Purification,structural data and biological properties of polysaccharide from Prunus amygdalus gum

This work demonstrates the efficiency of almond gum polysaccharides (AGPs) as bioactive compounds. AGPs were first extracted using H₂O₂, in the presence of NaOH, at different times and temperatures. The optimal extraction conditions were 4% H₂O₂ and 2 N NaOH, for 7 h at 50 °C, leading to an extraction yield of 58.2% (w/w). After a purification step, the retained AGPs were characterised using high‐performance liquid chromatography showing a molecular weight of 99.3 kDa. The monosaccharide composition of AGPs were assessed using gas chromatography–mass spectrometry. AGPs were found to be a complex heteropolysaccharide with a repeating unit mainly composed of galactose, arabinose, xylose, mannose, rhamnose, and glucuronic acid with the respective ratios: 45:26:7:10:1:11. The acidic nature of the polysaccharide is due to the presence of glucuronic acid. Total antioxidant activity, free radical‐scavenging activity and reducing power assay of AGPs were investigated. The obtained results showed high antioxidant activities of AGPs. Furthermore, beyond 60 mg mL^?1, AGPs exhibited bacterial growth inhibition for five pathogenic strains: Escherichia coli, Staphylococcus aureus, Enterococcus feacalis, Pseudomonas aeruginosa and Salmonella typhimurium.     

Novel T-rail electrodes for substrate removed low-voltage high-speed GaAs/AlGaAs electrooptic modulators

A novel traveling-wave electrode utilizing capacitively loaded T-rail elements was developed for low-voltage high-speed substrate-removed GaAs/AlGaAs electrooptic modulators. Electrodes with varying dimensions were fabricated and characterized. Electrode phase velocity, characteristic impedance, loss coefficient, and capacitive loading were extracted from the measured s-parameters up to 40 GHz. Electrode was also simulated using a finite-element solver. The measured and calculated electrode capacitance values were found to be in excellent agreement, showing that the electrode can be precisely designed. Approaches were outlined to provide a group velocity-matched very high-speed modulator electrode suitable for a low drive-voltage substrate-removed GaAs/AlGaAs electro-optic modulator     

Viscoelastic State for the Solutions of Spherically Symmetric Viscoelasticity Problems

The solutions of the spherically symmetric, linear, isothermal, and transient viscoelasticity problems via reciprocity theorem have been investigated for a specific material. The integral form of stress–strain relations has been used. The Laplace transform of a viscoelastic state, which is necessary for the integral equation arising as a result of reciprocity theorem, has been derived. This integral equation has been solved by Laplace transform. A sample problem has been solved to test the presented formulation. A numerical application of the analytic solution of this problem has been given.     

A comprehensive survey: artificial bee colony (ABC) algorithm and applications

Swarm intelligence (SI) is briefly defined as the collective behaviour of decentralized and self-organized swarms. The well known examples for these swarms are bird flocks, fish schools and the colony of social insects such as termites, ants and bees. In 1990s, especially two approaches based on ant colony and on fish schooling/bird flocking introduced have highly attracted the interest of researchers. Although the self-organization features are required by SI are strongly and clearly seen in honey bee colonies, unfortunately the researchers have recently started to be interested in the behaviour of these swarm systems to describe new intelligent approaches, especially from the beginning of 2000s. During a decade, several algorithms have been developed depending on different intelligent behaviours of honey bee swarms. Among those, artificial bee colony (ABC) is the one which has been most widely studied on and applied to solve the real world problems, so far. Day by day the number of researchers being interested in ABC algorithm increases rapidly. This work presents a comprehensive survey of the advances with ABC and its applications. It is hoped that this survey would be very beneficial for the researchers studying on SI, particularly ABC algorithm.     

Optimal Deep Learning Based Intruder Identification in Industrial Internet of Things Environment

With the increased advancements of smart industries, cybersecurity has become a vital growth factor in the success of industrial transformation. The Industrial Internet of Things (IIoT) or Industry 4.0 has revolutionized the concepts of manufacturing and production altogether. In industry 4.0, powerful Intrusion Detection Systems (IDS) play a significant role in ensuring network security. Though various intrusion detection techniques have been developed so far, it is challenging to protect the intricate data of networks. This is because conventional Machine Learning (ML) approaches are inadequate and insufficient to address the demands of dynamic IIoT networks. Further, the existing Deep Learning (DL) can be employed to identify anonymous intrusions. Therefore, the current study proposes a Hunger Games Search Optimization with Deep Learning-Driven Intrusion Detection (HGSODL-ID) model for the IIoT environment. The presented HGSODL-ID model exploits the linear normalization approach to transform the input data into a useful format. The HGSO algorithm is employed for Feature Selection (HGSO-FS) to reduce the curse of dimensionality. Moreover, Sparrow Search Optimization (SSO) is utilized with a Graph Convolutional Network (GCN) to classify and identify intrusions in the network. Finally, the SSO technique is exploited to fine-tune the hyper-parameters involved in the GCN model. The proposed HGSODL-ID model was experimentally validated using a benchmark dataset, and the results confirmed the superiority of the proposed HGSODL-ID method over recent approaches.     

Facile synthesis of CsPbBr3/PbSe composite clusters

In this work, CsPbBr₃ and PbSe nanocomposites were synthesized to protect perovskite material from self-enlargement during reaction. UV absorption and photoluminescence (PL) spectra indicate that the addition of Se into CsPbBr₃ quantum dots modified the electronic structure of CsPbBr₃, increasing the band gap from 2.38 to 2.48 eV as the Cs:Se ratio increased to 1:3. Thus, the emission color of CsPbBr₃ perovskite quantum dots was modified from green to blue by increasing the Se ratio in composites. According to X-ray diffraction patterns, the structure of CsPbBr₃ quantum dots changed from cubic to orthorhombic due to the introduction of PbSe at the surface. Transmission electron microscopy and X-ray photoemission spectroscopy confirmed that the atomic distribution in CsPbBr₃/PbSe composite clusters is uniform and the composite materials were well formed. The PL intensity of a CsPbBr₃/PbSe sample with a 1:1 Cs:Se ratio maintained 50% of its initial intensity after keeping the sample for 81 h in air, while the PL intensity of CsPbBr₃ reduced to 20% of its initial intensity. Therefore, it is considered that low amounts of Se could improve the stability of CsPbBr₃ quantum dots.     

A new supervised classification algorithm in artificial immune systems with its application to carotid artery Doppler signals to diagnose atherosclerosis

Because of its self-regulating nature, immune system has been an inspiration source for usually unsupervised learning methods in classification applications of Artificial Immune Systems (AIS). But classification with supervision can bring some advantages to AIS like other classification systems. Indeed, there have been some studies, which have obtained reasonable results and include supervision in this branch of AIS. In this study, we have proposed a new supervised AIS named as Supervised Affinity Maturation Algorithm (SAMA) and have presented its performance results through applying it to diagnose atherosclerosis using carotid artery Doppler signals as a real-world medical classification problem. We have employed the maximum envelope of the carotid artery Doppler sonograms derived from Autoregressive (AR) method as an input of proposed classification system and reached a maximum average classification accuracy of 98.93% with 10-fold cross-validation method used in training-test portioning. To evaluate this result, comparison was done with Artificial Neural Networks and Decision Trees. Our system was found to be comparable with those systems, which are used effectively in literature with respect to classification accuracy and classification time. Effects of system's parameters were also analyzed in performance evaluation applications. With this study and other possible contributions to AIS, classification algorithms with effective performances can be developed and potential of AIS in classification can be further revealed.