Studies on antimicrobial and wound healing applications of gauze coated with CHX–Ag hybrid NPs

In this study, chlorhexidine (CHX)–silver (Ag) hybrid nanoparticles (NPs) coated gauze was developed, and their bactericidal effect and in vivo wound healing capacities were tested. A new method was developed to synthesise the NPs, wherein Ag nitrate mixed with sodium (Na) metaphosphate and reduced using Na borohydride. Finally, CHX digluconate was added to form the hybrid NPs. To study the antibacterial efficacy of particles, the minimal inhibition concentration and biofilm degradation capacity against Gram‐positive and Gram‐negative bacteria was studied using Escherichia coli and Staphylococcus aureus. The results indicated that the NP inhibited biofilm formation and was bactericidal as well. The gauze was doped with NPs, and its wound healing property was evaluated using mice model. Results indicated that the wound healing process was fastened by using the NPs gauze doped with NPs without the administration of antibiotics.Inspec keywords: nanomedicine, nanoparticles, wounds, silver, cellular biophysics, biomedical materials, nanofabrication, microorganisms, antibacterial activityOther keywords: NPs gauze, antimicrobial wound healing applications, hybrid NPs, chlorhexidine–silver hybrid nanoparticles, CHX, coated gauze, bactericidal effect, minimal inhibition concentration, biofilm degradation capacity, Gram‐negative bacteria, wound healing property, wound healing process, in vivo wound healing capacities, Staphylococcus aureus, Escherichia coli, antibiotics administration, Na borohydride, Ag nitrate mixing, sodium metaphosphate, CHX digluconate, NP inhibited biofilm formation, Ag     

Synthesis of CeO2 nanorods with improved photocatalytic activity: comparison between precipitation and hydrothermal process

The main purpose of this article is to examine the surface free cerium oxide (CeO₂) nanostructures prepared by different methods. CeO₂ nanoparticles and nanorods were prepared by two different methods including precipitation and hydrothermal process. In precipitation process the nanoparticles were prepared at room temperature, while in hydrothermal process nanorods were prepared at high temperature. X-ray and electron diffraction analysis show the presence of CeO₂. X-ray photoelectron spectroscopy (XPS) confirms the presence of CeO₂ in both nanostructures. From BET, the specific surface area of nanorods (110 m²g^?1) is found to be higher than nanoparticles (52 m²g^?1). Also, the effect of morphology on their photodegradation of azo dye acid orange 7 (AO7) under UV–Visible light has been successfully investigated. The results show that the CeO₂ nanorods synthesized by hydrothermal method have high surface area and exhibit improved performance in the photocatalytic activity.     

Mosquito-larvicidal efficacy of gold nanoparticles synthesized from the seaweed,Turbinaria ornata (Turner) J.Agardh 1848

Malaria is considered a dreadful mosquito-borne infectious disease of human beings caused and spread by biting of the female mosquito Anopheles stephensi infected with a parasitic protozoan Plasmodium falciparum. Continuous application of chemicals/synthetic insecticides for vector control causes various problems such as resistant mechanism of mosquito, toxicity to nontarget aquatic organisms and disturbance to the microbial community of the soil. Currently, green synthesized nanoparticles are being employed in various biological processes including insect and pest control. The present investigation focused on the mosquito-larvicidal property of Turbinaria ornata-mediated gold nanoparticles (To-AuNPs) and its boiled aqueous extract (To-AE) against the malarial vector A. stephensi. The recorded lethal concentration (LC₅₀ and LC₉₀) values (µg/ml) of To-AE and To-AuNPs against fourth instar larvae of A. stephensi were 37.77 and 159.55 and 12.79 and 78.70, respectively. The To-AuNPs were characterized through UV-visible spectroscopy, Fourier transform infrared spectroscopy (FTIR), x-ray diffraction (XRD), field emission scanning electron microscopy (FESEM), high-resolution transmission electron microscopy (HRTEM), energy dispersive x-ray spectroscopy (EDX), zeta potential and dynamic light scattering (DLS) method. The presently synthesized gold nanoparticles through the single-step, eco-friendly method is a potentially effective mosquitocidal agent.     

Team NimbRo at MBZIRC 2017: Autonomous valve stem turning using a wrench

The Mohamed Bin Zayed International Robotics Challenge (MBZIRC) 2017 has defined ambitious new benchmarks to advance the state‐of‐the‐art in autonomous operation of ground‐based and flying robots. In this study, we describe our winning entry to MBZIRC Challenge 2: the mobile manipulation robot Mario. It is capable of autonomously solving a valve manipulation task using a wrench tool detected, grasped, and finally used to turn a valve stem. Mario’s omnidirectional base allows both fast locomotion and precise close approach to the manipulation panel. We describe an efficient detector for medium‐sized objects in three‐dimensional laser scans and apply it to detect the manipulation panel. An object detection architecture based on deep neural networks is used to find and select the correct tool from grayscale images. Parametrized motion primitives are adapted online to percepts of the tool and valve stem to turn the stem. We report in detail on our winning performance at the challenge and discuss lessons learned.     

Automatic segmentation of optic disc in retinal fundus images using semi-supervised deep learning

Diseases of the eye require manual segmentation and examination of the optic disc by ophthalmologists. Though, image segmentation using deep learning techniques is achieving remarkable results, it leverages on large-scale labeled datasets. But, in the field of medical imaging, it is challenging to acquire large labeled datasets. Hence, this article proposes a novel deep learning model to automatically segment the optic disc in retinal fundus images by using the concepts of semi-supervised learning and transfer learning. Initially, a convolutional autoencoder (CAE) is trained to automatically learn features from a large number of unlabeled fundus images available from the Kaggle’s diabetic retinopathy (DR) dataset. The autoencoder (AE) learns the features from the unlabeled images by reconstructing the input images and becomes a pre-trained network (model). After this, the pre-trained autoencoder network is converted into a segmentation network. Later, using transfer learning, the segmentation network is trained with retinal fundus images along with their corresponding optic disc ground truth images from the DRISHTI GS1 and RIM-ONE datasets. The trained segmentation network is then tested on retinal fundus images from the test set of DRISHTI GS1 and RIM-ONE datasets. The experimental results show that the proposed method performs on par with the state-of-the-art methods achieving a 0.967 and 0.902 dice score coefficient on the test set of the DRISHTI GS1 and RIM-ONE datasets respectively. The proposed method also shows that transfer learning and semi-supervised learning overcomes the barrier imposed by the large labeled dataset. The proposed segmentation model can be used in automatic retinal image processing systems for diagnosing diseases of the eye.

     

A deep ensemble network model for classifying and predicting breast cancer

Breast cancer is one of the leading causes of death among women worldwide. In most cases, the misinterpretation of medical diagnosis plays a vital role in increased fatality rates due to breast cancer. Breast cancer can be diagnosed by classifying tumors. There are two different types of tumors, such as malignant and benign tumors. Identifying the type of tumor is a tedious task, even for experts. Hence, an automated diagnosis is necessary. The role of machine learning in medical diagnosis is eminent as it provides more accurate results in classifying and predicting diseases. In this paper, we propose a deep ensemble network (DEN) method for classifying and predicting breast cancer. This method uses a stacked convolutional neural network, artificial neural network and recurrent neural network as the base classifiers in the ensemble. The random forest algorithm is used as the meta-learner for providing the final prediction. Experimental results show that the proposed DEN technique outperforms all the existing approaches in terms of accuracy, sensitivity, specificity, F-score and area under the curve (AUC) measures. The analysis of variance test proves that the proposed DEN model is statistically more significant than the other existing classification models; thus, the proposed approach may aid in the early detection and diagnosis of breast cancer in women, hence aiding in the development of early treatment techniques to increase survival rate.     

Hybrid Bacterial Foraging Optimization with Sparse Autoencoder for Energy Systems

The Internet of Things (IoT) technologies has gained significant interest in the design of smart grids (SGs). The increasing amount of distributed generations, maturity of existing grid infrastructures, and demand network transformation have received maximum attention. An essential energy storing model mostly the electrical energy stored methods are developing as the diagnoses for its procedure was becoming further compelling. The dynamic electrical energy stored model using Electric Vehicles (EVs) is comparatively standard because of its excellent electrical property and flexibility however the chance of damage to its battery was there in event of overcharging or deep discharging and its mass penetration deeply influences the grids. This paper offers a new Hybridization of Bacterial foraging optimization with Sparse Autoencoder (HBFOA-SAE) model for IoT Enabled energy systems. The proposed HBFOA-SAE model majorly intends to effectually estimate the state of charge (SOC) values in the IoT based energy system. To accomplish this, the SAE technique was executed to proper determination of the SOC values in the energy systems. Next, for improving the performance of the SOC estimation process, the HBFOA is employed. In addition, the HBFOA technique is derived by the integration of the hill climbing (HC) concepts with the BFOA to improve the overall efficiency. For ensuring better outcomes for the HBFOA-SAE model, a comprehensive set of simulations were performed and the outcomes are inspected under several aspects. The experimental results reported the supremacy of the HBFOA-SAE model over the recent state of art approaches.     

Composite controller for a hybrid power plant based on PV array fed wind-driven induction generator with battery storage

Hybrid autonomous power plants based on wind-driven induction generators excited by a photovoltaic (PV) array has been recently found to be a reliable power source for remote locations. In such schemes, a composite controller for both maximum power point tracking and voltage control has not been attempted so far. Hence a dc–dc converter with a dual control strategy of peak power tracking when the battery is connected and in voltage control mode in the absence of the battery is proposed in this paper. The development of such a controller is also presented. A d–q axes model of the proposed scheme is given and the results of the simulation of the entire scheme are also presented. The battery charging and discharging regimes have been distinctly identified for variations in irradiations and shaft-torque conditions. Close proximity between the calculated peak power for different irradiations and the peak power observed from the simulated results brings out the significance of the proposed controller. Copyright © 2006 John Wiley & Sons, Ltd.     

Investigation of the quasi-ternary system LaMnO3–LaCoO3–“LaCuO3”. II: The series LaMn0.25?xCo0.75?xCu2xO3?δ and LaMn0.75?xCo0.25?xCu2xO3?δ

This paper investigates the crystal structure, thermal expansion, and electrical conductivity of two series of perovskites (LaMn_0.25−x Co_0.75−x Cu_2x O_3−δ and LaMn_0.75−x Co_0.25−x Cu_2x O_3−δ with x = 0, 0.025, 0.05, 0.1, 0.15, 0.2, and 0.25) in the quasi-ternary system LaMnO₃–LaCoO₃–“LaCuO₃”. The Mn/Co ratio was found to have a stronger influence on these properties than the Cu content. In comparison to the Co-rich series (LaMn_0.25−x Co_0.75−x Cu_2x O_3−δ), the Mn-rich series (LaMn_0.75−x Co_0.25−x Cu_2x O_3−δ) showed a much higher Cu solubility. All compositions in this series were single-phase materials after calcination at 1100 °C. The Co-rich series showed higher thermal expansion coefficients (α_max = 19.6 × 10⁻⁶ K⁻¹) and electrical conductivity (σ_max = 730 S/cm at 800 °C) than the Mn-rich series (α_max = 10.6 × 10⁻⁶ K⁻¹, σ_max = 94 S/cm at 800 °C). Irregularities in the thermal expansion curves indicated phase transitions at 150–350 °C for the Mn-rich series, while partial melting occurred at 980–1000 °C for the Co-rich series with x > 0.15. I. Arul Raj—on leave from Central Electrochemical Research Institute, Karaikudi, 630006 India.