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Poulose, Anesh Manjaly,Elnour, Ahmed Yagoub,Anis, Arfat,Shaikh, Hamid,Al-Zahrani, S.M.,George, Justin,Al-Wabel, Mohammad I.,Usman, Adel R.,Ok, Yong Sik,Tsang, Daniel C.W.,Sarmah, Ajit K. Elsevier 2018 The Science of the total environment Vol.619 No.-
<P><B>Abstract</B></P> <P>The application of biochar (BC) as a filler in polymers can be viewed as a sustainable approach that incorporates pyrolysed waste based value-added material and simultaneously mitigate bio-waste in a smart way. The overarching aim of this work was to investigate the electrical, mechanical, thermal and rheological properties of biocomposite developed by utilizing date palm waste-derived BC for the reinforcing of polypropylene (PP) matrix. Date palm waste derived BC prepared at (700 and 900°C) were blended at different proportions with polypropylene and the resultant composites (BC/PP) were characterized using an array of techniques (scanning electron microscope, energy-dispersive X-ray spectroscopy and Fourier transform infra-red spectroscopy). Additionally the thermal, mechanical, electrical and rheological properties of the BC/PP composites were evaluated at different loading of BC content (from 0 to15% w/w). The mechanical properties of BC/PP composites showed an improvement in the tensile modulus while that of electrical characterization revealed an enhanced electrical conductivity with increased BC loading. Although the BC incorporation into the PP matrix has significantly reduced the total crystallinity of the resulted composites, however; a positive effect on the crystallization temperature (T<SUB>c</SUB>) was observed. The rheological characterization of BC/PP composites revealed that the addition of BC had minimal effect on the storage modulus (G′) compared to the neat (PP).</P> <P><B>Highlights</B></P> <P> <UL> <LI> Date palm waste derived biochar was used as filler for polymer composites' applications. </LI> <LI> Biochar/polypropylene (BC/PP) composites' properties such as electrical, mechanical, thermal and rheological were investigated. </LI> <LI> The BC/PP composites' surface resistivity was decreased by four orders of magnitude. </LI> </UL> </P> <P><B>Graphical abstract</B></P> <P>[DISPLAY OMISSION]</P>
Kottackal Poulose Martin,Aneta Sabovljevic,Joseph Madassery 한국작물학회 2011 Journal of crop science and biotechnology Vol.14 No.3
High-frequency transgenic plant regeneration and production of plumbagin were accomplished from hairy roots induced by Agrobacterium rhizogenes strain A4M70GUS on Plumbago indica L. Of the two types of hairy roots developed on Murashige and Skoog (MS) basal medium, Type I was long and thick with lesser branches and root hairs, and Type II was highly-branched short and slender with tufts of root hairs. Of the different lines grown in half-strength MS liquid basal media, root line 5 (R5) of the Type I yielded the highest plumbagin (0.92% DW) and was significantly different to that of the in vitro control. R5 showed a stable production of plumbagin (1.09% DW) in subsequent cultures. Elicitation of R5 with 50 μM methyl jasmonate for 48 h increased the yield of plumbagin to 5.0% DW, and was superior to 100 μM acetylsalicylic acid (3.8% DW). Plumbagin yield was five times that of twoyear-old ex vitro roots. Histochemical assay and PCR analysis using the primers of uidA coding region confirmed the transformation. Hairy root segments cultured on MS medium containing 8.8 μM benzyladenine and 2.5 μM indole-3-butyric acid induced a mean of 9.1 shoots. Subsequent culture of the isolated shoots developed more than 50 normal shoots per culture. The root-free shoots were rooted on half-strength MS basal medium. The plantlets transferred in field conditions grew normally and exhibited 90% survival. Transgenic plant regeneration and hairy root induction in P. indica serves as reliable source of plumbagin which in turn cut off the mass destruction of the plant species.
Facial Emotion Recognition Using 3D Face Reconstruction
Alwin Poulose,Chinthala Sreya Reddy,Jung Hwan Kim,Dong Seog Han 한국통신학회 2021 한국통신학회 학술대회논문집 Vol.2021 No.6
In recent days, autonomous driving systems (ADS) effectively utilize facial emotion recognition (FER) results for safe driving. In FER, the system provides the user emotions such as happy, sad, anger, surprise, disgust, fear, or neutral. These emotions provide helpful information for safe driving and reduce the chances of road accidents. The conventional FER approaches use 2D images as their inputs and classify the user emotions. However, the 2D face images in the conventional FER approaches have limited features for model training. In addition, the features from the 2D face images themselves are not sufficient for accurate emotion classification. To reduce the feature extraction issues in the conventional FER approaches, we propose a 3D face image-based FER approach that uses the 3D face reconstruction technique for converting the 2D face images into 3D face images. The deep convolutional neural networks (DCNNs) used in the proposed FER approach efficiently use the 3D face images as inputs and classify the user emotions with minimum errors. The experiment results show that the proposed 3D face image-based FER approach achieves 99% classification accuracy which is better than the conventional 2D face image-based FER approach.
Wi-Fi RSSI Heat Maps Based Indoor Localization System Using Deep Convolutional Neural Networks
Alwin Poulose,Dong Seog Han 한국방송·미디어공학회 2020 한국방송공학회 학술발표대회 논문집 Vol.2020 No.7
An indoor localization system that uses Wi-Fi RSSI signals for localization gives accurate user position results. The conventional Wi-Fi RSSI signal based localization system uses raw RSSI signals from access points (APs) to estimate the user position. However, the RSSI values of a particular location are usually not stable due to the signal propagation in the indoor environments. To reduce the RSSI signal fluctuations, shadow fading, multipath effects and the blockage of Wi-Fi RSSI signals, we propose a Wi-Fi localization system that utilizes the advantages of Wi-Fi RSSI heat maps. The proposed localization system uses a regression model with deep convolutional neural networks (DCNNs) and gives accurate user position results for indoor localization. The experiment results demonstrate the superior performance of the proposed localization system for indoor localization.