Hyperspectral target detection via discrete wavelet-based spectral fringe-adjusted joint transform correlation

Spectral variability remains a major challenge for target detection in hyperspectral imagery (HSI). Recently, the spectral fringe-adjusted joint transform correlation (SFJTC) technique has been used effectively for hyperspectral target detection applications. In this paper, we propose to use discrete wavelet transform (DWT) coefficients of the signatures as features for detection in order to make the SFJTC technique more insensitive to spectral variability. We devised a supervised training algorithm that uses the pure target signature and randomly selected samples from input scenery to select an optimal set of DWT coefficients for detection. We have inserted target signatures into urban and vegetative hyperspectral scenery with varying levels of spectral variability to explore the performance of our DWT-based SFJTC technique in different operating conditions. Detection results in the form of receiver-operating-characteristic (ROC) curves and area-under-the-ROC (AUROC) curves show that the proposed scheme yields the largest mean AUROC values compared to SFJTC using the original signatures and traditional hyperspectral detection algorithms.     

A comparison of methods for assessing dough and gluten strength of durum wheat and their relationship to pasta cooking quality

The best rheological test to differentiate dough and gluten strength and predict cooking quality of different durum wheat cultivars is not recognised yet. Sixteen durum wheat cultivars were grown at three locations in North Dakota using a randomised complete block design to compare different methods for measuring dough/gluten strength and to relate their results to pasta cooking quality. Different rheological tests were used to distinguish the weak, medium strong, strong and very strong gluten cultivars. Alveograph, gluten index and glutograph were the only tests that could differentiate between medium strong and strong gluten samples. Alveograph was the best method to predict gluten strength where few samples are available for assessment. In comparison with alveograph, the gluten index was faster and required less semolina and gave similar results as the alveograph. All tests had significant correlation with cooked spaghetti firmness and negative correlation with cooked weight.     

A resource-efficient encryption algorithm for multimedia big data

Nowadays, multimedia is considered to be the biggest big data as it dominates the traffic in the Internet and mobile phones. Currently symmetric encryption algorithms are used in IoT but when considering multimedia big data in IoT, symmetric encryption algorithms incur more computational cost. In this paper, we have designed and developed a resource-efficient encryption system for encrypting multimedia big data in IoT. The proposed system takes the advantages of the Feistel Encryption Scheme, an Advanced Encryption Standard (AES), and genetic algorithms. To satisfy high throughput, the GPU has also been used in the proposed system. This system is evaluated on real IoT medical multimedia data to benchmark the encryption algorithms such as MARS, RC6, 3-DES, DES, and Blowfish in terms of computational running time and throughput for both encryption and decryption processes as well as the avalanche effect. The results show that the proposed system has the lowest running time and highest throughput for both encryption and decryption processes and highest avalanche effect with compared to the existing encryption algorithms. To satisfy the security objective, the developed algorithm has better Avalanche Effect with compared to any of the other existing algorithms and hence can be incorporated in the process of encryption/decryption of any plain multimedia big data. Also, it has shown that the classical and modern ciphers have very less Avalanche Effect and hence cannot be used for encryption of confidential multimedia messages or confidential big data. The developed encryption algorithm has higher Avalanche Effect and for instance, AES in the proposed system has an Avalanche Effect of %52.50. Therefore, such system is able to secure the multimedia big data against real-time attacks.

     

Detecting and Classifying Darknet Traffic Using Deep Network Chains

The anonymity of the darknet makes it attractive to secure communication lines from censorship. The analysis, monitoring, and categorization of Internet network traffic are essential for detecting darknet traffic that can generate a comprehensive characterization of dangerous users and assist in tracing malicious activities and reducing cybercrime. Furthermore, classifying darknet traffic is essential for real-time applications such as the timely monitoring of malware before attacks occur. This paper presents a two-stage deep network chain for detecting and classifying darknet traffic. In the first stage, anonymized darknet traffic, including VPN and Tor traffic related to hidden services provided by darknets, is detected. In the second stage, traffic related to VPNs and Tor services is classified based on their respective applications. The methodology of this paper was verified on a benchmark dataset containing VPN and Tor traffic. It achieved an accuracy of 96.8% and 94.4% in the detection and classification stages, respectively. Optimization and parameter tuning were performed in both stages to achieve more accurate results, enabling practitioners to combat alleged malicious activities and further detect such activities after outbreaks. In the classification stage, it was observed that the misclassifications were due to the audio and video streaming commonly used in shared real-time protocols. However, in cases where it is desired to distinguish between such activities accurately, the presented deep chain classifier can accommodate additional classifiers. Furthermore, additional classifiers could be added to the chain to categorize specific activities of interest further.     

Intracellular Ionic Strength Sensing Using NanoLuc

Intracellular ionic strength regulates myriad cellular processes that are fundamental to cellular survival and proliferation, including protein activity, aggregation, phase separation, and cell volume. It could be altered by changes in the activity of cellular signaling pathways, such as those that impact the activity of membrane-localized ion channels or by alterations in the microenvironmental osmolarity. Therefore, there is a demand for the development of sensitive tools for real-time monitoring of intracellular ionic strength. Here, we developed a bioluminescence-based intracellular ionic strength sensing strategy using the Nano Luciferase (NanoLuc) protein that has gained tremendous utility due to its high, long-lived bioluminescence output and thermal stability. Biochemical experiments using a recombinantly purified protein showed that NanoLuc bioluminescence is dependent on the ionic strength of the reaction buffer for a wide range of ionic strength conditions. Importantly, the decrease in the NanoLuc activity observed at higher ionic strengths could be reversed by decreasing the ionic strength of the reaction, thus making it suitable for sensing intracellular ionic strength alterations. Finally, we used an mNeonGreen–NanoLuc fusion protein to successfully monitor ionic strength alterations in a ratiometric manner through independent fluorescence and bioluminescence measurements in cell lysates and live cells. We envisage that the biosensing strategy developed here for detecting alterations in intracellular ionic strength will be applicable in a wide range of experiments, including high throughput cellular signaling, ion channel functional genomics, and drug discovery.     

Design,Synthesis, Biological Evaluation, 2D-QSAR Modeling,and Molecular Docking Studies of Novel 1H-3-Indolyl Derivatives as Significant Antioxidants

Novel candidates of 3-(4-(thiophen-2-yl)-pyridin/pyran/pyrimidin/pyrazol-2-yl)-1H-indole derivatives (2–12) were designed by pairing the pyridine/pyrane/pyrimidine/pyrazole heterocycles with indole and thiophene to investigate their potential activities as (2,2′-azinobis (3-ethylbenzothiazoline-6-sulfonic acid) inhibitors. The purpose of these derivatives’ modification is to create high-efficiency antioxidants, especially against ABTS, as a result of the efficiency of this set of key heterocycles in the inhibition of ROS. Herein, 2D QSAR modeling was performed to recommend the most promising members for further in vitro investigations. Furthermore, the pharmacological assay for antioxidant activity evaluation of the yielded indole-based heterocycles was tested against ABTS (2,2′-azinobis (3-ethylbenzothiazoline-6-sulfonic acid); by utilizing ascorbic acid as the standard. Candidate 10 showed higher antioxidant activity (IC₅₀ = 28.23 μg/mL) than ascorbic acid itself which achieved (IC₅₀ = 30.03 μg/mL). Moreover, molecular docking studies were performed for the newly designed and synthesized drug candidates to propose their mechanism of action as promising cytochrome c peroxidase inhibitors compared to ascorbic acid as a reference standard. Our findings could be promising in the medicinal chemistry scope for further optimization of the newly designed and synthesized compounds regarding the introduced structure-activity relationship study (SAR) in order to get a superior antioxidant lead compound in the near future.     

Effect of electric potential into conductance silicon nanowire distribution

Microsystem Technologies - Distribution of electric potential and space charge in a silicon nanowire has been investigated. First, the model of the nanowire is generated with concern the physics...