Thermal blooming and photoluminescence characterizations of sol–gel CdO–SiO<Subscript>2</Subscript> with different nanocomposite

The CdO NPs was synthesized using the sol–gel method and the nanoparticles were characterized using an UV–Vis spectrophotometer, with shape and size were examined by SEM and XRD. The XRD analysis respects the Bragg’s law and confirmed the crystalline nature of CdO nanoparticles. From the XRD, the average size of CdO NPs was found to be around 41 nm. The photoluminescence spectra of the CdO NPs, as recorded at room temperature, were excited at 300 nm wavelength. The broad emission peaks were between 600 and 650 nm (orange emission). The optical limiting performance of the nanocomposite was described in the sol–gel state. Also, this study has observed and studied the diffraction rings generated in CdO NPs using the same CW laser. The number of rings increases almost exponentially with an increasing volume fraction of SiO₂ in the nanocomposites. The refractive index change, Δn, and effective nonlinear refractive index, n ₂, were found to be 10^?4 and 10^?8 cm²/W, respectively. The effective nonlinear refractive index, n ₂, was determined based on the observed number of rings. The threshold values of the CdO, CdO–2SiO₂ and CdO–5SiO₂ nanocomposites are 7.1, 6.55 and 6.34 mW, respectively. This large nonlinearity is attributed to the thermal effect. The present studies suggest that the nanocomposite is a potential candidate for optical device applications such as the optical limiters. The thermal blooming technique was applied to evaluate the thermo-optic coefficient and thermal diffusivity of the CdO NPs. In the thermal blooming experimental setup a transistor–transistor logic modulated CW laser of wavelength 532 nm was used as the excitation source.     

Assessing abrasion performance of self-consolidating concrete containing synthetic fibers using acoustic emission analysis

The key objective of this investigation was to evaluate the abrasion resistance of self-consolidating concrete (SCC) with and without synthetic fibers (SynFs). The abrasion resistance of normal concrete was also investigated in this study for comparison. The abrasion test was performed on concrete specimens according to the rotating-cutter method along with continuous monitoring of acoustic emission (AE) using attached AE sensors. The effects of changing concrete type and incorporating various types (flexible and semi-rigid) and lengths of SynFs on the abrasion behaviour were investigated with the aid of AE analysis. AE signal characteristics such as amplitude, signal strength, number of hits, and duration were gathered during testing. Furthermore, the collected AE data was used to complete b-value analysis as well as intensity analysis resulting in three additional parameters: b-value, severity (S_r), and historic index (H(t)). The results showed that the AE parameters were directly correlated with the abrasion damage in all tested mixtures. Adding SynFs to all SCC mixtures enhanced their abrasion resistance. The flexible fibers variety exhibited better abrasion performance on average than the semi-rigid fibers. Meanwhile, longer fibers showed lower abrasion resistance than the shorter ones with the same type. The results also indicated that AE intensity analysis was able to determine the ranges for H(t) and S_r that identify the extent of damage due to abrasion of SynF-reinforced SCC.     

The reference temperature dependence of Young’s modulus of two-temperature thermoelastic damping of gold nano-beam

This work is concerning with the study of the thermoelastic damping of a nanobeam resonator in the context of the two-temperature generalized thermoelasticity theory. An explicit formula of thermoelastic damping has been derived when Young’s modulus is a function of the reference temperature. Influences of the beam height and Young’s modulus have been studied with some comparisons between the Biot model and the Lord–Shulman model (L–S) for one- and two-temperature types. Numerical results show that the values of the thermal relaxation parameter and the two-temperature parameter have a strong influence on thermoelastic damping at nanoscales.     

Silver nanoparticles reduce the apoptosis induced by tumor necrosis factor-α

Silver nanoparticles (AgNPs) are widely known to have anti-inflammatory properties, but the exact mechanism underlying this anti-inflammatory effect is not clearly understood. Tumor necrosis factor-α (TNFα) is a major pro-inflammatory cytokine that is expressed in the early stage of cell inflammation and induces apoptosis by several known pathways. Our study aimed to investigate the effect of AgNPs on the response of lung epithelial cells to TNFα and the molecular mechanism of this response. Lung epithelial cell line NCI-H292 cells were exposed to AgNPs (5 µg/mL) and/or TNFα (20 ng/mL) for 24 h, then cellular uptake was analyzed using flow cytometry. Our results showed that AgNPs were taken up by cells in a dose-dependent manner and that the cellular uptake ratio of AgNPs was significantly increased in the presence of TNFα. Apoptosis assays indicated that exposure to AgNPs significantly decreased the apoptotic effect of TNFα. Confocal microscopy was used to localize the tumor necrosis factor receptor 1 (TNFR1) and revealed that TNFR1 localized on the surface of cells exposed to TNFα. In contrast, TNFR1 localized inside cells exposed to both AgNPs and TNFα, with very few receptors scattered on the cell membrane. The results indicated that AgNPs reduced the cell surface TNFR1 expression level. The results suggested that the reduction of surface TNFR1 reduced cellular response to TNFα, resulting in an anti-apoptotic effect.     

Green synthesis of silver nanoparticles using flower extract of Malva sylvestris and investigation of their antibacterial activity

High‐quality colloidal silver nanoparticles (AgNP) were synthesised via a green approach by using hydroalcoholic extracts of Malva sylvestris. Silver nitrate was used as a substrate ion while the plant extract successfully played the role of reducing and stabilising agents. The synthesised nanoparticles were carefully characterised by using transmission electron microscopy, atomic‐force microscopy, energy dispersive X‐ray spectroscopy, Fourier transform infrared spectroscopy and UV–vis spectroscopy. The maximum absorption wavelengths of the colloidal solutions synthesised using 70 and 96% ethanol and 100% methanol, as extraction solvents, were 430, 485 and 504 nm, respectively. Interestingly, the size distribution of nanoparticles depended on the used solvent. The best particle size distribution belonged to the nanoparticles synthesised by 70% ethanol extract, which was 20–40 nm. The antibacterial activity of the synthesised nanoparticles was studied on Escherichia coli, Staphylococcus aureus and Streptococcus pyogenes using disk diffusion, minimum inhibitory concentrations and minimum bactericidal concentrations assays. The best antibacterial activity obtained for the AgNPs produced by using 96% ethanolic extract.Inspec keywords: silver, nanoparticles, nanofabrication, antibacterial activity, colloids, particle size, transmission electron microscopy, atomic force microscopy, X‐ray chemical analysis, Fourier transform spectra, infrared spectra, ultraviolet spectra, visible spectra, microorganisms, nanomedicine, biomedical materialsOther keywords: Green synthesis, flower extract, Malva sylvestris, antibacterial activity, high‐quality colloidal silver nanoparticles, hydroalcoholic extracts, plant extract, reducing agents, stabilising agents, transmission electron microscopy, atomic‐force microscopy, energy dispersive X‐ray spectroscopy, Fourier transform infrared spectroscopy, UV– vis spectroscopy, colloidal solutions, particle size distribution, Escherichia coli, Staphylococcus aureus, Streptococcus pyogenes, disk diffusion, minimum inhibitory concentrations, minimum bactericidal concentrations assays, ethanolic extract, size 430 nm, size 485 nm, size 504 nm, size 20 nm to 40 nm, Ag     

Engineering carbon quantum dots for photomediated theranostics

Carbon quantum dots (CQDs) have emerged as potential alternatives to classical metal-based semiconductor quantum dots (QDs) due to the abundance of their precursors, their ease of synthesis, high biocompatibility, low cost, and particularly their strong photoresponsiveness, tunability, and stability. Light is a versatile, tunable stimulus that can provide spatiotemporal control. Its interaction with CQDs elicits interesting responses such as wavelength-dependent optical emissions, charge/electron transfer, and heat generation, processes that are suitable for a range of photomediated bioapplications. The carbogenic core and surface characteristics of CQDs can be tuned through versatile engineering strategies to endow specific optical and physicochemical properties, while conjugation with specific moieties can enable the design of targeted probes. Fundamental approaches to tune the responses of CQDs to photo-interactions and the design of bionanoprobes are presented, which enable biomedical applications involving diagnostics and therapeutics. These strategies represent comprehensive platforms for engineering multifunctional probes for nanomedicine, and the design of QD probes with a range of metal-free and emerging 2D materials.

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Quantum Inspired Broadband Photonic Absorber: Potential Application as Solar Sail along with Mobility Analysis

Electromagnetic wideband absorption is still perceived as a critical and formidable challenge to address with an unambiguous photonic absorber. Subwavelength metamaterial (MM) unit cells with unique and controlled features have recently gained considerable interest. However, meta-atoms, generated using a quantum-inspired pattern distribution, are underwhelming in existing literature to design photonic absorbers and their potential application to manufacture solar sails is still quite uncommon. In this article, to create a flexible, polarization-insensitive, ultrathin, and broadband MM absorber, quantum interference pattern-inspired design is utilized. Herein, a novel approach to fabricating solar sails for the space exploration incorporates the proposed broadband photonic absorber rather than conventional reflectors. The quantum-inspired meta-absorber (QIMA) exhibits an absorption of over 91% for the visible domain, i.e., 380–800 nm under a conventional plane-polarized source. It is shown in the study that broadband absorbers are almost equivalent to excellent reflectors to design the solar sails in terms of the time-averaged force calculated by utilizing the Maxwell stress tensor method. Thus, the QIMA has the potential to be a viable alternative to reflectors in the design of futuristic solar sails for space exploration. The interference theory model is also utilized to assure the dependability of calculated data, and additionally, the standard AM1.5 solar spectrum is utilized to demonstrate the QIMA's solar-harvesting potentiality.     

Leveraging Transfer Learning for Spatio-Temporal Human Activity Recognition from Video Sequences

Human Activity Recognition (HAR) is an active research area due to its applications in pervasive computing, human-computer interaction, artificial intelligence, health care, and social sciences. Moreover, dynamic environments and anthropometric differences between individuals make it harder to recognize actions. This study focused on human activity in video sequences acquired with an RGB camera because of its vast range of real-world applications. It uses two-stream ConvNet to extract spatial and temporal information and proposes a fine-tuned deep neural network. Moreover, the transfer learning paradigm is adopted to extract varied and fixed frames while reusing object identification information. Six state-of-the-art pre-trained models are exploited to find the best model for spatial feature extraction. For temporal sequence, this study uses dense optical flow following the two-stream ConvNet and Bidirectional Long Short Term Memory (BiLSTM) to capture long-term dependencies. Two state-of-the-art datasets, UCF101 and HMDB51, are used for evaluation purposes. In addition, seven state-of-the-art optimizers are used to fine-tune the proposed network parameters. Furthermore, this study utilizes an ensemble mechanism to aggregate spatial-temporal features using a four-stream Convolutional Neural Network (CNN), where two streams use RGB data. In contrast, the other uses optical flow images. Finally, the proposed ensemble approach using max hard voting outperforms state-of-the-art methods with 96.30% and 90.07% accuracies on the UCF101 and HMDB51 datasets.     

Classification of Electroencephalogram Signals Using LSTM and SVM Based on Fast Walsh-Hadamard Transform

Classification of electroencephalogram (EEG) signals for humans can be achieved via artificial intelligence (AI) techniques. Especially, the EEG signals associated with seizure epilepsy can be detected to distinguish between epileptic and non-epileptic regions. From this perspective, an automated AI technique with a digital processing method can be used to improve these signals. This paper proposes two classifiers: long short-term memory (LSTM) and support vector machine (SVM) for the classification of seizure and non-seizure EEG signals. These classifiers are applied to a public dataset, namely the University of Bonn, which consists of 2 classes –seizure and non-seizure. In addition, a fast Walsh-Hadamard Transform (FWHT) technique is implemented to analyze the EEG signals within the recurrence space of the brain. Thus, Hadamard coefficients of the EEG signals are obtained via the FWHT. Moreover, the FWHT is contributed to generate an efficient derivation of seizure EEG recordings from non-seizure EEG recordings. Also, a k-fold cross-validation technique is applied to validate the performance of the proposed classifiers. The LSTM classifier provides the best performance, with a testing accuracy of 99.00%. The training and testing loss rates for the LSTM are 0.0029 and 0.0602, respectively, while the weighted average precision, recall, and F1-score for the LSTM are 99.00%. The results of the SVM classifier in terms of accuracy, sensitivity, and specificity reached 91%, 93.52%, and 91.3%, respectively. The computational time consumed for the training of the LSTM and SVM is 2000 and 2500 s, respectively. The results show that the LSTM classifier provides better performance than SVM in the classification of EEG signals. Eventually, the proposed classifiers provide high classification accuracy compared to previously published classifiers.     

A recursive state estimator in the presence of state inequality constraints

This paper proposes an optimal recursive estimator to estimate the states of a stochastic discrete time linear dynamic system when the states of the system are constrained with inequality constraints. The case when the constraints are strictly satisfied is treated independently from the case when some of the constraints are violated. For the first case, the well known Kalman filter estimator is used. In the second case, an algorithm which uses a series of successive orthogonalizations on the measurement subspaces is employed to obtain the optimal estimate. It is shown that the proposed estimator has several attractive properties such that it is an unbiased estimator. More importantly, compared to other estimator found in the literature, the proposed estimator needs less computational efforts, is numerically more stable and it leads to a smaller variance. To show the effectiveness of the proposed estimator, several simulation results are presented and discussed.