Facile Deposition of Silver Nanoparticles on Photonic Cellulose Nanocrystals Films: A Study on Solvent Stability and Post Antibacterial Activity

Cellulose nanocrystals (CNCs) incorporated with silver nanoparticles (AgNPs) photonic films have drawn considerable attention due to their plasmonic chiroptical activity. However, the exploitation of some fundamental properties for practical use such as the affinity analysis of metal nanoparticles attached to the surface of photonic films according to the solvent compatibility and antibacterial activity under physical conditions has yet not been studied. Hence, a facile process of in situ deposition of AgNPs into the chiral structure of CNC films is proposed. CNC photonic films, cross-linked by glutaraldehyde are prepared. This interaction generated the solvents-stable photonic film with a considerable amount of unreacted aldehyde functional groups that facilitates the reduction of Ag salt to AgNPs. The formed AgNPs in the photonic films show excellent stability over immersion in various polar and non-polar solvents. The post-solvent treated photonic films display excellent contact-based antibacterial behavior against gram-negative Escherichia coli.     

An Improved Hybrid Indoor Positioning Algorithm via QPSO and MLP Signal Weighting

Accurate location or positioning of people and self-driven devices in large indoor environments has become an important necessity The application of increasingly automated self-operating moving transportation units, in large indoor spaces demands a precise knowledge of their positions. Technologies like WiFi and Bluetooth, despite their low-cost and availability, are sensitive to signal noise and fading effects. For these reasons, a hybrid approach, which uses two different signal sources, has proven to be more resilient and accurate for the positioning determination in indoor environments. Hence, this paper proposes an improved hybrid technique to implement a fingerprinting based indoor positioning, using Received Signal Strength information from available Wireless Local Area Network access points, together with the Wireless Sensor Networks technology. Six signals were recorded on a regular grid of anchor points, covering the research space. An optimization was performed by relative signal weighting, to minimize the average positioning error over the research space. The optimization process was conducted using a standard Quantum Particle Swarm Optimization, while the position error estimate for all given sets of weighted signals was performed using a Multilayer Perceptron (MLP) neural network. Compared to our previous research works, the MLP architecture was improved to three hidden layers and its learning parameters were finely tuned. These experimental results led to the 20% reduction of the positioning error when a suitable set of signal weights was calculated in the optimization process. Our final achieved value of 0.725 m of the location incertitude shows a sensible improvement compared to our previous results.     

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.     

Double Layer Coatings: A New Technique for Maintaining Physico-Chemical Characteristics and Antioxidant Properties of Dragon Fruit During Storage

A double layer coating was evaluated for maintenance of quality of dragon fruit during storage at 10?±?2 °C and 80?±?5 % RH for 28 days. Significant differences (p?<?0.05) were observed between control and the treated fruit. However, a double layer coating with 600 nm droplet size?+?1.0 % conventional chitosan showed promising results in all the tested parameters, while the fruit treated with 1,000 nm droplet size?+?1.0 % conventional chitosan showed some negative effects on fruit surface. Increase in weight loss was 12.0 % in fruit treated with 600 nm droplet size and 1.0 % conventional chitosan as compared to the control. Antioxidants and gaseous analysis also proved the efficacy of double layer coatings with 600 nm droplet size?+?1.0 % conventional chitosan. Thus it can be concluded from the present investigation that double layer coating could be used for maintaining quality in dragon fruit for up to 28 days without any off-flavours.     

A systematic mapping of semi-formal and formal methods in requirements engineering of industrial Cyber-Physical systems

Journal of Intelligent Manufacturing - The requirements engineering of Industrial Cyber-Physical Systems is extremely challenging due to large system sizes, component heterogeneity, involvement of...     

Monitoring the degradation of a solid oxide fuel cell stack during 10,000 h via electrochemical impedance spectroscopy

A 5-cell solid oxide fuel cell stack was tested during 10,000 h of continuous operation with simulated reformate gas as fuel (71 vol.% H₂, 20.7 vol.% CO₂ and 8.3 vol.% steam) under high fuel utilization (73%) and constant current load (0.5 A cm^?2 or 25 A) at 750 °C. In situ electrochemical impedance spectroscopy was used to monitor the evolution of ohmic and polarisation resistances of individual cells in the stack without interrupting the current load. Impedance spectra were recorded on each cell periodically (every 1000 h) or after uncontrolled incidents happened with the test setup. It has been found that the stack degradation is mainly attributed to the increased ohmic resistance, pointing to possible causes such as interconnect corrosion and reduced effective contact areas between cells and interconnects. The degradation rate during the first 5000 h was about 1% kh^?1, but increased afterwards up to 1.5% kh^?1 due to the impact of incidents. Both types of incidents (fuel supply fluctuations and overloading failure of the electronic load) were complicated by inhomogeneous fuel distribution among cells, leading to most probably partial re-oxidation of the anode, accelerating the stack degradation.     

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.     

Green synthesised zinc oxide nanostructures through Periploca aphylla extract shows tremendous antibacterial potential against multidrug resistant pathogens

To grapple with multidrug resistant bacterial infections, implementations of antibacterial nanomedicines have gained prime attention of the researchers across the globe. Nowadays, zinc oxide (ZnO) at nano‐scale has emerged as a promising antibacterial therapeutic agent. Keeping this in view, ZnO nanostructures (ZnO‐NS) have been synthesised through reduction by P. aphylla aqueous extract without the utilisation of any acid or base. Structural examinations via scanning electron microscopy (SEM) and X‐ray diffraction have revealed pure phase morphology with highly homogenised average particle size of 18 nm. SEM findings were further supplemented by transmission electron microscopy examinations. The characteristic Zn–O peak has been observed around 363 nm using ultra‐violet–visible spectroscopy. Fourier‐transform infrared spectroscopy examination has also confirmed the formation of ZnO‐NS through detection of Zn–O bond vibration frequencies. To check the superior antibacterial activity of ZnO‐NS, the authors'' team has performed disc diffusion assay and colony forming unit testing against multidrug resistant E. coli, S. marcescens and E. cloacae. Furthermore, protein kinase inhibition assay and cytotoxicity examinations have revealed that green fabricated ZnO‐NS are non‐hazardous, economical, environmental friendly and possess tremendous potential to treat lethal infections caused by multidrug resistant pathogens.Inspec keywords: nanomedicine, zinc compounds, II‐VI semiconductors, wide band gap semiconductors, nanoparticles, scanning electron microscopy, X‐ray diffraction, antibacterial activity, transmission electron microscopy, particle size, Fourier transform infrared spectra, ultraviolet spectra, visible spectra, enzymes, biochemistry, molecular biophysics, microorganisms, drugs, toxicology, bonds (chemical), semiconductor growth, nanofabrication, vibrational modesOther keywords: green synthesised zinc oxide nanostructures, Periploca aphylla extract, antibacterial potential, multidrug resistant pathogens, multidrug resistant bacterial infections, antibacterial nanomedicines, P. aphylla aqueous extract, structural examinations, scanning electron microscopy, X‐ray diffraction, pure phase morphology, homogenised average particle size, SEM, transmission electron microscopy, Fourier‐transform infrared spectroscopy, bond vibration frequency, antibacterial activity, disc diffusion assay, colony forming unit testing, S. marcescens, E. cloacae, E. coli, ultraviolet‐visible spectroscopy, protein kinase inhibition assay, cytotoxicity, lethal infections, ZnO