Performance analysis of intelligent reflecting surface-assisted mobile wireless networks subjected to generalized Gaussian noise

Different from the prior works, this paper presents the performance analysis of an intelligent reflecting surface (IRS)-assisted communication link in a static and mobile scenario impaired by Rayleigh fading and additive white generalized Gaussian noise (AWGGN). Precisely, the IRS is configured as an intelligent access point, and the mobile behavior of the nodes is characterized by the random waypoint (RWP) model. To this end, closed-form expressions of average bit error rate (BER), average channel capacity (ACC), and outage probability (OP) in both static and mobile scenarios are obtained. To gain further insight into the system performance at high signal-to-noise ratio (SNR), asymptotic expressions are obtained. Moreover, the effect of the number of reflective elements ( $N$) and the shaping parameter ( $\lambda$) on the system performance is thoroughly studied. The results indicate that introduction of IRS leads to significant improvement in the overall system performance. The derived results are corroborated with Monte Carlo simulations.     

CuCe-Ferrite/TiO2 Nanocomposite as an Efficient Magnetically Separable Photocatalyst for Dye Pollutants Decolorization

In this work, a magnetically separated photocatalyst with great efficiency CuCe-Ferrite/TiO₂ composite was prepared and characterized by X-ray diffraction (XRD), UV–Vis spectrophotometry, Fourier transformer infra-red spectroscopy (FTIR), field emission scanning electron microscopy (FE-SEM), high resolution transmission electron microscopy (HR-TEM), energy dispersive X-ray spectroscopy (EDX) and vibrating sample magnetometer (VSM). Single-phase cubic spinel was formed by calcining the prepared sample at a temperature of 550 °C, according to the results. Different concentrations of reactive red 250 (RR250) dye photodegradation was evaluated using different doses of CuCe-ferrite/ TiO₂ and TiO₂ NPs. Higher efficiency of RR250 photodegradation up to 100% was obtained using CuCe-ferrite/ TiO₂. The photodegradation efficiency was confirmed using chemical oxygen demand (COD) test of both treated and untreated samples. The oxidation process was mostly mediated by photogenerated ^.O^2? according to scavenger test results. The catalyst possess higher photodegradation efficiency even after regeneration for ten times.

Graphical Abstract

     

Cytotoxicities of Tumor-Seeking Dyes: Impact on Future Clinical Trials

Heptamethine (Cy7) dyes with meso-Cl substituents injected intravenously (iv) into mice accumulate in tumors and persist there over several days. We believe this occurs via meso-Cl displacement by the only free cysteine residues of albumin; therefore, conjugating tumor-seeking dyes with fragments can increase selective therapeutic delivery to tumors and drug residence. This strategy has elevated significance recently because the first tumor-seeking dye-drug conjugate has moved into clinical trials. Options for further clinical research include modifying the dye, and use of preformed albumin adducts instead of dyes alone. Herein we show correlations of cytotoxicities, lipophilicities, organelle localization, apoptosis, cell-cycle arrest, wound healing/migration assays, and reactivities/affinities with human serum albumin are difficult to observe. However, our studies arrived at an important conclusion: preformed dye-drug-HSA adducts are less cytotoxic, and therefore preferable for subsequent clinical work, relative to direct injection of meso-Cl-containing forms.     

Genetics Based Compact Fuzzy System for Visual Sensor Network

As a component of Wireless Sensor Network (WSN), Visual-WSN (VWSN) utilizes cameras to obtain relevant data including visual recordings and static images. Data from the camera is sent to energy efficient sink to extract key-information out of it. VWSN applications range from health care monitoring to military surveillance. In a network with VWSN, there are multiple challenges to move high volume data from a source location to a target and the key challenges include energy, memory and I/O resources. In this case, Mobile Sinks(MS) can be employed for data collection which not only collects information from particular chosen nodes called Cluster Head (CH), it also collects data from nearby nodes as well. The innovation of our work is to intelligently decide on a particular node as CH whose selection criteria would directly have an impact on QoS parameters of the system. However, making an appropriate choice during CH selection is a daunting task as the dynamic and mobile nature of MSs has to be taken into account. We propose Genetic Machine Learning based Fuzzy system for clustering which has the potential to simulate human cognitive behavior to observe, learn and understand things from manual perspective. Proposed architecture is designed based on Mamdani’s fuzzy model. Following parameters are derived based on the model residual energy, node centrality, distance between the sink and current position, node centrality, node density, node history, and mobility of sink as input variables for decision making in CH selection. The inputs received have a direct impact on the Fuzzy logic rules mechanism which in turn affects the accuracy of VWSN. The proposed work creates a mechanism to learn the fuzzy rules using Genetic Algorithm (GA) and to optimize the fuzzy rules base in order to eliminate irrelevant and repetitive rules. Genetic algorithm-based machine learning optimizes the interpretability aspect of fuzzy system. Simulation results are obtained using MATLAB. The result shows that the classification accuracy increase along with minimizing fuzzy rules count and thus it can be inferred that the suggested methodology has a better protracted lifetime in contrast with Low Energy Adaptive Clustering Hierarchy (LEACH) and LEACH-Expected Residual Energy (LEACH-ERE).     

Fracture Toughness Characteristics of Stacked Bilayer Graphene via Far-Field Displacement Measurements

Mechanical properties of graphene, e.g., strength, modulus, and fracture toughness are extremely sensitive to flaws. Here the fracture properties of stacked bilayer graphene sheets (SBLG) are reported, obtained via stacking two individually grown graphene sheets. The SBLG is presented here as a building block for flaw-resilient nanomaterials. The fracture properties of freestanding SBLG sheets, suspended on transmission electron microscope (TEM) grids, are characterized by stretching the TEM grid inside an scanning electron microscope (SEM) chamber and monitoring the local displacements in real-time. The fracture toughness is measured and expressed as a function of the critical displacement required to propagate existing cracks in the experiment via computational models. This approach decouples force and displacements measurements, and utilizes the known elastic modulus along with the known displacement boundary conditions at the onset of crack growth to estimate the far field force and stress. This strategy represents a breakthrough in nanoscale fracture mechanics for statistical analysis and high throughput experimens on multiple samples at a time. Results demonstrate that the SBLG is markedly tougher than as-grown single or multilayer graphene, with a mode I fracture toughness of ≈28.06 ± 7.5 MPa$\sqrt{m}$. The mechanisms leading to a higher toughness of SBLG are also analyzed and discussed.