Recovery of naringin from kinnow (<Emphasis Type="Italic">Citrus reticulata Blanco</Emphasis>) peels by adsorption–desorption technique using an indigenous resin

A process technology has been developed for recovery of naringin from kinnow (citrus) peels, which is a waste. The kinnow peels were boiled with water to extract naringin into water. It was adsorbed on an indigenous macroporous resin, Indion PA-500. Naringin was recovered from the saturated resin by desorption with ethanol as a solvent. The equilibrium and kinetic studies for both adsorption and desorption are presented. The Langmuir isotherm described the adsorption equilibrium data. However, desorption data were best described by the Toth isotherm. Adsorption and desorption kinetic data were found to follow a pseudo-second-order rate equation and second-order rate equation, respectively. Recovery of naringin was about 49% w/w (based on naringin present in peel-boiled extract). The purity of final products was 91–94% w/w.     

Symbiotic Organisms Search with Deep Learning Driven Biomedical Osteosarcoma Detection and Classification

Osteosarcoma is one of the rare bone cancers that affect the individuals aged between 10 and 30 and it incurs high death rate. Early diagnosis of osteosarcoma is essential to improve the survivability rate and treatment protocols. Traditional physical examination procedure is not only a time-consuming process, but it also primarily relies upon the expert’s knowledge. In this background, the recently developed Deep Learning (DL) models can be applied to perform decision making. At the same time, hyperparameter optimization of DL models also plays an important role in influencing overall classification performance. The current study introduces a novel Symbiotic Organisms Search with Deep Learning-driven Osteosarcoma Detection and Classification (SOSDL-ODC) model. The presented SOSDL-ODC technique primarily focuses on recognition and classification of osteosarcoma using histopathological images. In order to achieve this, the presented SOSDL-ODC technique initially applies image pre-processing approach to enhance the quality of image. Also, MobileNetv2 model is applied to generate a suitable group of feature vectors whereas hyperparameter tuning of MobileNetv2 model is performed using SOS algorithm. At last, Gated Recurrent Unit (GRU) technique is applied as a classification model to determine proper class labels. In order to validate the enhanced osteosarcoma classification performance of the proposed SOSDL-ODC technique, a comprehensive comparative analysis was conducted. The obtained outcomes confirmed the betterment of SOSDL-ODC approach than the existing approaches as the former achieved a maximum accuracy of 97.73%.     

Correction to: The circular agricultural system is more sustainable: emergy analysis

Clean Technologies and Environmental Policy - In the original publication, the incorrect graphical abstract figure was published. The correct graphical abstract of the article is provided below.     

Deep Learning Enabled Object Detection and Tracking Model for Big Data Environment

Recently, big data becomes evitable due to massive increase in the generation of data in real time application. Presently, object detection and tracking applications becomes popular among research communities and finds useful in different applications namely vehicle navigation, augmented reality, surveillance, etc. This paper introduces an effective deep learning based object tracker using Automated Image Annotation with Inception v2 based Faster RCNN (AIA-IFRCNN) model in big data environment. The AIA-IFRCNN model annotates the images by Discriminative Correlation Filter (DCF) with Channel and Spatial Reliability tracker (CSR), named DCF-CSRT model. The AIA-IFRCNN technique employs Faster RCNN for object detection and tracking, which comprises region proposal network (RPN) and Fast R-CNN. In addition, inception v2 model is applied as a shared convolution neural network (CNN) to generate the feature map. Lastly, softmax layer is applied to perform classification task. The effectiveness of the AIA-IFRCNN method undergoes experimentation against a benchmark dataset and the results are assessed under diverse aspects with maximum detection accuracy of 97.77%.     

Peer-to-peer indirect reciprocity via personal currency

Motivating peers to contribute services is critical to the success of peer-to-peer (P2P) systems. Incentive protocols use reciprocity to enforce contributions. Indirect reciprocity schemes are more efficient than direct reciprocity schemes for large-scale P2P systems under high churn rate. In this paper, we propose an indirect reciprocity scheme, called FairTrade, in which peers issue personal currencies to trade services in a P2P system. Personal currency enables indirect reciprocity without relying on any central banks or authorities. It wins extra robustness over global currency as well as much improved trading flexibility and efficiency over direct reciprocity schemes. The acceptance degree of a personal currency depends on the issuer’s service capability and reliance. Peer credit limit is introduced to represent the amount of personal currency that will be accepted by other peers. Every peer as a creditor applies a Bayesian network model to setting peer credit limit for a trading partner peer as a creditee. The Bayesian network model learns the creditee’s capability and reliability and anticipates the associated profits and risks for credit setting. Using simulations on a file-sharing P2P system, we demonstrate that FairTrade achieves 100%$100\%$ success rate of download requests without malicious peers, and maintains over 90%$90\%$ success rate even with 50%$50\%$ malicious nodes. The system warms up quickly and does not assume any altruistic service or other kind of help. On average, the system traffic stabilizes before peers issue their second download requests. All these good performances are achieved with extremely low trading overhead, which takes up less than 1%$1\%$ of the total traffic.     

Richardson Extrapolation of Iterated Discrete Galerkin Method for Eigenvalue Problem of a Two Dimensional Compact Integral Operator

We consider approximation of eigenelements of a two-dimensional compact integral operator with a smooth kernel by discrete Galerkin and iterated discrete Galerkin methods. By choosing numerical quadrature appropriately, we obtain superconvergence rates for eigenvalues and iterated eigenvectors, and for gap between the spectral subspaces. We propose an asymptotic error expansions of the iterated discrete Galerkin method and asymptotic error expansion of approximate eigenvalues. We then apply Richardson extrapolation to obtain improved error bounds for the eigenvalues. Numerical examples are presented to illustrate theoretical estimate.     

Performance characterization of multi-thread and multi-core processors based XML application oriented networking systems

There is a growing trend to insert application intelligence into network devices. Processors in this type of Application Oriented Networking (AON) devices are required to handle both packet-level network I/O intensive operations as well as XML message-level CPU intensive operations. In this paper, we investigate the performance effect of symmetric multi-processing (SMP) via (1) hardware multi-threading, (2) uni-processor to dual-processor architectures, and (3) single to dual and quad core processing, on both packet-level and XML message-level traffic. We use AON systems based on Intel Xeon processors with hyperthreading, Pentium M based dual-core processors, and Intel’s dual quad-core Xeon E5335 processors. We analyze and cross-examine the SMP effect from both highlevel performance as well as processor microarchitectural perspectives. The evaluation results will not only provide insight to microprocessor designers, but also help system architects of AON types of device to select the right processors.     

Coot Optimization with Deep Learning-Based False Data Injection Attack Recognition

The recent developments in smart cities pose major security issues for the Internet of Things (IoT) devices. These security issues directly result from inappropriate security management protocols and their implementation by IoT gadget developers. Cyber-attackers take advantage of such gadgets’ vulnerabilities through various attacks such as injection and Distributed Denial of Service (DDoS) attacks. In this background, Intrusion Detection (ID) is the only way to identify the attacks and mitigate their damage. The recent advancements in Machine Learning (ML) and Deep Learning (DL) models are useful in effectively classifying cyber-attacks. The current research paper introduces a new Coot Optimization Algorithm with a Deep Learning-based False Data Injection Attack Recognition (COADL-FDIAR) model for the IoT environment. The presented COADL-FDIAR technique aims to identify false data injection attacks in the IoT environment. To accomplish this, the COADL-FDIAR model initially pre-processes the input data and selects the features with the help of the Chi-square test. To detect and classify false data injection attacks, the Stacked Long Short-Term Memory (SLSTM) model is exploited in this study. Finally, the COA algorithm effectively adjusts the SLTSM model’s hyperparameters effectively and accomplishes a superior recognition efficiency. The proposed COADL-FDIAR model was experimentally validated using a standard dataset, and the outcomes were scrutinized under distinct aspects. The comparative analysis results assured the superior performance of the proposed COADL-FDIAR model over other recent approaches with a maximum accuracy of 98.84%.     

Dense spatially-weighted attentive residual-haze network for image dehazing

Haze severely affects computer vision algorithms by degrading the quality of captured images and results in image data loss. With several available approaches for dehazing, single image dehazing is most preferred and challenging. We proposed a Dense Spatially-weighted Attentive Residual-haze Network (DSA Net), a novel end-to-end Encoder-decoder architecture to learn the residual haze layer between the hazy and haze-free image. We use encoder-decoder blocks with multiple skip connections to improve feature propagation. Feature Learning block uses a novel Residual Inception fused with Attention (RIA) block to learn the complex non-linearity from features extracted from the encoder part. Learning residual image is more straightforward than the whole haze-free image, and it improves the ability of the network to estimate the haze thickness accurately. DSA Net learns this less complex residual-map from the hazy input image and subtracts it from the input to obtain the dehazed images. Detail ablation study shows the effectiveness of different modules used in our architecture. Experiment results on different haze conditions demonstrate that our method shows significant improvement over other state-of-the-art methods.

     

Poly(acrylonitrile‐butadiene‐styrene)/Poly(ether ether ketone) Blends: an Attempt Towards a Polymer Reinforced Polymer Composite

Summary: Blends of poly(acrylonitrile‐butadiene‐styrene) (ABS) and poly(ether ether ketone) (PEEK), in which PEEK has been used as a reinforcing medium for the ABS matrix in ratios up to 20 wt.‐% of the blend, were prepared by melt mixing using a laboratory mixer. All the blend compositions were processed at the ABS processing temperature so that the PEEK was dispersed in the ABS matrix without actually melting. Differential scanning calorimetry (DSC) and dynamic mechanical analysis (DMA) studies revealed that the glass transition temperature (T_g) of the ABS phase in the blend did not show any appreciable change with composition. The dynamic storage modulus measured by DMA was found to be higher for the blend as compared to pure ABS due to reinforcement of the matrix by PEEK. The tensile strength and modulus behavior of these blends were found to follow the curves predicted using models proposed for composite systems having perfect adhesion, which shows the presence of some physical interaction between the blend components. The good tensile properties of the blend have been correlated with the observed morphology. The disperse phase in the blend has been found to be present in extremely small (sub‐micron) dimensions, which not only provides more surface area for possible interactions between the blend components but also result in efficient stress transfer between the matrix and the dispersed phase during the tensile tests. The thermal stability of the blends was investigated using thermogravimetric analysis (TGA). TGA further revealed that the constituents degraded at their respective decomposition temperatures.

SEM micrograph of tensile fractured surface of an ABS/PEEK 90/10 blend.