Mechanical behavior of carbon nanofibre‐reinforced epoxy composites

Epoxy resins are widely used in a variety of applications because of their high chemical and corrosion resistance and good mechanical properties. But few types of epoxy resins are brittle and possess low toughness which makes them unsuitable for several structural applications. In this work, carbon nanofibres have been dispersed uniformly into the epoxy resin at a very low concentration (0.07 vol. %). Improvement of 98% in Young modulus, 24% in breaking stress and 144% in work of rupture was achieved in the best sample. The emphasis is on achieving uniform dispersion of carbon nanofibers into epoxy resin using a combination of techniques such as ultrasonication, use of solvent and surfactants. The fracture surfaces of the specimens were studied under scanning electron microscope to see the fracture mechanism of nanocomposites under tensile load and correlate it to the enhancement in their properties. © 2010 Wiley Periodicals, Inc. J Appl Polym Sci, 2010     

C/C composite, carbon nanotube and paraffin wax hybrid systems for the thermal control of pulsed power in electronics

The design of a composite hybrid thermal control (TC) system for the thermal protection of electronics against transient pulsed power heat loads was investigated. Three types of materials: paraffin wax, carbon nanotubes (CNTs), and C/C composites are used in a novel design configuration to produce the hybrid TC system design. The hybrid TC system is tested for three cases: (1) TC system without CNT additives and thermal interface material (TIM), (2) TC system with CNT additives, and (3) TC system with both CNT additives and TIM. For each case the samples were subjected to uniform power condition and two types of varying power conditions. The TC system corresponding to case-2 showed a reasonable decrease in heater and junction temperatures as compared to case-1. However, substantial drop in heater and junction temperatures was found for case-3 as compared to case-1 and case-2. The results demonstrated the importance of using CNTs as an additive in paraffin wax and as a TIM in improving the thermal performance of a TC unit for protection against pulsed heat loads in electronics.     

Surfactant mediated growth of ZnO nanostructures and their dye sensitized solar cell properties

Single crystalline and highly aligned ZnO nanorods, faceted microrods, nanoneedles and nanotowers were grown onto glass substrates by a facile aqueous chemical method at relatively low temperature (90 °C). Various structure directing agents or organic surfactants such as diaminopropane (DAP), polyacrylic acid (PAA) and polyethylenimine (PEI) were used to modify the surface morphology. The samples were characterized by X-ray diffraction (XRD), scanning electron microscopy (SEM) and optical absorption. It was found that, vertically aligned ZnO nanorods formation takes place with preferential orientation along (002) plane. The organic surfactants play an important role in modifying the morphology. The samples were further used to fabricate dye sensitized solar cells. The highest photocurrent (670 μA) and efficiency were observed for the ZnO:PEI sample.     

Preparation and characterization of CdS–Bi2S3 nanocomposite thin film by successive ionic layer adsorption and reaction (SILAR) method

CdS, Bi₂S₃ and CdS–Bi₂S₃ nanocomposite thin films were grown by successive ionic layer adsorption and reaction method (SILAR) onto the glass substrates at room temperature. These films were characterized by X-ray diffraction (XRD), scanning electron microscope (SEM) and electrical measurement systems. A comparative study was made between CdS, Bi₂S₃ and CdS–Bi₂S₃ nanocomposite thin films. The XRD patterns reveal that CdS, Bi₂S₃ and CdS–Bi₂S₃ nanocomposite thin film have hexagonal, orthorhombic and mixed phase of hexagonal CdS and orthorhombic Bi₂S₃ crystal structure, respectively. SEM images showed uniform deposition of the material over the entire glass substrate. The energy band gap for CdS, Bi₂S₃ and CdS–Bi₂S₃ thin films were revealed from the optical studies and were found to be 2.4, 1.6 and 1.69 eV, respectively. The thermoemf measurements of CdS–Bi₂S₃ nanocomposite thin film revealed n-type electrical conductivity, while the I–V measurement of CdS, Bi₂S₃ and CdS–Bi₂S₃ nanocomposite thin film under dark and illumination condition (100 mW/cm²) exhibited photoconductivity phenomena suggesting its applicability in photosensors devices.     

Optimizing Steering Angle Predictive Convolutional Neural Network for Autonomous Car

Deep learning techniques, particularly convolutional neural networks (CNNs), have exhibited remarkable performance in solving vision-related problems, especially in unpredictable, dynamic, and challenging environments. In autonomous vehicles, imitation-learning-based steering angle prediction is viable due to the visual imagery comprehension of CNNs. In this regard, globally, researchers are currently focusing on the architectural design and optimization of the hyperparameters of CNNs to achieve the best results. Literature has proven the superiority of metaheuristic algorithms over the manual-tuning of CNNs. However, to the best of our knowledge, these techniques are yet to be applied to address the problem of imitation-learning-based steering angle prediction. Thus, in this study, we examine the application of the bat algorithm and particle swarm optimization algorithm for the optimization of the CNN model and its hyperparameters, which are employed to solve the steering angle prediction problem. To validate the performance of each hyperparameters’ set and architectural parameters’ set, we utilized the Udacity steering angle dataset and obtained the best results at the following hyperparameter set: optimizer, Adagrad; learning rate, 0.0052; and nonlinear activation function, exponential linear unit. As per our findings, we determined that the deep learning models show better results but require more training epochs and time as compared to shallower ones. Results show the superiority of our approach in optimizing CNNs through metaheuristic algorithms as compared with the manual-tuning approach. Infield testing was also performed using the model trained with the optimal architecture, which we developed using our approach.     

Optimality of Solution with Numerical Investigation for Coronavirus Epidemic Model

The novel coronavirus disease, coined as COVID-19, is a murderous and infectious disease initiated from Wuhan, China. This killer disease has taken a large number of lives around the world and its dynamics could not be controlled so far. In this article, the spatio-temporal compartmental epidemic model of the novel disease with advection and diffusion process is projected and analyzed. To counteract these types of diseases or restrict their spread, mankind depends upon mathematical modeling and medicine to reduce, alleviate, and anticipate the behavior of disease dynamics. The existence and uniqueness of the solution for the proposed system are investigated. Also, the solution to the considered system is made possible in a well-known functions space. For this purpose, a Banach space of function is chosen and the solutions are optimized in the closed and convex subset of the space. The essential explicit estimates for the solutions are investigated for the associated auxiliary data. The numerical solution and its analysis are the crux of this study. Moreover, the consistency, stability, and positivity are the indispensable and core properties of the compartmental models that a numerical design must possess. To this end, a nonstandard finite difference numerical scheme is developed to find the numerical solutions which preserve the structural properties of the continuous system. The M-matrix theory is applied to prove the positivity of the design. The results for the consistency and stability of the design are also presented in this study. The plausibility of the projected scheme is indicated by an appropriate example. Computer simulations are also exhibited to conclude the results.     

Heterogeneous Cubic Multidimensional Integrated Circuit for Water and Food Security in Fish Farming Ponds

Among major food production sectors, world aquaculture shows the highest growth rate, providing more than 50% of the global seafood market. However, water pollution in fish farming ponds is regarded as the leading cause of fish death and financial losses in the market. Here, an Internet of Things system based on a cubic multidimensional integration of circuit (MD‐IC) is demonstrated for water and food security applications in fish farming ponds. Both faces of the silicon substrate are used for thin‐film‐based device fabrication. The devices are interconnected via through‐silicon‐vias, resulting in a bifacial complementary metal‐oxide‐semiconductor‐compatible electronics system. The demonstrated cubic MD‐IC is a complete, small, and lightweight system that can be easily deployed by farmers with no need for specialists. The system integrates on its outer sides simultaneous air and water quality monitoring devices (temperature, electrical conductivity, ammonia, and pH sensors), solar cells for energy‐harvesting, and antenna for real‐time data‐transfer, while data‐management circuitry and a solid‐state battery are integrated on its internal faces. Microfluidic cooling technology is used for thermal management in the MD‐IC. Finally, a biofriendly polymeric encapsulation is used to waterproof the embedded electronics, improve the mechanical robustness, and allow the system to float on the surface of the water.     

Solving the Multidimensional Multiple-choice Knapsack Problem by constructing convex hulls

This paper presents a heuristic to solve the Multidimensional Multiple-choice Knapsack Problem (MMKP), a variant of the classical 0–1 Knapsack Problem. We apply a transformation technique to map the multidimensional resource consumption to single dimension. Convex hulls are constructed to reduce the search space to find the near-optimal solution of the MMKP. We present the computational complexity of solving the MMKP using this approach. A comparative analysis of different heuristics for solving the MMKP has been presented based on the experimental results.     

Synthesis and enhanced ethanol sensing performance of nanostructured Sr doped SnO<Subscript>2</Subscript> thick film sensor

In the present paper we have synthesized pristine and Sr doped SnO₂ in order to prepare a selective ethanol sensor with rapid response–recovery time and good repeatability. Pristine as well as Sr (2, 4 and 6 mol%) doped SnO₂ nanostructured powder was synthesized by using a facile co-precipitation method. The samples were characterized by TG–DTA, XRD, HR-TEM, SAED, FEG-SEM, SEM–EDAX, XPS, UV–Vis and FTIR spectroscopy techniques. The gas response performance of sensor towards ethanol, acetone, liquid petroleum gas and ammonia has been carried out. The results demonstrate that Sr doping in SnO₂ systematically decreases crystallite size, increases the porosity and hence enhances the gas response properties of pristine SnO₂ viz. lower operating temperature, higher ethanol response and better selectivity towards ethanol. The response and recovery time for 4 mol% Sr doped SnO₂ thick film sensor at the operating temperature of 300 °C were 2 and 7 s, respectively.     

Automatic visual speech segmentation and recognition using directional motion history images and Zernike moments

Appearance-based visual speech recognition using only video signals is presented. The proposed technique is based on the use of directional motion history images (DMHIs), which is an extension of the popular optical-flow method for object tracking. Zernike moments of each DMHI are computed in order to perform the classification. The technique incorporates automatic temporal segmentation of isolated utterances. The segmentation of isolated utterance is achieved using pair-wise pixel comparison. Support vector machine is used for classification and the results are based on leave-one-out paradigm. Experimental results show that the proposed technique achieves better performance in visemes recognition than others reported in literature. The benefit of this proposed visual speech recognition method is that it is suitable for real-time applications due to quick motion tracking system and the fast classification method employed. It has applications in command and control using lip movement to text conversion and can be used in noisy environment and also for assisting speech impaired persons.