary movement can be predicted before movement occurs. Significance The successful prediction of human movement intention will provide further insight into how the brain prepares for movement, as well as the potential for direct cortical control of a device which may be faster than normal physical control.

Pt nanoparticles were introduced on the surface of ZnO nanowires using a chemically driven self-assembly method. Through this controllable method, Pt-nanoparticle-functionalized ZnO nanowires (Pt NPs-ZnO NWs) with uniform particle dispersion, tunable Pt particle sizes, and narrow particle size distribution were obtained. Changes in the morphology of the decorative preparation were observed as the amount of linker reagent and the concentration of Pt nanoparticle solution were altered. The as-prepared Pt NPs-ZnO NWs with optimal morphology showed excellent gas sensing and photocatalytic performance. Tuning of the functionalities of photocatalytic and gas sensors can be obtained by tailoring the morphology of Pt NP-ZnO NW composite materials.

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The precise monitoring of H2S has aroused immense research interest in the biological and biomedical fields since it is exposed as a third endogenous gasotransmitter. Hence, there is an urgent requisite to explore an ultrasensitive and economical H2S detection system. Herein, we report a simple strategy to configure an extremely sensitive electrochemical sensor with a 2D nanosheet-shaped layered double hydroxide (LDH) wrapped carbon nanotubes (CNTs) nanohybrid (CNTs@LDH), where a series of CNTs@CuMn-LDH nanohybrids with varied amounts of LDH nanosheets grafted on a conductive CNTs backbone has been synthesized via a facile coprecipitation approach. Taking advantage of the unique core–shell structure, the integrated electrochemically active CuMn-LDH nanosheets on the conductive CNTs scaffold, the maximum interfacial collaboration, and the superior specific surface area with a plethora of surface active sites and ultrathin LDH layers, the as-prepared CNTs@CuMn-LDH nanoarchitectures have exhibited superb electrocatalytic activity toward H2S oxidation. Under the optimum conditions, the electrochemical sensor based on the CNTs@CuMn-LDH nanohybrid shows remarkable sensing performances for H2S determination in terms of a wide linear range and a low detection limit of 0.3 nM (S/N = 3), high selectivity, reproducibility, and durability. With marvelous efficiency achieved, the proposed sensing platform has been practically used in in situ detection of abiotic H2S efflux produced by sulfate reducing bacteria and real-time in vitro tracking of H2S concentrations from live cells after being excreted by a stimulator which in turn might serve as early diseases diagnosis. Thus, our core–shell hybrid nanoarchitectures fabricated via structural integration strategy will open new horizons in material synthesis, biosensing systems, and clinical chemistry.