New nanocomposite based on poly(lactic-co-glycolic acid) copolymer and magnetite. Synthesis and characterization

The study presents the preparation of the new magnetic nanocomposite based on PLGA and magnetite. The PLGA used to obtain the magnetic nanocomposites was synthesized by the copolymerization of lactic acid with glycolic acid, in the presence of tin octanoate [Sn(Oct)₂] as catalyst, by polycondensation procedure. Magnetite was obtained by co-precipitation from aqueous salt solutions FeCl₂/FeCl₃. The particles size of magnetite was 420 nm, and the saturation magnetization 62.78 emu/g, while the PLGA/magnetite nanocomposite size was 864 nm and the saturation magnetization 39.44 emu/g. The magnetic nanocomposites were characterized by FT-IR, DLS technique, SEM, VSM and simultaneous thermal analyses (TG–FTIR–MS). The polymer matrix PLGA acts as a shell and carrier for the active component, while magnetite is the component which makes targeting possible by external magnetic field manipulation. Based on the gases resulted by thermal degradation of PLGA copolymer, using the simultaneous analysis TG–FTIR–MS, a possible degradation mechanism was proposed.     

Characteristics and formation mechanism of porosities in CFRP during laser joining of CFRP and steel

An experimental investigation on the mechanism of porosity formation during the laser joining of carbon fiber reinforced polymer (CFRP) and steel is presented. The porosity morphology and distribution were characterized by optical and scanning electron microscopy, and the thermal pyrolysis behaviors were investigated by thermal analysis and designed back-side cooling experiments. The results show that there are two types of porosities in CFRP. Porosity I only appears when the heat input is more than 77.8 J/mm. It has a smooth inner wall and distributes near the bonding interface between CFRP and steel at the central area of melted zone, which is caused by gaseous products such as CO₂, NH₃, H₂O, and hydrocarbons produced by the pyrolysis of CFRP. Porosity II can be seen under all joining conditions. It has a rough inner wall and distributes far away from the bonding interface, concentrating at the final solidification locations. Porosity II is caused by the shrinkage of melted CFRP during solidification stage.     

Rapid detection of viable Bacillus cereus emetic and enterotoxic strains in food by coupling propidium monoazide and multiplex PCR (PMA-mPCR)

Bacillus cereus can cause emetic and diarrheal food poisoning. It is widespread in nature and therefore, considered a major foodborne pathogen. To develop a sensitive and reliable assay for detecting enterotoxin genes (nheA, entFM, hblD, cytK) and emetic toxin (ces), specific primers each targeting one individual gene were designed. Propidium monoazide (PMA) was coupled with the developed multiplex PCR (mPCR) for the detection of viable B. cereus. The inclusivity and exclusivity of the PMA-mPCR was confirmed using a panel of 44 strains including 17 emetic and 9 enterotoxic B. cereus reference strains and 18 non-target strains. The limit of detection (LOD) without PMA treatment in pure DNA was 2 pg/reaction tube. The LOD of mPCR assay in pure heat-killed dead bacteria was 4.0 × 10² CFU/mL. Also, the LOD on the viable bacteria with or without PMA treatment was similar (3.8 × 10² CFU/mL) showing that the PMA treatment did not significantly decrease sensitivity. Finally, the newly developed PMA-mPCR successfully detected 4.8 × 10³ and 3.6 × 10³ CFU/g of viable B. cereus F4810/72 (emetic) and B. cereus ATCC 12480 (enterotoxic) reference strains, respectively, in food samples. Hence, this study combines PMA and mPCR to detect viable B. cereus with a wide range of toxin detection (5 toxins). Thus, the novel PMA-mPCR assay developed in this study is a rapid and efficient diagnostic tool for the monitoring of viable B. cereus in food samples and potentially other samples via appropriate DNA extraction.     

Formulation of sage essential oil (Salvia officinalis,L.) monoterpenes into chitosan hydrogels and permeation study with GC-MS analysis

This study deals with the formulation of natural drugs into hydrogels. For the first time, compounds from the sage essential oil were formulated into chitosan hydrogels. A sample preparation procedure for hydrophobic volatile analytes present in a hydrophilic water matrix along with an analytical method based on the gas chromatography coupled with the mass spectrometry (GC-MS) was developed and applied for the evaluation of the identity and quantity of essential oil components in the hydrogels and saline samples. The experimental results revealed that the chitosan hydrogels are suitable for the formulation of sage essential oil. The monoterpene release can be effectively controlled by both chitosan and caffeine concentration in the hydrogels. Permeation experiment, based on a hydrogel with the optimized composition [3.5% (w/w) sage essential oil, 2.0% (w/w) caffeine, 2.5% (w/w) chitosan and 0.1% (w/w) Tween-80] in donor compartment, saline solution in acceptor compartment, and semi-permeable cellophane membrane, demonstrated the useful permeation selectivity. Here, (according to lipophilicity) an enhanced permeation of the bicyclic monoterpenes with antiflogistic and antiseptic properties (eucalyptol, camphor and borneol) and, at the same time, suppressed permeation of toxic thujone (not exceeding its permitted applicable concentration) was observed. These properties highlight the pharmaceutical importance of the developed chitosan hydrogel formulating sage essential oil in the dermal applications.     

Parallel chaos optimization algorithm with migration and merging operation

Chaos optimization algorithm (COA) utilizes the chaotic maps to generate the pseudo-random sequences mapped as the decision variables for global optimization applications. A kind of parallel chaos optimization algorithm (PCOA) has been proposed in our former studies to improve COA. The salient feature of PCOA lies in its pseudo-parallel mechanism. However, all individuals in the PCOA search independently without utilizing the fitness and diversity information of the population. In view of the limitation of PCOA, a novel PCOA with migration and merging operation (denoted as MMO-PCOA) is proposed in this paper. Specifically, parallel individuals are randomly selected to be conducted migration and merging operation with the so far parallel solutions. Both migration and merging operation exchange information within population and produce new candidate individuals, which are different from those generated by stochastic chaotic sequences. Consequently, a good balance between exploration and exploitation can be achieved in the MMO-PCOA. The impacts of different one-dimensional maps and parallel numbers on the MMO-PCOA are also discussed. Benchmark functions and parameter identification problems are used to test the performance of the MMO-PCOA. Simulation results, compared with other optimization algorithms, show the superiority of the proposed MMO-PCOA algorithm.     

Mechanical damage characterization of glass fiber-reinforced polymer laminates by ultrasonic maps

A method for simultaneous measurement of the thickness and density for Glass Fiber-Reinforced Polymer (GFRP) laminate plates with ultrasonic waves in C-Scan mode is presented in the form of maps. The method uses three different signals in immersion pulse-echo C-Scan mode. The maps obtained based on the density show the heterogeneity of the material at high resolution at the pixel level (1 × 1 mm²) and therefore they represent an efficient tool to assess and evaluate the damage of the composite structures after manufacturing and after an applied mechanical loading.     

An adaptive image steganographic scheme based on Noise Visibility Function and an optimal chaotic based encryption method

Steganography is the science of hiding secret message in an appropriate digital multimedia in such a way that the existence of the embedded message should be invisible to anyone apart from the sender or the intended recipient. This paper presents an irreversible scheme for hiding a secret image in the cover image that is able to improve both the visual quality and the security of the stego-image while still providing a large embedding capacity. This is achieved by a hybrid steganography scheme incorporates Noise Visibility Function (NVF) and an optimal chaotic based encryption scheme. In the embedding process, first to reduce the image distortion and to increase the embedding capacity, the payload of each region of the cover image is determined dynamically according to NVF. NVF analyzes the local image properties to identify the complex areas where more secret bits should be embedded. This ensures to maintain a high visual quality of the stego-image as well as a large embedding capacity. Second, the security of the secret image is brought about by an optimal chaotic based encryption scheme to transform the secret image into an encrypted image. Third, the optimal chaotic based encryption scheme is achieved by using a hybrid optimization of Particle Swarm Optimization (PSO) and Genetic Algorithm (GA) which is allowing us to find an optimal secret key. The optimal secret key is able to encrypt the secret image so as the rate of changes after embedding process be decreased which results in increasing the quality of the stego-image. In the extracting process, the secret image can be extracted from the stego-image losslessly without referring to the original cover image. The experimental results confirm that the proposed scheme not only has the ability to achieve a good trade-off between the payload and the stego-image quality, but also can resist against the statistics and image processing attacks.     

Multiobjective PSO based adaption of neural network topology for pixel classification in satellite imagery

The proposed work involves the multiobjective PSO based adaption of optimal neural network topology for the classification of multispectral satellite images. It is per pixel supervised classification using spectral bands (original feature space). This paper also presents a thorough experimental analysis to investigate the behavior of neural network classifier for given problem. Based on 1050 number of experiments, we conclude that following two critical issues needs to be addressed: (1) selection of most discriminative spectral bands and (2) determination of optimal number of nodes in hidden layer. We propose new methodology based on multiobjective particle swarm optimization (MOPSO) technique to determine discriminative spectral bands and the number of hidden layer node simultaneously. The accuracy with neural network structure thus obtained is compared with that of traditional classifiers like MLC and Euclidean classifier. The performance of proposed classifier is evaluated quantitatively using Xie-Beni and β indexes. The result shows the superiority of the proposed method to the conventional one.     

Iterative equalization combining algorithm in intersymbol interference channel

To realize joint optimization of spatial diversity and equalization combining in the intersymbol interference (ISI) channel, an iterative equalization combining algorithm is proposed. The proposed algorithm uses the coefficients of Turbo equalization to calculate the combination weights without estimating the signal to noise ratio in each diversity branch. The equalized symbols from different diversity branches are combined, and the extrinsic information output from the decoder is fed back to the equalizers, so as to exchange soft information between the equalizers and the decoder. The performance of the proposed algorithm is analyzed using the extrinsic information transfer (EXIT) chart and verified by simulations. Results show that our approach fully exploits time domain information from the multipath channel and spatial domain information from multi receiving antennas, which efficiently improve the performance of the receiver in the severe ISI channel.     

Genetic algorithm and self organizing map based fuzzy hybrid intelligent method for color image segmentation

The grouping of pixels based on some similarity criteria is called image segmentation. In this paper the problem of color image segmentation is considered as a clustering problem and a fixed length genetic algorithm (GA) is used to handle it. The effectiveness of GA depends on the objective function (fitness function) and the initialization of the population. A new objective function is proposed to evaluate the quality of the segmentation and the fitness of a chromosome. In fixed length genetic algorithm the chromosomes have same length, which is normally set by the user. Here, a self organizing map (SOM) is used to determine the number of segments in order to set the length of a chromosome automatically. An opposition based strategy is adopted for the initialization of the population in order to diversify the search process. In some cases the proposed method makes the small regions of an image as separate segments, which leads to noisy segmentation. A simple ad hoc mechanism is devised to refine the noisy segmentation. The qualitative and quantitative results show that the proposed method performs better than the state-of-the-art methods.