Effect of processing routes on mechanical and thermal properties of copper–graphene composites

In this paper, copper–graphene composites were fabricated by using two different processing routes (ball milling (BM) and ultrasonication) followed by spark plasma sintering. Vickers hardness and anisotropic thermal conductivity of the composites were measured and observed that ultrasonicated fabricated composites gave better result compared with BM composite and even from pure copper. The hardness values obtained for ultrasonicated copper–graphene composite were 69?HV (57% higher) and thermal conductivity 387?W/m?K (13% higher) by using only 0.5?wt-% of graphene, while for pure copper the values were 44?HV and 341?W/m?K. The value of anisotropic thermal conductivity ultrasonicated composites was also 1.97 which is much higher than pure copper 0.94.     

Antioxidant Activity and Amino Acid Profiling of Protein Hydrolysates from the Skin of Sphyraena barracuda and Lepturacanthus savala

The antioxidant activity of protein hydrolysates prepared from the skin of Sphyraena barracuda (Seela fish) and Lepturacanthus savala (Ribbon fish), using the commercial enzymes pepsin, trypsin, and papain were determined. The protein hydrolysate showed high antioxidant activity in which trypsin hydrolysate of the skin of both seela and ribbon fish proved good DPPH scavenging activity with 66 and 60% (p < 0.05), respectively. These active hydrolysates were purified using fast protein liquid chromatography on ion exchange and gel filtration chromatography procedure and the active purified fractions were determined using electron spin resonance spectrophotometer against 2,2-diphenyl-1-picrylhydrazyl (DPPH) and hydroxyl radicals. Further the purified fractions were analyzed for amino acid composition using high performance liquid chromatography and it consisted of antioxidant inducing amino acids along with a considerable quantity of essential amino acids.     

Numerical analyses on thermal stress distribution induced from impact compression in 3D carbon fiber/epoxy braided composite materials

Thermal stress and thermomechanical coupling behaviors are essential to design 3D braided composite materials under impact loading. Here we report the thermal/mechanical coupling properties of 3D carbon fiber/epoxy matrix braided composite under impact compressions. The compressive behaviors of the 3D braided composites with different braiding angles have been tested. A high-speed camera system was used to capture the compressive deformation and damage developments. Two material models described as “with thermal parameter (WT) model” and “without thermal parameter (NT) model” were established to calculate thermal stress induced from impact compression. Both the two models give reasonable predictions. The plastic deformation energy of the composite is found to increase with braiding angle, resulting in higher temperature and greater thermal stress. The difference between the stress curves obtained from two models increases with the increase in braiding angle. Although the effect of thermal stress on the stress–strain curve obtained from two models is not obvious for the composite with smaller braided angle, the stress level of the resin and fiber tows of the WT model is higher than that of the NT model. The resin of the WT model has damages earlier and more serious. Likewise, the interface damage is also more serious.     

Antioxidant proteins and peptides to enhance the oxidative stability of meat and meat products: A comprehensive review

Lipid oxidation is among the major flaw-grounding processes in meat and meat-based products that can affect interactions among lipids and proteins, leading to critically undesirable changes. Therefore, it is imperative to control lipid oxidation in meat allied products to enhance consumer acceptability. Moreover, lipid oxidation is somber dilemma visage by the meat processing industry, affects food constituents, leading to detrimental alterations that can impart the deleterious effects on human health upon consumption. Various synthetic (butylated hydroxyanisole (BHA), butylated hydroxytoluene (BHT), and tertiary butylhydroquinone (TBHQ)) and natural antioxidants (vitamin C, vitamin A, tocopherols, especially vitamin E, flavonoids particularly quercetin, proteins, and peptides) as well as preservatives are employed to extend the storability of meat and resultants products; however, great consideration is paid to the utilization of natural antioxidants due to the harmful side effects imparted by synthetic counterparts. Recently, bioactive peptides are claimed to thwart lipid oxidation in meat and other products; in addition, these antioxidant peptides have also been reported to possess substantial health-promoting potential besides controlling oxidation. Therefore, the present review is intended to emphasize the sources, production methods, and applications of antioxidant proteins and peptides to control oxidative degradation in meat products and the potential health benefits of bioactive peptides. Furthermore, the techniques available for the extraction, characterization, and assessment of the antioxidant capability of bioactive peptides are discussed critically in this review.     

Investigation of the distortion factor for Bussgang noise cancellation iterative receivers

Orthogonal frequency‐division multiplexing (OFDM) is considered an efficient modulation approach in the field of wireless systems. However, the significant high peak to average power ratios (PAPR) are one of the prominant shortcomings of the OFDM systems leading to severe signal distortions and acute power inefficiency. Iterative receivers, which use Bussgang algorithm approach along with clipping at the transmitter, have been presented in the literature. This methodology is majorly receiver based in order to compensate the effects of the clipping noise that was added at the transmitter. These receivers are used to analyse the distortions of clipping noise directly subtracting from the signals that are received in order to get the original signal. We demonstrate in this study a detailed and technical investigation of the Bussgang noise cancellation (BNC) receiver along with some significant results. Simulation results are presented to show that the BNC iterative receiver does not estimate the accurate clipping noise, and the performance may be improved if at the receiver end, it is somehow capable to analyze the precise clipping noise for each OFDM signal.     

Development of Social Media Analytics System for Emergency Event Detection and Crisis Management

Social media platforms have proven to be effective for information gathering during emergency events caused by natural or human-made disasters. Emergency response authorities, law enforcement agencies, and the public can use this information to gain situational awareness and improve disaster response. In case of emergencies, rapid responses are needed to address victims’ requests for help. The research community has developed many social media platforms and used them effectively for emergency response and coordination in the past. However, most of the present deployments of platforms in crisis management are not automated, and their operational success largely depends on experts who analyze the information manually and coordinate with relevant humanitarian agencies or law enforcement authorities to initiate emergency response operations. The seamless integration of automatically identifying types of urgent needs from millions of posts and delivery of relevant information to the appropriate agency for timely response has become essential. This research project aims to develop a generalized Information Technology (IT) solution for emergency response and disaster management by integrating social media data as its core component. In this paper, we focused on text analysis techniques which can help the emergency response authorities to filter through the sheer amount of information gathered automatically for supporting their relief efforts. More specifically, we applied state-of-the-art Natural Language Processing (NLP), Machine Learning (ML), and Deep Learning (DL) techniques ranging from unsupervised to supervised learning for an in-depth analysis of social media data for the purpose of extracting real-time information on a critical event to facilitate emergency response in a crisis. As a proof of concept, a case study on the COVID-19 pandemic on the data collected from Twitter is presented, providing evidence that the scientific and operational goals have been achieved.     

Correction to: A review of machine learning-based human activity recognition for diverse applications

Neural Computing and Applications -     

Digital watermarking using Hall property image decomposition method

Most of the existing singular value decomposition-based digital watermarking methods are not robust to geometric rotation, which change the pixels’ locations without maintaining the corresponding changes to the pixel’s intensity values of entire image and yield high computational cost. To answer this, we propose a digital image watermarking algorithm using the Hall property. In the proposed method, a digital watermark image is factorized into lower-triangular, upper-triangular, and permutation matrices. The permutation matrix is used as the valid key matrix for authentication of the rightful ownership of the watermark image. The product of the lower and upper triangular matrices is processed with a few iterations of the Arnold transformation to obtain the scrambled data. The scrambled data are embedded into particular sub-bands of a cover image using Wavelet transform. Our experiments show that the proposed algorithm is highly reliable and computationally efficient compared with state-of-the-art methods that are based on singular value decomposition.     

Image denoising with norm weighted fusion estimators

In recent era, the weighted matrix rank minimization is used to reduce image noise, promisingly. However, low-rank weighted conditions may cause oversmoothing or oversharpening of the denoised image. This demands a clever engineering algorithm. Particularly, to remove heavy noise in image is always a challenging task, specially, when there is need to preserve the fine edge structures. To attain a reliable estimate of heavy noise image, a norm weighted fusion estimators method is proposed in wavelet domain. This holds the significant geometric structure of the given noisy image during the denoising process. Proposed method is applied on standard benchmark images, and simulation results outperform the most popular rivals of noise reduction approaches, such as BM3D, EPLL, LSSC, NCSR, SAIST, and WNNM in terms of the quality measurement metric PSNR (dB) and structural analysis SSIM indices.