Chemical reaction optimization with greedy strategy for the 0–1 knapsack problem

The 0–1 knapsack problem (KP01) is a well-known combinatorial optimization problem. It is an NP-hard problem which plays important roles in computing theory and in many real life applications. Chemical reaction optimization (CRO) is a new optimization framework, inspired by the nature of chemical reactions. CRO has demonstrated excellent performance in solving many engineering problems such as the quadratic assignment problem, neural network training, multimodal continuous problems, etc. This paper proposes a new chemical reaction optimization with greedy strategy algorithm (CROG) to solve KP01. The paper also explains the operator design and parameter turning methods for CROG. A new repair function integrating a greedy strategy and random selection is used to repair the infeasible solutions. The experimental results have proven the superior performance of CROG compared to genetic algorithm (GA), ant colony optimization (ACO) and quantum-inspired evolutionary algorithm (QEA).     

Mosaic organization of DNA nucleotides

Long-range power-law correlations have been reported recently for DNA sequences containing noncoding regions. We address the question of whether such correlations may be a trivial consequence of the known mosaic structure ("patchiness") of DNA. We analyze two classes of controls consisting of patchy nucleotide sequences generated by different algorithms--one without and one with long-range power-law correlations. Although both types of sequences are highly heterogenous, they are quantitatively distinguishable by an alternative fluctuation analysis method that differentiates local patchiness from long-range correlations. Application of this analysis to selected DNA sequences demonstrates that patchiness is not sufficient to account for long-range correlation properties.     

Long-range correlation properties of coding and noncoding DNA sequences: GenBank analysis

An open question in computational molecular biology is whether long-range correlations are present in both coding and noncoding DNA or only in the latter. To answer this question, we consider all 33301 coding and all 29453 noncoding eukaryotic sequences--each of length larger than 512 base pairs (bp)--in the present release of the GenBank to dtermine whether there is any statistically significant distinction in their long-range correlation properties. Standard fast Fourier transform (FFT) analysis indicates that coding sequences have practically no correlations in the range from 10 bp to 100 bp (spectral exponent beta=0.00 +/- 0.04, where the uncertainty is two standard deviations). In contrast, for noncoding sequences, the average value of the spectral exponent beta is positive (0.16 +/- 0.05) which unambiguously shows the presence of long-range correlations. We also separately analyze the 874 coding and the 1157 noncoding sequences that have more than 4096 bp and find a larger region of power-law behavior. We calculate the probability that these two data sets (coding and noncoding) were drawn from the same distribution and we find that it is less than 10(-10). We obtain independent confirmation of these findings using the method of detrended fluctuation analysis (DFA), which is designed to treat sequences with statistical heterogeneity, such as DNA's known mosaic structure ("patchiness") arising from the nonstationarity of nucleotide concentration. The near-perfect agreement between the two independent analysis methods, FFT and DFA, increases the confidence in the reliability of our conclusion.     

Accuracy verification of a simple local three-dimensional displacement measurement method of DIC with two images coordinates

There are two methods applied for three-dimensional digital image correlation method to measure three-dimensional displacement. One is to measure the spatial coordinates of measuring points by analyzing the images. Then, the displacement vectors of these points can be calculated using the spatial coordinates of these points obtained at different stages. The other is to calibrate the parameters for individual measuring points locally. Then, the local displacements of these points can be measured directly. This study proposes a simple local three-dimensional displacement measurement method. Without any complicated distortion correction processes, this method can be used to measure small displacement in the three-dimensional space through a simple calibration process. A laboratory experiment and field experiment are carried out to prove the accuracy of this proposed method. Laboratory test errors of one-dimensional experiment are similar to the accuracy of the XYZ table; the error in Z-direction is only 0.0025% of the object distance. The measurement error of laboratory test is about 0.0033% of the object distance for local three-dimensional displacement measurement test. Test and analysis results of field test display that in-plane displacement error is only 0.12 mm, and the out-of-plane error is 1.1 mm for 20 m × 30 m measuring range. The out-of-plane error is only about 10 PPM of the object distance. These test and analysis results show that this proposed method can achieve very high accuracy under small displacement for both of laboratory and field tests.     

Smart Utilization of Carbon Dots in Semiconductor Photocatalysis

Efficient capture of solar energy will be critical to meeting the energy needs of the future. Semiconductor photocatalysis is expected to make an important contribution in this regard, delivering both energy carriers (especially H₂) and valuable chemical feedstocks under direct sunlight. Over the past few years, carbon dots (CDs) have emerged as a promising new class of metal‐free photocatalyst, displaying semiconductor‐like photoelectric properties and showing excellent performance in a wide variety of photoelectrochemical and photocatalytic applications owing to their ease of synthesis, unique structure, adjustable composition, ease of surface functionalization, outstanding electron‐transfer efficiency and tunable light‐harvesting range (from deep UV to the near‐infrared). Here, recent advances in the rational design of CDs‐based photocatalysts are highlighted and their applications in photocatalytic environmental remediation, water splitting into hydrogen, CO₂ reduction, and organic synthesis are discussed.     

Conformations and folding of lysozyme ions in vacuo

Proton transfer reactivity of isolated charge states of the protein hen egg-white lysozyme shows that multiple distinct conformations of this protein are stable in the gas phase. The reactivities of the 9+ and 10+ charge state ions, formed by electrospray ionization of "native" (disulfide-intact) and "denatured" (disulfide-reduced) solutions, are consistent with values calculated for ions in their crystal structure and fully denatured conformations, respectively. Charge states below 8+ of both forms, formed by proton stripping, have similar or indistinguishable reactivities, indicating that the disulfide-reduced ions fold in the gas phase to a more compact conformation.     

Financial volatility trading using a self-organising neural-fuzzy semantic network and option straddle-based approach

Financial volatility refers to the intensity of the fluctuations in the expected return on an investment or the pricing of a financial asset due to market uncertainties. Hence, volatility modeling and forecasting is imperative to financial market investors, as such projections allow the investors to adjust their trading strategies in anticipation of the impending financial market movements. Following this, financial volatility trading is the capitalization of the uncertainties of the financial markets to realize investment profits in times of rising, falling and side-way market conditions. In this paper, an intelligent straddle trading system (framework) that consists of a volatility projection module (VPM) and a trade decision module (TDM) is proposed for financial volatility trading via the buying and selling of option straddles to help a human trader capitalizes on the underlying uncertainties of the Hong Kong stock market. Three different measures, namely: (1) the historical volatility (HV), (2) implied volatility (IV) and (3) model-based volatility (MV) of the Hang Seng Index (HSI) are employed to quantify the implicit volatility of the Hong Kong stock market. The TDM of the proposed straddle trading system combines the respective volatility measures with the well-established moving-averages convergence/divergence (MACD) principle to recommend trading actions to a human trader dealing in HSI straddles. However, the inherent limitation of the MACD trading rule is that it generates time-delayed trading signals due to the use of moving averages, which are essentially lagging trend indicators. This drawback is intuitively addressed in the proposed straddle trading system by applying the VPM to compute future projections of the volatility measures of the HSI prior to the activation of the TDM. The VPM is realized by a self-organising neural-fuzzy semantic network named the evolving fuzzy semantic memory (eFSM) model. As compared to existing statistical and computational intelligence based modeling techniques currently employed for financial volatility modeling and forecasting, eFSM possesses several desirable attributes such as: (1) an evolvable knowledge base to continuously address the non-stationary characteristics of the Hong Kong stock market; (2) highly formalized human-like information computations; and (3) a transparent structure that can be interpreted via a set of linguistic IF–THEN semantic fuzzy rules. These qualities provide added credence to the computed HSI volatility projections. The volatility modeling and forecasting performances of the eFSM, when benchmarked to several established modeling techniques, as well as the observed trading returns of the proposed straddle trading system, are encouraging.     

A multi-strategy knowledge interoperability framework for heterogeneous learning objects

This paper presents a knowledge exchange framework that can leverage the interoperability among semantically heterogeneous learning objects. With the release of various e-Learning standards, learning contents and digital courses are easy to achieve cross-platform sharing, exchanging, and even reorganizing. However, knowledge sharing in semantic level is still a challenge due to that the learning materials can be presented in any form, such as audios, videos, web pages, and even flash files. The proposed knowledge exchange framework allows users to share their learning materials (also called “learning objects”) in semantic level automatically. This framework contains two methodologies: the first is a semantic mapping between knowledge bases (i.e. ontologies) which have essentially similar concepts, and the second is an ontology-based classification algorithm for sharable learning objects. The proposed algorithm adopts the IMS DRI standard and classifies the sharable learning objects from heterogeneous repositories into a local knowledge base by their inner meaning instead of keyword matching. Significance of this research lies in the semantic inferring rules for ontology mapping and learning objects classification as well as the full automatic processing and self-optimizing capability. Focused on digital learning materials and contrasted to other traditional technologies, the proposed approach has experimentally demonstrated significantly improvement in performance.