Dynamic S-Box Generation Using Novel Chaotic Map with Nonlinearity Tweaking

A substitution box (S-Box) is a crucial component of contemporary cryptosystems that provide data protection in block ciphers. At the moment, chaotic maps are being created and extensively used to generate these S-Boxes as a chaotic map assists in providing disorder and resistance to combat cryptanalytical attempts. In this paper, the construction of a dynamic S-Box using a cipher key is proposed using a novel chaotic map and an innovative tweaking approach. The projected chaotic map and the proposed tweak approach are presented for the first time and the use of parameters in their working makes both of these dynamic in nature. The tweak approach employs cubic polynomials while permuting the values of an initial S-Box to enhance its cryptographic fort. Values of the parameters are provided using the cipher key and a small variation in values of these parameters results in a completely different unique S-Box. Comparative analysis and exploration confirmed that the projected chaotic map exhibits a significant amount of chaotic complexity. The security assessment in terms of bijectivity, nonlinearity, bits independence, strict avalanche, linear approximation probability, and differential probability criteria are utilized to critically investigate the effectiveness of the proposed S-Box against several assaults. The proposed S-Box’s cryptographic performance is comparable to those of recently projected S-Boxes for its adaption in real-world security applications. The comparative scrutiny pacifies the genuine potential of the proposed S-Box in terms of its applicability for data security.     

Edge aware anisotropic diffusion for 3D scalar data

In this paper we present a novel anisotropic diffusion model targeted for 3D scalar field data. Our model preserves material boundaries as well as fine tubular structures while noise is smoothed out. One of the major novelties is the use of the directional second derivative to define material boundaries instead of the gradient magnitude for thresholding. This results in a diffusion model that has much lower sensitivity to the diffusion parameter and smoothes material boundaries consistently compared to gradient magnitude based techniques. We empirically analyze the stability and convergence of the proposed diffusion and demonstrate its de-noising capabilities for both analytic and real data. We also discuss applications in the context of volume rendering.     

Profiling drivers based on driver dependent vehicle driving features

This work addresses the problem of profiling drivers based on their driving features. A purpose-built hardware integrated with a software tool is used to record data from multiple drivers. The recorded data is then profiled using clustering techniques. k-means has been used for clustering and the results are counterchecked with Fuzzy c-means (FCM) and Model Based Clustering (MBC). Based on the results of clustering, a classifier, i.e., an Artificial Neural Network (ANN) is trained to classify a driver during driving in one of the four discovered clusters (profiles). The performance of ANN is compared with that of a Support Vector Machine (SVM). Comparison of the clustering techniques shows that different subsets of the recorded dataset with a diverse combination of attributes provide approximately the same number of profiles, i.e., four. Analysis of features shows that average speed, maximum speed, number of times brakes were applied, and number of times horn was used provide the information regarding drivers’ driving behavior, which is useful for clustering. Both one versus one (SVM) and one versus rest (SVM) method for classification have been applied. Average accuracy and average mean square error achieved in the case of ANN was 84.2 % and 0.05 respectively. Whereas the average performance for SVM was 47 %, the maximum performance was 86 % using RBF kernel. The proposed system can be used in modern vehicles for early warning system, based on drivers’ driving features, to avoid accidents.     

Sensitivity of CFP/YFP and GFP/mCherry pairs to donor photobleaching on FRET determination by fluorescence lifetime imaging microscopy in living cells

Fluorescent protein-based FRET is a powerful method for visualizing protein-protein interactions and biochemical reactions in living cells. It can be difficult, however, to avoid photobleaching when observing fluorescent cells under the microscope, especially those expressing CFP. We compared the sensitivity of two protein-based FRET pairs to light-induced fluorescence changes in the donor, on FRET determination by fluorescence lifetime imaging microscopy (FLIM). Thanks to the very low excitation light levels of the time- and space-correlated single photon counting (TSCSPC) method, FLIM acquisitions were achieved without donor photobleaching. Here, we show that photobleaching of CFP by a mercury lamp under the microscope induced a decrease in the mean fluorescence lifetime, which interfered with FRET determination between CFP and YFP. Importantly, the range of light-induced variation of the mean fluorescence lifetime of CFP was not proportional to the decrease in the steady state fluorescence intensity and varied from cell to cell. The choice of the CFP/YFP pair therefore requires that the cells be observed and analyzed at very low light levels during the whole FRET experiment. In contrast, the GFP/mCherry pair provided an accurate FRET measurement by FLIM, even if some GFP photobleaching took place. We thus demonstrate that CFP can be an unreliable donor for FRET determination in living cells, due to its photosensitivity properties. We demonstrate that the GFP/mCherry pair is better suited for FRET measurement by FLIM in living cells than the CFP/YFP pair.     

Reactions with 5-substituted 4-arylimino-2-thiazolidinones

The reaction of 5-arylidene-4-arylimino-2-thiazolidinones 4 with GRIGNARD reagents effected 1,4-addition to the exocyclic  CC bond with the formation of 5 . Alkylation of the arylidene derivatives 4 with diazomethane or methyl iodide results in the formation of the N-methyl derivatives 6 . Treatment of 6 with phenylmagnesium bromide effects addition of the reagent to the double bond of the lateral chain to yield products, proved to have structure 8 . The 5-arylazo derivatives 9 of 4-arylimino-2-thiazolidinones were obtained by coupling 4 with aromatic diazonium chlorides. Compounds 9 were N-methylated with diazomethane to yield 10 .     

Microscopic melanoma detection and classification: A framework of pixel‐based fusion and multilevel features reduction

The numbers of diagnosed patients by melanoma are drastic and contribute more deaths annually among young peoples. An approximately 192,310 new cases of skin cancer are diagnosed in 2019, which shows the importance of automated systems for the diagnosis process. Accordingly, this article presents an automated method for skin lesions detection and recognition using pixel‐based seed segmented images fusion and multilevel features reduction. The proposed method involves four key steps: (a) mean‐based function is implemented and fed input to top‐hat and bottom‐hat filters which later fused for contrast stretching, (b) seed region growing and graph‐cut method‐based lesion segmentation and fused both segmented lesions through pixel‐based fusion, (c) multilevel features such as histogram oriented gradient (HOG), speeded up robust features (SURF), and color are extracted and simple concatenation is performed, and (d) finally variance precise entropy‐based features reduction and classification through SVM via cubic kernel function. Two different experiments are performed for the evaluation of this method. The segmentation performance is evaluated on PH2, ISBI2016, and ISIC2017 with an accuracy of 95.86, 94.79, and 94.92%, respectively. The classification performance is evaluated on PH2 and ISBI2016 dataset with an accuracy of 98.20 and 95.42%, respectively. The results of the proposed automated systems are outstanding as compared to the current techniques reported in state of art, which demonstrate the validity of the proposed method.     

Reactivity of the exocyclic double bond in 4-arylidene-2-phenyl-2-imidazolin-5-ones toward organomagnesium compounds. I

4-Arylidene-2-phenyl-1-substituted-2-imidazolin-5-ones 1a – d react with organomagnesium compounds, in ether at room temperature, to yield, as the main isolable products, the 4-disubstituted-methyl derivatives 2a – h . On the other hand, 1a – d react with phenylmagnesium bromide in a boiling ether-benzene mixture to give, among the reaction products, 2a – d and the 4-diarylmethylene derivatives 3a – d . Spectral data of 2 and 3 are discussed. Treatment of the GRIGNARD products 2a – c with formaldehyde and piperidine or morpholine effects aminomethylation at C-4 with the formation of the MANNICH bases 6a – f .     

Selection of optimal denoising-based regularization hyper-parameters for performance improvement in a sensor validation model

Multilayered auto-associative neural architectures have widely been used in empirical sensor modeling. Typically, such empirical sensor models are used in sensor calibration and fault monitoring systems. However, simultaneous optimization of related performance metrics, i.e., auto-sensitivity, cross-sensitivity, and fault-detectability, is not a trivial task. Learning procedures for parametric and other relevant non-parametric empirical models are sensitive to optimization and regularization methods. Therefore, there is a need for active learning strategies that can better exploit the underlying statistical structure among input sensors and are simple to regularize and fine-tune. To this end, we investigated the greedy layer-wise learning strategy and denoising-based regularization procedure for sensor model optimization. We further explored the effects of denoising-based regularization hyper-parameters such as noise-type and noise-level on sensor model performance and suggested optimal settings through rigorous experimentation. A visualization procedure was introduced to obtain insight into the internal semantics of the learned model. These visualizations allowed us to suggest an implicit noise-generating process for efficient regularization in higher-order layers. We found that the greedy-learning procedure improved the overall robustness of the sensor model. To keep experimentation unbiased and immune to noise-related artifacts in real sensors, the sensor data were sampled from simulators of a nuclear steam supply system of a pressurized water reactor and a Tennessee Eastman chemical process. Finally, we compared the performance of an optimally regularized sensor model with auto-associative neural network, auto-associative kernel regression, and fuzzy similarity-based sensor models.     

Banana peel: an effective biosorbent for aflatoxins

This work reports the application of banana peel as a novel bioadsorbent for in vitro removal of five mycotoxins (aflatoxins (AFB1, AFB2, AFG1, AFG2) and ochratoxin A). The effect of operational parameters including initial pH, adsorbent dose, contact time and temperature were studied in batch adsorption experiments. Scanning electron microscopy (SEM), Fourier transform infrared spectroscopy (FTIR) and point of zero charge (pH_pzc) analysis were used to characterise the adsorbent material. Aflatoxins’ adsorption equilibrium was achieved in 15 min, with highest adsorption at alkaline pH (6–8), while ochratoxin has not shown any significant adsorption due to surface charge repulsion. The experimental equilibrium data were tested by Langmuir, Freundlich and Hill isotherms. The Langmuir isotherm was found to be the best fitted model for aflatoxins, and the maximum monolayer coverage (Q₀) was determined to be 8.4, 9.5, 0.4 and 1.1 ng mg^?1 for AFB1, AFB2, AFG1 and AFG2 respectively. Thermodynamic parameters including changes in free energy (ΔG), enthalpy (ΔH) and entropy (ΔS) were determined for the four aflatoxins. Free energy change and enthalpy change demonstrated that the adsorption process was exothermic and spontaneous. Adsorption and desorption study at different pH further demonstrated that the sorption of toxins was strong enough to sustain pH changes that would be experienced in the gastrointestinal tract. This study suggests that biosorption of aflatoxins by dried banana peel may be an effective low-cost decontamination method for incorporation in animal feed diets.