Titania nanotubes (TNT) prepared by anodization of Ti foils were used for water splitting in a standalone cell. The concentration polarization between the anode side (1M NaOH) and cathode side (0.5 M H2SO4) ensured that the water splitting reaction could take place with no external bias and separate H2 and O2 evolution could be achieved. The destructi...

This paper presents a novel formal development of a non-trivial parallel program: Simpson's implementation of asynchronous communication mechanisms (ACMs). Although the correctness of the "4-slot algorithm" has been shown elsewhere, earlier developments are by no means intuitive. The aims of this paper include both the presentation of an understandable (yet formal) design history and the establ...

The most popular tools for stream data mining are based on decision trees. In previous 15 years, all designed methods, headed by the very fast decision tree algorithm, relayed on Hoeffding's inequality and hundreds of researchers followed this scheme. Recently, we have demonstrated that although the Hoeffding decision trees are an effective tool for dealing with stream data, they are a purely h...

Alstromeria has beautiful flowers that is popular in the world. One of the most problem in commercial of this flower is ethylene sensitivity and bacterial contamination in stem end of cut flowers. In this experiment, effect of essential oils of bitter orange (0, 25 and 50 mg l-1) and lawson cypress (0, 100 and 200 mg l-1) and nonchemical treatment (with and without) with 18 treatments, 3 replic...

The increase in data volume is challenging the suitability of non-distributed and non-scalable algorithms, despite advancements hardware. An example this challenge clustering. Considering that optimal clustering algorithms scale poorly with increased or are intrinsically non-distributed, accurate large datasets increasingly resource-heavy, relying on substantial expensive compute nodes. This sc...

This article reports on our work on the clustering of non-uniformly distributed data. An innovative method, termed incremental splitting, is presented. Taking the K-means method as the core, the proposed approach splits only clusters with the largest total error in each iteration. This heuristic has the effect of allocating more clusters to those regions having more sample data. Consistent expe...