Combining multispectral reflectance and fluorescence imaging for identifying bruises and stem-end/calyx regions on Golden Delicious apples

As a part of the project for detecting bruises on Golden Delicious apple using vision system, the present paper shows a method that could separate the stem-end/calyx regions from the true bruises by combining the information of hyperspectral reflectance and fluorescence images. The images were scanned between 400 nm and 1,000 nm with a hyperspectral imaging system. Different light sources were constructed for capturing the reflectance and fluorescent images. Compared to the reflectance signal, the fluorescence signatures are much less intense, so that only the fluorescence of chlorophyll waveband was further examined (i.e., 685 nm). The analysis showed that the Principal Components scores images, which were based on the reflectance images, can be used for separating the bruised areas as well as the stem-end/calyx regions from the sound apple tissues; whereas only the stem-end/calyx was able to be recognized from the fluorescence images. For the samples investigated in this study, no stem-end/calyx regions were misrecognized as bruises; however, about 12% of bruised surfaces were misclassified as stem-end or calyx regions. All of the healthy tissues were correctly recognized as non-stem-end/calyx regions. The classification results indicated that combining multispectral reflectance and fluorescence imaging may help to identify the stem-end/calyx regions from the true bruised tissue and therefore to improve the accuracy for bruise detection on Golden Delicious apples.     

Development of a rapid method based on front-face fluorescence spectroscopy for the monitoring of egg freshness: 2—evolution of egg yolk

This preliminary study is devoted to the application of front-face fluorescence spectroscopy to the study of egg yolks during storage. A total of 79 eggs stored for 1, 2, 3, 4, 5, 9, 10, 12, 16, 18, 23, 25 and 29 days at room temperature were analysed. The fluorescence emission spectra of tryptophan residues (excitation: 290 nm; emission: 305–430 nm) of proteins and the excitation spectra of vitamin A (emission: 410 nm; excitation: 270–350 nm) were recorded directly on egg yolk samples. Factorial discriminant analysis (FDA) was used to classify the eggs according to their date after they were laid. Using tryptophan fluorescence spectra, correct classification was observed for 57.1 and 51.9% for the calibration and the validation sets, respectively. Better classification (94.9 and 91.4% of the calibration and validation samples, respectively) was obtained from the vitamin A fluorescence spectra. The first five principal components (PCs) of the principal component analysis (PCA) extracted from each data set (tryptophan and vitamin A fluorescence spectra) were pooled (concatenated) into a single-matrix and analysed by FDA. Correct classifications were obtained for 97.5% of the calibration and 96.3.1% of the validation spectra. The discrimination of the investigated egg yolks according to their storage time was excellent. It was concluded that the concatenation of different fluorescence spectra might be considered as a promising indicator of shell egg freshness when they are used in egg products.     

Dynamic testing rheology and fluorescence spectroscopy investigations of surface to centre differences in ripened soft cheeses

The viscoelastic properties and the matrix structures of three different retailed soft cheeses (M1, M2 and M3), for which the manufacturing process was varied, were studied from the surface to the centre of the cheese using dynamic rheology and front-face fluorescence spectroscopy. The storage modulus (G′) and the loss modulus (G″) values of the samples increased from the surface to the inner part of the cheeses, while strain and tan δ decreased. Protein tryptophan (excitation: 290 nm; emission: 305–400 nm) and vitamin A (emission: 410 nm; excitation: 250–350 nm) spectra were recorded at 20°C in samples cut from the surface to the centre. For each cheese, the data sets containing fluorescence spectra and rheology data were evaluated using multidimensional statistical methods. In addition, the three cheeses were well discriminated by their spectra by applying factorial discriminant analysis. From the tryptophan fluorescence data sets, 94% and 87.7% good classifications were observed for calibration and validation groups, respectively. A better classification (100% and 96% for principal and test samples) was obtained from the vitamin A spectra. Canonical correlation analysis was performed on the rheology and tryptophan fluorescence spectral data sets, and on the rheology and vitamin A fluorescence spectra data sets. The two groups of variables were found to be highly correlated since the squared canonical coefficients for canonical variates 1, 2, 3, 4 were higher than 0.98. These high correlations indicate that cheese rheology is a reflection of its structure at the molecular level.     

Mid infrared and fluorescence spectroscopies coupled with factorial discriminant analysis technique to identify sheep milk from different feeding systems

Mid infrared spectroscopy (MIR) combined with multivariate data analysis was used to discriminate between ewes milk samples according to their feeding systems (controls, ewes fed scotch bean and ewes fed soybean). The MIR spectra were scanned throughout the first 11 weeks of the lactation stage. When factorial discriminant analysis (FDA) with leave one-out cross-validation was applied, separately, to the three spectral regions in the MIR (i.e. 3000–2800, 1700–1500 and 1500–900 cm⁻¹), the classification rate was not satisfactory. Therefore, the first principal component (PCs) scores (corresponding to 3, 10 and 10 for, respectively, the 3000–2800, 1700–1500 and 1500–900 cm⁻¹) of the principal component analysis (PCA) extracted from each of the data sets were pooled (concatenated) into a single matrix and analysed by FDA. Correct classification amounting to 71.7% was obtained. Finally, the same procedure was applied to the MIR and fluorescence data sets and 98% of milk samples were found to be correctly classified. Milk samples belonging to control and soybean groups were 100% correctly classified. Regarding milk samples originating from the scotch bean group, only 2 out of 33 samples were misclassified. It was concluded that concatenation of the data sets collected from the two spectroscopic techniques is an efficient tool for authenticating milk samples according to their feeding systems, regardless of the lactation stage.     

Computation of Minimal Siphons in Petri Nets Using Problem Partitioning Approaches

A large amount of research has shown the vitality of siphon enumeration in the analysis and control of deadlocks in various resource-allocation systems modeled by Petri nets(PNs).In this paper,we propose an algorithm for the enumeration of minimal siphons in PN based on problem decomposition.The proposed algorithm is an improved version of the global partitioning minimal-siphon enumeration(GPMSE)proposed by Cordone et al.(2005)in IEEE Transactions on Systems,Man,and Cybernetics-Part A:Systems and Humans,which is widely used in the literature to compute minimal siphons.The experimental results show that the proposed algorithm consumes lower computational time and memory compared with GPMSE,which becomes more evident when the size of the handled net grows.     

MOS cascode-mirror biasing circuit operating at any current levelwith minimal output saturation voltage

A cascode biasing circuit is proposed which fixes the source voltage of the cascode transistor equal to the saturation voltage of the mirror transistor. The mirror can operate at any current level from weak to strong inversion. The design is based on ratios, and is technology-independent. Since the circuit ensures the smallest possible output saturation voltage, it has potential applications in all fields of low-voltage micropower design     

Design and synthesis of mixed micellar system for enhanced anticancer efficacy of Paclitaxel through its co-delivery with Naringin

Emergence of multidrug resistance (MDR) has limited the success of chemotherapeutic agents. Reversal of drugs efflux systems through combination therapy has got wider attention for increasing anticancer drugs efficacy. This study aims at co-encapsulation of Paclitaxel with Naringin in mixed polymeric micelles for enhanced anticancer activity of the drug. Drug-loaded micelles were prepared using two different amphiphilic block co-polymers and were characterized for morphology, size, zeta potential, drug encapsulation, in vitro release and stability using atomic force microscope (AFM), zetasizer, UV spectrophotometer, and FT-IR. MTT assay and fluorescence microscopy were used for in vitro cytotoxicity and cellular uptake studies. Nano-size micelles with spherical morphology and negative charge encapsulated 76.52?±?0.94% and 32.87 0.61% Paclitaxel and Naringin, respectively. The micelles were thermally stable and retained 87.05?±?0.69% and 92.88?±?2.17% Paclitaxel and Naringin upon one-month storage. Maximum drug release was achieved at fourth hour of the study for both the loaded drugs. Paclitaxel co-encapsulation with Naringin synergistically improved its intracellular uptake and 65% in vitro cytotoxicity against breast cancer cells was achieved at its lower dose of 15?µg/mL. Results suggest that co-encapsulation of Paclitaxel with Naringin in mixed micelles is an effective strategy for achieving its higher anticancer activity.     

Risk based intrusion detection system in software defined networking

Software defined networking (SDN) separates control from data operations. However, this technology adds a new security cost to the network architecture because of the ongoing and developing security vulnerabilities. An intrusion detection system must be continuously improved and integrated into the SDN architecture in order to provide a network defense against attacks. In this study, we propose a continual learning system based on risk assessment to detect intrusion in SDN. We suggest a technique for continually enhancing datasets to produce a more accurate prediction. The proposed system includes various processes, including risk assessment and the selection of the deep learning (DL) approach. We propose assessing the risks related to different intrusion types. Based on the risk value, we can identify which intrusion types are more important and have a dangerous impact. We use the risk values to choose the most appropriate DL approach and for the dataset's continual enrichment. We compare different DL methods using the standard metrics and two proposed metrics. Then, we propose to use a method based on the bit alternation approach to obtain a unique metric for decision-making. Finally, we have studied the efficacy of our system using two case studies.