Eco-friendly electronics,based on nanocomposites of biopolyester reinforced with carbon nanotubes: a review

ABSTRACT

Fabrication of electronic materials from nanocomposite of biopolyesters reinforced with carbon nanotubes can be regarded as the effective alternative for conventional nanocomposites consisting of non-biodegradable polymers. Commercial availability of biopolyester-based nanocomposites is limited because of their high cost compared to other polymers, but the factor of their compostable nature is worthless for environmental protection. Such nanocomposites have potential applications in biodegradable sensors, EMI materials, etc. In this review, the current progress of biopolyester/CNTs nanocomposites in the field of biodegradable electronics is reviewed and also the impact of CNTs dispersion on electrical, thermal and mechanical properties of eco composites is stipulated.     

KP-Rank: a semantic-based unsupervised approach for keyphrase extraction from text data

Automatic key concept identification from text is the main challenging task in information extraction, information retrieval, digital libraries, ontology learning, and text analysis. The main difficulty lies in the issues with the text data itself, such as noise in text, diversity, scale of data, context dependency and word sense ambiguity. To cope with this challenge, numerous supervised and unsupervised approaches have been devised. The existing topical clustering-based approaches for keyphrase extraction are domain dependent and overlooks semantic similarity between candidate features while extracting the topical phrases. In this paper, a semantic based unsupervised approach (KP-Rank) is proposed for keyphrase extraction. In the proposed approach, we exploited Latent Semantic Analysis (LSA) and clustering techniques and a novel frequency-based algorithm for candidate ranking is introduced which considers locality-based sentence, paragraph and section frequencies. To evaluate the performance of the proposed method, three benchmark datasets (i.e. Inspec, 500N-KPCrowed and SemEval-2010) from different domains are used. The experimental results show that overall, the KP-Rank achieved significant improvements over the existing approaches on the selected performance measures.

     

Sensor Fault Diagnosis and Unknown Disturbances Estimation of High Switching Frequency Single-phase PWM Rectifier

International Journal of Control, Automation and Systems - The fault and disturbances estimation has an important role in the modern traction railway system. This paper proposes a unique method for...     

Performance tuning of glass fiber/epoxy composites through interfacial modification upon integrating with dendrimer functionalized graphene oxide

In this study, glass fiber/epoxy composites were interfacially tailored by introducing polyamidoamine (PAM) dendrimer functionalized graphene oxide (GO) into epoxy matrix. Two different composites each containing varying loading fraction (0.5, 1.0, and 1.5 wt%) of GO and GO-PAM were fabricated via hot press processing. Composites were evaluated for interlaminar shear strength (ILSS), dynamic mechanical properties and thermal conductivity. The inclusion of 1.5 wt% GO-PAM resulted ~57.3%, ~42.7%, and ~54% enhancement in ILSS, storage modulus and thermal conductivity, respectively. Almost, ~71% reduction in coefficient of thermal expansion was also observed at same GO-PAM loading. Moreover, higher glass transition temperature was observed with GO-PAM addition. GO-PAM substantially improved fiber/matrix interfacial adhesion, which was witnessed through scanning electron microscopy. The enhanced thermo-mechanical performance was attributed to interfacial covalent interactions engendered by ring opening reaction between epoxy and amine moieties of PAM dendrimers. These multiscale composites with extraordinary functional properties can outperform conventional counterparts with improved reliability and performance.     

Your comments matter: incorporating viewers’ comments for ranking online video content using bibliometrics

ABSTRACT

The quality of user-generated content over World Wide Web media is a matter of serious concern for both creators and users. To measure the quality of content, webometric techniques are commonly used. In recent times, bibliometric techniques have been introduced to good effect for evaluation of the quality of user-generated content, which were originally used for scholarly data. However, the application of bibliometric techniques to evaluate the quality of YouTube content is limited to h-index and g-index considering only views. This paper advocates for and demonstrates the adaptation of existing Bibliometric indices including h-index, g-index and M-index exploiting both views and comments and proposes three indices h_vc, g_vc and m_vc for YouTube video channel ranking. The empirical results prove that the proposed indices using views along with the comments outperform the existing approaches on a real-world dataset of YouTube.     

Alpha Fusion Adversarial Attack Analysis Using Deep Learning

The deep learning model encompasses a powerful learning ability that integrates the feature extraction, and classification method to improve accuracy. Convolutional Neural Networks (CNN) perform well in machine learning and image processing tasks like segmentation, classification, detection, identification, etc. The CNN models are still sensitive to noise and attack. The smallest change in training images as in an adversarial attack can greatly decrease the accuracy of the CNN model. This paper presents an alpha fusion attack analysis and generates defense against adversarial attacks. The proposed work is divided into three phases: firstly, an MLSTM-based CNN classification model is developed for classifying COVID-CT images. Secondly, an alpha fusion attack is generated to fool the classification model. The alpha fusion attack is tested in the last phase on a modified LSTM-based CNN (CNN-MLSTM) model and other pre-trained models. The results of CNN models show that the accuracy of these models dropped greatly after the alpha-fusion attack. The highest F1 score before the attack was achieved is 97.45 And after the attack lowest F1 score recorded is 22%. Results elucidate the performance in terms of accuracy, precision, F1 score and Recall.     

The Double Edge Sword Based Distributed Executor Service

Scalability is one of the most important quality attribute of software-intensive systems, because it maintains an effective performance parallel to the large fluctuating and sometimes unpredictable workload. In order to achieve scalability, thread pool system (TPS) (which is also known as executor service) has been used extensively as a middleware service in software-intensive systems. TPS optimization is a challenging problem that determines the optimal size of thread pool dynamically on runtime. In case of distributed-TPS (DTPS), another issue is the load balancing b/w available set of TPSs running at backend servers. Existing DTPSs are overloaded either due to an inappropriate TPS optimization strategy at backend servers or improper load balancing scheme that cannot quickly recover an overload. Consequently, the performance of software-intensive system is suffered. Thus, in this paper, we propose a new DTPS that follows the collaborative round robin load balancing that has the effect of a double-edge sword. On the one hand, it effectively performs the load balancing (in case of overload situation) among available TPSs by a fast overload recovery procedure that decelerates the load on the overloaded TPSs up to their capacities and shifts the remaining load towards other gracefully running TPSs. And on the other hand, its robust load deceleration technique which is applied to an overloaded TPS sets an appropriate upper bound of thread pool size, because the pool size in each TPS is kept equal to the request rate on it, hence dynamically optimizes TPS. We evaluated the results of the proposed system against state of the art DTPSs by a client-server based simulator and found that our system outperformed by sustaining smaller response times.     

Trimetallic PtCoFe Alloy Monolayer Superlattices as Bifunctional Oxygen‐Reduction and Ethanol‐Oxidation Electrocatalysts

A synthesis strategy for the preparation of trimetallic PtCoFe alloy nanoparticle superlattices is reported. Trimetallic PtCoFe alloy monolayer array of nanoparticle superlattices with a large density of high index facets and platinum‐rich surface are successfully prepared by coreduction of metal precursors in formamide solvent. The concentration of cetyl trimethyl ammonium bromide plays a vital role for the formation of a monolayer array of nanoparticle superlattices, while the size of nanoparticles is determined by NaI. The prepared monolayer array of nanoparticle superlattices is the superior catalyst for oxygen reduction reaction as well as for ethanol oxidation owing to their specific structural and compositional characteristics.     

Abiotic stress of ZnO‐PEG,ZnO‐PVP,CuO‐PEG and CuO‐PVP nanoparticles enhance growth,sweetener compounds and antioxidant activities in shoots of Stevia rebaudiana Bertoni

Nanoparticles are known to play remarkable role as abiotic stress elicitors in plants. This study reports the comparative analysis of effects produced by capped [zinc oxide (ZnO)‐polyethylene glycol (PEG), ZnO‐polyvinyl pyrrolidone (PVP), copper oxide (CuO)‐PEG, CuO‐PVP] and uncapped (ZnO and CuO) nanoparticles on the medicinal plant, Stevia rebaudiana raised in vitro for the production of commercially important sweetener compounds. In context of shoot organogenesis, ZnO‐PEG, ZnO‐PVP, CuO‐PEG, CuO‐PVP were employed to the growth medium that resulted in increased growth parameters, and larger content of steviol glycosides as compared to the shoots raised in medium containing ZnO and CuO, revealed by high‐performance liquid chromatography. In the meanwhile, non‐enzymatic antioxidant activities including total phenolic content, total flavonoid content, total antioxidant capacity, total reducing power and 2,2‐diphenyl‐1‐picryl hydrazyl‐free radical scavenging activity were calculated and showed comparatively greater amounts in shoots grown in medium containing capped ZnO or CuO nanoparticles. Furthermore, the ZnO and its derivatives revealed to be more reactive at 1 mg/l of concentration. Whereas, the CuO and its derivatives produced greater response on Stevia at 10 mg/l concentration of nanoparticles. This study paves the way for more such studies encompassing capped and uncapped nanoparticles and their ultimate effect on in‐vitro grown plant tissues for the production of active metabolites on industrial scale.Inspec keywords: nanoparticles, chromatography, resins, polymers, zinc compounds, copper compounds, food technology, agricultureOther keywords: nanoparticles, sweetener compounds, antioxidant activities, Stevia rebaudiana Bertoni, abiotic stress elicitors, zinc oxide‐polyethylene glycol, zinc oxide‐polyvinyl pyrrolidone, copper oxide‐polyethylene glycol, medicinal plant, shoot organogenesis, steviol glycosides, high‐performance liquid chromatography, nonenzymatic antioxidant activities, total phenolic content, total flavonoid content, total antioxidant capacity, total reducing power, 2,2‐diphenyl‐1‐picryl hydrazylfree radical scavenging activity, derivatives, active metabolite production, copper oxide‐polyvinyl pyrrolidone