An Optimized Algorithm for CR-MIMO Wireless Networks

With the rapid development of wireless communication technology, the spectrum resources are increasingly strained which needs optimal solutions. Cognitive radio (CR) is one of the key technologies to solve this problem. Spectrum sensing not only includes the precise detection of the communication signal of the primary user (PU), but also the precise identification of its modulation type, which can then determine the a priori information such as the PU’ service category, so as to use this information to make the cognitive user (CU) aware to discover and use the idle spectrum more effectively, and improve the spectrum utilization. Spectrum sensing is the primary feature and core part of CR. Classical sensing algorithms includes energy detection, cyclostationary feature detection, matched filter detection, and so on. The energy detection algorithm has a simple structure and does not require prior knowledge of the PU transmitter signal, but it is easily affected by noise and the threshold is not easy to determine. The combination of multiple-input multiple-output (MIMO) with CR improves the spectral efficiency and multi-path fading utilization. To best utilize the PU spectrum while minimizing the overall transmit power, an iterative technique based on semidefinite programming (SDP) and minimum mean squared error (MMSE) is proposed. Also, this article proposed a new method for max-min fairness beamforming. When compared to existing algorithms, the simulation results show that the proposed algorithms perform better in terms of total transmitted power and signal-to-interference plus noise ratio (SINR). Furthermore, the proposed algorithm effectively improved the system performance in terms of number of iterations, interference temperature threshold and balance SINR level which makes it superior over the conventional schemes.     

Synthesis,characterization, and voltammetric hydrogen peroxide sensing on novel monometallic (Ag,Co/MWCNT) and bimetallic (AgCo/MWCNT) alloy nanoparticles

Multiwall carbon nanotube supported (MWCNT) Ag, Co, and Ag-Co alloy nanocatalysts were synthesized at varying metal loadings by borohydride reduction methods without stabilizers to obtain enhanced hydrogen peroxide sensitivity. The resulting materials were characterized employing Scanning electron microscopy (SEM), Energy dispersive X-ray spectroscopy (EDX), and X-ray diffraction (XRD). For electrochemical measurements carried out cyclic voltammetry (CV) and differential pulse voltammetry (DPV), glassy carbon electrode (GCE) was modified with Ag/MWCNT, Co/MWCNT, and Ag-Co/MWCNT alloy nanoparticles. Ag-Co/MWCNT/GCE exhibited the highest performance toward electrochemical oxidation of H₂O₂ in 0.1 M phosphate buffered solution (PBS). Furthermore, the sensitivity and the limit of detection values for Ag-Co/MWCNT/GCE were obtained as 57.14 µA cm^?2 mM^?1and 0.74 µM, respectively. However, the sensitivity values for Ag/MWCNT/GCE, and Co/MWCNT/GCE are 41.66 and 13.88 µA cm^?2 mM^?1, respectively. The LOD values were predicted as 1.84 µM for Ag/MWCNT/GCE and 3.3 µM for Co/MWCNT/GCE.

In addition, the interference experiment indicated that the Ag-Co/MWCNT alloy nanoparticles have good selectivity toward H₂O₂.     

Compact Bat Algorithm with Deep Learning Model for Biomedical EEG EyeState Classification

Electroencephalography (EEG) eye state classification becomes an essential tool to identify the cognitive state of humans. It can be used in several fields such as motor imagery recognition, drug effect detection, emotion categorization, seizure detection, etc. With the latest advances in deep learning (DL) models, it is possible to design an accurate and prompt EEG EyeState classification problem. In this view, this study presents a novel compact bat algorithm with deep learning model for biomedical EEG EyeState classification (CBADL-BEESC) model. The major intention of the CBADL-BEESC technique aims to categorize the presence of EEG EyeState. The CBADL-BEESC model performs feature extraction using the ALexNet model which helps to produce useful feature vectors. In addition, extreme learning machine autoencoder (ELM-AE) model is applied to classify the EEG signals and the parameter tuning of the ELM-AE model is performed using CBA. The experimental result analysis of the CBADL-BEESC model is carried out on benchmark results and the comparative outcome reported the supremacy of the CBADL-BEESC model over the recent methods.     

Toward the understanding of the metabolism of levodopa I. DFT investigation of the equilibrium geometries, Acid-base properties and levodopa-water complexes

Levodopa (LD) is used to increase dopamine level for treating Parkinson's disease. The major metabolism of LD to produce dopamine is decarboxylation. In order to understand the metabolism of LD; the electronic structure of levodopa was investigated at the Density Functional DFT/B3LYP level of theory using the 6-311+G** basis set, in the gas phase and in solution. LD is not planar, with the amino acid side chain acting as a free rotator around several single bonds. The potential energy surface is broad and flat. Full geometry optimization enabled locating and identifying the global minimum on this Potential energy surface (PES). All possible protonation/deprotonation forms of LD were examined and analyzed. Protonation/deprotonation is local in nature, i.e., is not transmitted through the molecular framework. The isogyric protonation/deprotonation reactions seem to involve two subsequent steps: First, deprotonation, then rearrangement to form H-bonded structures, which is the origin of the extra stability of the deprotonated forms. Natural bond orbital (NBO) analysis of LD and its deprotonated forms reveals detailed information of bonding characteristics and interactions across the molecular framework. The effect of deprotonation on the donor-acceptor interaction across the molecular framework and within the two subsystems has also been examined. Attempts to mimic the complex formation of LD with water have been performed.     

Experimental Results of the Dichotomic Sampling in Circular Ring Positron Emission Tomograph

Experimental verification of the new dichotomic sampling scheme was attempted and applied to a circular ring PET system to improve the sampling thereby the overall system resolution and its performances. In the experiment, two different types of aperture collimators were adopted for high resolution (HR) and very high resolution (VHR) imaging. In HR mode, a resolution of 6.5 mm FWHM was obtained without appreciable degradation in overall sensitivity which represents a threefold resolution improvement over the original system. In phantom studies with HR mode a sensitivity of 4500 counts/sec/?Ci/cc was obtained for a 20 cm diameter uniform phantom filled with water. VHR mode experiment was also conducted to observe the ultimate resolution capability of DICHOTOM-I system and resolution of 4.2 mm FWHM was obtained at the expense of sensitivity reduction by a factor of four from HR mode experiment.     

Innovative Carbon Dioxide‐Capturing Organic Solvent: Reaction Mechanism and Kinetics

The reaction rates of CO₂ with an innovative CO₂‐capturing organic solvent (CO₂COS), consisting of blends of 2‐tert‐butyl‐1,1,3,3‐tetramethylguanidine (BTMG) and 1‐propanol, were obtained as function of BTMG concentration and temperature. A stopped‐flow apparatus with conductivity detection was used. The reaction was modeled by means of a modified termolecular reaction mechanism which resulted in a second‐order rate constant, and activation energies were calculated for a defined temperature range. Quantum chemical calculations at the B3LYP/6‐31G(d) level also produced the activation energy of this reaction system which strongly supports the experimental findings.     

A New Caged‐Glutamine Derivative as a Tool To Control the Assembly of Glutamine‐Containing Amyloidogenic Peptides

We present the design, synthesis, and characterization of a novel photocaged glutamine derivative (modified on the side chain of glutamine), and describe its use in enhancing peptide stability and solubility. Our results demonstrate that this approach can be used to develop molecular switches to control the folding and β‐sheet formation of amyloidogenic peptides.     

Degradation of thermomechanical performance and lifetime estimation of multilayer greenhouse polyethylene films under simulated climatic conditions

The main objective of this work is to investigate the impact of degradation of low‐density polyethylene multilayer films, used as greenhouses covers, under some desert simulated climatic conditions on their mechanical behavior, thermal stability and lifetime. The climatic conditions considered are temperature, ultraviolet (UV) radiation, and humidity which are the most detrimental factors in the ageing of the polymeric greenhouses covers. At the molecular level, the combined effects of these environmental conditions cause oxidation and severe structural modifications of the polymer which are the main mechanism of degradation. In this work, multilayer polyethylene films with 180 μm overall thickness are artificially aged at different twelve combined climatic conditions of temperature, UV radiation and humidity. Deferential scanning calorimetry (DSC) analyses and mechanical tests were carried out to characterize the material and evaluate the degradation level of the thermal, physicochemical, and mechanical properties of the films. The results revealed that, the investigated broad range of climatic conditions have remarkable deteriorative impact on the performance of the film and its functionality; the highest degradation rate was under the combined effect of UV radiation and temperature ageing condition. The correlation between the created modifications in the material structure and the degradation level of cover properties and its lifetime is discussed. POLYM. ENG. SCI., 55:287–298, 2015. © 2014 Society of Plastics Engineers     

Proteomic Identification of Target Proteins of Thiodigalactoside in White Adipose Tissue from Diet-Induced Obese Rats

Previously, galectin-1 (GAL1) was found to be up-regulated in obesity-prone subjects, suggesting that use of a GAL1 inhibitor could be a novel therapeutic approach for treatment of obesity. We evaluated thiodigalactoside (TDG) as a potent inhibitor of GAL1 and identified target proteins of TDG by performing comparative proteome analysis of white adipose tissue (WAT) from control and TDG-treated rats fed a high fat diet (HFD) using two dimensional gel electrophoresis (2-DE) combined with MALDI-TOF-MS. Thirty-two spots from a total of 356 matched spots showed differential expression between control and TDG-treated rats, as identified by peptide mass fingerprinting. These proteins were categorized into groups such as carbohydrate metabolism, tricarboxylic acid (TCA) cycle, signal transduction, cytoskeletal, and mitochondrial proteins based on functional analysis using Protein Annotation Through Evolutionary Relationship (PANTHER) and Database for Annotation, Visualization, Integrated Discovery (DAVID) classification. One of the most striking findings of this study was significant changes in Carbonic anhydrase 3 (CA3), Voltage-dependent anion channel 1 (VDAC1), phosphatidylethanolamine-binding protein 1 (PEBP1), annexin A2 (ANXA2) and lactate dehydrogenase A chain (LDHA) protein levels between WAT from control and TDG-treated groups. In addition, we confirmed increased expression of thermogenic proteins as well as reduced expression of lipogenic proteins in response to TDG treatment. These results suggest that TDG may effectively prevent obesity, and TDG-responsive proteins can be used as novel target proteins for obesity treatment.