Numerical and experimental investigation on the thermochemical gasification potential of Cocoa pod husk (Theobroma Cacoa) in an open-core gasifier

Clean Technologies and Environmental Policy - Thermochemical conversion is a promising technology to generate producer gas (PG) from different types of agroforestry biomass residues. To use an...     

Experimental investigation and statistical analysis of additively manufactured onyx-carbon fiber reinforced composites

Availability of additive manufacturing has influenced the scientific community to improve on production and versatility of the components created with several associated technologies. Adding multiple substances through superimposing levels is considered as a part of three-dimensional (3D) printing innovations to produce required products. These technologies are experiencing an increase in development nowadays. It requires frequently adding substance and has capacity to fabricate extremely complex geometrical shapes. However, the fundamental issues with this advancement include alteration of capacity to create special products with usefulness and properties at an economically viable price. In this study, significant procedural parameters: layer designs/ patterns (hexagonal, rectangular and triangular) and infill densities (30%, 40%, and 50%) were considered to investigate into their effects on mechanical behaviors off fused deposition modeling or 3D-printed onyx-carbon fiber reinforced composite specimens, using a high-end 3D printing machine. Mechanical (tensile and impact) properties of the printed specimens were conclusively analyzed. From the results obtained, it was observed that better qualities were achieved with an increased infill density, and rectangular-shaped design exhibited an optimum or maximum tensile strength and energy absorption rate, when compared with other counterparts. The measurable relapse conditions were viably evolved to anticipate the real mechanical qualities with an accuracy of 96.4%. In comparison with other patterns, this was more closely predicted in the rectangular design, using regression models. The modeled linear regression helps to define the association of two dependent variables linked with properties of the dissimilar composite material natures. The models can further predict response of the quantities before and also guide practical applications.     

3D printing-assisted combinatorial approach for designing mechanically-tunable and vascular supportive nanofibrous membranes to repair perforated eardrum

Perforation of eardrum or tympanic membrane (TM) is a common clinical condition, which occurs due to infection or injury of the eardrum, and could results in varying degrees of conductive hearing loss among all ages. In this study, the authors report the combinatorial approach of designing mechanically-tunable and vascular supportive nanofibrous membranes by 3D printing-assisted electrospinning (e-spin) using polycaprolactone (PCL) and gelatin with different mass ratios suitable to repair a perforated eardrum. The physicochemical, mechanical, and biological properties of the membranes were characterized. The results show that the membrane has nanofibrous morphology with fibers are of varying size (400–600 nm in diameter) depending on processing conditions. The wettability and mechanical properties of the membrane can be tuned by regulating the gelatin content. Moreover, a biomimetic repair strategy inspired by chicken eggshell membrane, often used in wound dressings, was also presented for study and results show that the suture retention strength of the fabricated membrane can meet clinical translational requirements to promote TM healing. The vascular cell responsiveness of PCL/gelatin nanofibrous membrane was evaluated using human umbilical vein endothelial cells (HUVECs) and the results showed satisfactory biocompatibility, vascular cell responsiveness, and cell proliferation. The findings of this study demonstrate that the combinatorically engineered PCL/gelatin nanofibrous membrane has great potential for repairing perforated eardrum.     

Printed Planar Monopole Antenna Design for Ultra-Wideband Communications

This paper presents an ultra wideband (UWB) planar printed monopole antenna fed by microstrip line. The antenna configuration contains a beveled ground plane. The beveled partial ground plane improves the impedance bandwidth. The measured frequency response demonstrates that the fabricated antenna exhibits an impedance bandwidth of 7.9 GHz over 3.1 to 11 GHz for VSWR < 2. The proposed antenna has ultra-wideband characteristics with omnidirectional radiation pattern and stable gain. Ultra-wideband performance of the proposed antenna is examined through the simulated surface current distributions. Measured results confirm that the antenna is suitable for UWB applications due to its compact size and high performance characteristics.     

Optimization of weld bead geometry for stainless steel claddings deposited by FCAW

The quality of cladded components depends on the weld bead geometry, coefficients of shape of welds and dilution, which have to be controlled. Optimum range of bead parameters and dilution are required for better economy and to ensure the desired mechanical and corrosion resistant properties of the overlay. The above objectives can easily be achieved by developing mathematical equations to predict the weld bead geometry. This paper presents the development of such equations using the data obtained by conducting three factor five level factorial experiments. The experiments were conducted by depositing Type AISI 317L flux cored stainless steel wire onto IS: 2062 structural steel base plate. The results of the confirmation experiments showed that the models developed are able to predict the bead geometries and dilution with reasonable accuracy. The studies have indicated that both main and interaction effects of the process variables play a major role in determining the bead dimensions and dilution, and the effect of interaction between the process variables cannot be neglected. The process parameters were also optimized using response surface methodology (RSM) which will help the plant engineers to select and control the process variables effectively, to achieve the desired clad qualities.     

Visibility improvement of underwater turbid image using hybrid restoration network with weighted filter

Due to the attenuation of light passes through water, the captured underwater images suffer from low-contrast, halo artifacts, etc. To address this issue, the hybrid network with a weighted filter is proposed to improve the visibility of the obscured (turbid) images. In the captured image, the brighter pixels (near-to-source) are called foreground regions and the darker pixels (far-from-source) are called background regions. In order to ensure the adaptability of the proposed algorithm, the considered datasets are collected on different atmospheric light such as pond, lake, and fisheries tank. The foreground area of an image can be enhanced using the thresholding and masking technique. The background hazy region can be recovered by a hybrid Dehazenet called Generative Adversarial Network and Convolutional Neural Network. With this, the transmission map with high accuracy and color deviation can be addressed. Then both the regions are blended and the Amended Unsharp Mask filter is used to toughen the distorted edges. Finally, the blended restored image is weighted with a contrast factor to obtain the visibility improved image. The subjective and objective evaluation is done on considering the standard non-reference metric called Underwater Image Quality Measure comprises measures of color, sharpness, and contrast for a variety of water types with different atmospheric light. It is observed that the proposed technique showed a metric improvement of 57% compared to other existing techniques in an average manner. Overall, it is inferred that the proposed technique produces better results in both subjective and objective evaluation, thus it outperforms other state-of-the-art techniques.

     

Decision Tree-Based Entries Reduction scheme using multi-match attributes to prevent flow table overflow in SDN environment

The software-defined networking is used extensively in data centers that provide centralized control for the widely deployed networking resources. The traffic is shaped by rules created by the controller dynamically without modifying the individual switch. The key component that stores rules which are used to process the flows is the flow table which resides in the ternary content addressable memory. The current commercial OpenFlow appliances accommodate limited entries up to 8000 due to its high cost and high power consumption. There are two issues to be considered, where (1) flow table's inability to provide rules during flow table overflow leads to dropping of incoming packets and (2) the significant amount of rule replacement occurs when the traffic in data centers increases which creates massive route requests to controller creating overhead. The proposed scheme prevents flow table overflow using the robust machine learning algorithm called decision tree (Iterative Dichotomiser 3) that allows the flow table to learn its high prioritized fine-grained entries by means of multiple matching attributes. The entries are classified, and the usual eviction process is replaced by pushing the low important entries into counting bloom filter which acts as a cache to prevent flow entry miss. The simulations were carried out using real-time network traffic datasets, and the comparisons with the various existing schemes prove that the proposed approach reduces 99.99% of the controller's overhead and the entries are minimized to 99% providing extra space for new flows.     

Energy-efficient routing protocol and optimized passive clustering in WSN for SMART grid applications

Due to low cost, ease of implementation and flexibility of wireless sensor networks (WSNs), WSNs are considered to be an essential technology to support the smart grid (SG) application. The prime concern is to increase the lifetime in order to find the active sensor node and thereby to find once the sensor node (SN) dies in any region. For this reason, an energy-efficient Dynamic Source Routing (DSR) protocol needs to provide the right stability region with a prolonged network lifetime. This work is an effort to extend the network's existence by finding and correcting the considerable energy leveraging behaviors of WSN. We build a comprehensive model based on real measures of SG path loss for different conditions by using the characteristics of WSN nodes and channel characteristics. This method also establishes a hierarchical network structure of balanced clusters and an energy-harvesting SN. The cluster heads (CHs) are chosen by these SN using a low overhead passive clustering strategy. The cluster formation method is focused on the use of passive clustering of the particle swarm optimization (PSO). For the sake of eliminating delayed output in the WSN, energy competent dynamic source routing protocol (EC-DSR) is used. Chicken swarm optimization (CSO) in which optimum cluster path calculation shall be done where distance and residual energy should be regarded as limitation. Finally, the results are carried out with regard to the packet distribution ratio, throughput, overhead management, and average end-to-end delay to demonstrate the efficiency of the proposed system.     

Delay‐dependent fault‐tolerant controller for time‐delay systems with randomly occurring uncertainties

This paper addresses the passivity‐based control problem for a class of time‐varying delay systems subject to nonlinear actuator faults and randomly occurring uncertainties via fault‐tolerant controller. More precisely, the uncertainties are described in terms of stochastic variables, which satisfies Bernoulli distribution, and the existence of actuator faults are assumed not only linear but also nonlinear, which is a more general one. The main objective of this paper is to design a state feedback‐reliable controller such that the resulting closed‐loop time‐delay system is stochastically stable under a prescribed mixed and passivity performance level γ>0 in the presence of all admissible uncertainties and actuator faults. Based on Lyapunov stability method and some integral inequality techniques, a new set of sufficient conditions is obtained in terms of linear matrix inequality (LMI) constraints to ensure the asymptotic stability of the considered system. Moreover, the control design parameters can be computed by solving a set of LMI constraints. Finally, two examples including a quarter‐car model are provided to show the efficiency and usefulness of the proposed control scheme. Copyright © 2017 John Wiley & Sons, Ltd.     

Species classification of aquatic plants using PSVM and ANFIS

This work presents a method for plant species identification using the images of flowers. It focuses on the stable feature extraction of flowers such as color, texture and shape features in addition to fractal dimension. Color based segmentation using K-means clustering and active contour model is used to extract the color features. Texture segmentation using texture filter is used to segment the image and obtain texture features. Sobel, Prewitt and Robert operators are used to extract the boundary of image and to obtain the shape features. Classification of the plants is done using Proximal Support Vector Machine (PSVM) and Adaptive Neuro Fuzzy Inference System (ANFIS) classifiers.