Color image DNA encryption using mRNA properties and non-adjacent coupled map lattices

This paper proposes a novel algorithm for encrypting color images. The innovation in this study is the use of messenger ribonucleic acid (mRNA) encoding to import into Deoxyribonucleic acid (DNA) encoding. For permutation of the plain image bits, we use Arnold’s Cat Map at the bit-level. Then, using Non-Adjacent Coupled Map Lattices (NCML), we apply diffusion operations to the permuted color channels. We also provide the upgrade of the diffusion phase with DNA encoding. In the proposed algorithm, the choices are random depending on the secret key, which is implemented using a simple logistic map. Hashing the string entered by the user, the secret key, parameters, and initial values are generated by the Double MD5 method. The results of tests and security analysis showed that the results of encryption with this scheme are effective, and the key space is large enough to withstand common attacks.

     

Investigation of effective dimensionless numbers on initiation of instability in combustion of moisty organic dust简

In this work, the effect of various effective dimensionless numbers and moisture contents on initiation of instability in combustion of moisty organic dust is calculated. To have reliable model, effect of thermal radiation is taken into account. One- dimensional flame structure is divided into three zones： preheat zone, reaction zone and post-flame zone. To investigate pulsating characteristics of flame, governing equations are rewritten in dimensionless space-time （（, r/, ~） coordinates. By solving these newly achieved governing equations and combining them, which is completely discussed in body of article, a new expression is obtained. By solving this equation, it is possible to predict initiation of instability in organic dust flame. According to the obtained results by increasing Lewis number, threshold of instability happens sooner. On the other hand, pulsating is postponed by increasing Damk6hler number, pyrolysis temperature or moisture content. Also, by considering thermal radiation effect, burning velocity predicted by our model is closer to experimental results.     

Selective harmonic elimination method for wide range of modulation indexes in multilevel inverters using ICA

Selective harmonic elimination（SHE） in multilevel inverters is an intricate optimization problem that involves a set of nonlinear transcendental equations which have multiple local minima. A new advanced objective function with proper weighting is proposed and also its efficiency is compared with the objective function which is more similar to the proposed one. To enhance the ability of the SHE in eliminating high number of selected harmonics, at each level of the output voltage, one slot is created. The SHE problem is solved by imperialist competitive algorithm（ICA）. The conventional SHE methods cannot eliminate the selected harmonics and satisfy the fundamental component in some ranges of modulation indexes. So, to surmount the SHE defect, a DC-DC converter is applied. Theoretical results are substantiated by simulations and experimental results for a 9-level multilevel inverter. The obtained results illustrate that the proposed method successfully minimizes a large number of identified harmonics which consequences very low total harmonic distortion of output voltage.     

Application of thermal ultrasound-assisted liquid–liquid micro-extraction coupled with HPLC-UV for rapid determination of synthetic phenolic antioxidants in edible oils

A simple, fast, and reliable liquid–liquid micro-extraction (LLME) method assisted by thermal ultrasound approach was developed for simultaneous determination of synthetic phenolic antioxidants (SPAs) in edible oils by high-performance liquid chromatography equipped with ultraviolet detector (HPLC-UV). The synthetic antioxidants were propyl gallate (PG), butylated hydroxyanisole (BHA), tert-butylhydroquinone (TBHQ), and butylated hydroxyltoluene (BHT). The best extraction conditions were observed were methanol/acetonitrile (1:1, v/v) as the solvent, ultrasound at 4 min, and a temperature of 40°C. The linearity of the calibration curves for the optimum conditions were R² > 0.989 for all of the SPAs in a range from 1–200 μg ml⁻¹. Relative standard deviation (RSD %) for five analysis was in range of 2.83% to 4.21%. Limit of detection (LOD) and limit of quantification (LOQ) were obtained in range of 0.012–0.06 and 0.04–0.2 μg g⁻¹, respectively. With regard to recovery, a range of 91%–116% was calculated for the spiked edible oils.     

A bio-inspired gelatin-based pH- and thermal-sensitive magnetic hydrogel for in vitro chemo/hyperthermia treatment of breast cancer cells

Gelatin (Gel)-based pH- and thermal-responsive magnetic hydrogels (MH-1 and MH-2) were designed and developed as novel drug delivery systems (DDSs) for cancer chemo/hyperthermia therapy. For this goal, Gel was functionalized with methacrylic anhydride (GelMA), and then copolymerized with (2-dimethylaminoethyl) methacrylate (DMAEMA) monomer in the presence of methacrylate-end capped magnetic nanoparticles (MNPs) as well as triethylene glycol dimethacrylate (TEGDMA; as crosslinker). Afterward, a thiol-end capped poly(N-isopropylacrylamide) (PNIPAAm-SH) was synthesized through an atom transfer radical polymerization technique, and then attached onto the hydrogel through “thiol-ene” click grafting. The preliminary performances of developed MHs for chemo/hyperthermia therapy of human breast cancer was investigated through the loading of doxorubicin hydrochloride (Dox) as an anticancer agent followed by cytotoxicity measurement of drug-loaded DDSs using MTT assay by both chemo- and chemo/hyperthermia-therapies. Owing to porous morphologies of the fabricated magnetic hydrogels according to scanning electron microscopy images and strong physicochemical interactions (e.g., hydrogen bonding) the drug loading capacities of the MH-1 and MH-2 were obtained as 72 ± 1.4 and 77 ± 1.8, respectively. The DDSs exhibited acceptable pH- and thermal-triggered drug release behaviors. The MTT assay results revealed that the combination of hyperthermia therapy and chemotherapy has synergic effect on the anticancer activities of the developed DDSs.     

A study on melt recycling of bio-based polypropylene/thermoplastic starch compound

The compulsion to use bioplastics has increased significantly today. One of the important aspects of plastics is their recyclability. Therefore, the important question of this research is that although bio-based compounds containing starch are sensitive to thermal-mechanical recycling processes, are such products thermally recyclable? To answer the question, polypropylene (PP)/thermoplastic starch (TPS) compound granules were extruded up to five times, and in the other part, single-extruded granules were blended at different ratios with virgin granules by extrusion. In order to characterize these samples, Fourier transform infrared spectroscopy, thermogravimetric analysis, differential scanning calorimetry, rotational disc rheometry, tensile properties, and appearance evaluation were used. The results showed that it is possible to recycle PP/TPS granules up to four times repetition of the extrusion operation and the fifth repetition also showed slight changes. There was also a blend of single-extruded granules with virgin material up to a 50:50% composition without significant variation.     

Privacy Preserving Geo-Linkage in the Big Urban Data Era

Big data technologies and a range of Government open data initiatives provide the basis for discovering new insights into cities; how they are planned, how they managed and the day-to-day challenges they face in health, transport and changing population profiles. The Australian Urban Research Infrastructure Network (AURIN – www.aurin.org.au) project is one example of such a big data initiative that is currently running across Australia. AURIN provides a single gateway providing online (live) programmatic access to over 2000 data sets from over 70 major and typically definitive data-driven organizations across federal and State government, across industry and across academia. However whilst open (public) data is useful to bring data-driven intelligence to cities, more often than not, it is the data that is not-publicly accessible that is essential to understand city challenges and needs. Such sensitive (unit-level) data has unique requirements on access and usage to meet the privacy and confidentiality demands of the associated organizations. In this paper we highlight a novel geo-privacy supporting solution implemented as part of the AURIN project that provides seamless and secure access to individual (unit-level) data from the Department of Health in Victoria. We illustrate this solution across a range of typical city challenges in localized contexts around Melbourne. We show how unit level data can be combined with other data in a privacy-protecting manner. Unlike other secure data access and usage solutions that have been developed/deployed, the AURIN solution allows any researcher to access and use the data in a manner that meets all of the associated privacy and confidentiality concerns, without obliging them to obtain ethical approval or any other hurdles that are normally put in place on access to and use of sensitive data. This provides a paradigm shift in secure access to sensitive data with geospatial content.     

Regularized continuous estimation of distribution algorithms

Regularization is a well-known technique in statistics for model estimation which is used to improve the generalization ability of the estimated model. Some of the regularization methods can also be used for variable selection that is especially useful in high-dimensional problems. This paper studies the use of regularized model learning in estimation of distribution algorithms (EDAs) for continuous optimization based on Gaussian distributions. We introduce two approaches to the regularized model estimation and analyze their effect on the accuracy and computational complexity of model learning in EDAs. We then apply the proposed algorithms to a number of continuous optimization functions and compare their results with other Gaussian distribution-based EDAs. The results show that the optimization performance of the proposed RegEDAs is less affected by the increase in the problem size than other EDAs, and they are able to obtain significantly better optimization values for many of the functions in high-dimensional settings.     

Human eye sclera detection and tracking using a modified time-adaptive self-organizing map

This paper investigates the use of time-adaptive self-organizing map (TASOM)-based active contour models (ACMs) for detecting the boundaries of the human eye sclera and tracking its movements in a sequence of images. The task begins with extracting the head boundary based on a skin-color model. Then the eye strip is located with an acceptable accuracy using a morphological method. Eye features such as the iris center or eye corners are detected through the iris edge information. TASOM-based ACM is used to extract the inner boundary of the eye. Finally, by tracking the changes in the neighborhood characteristics of the eye-boundary estimating neurons, the eyes are tracked effectively. The original TASOM algorithm is found to have some weaknesses in this application. These include formation of undesired twists in the neuron chain and holes in the boundary, lengthy chain of neurons, and low speed of the algorithm. These weaknesses are overcome by introducing a new method for finding the winning neuron, a new definition for unused neurons, and a new method of feature selection and application to the network. Experimental results show a very good performance for the proposed method in general and a better performance than that of the gradient vector field (GVF) snake-based method.