Counterfeiting of valuable documents, currency and branded products is a challenging problem that has serious economic, security and health ramifications for governments, businesses and consumers all ...over the world. It is estimated that counterfeiting represents a multi-billion dollar underground economy with counterfeit products being produced on a large scale every year. Counterfeiting is an increasingly high-tech crime and calls for high-tech solutions to prevent and deter the acts of counterfeiting. The present review briefly outlines and addresses the key challenges in this area, including the above mentioned concerns for anti-counterfeiting applications. This article describes a unique combination of all possible kinds of security ink formulations based on lanthanide doped luminescent nanomaterials, quantum dots (semiconductor and carbon based), metal organic frameworks as well as plasmonic nanomaterials for their possible use in anti-counterfeiting applications. Moreover, in this review, we have briefly discussed and described the historical background of luminescent nanomaterials, basic concepts and detailed synthesis methods along with their characterization. Furthermore, we have also discussed the methods adopted for the fabrication and design of luminescent security inks, various security printing techniques and their anti-counterfeiting applications.
The present review provides modern strategies for various kinds of luminescent nanomaterial based security inks for high end anti-counterfeiting applications.
Antibiotic is one of the most significant discoveries and have brought a revolution in the field of medicine for human therapy. In addition to the medical uses, antibiotics have broad applications in ...agriculture and animal husbandry. In developing nations, antibiotics use have helped to increase the life expectancy by lowering the deaths due to bacterial infections, but the risks associated with antibiotics pollution is largely affecting people. Since antibiotics are released partially degraded and undegraded into environment creating antibiotic pollution, and its bioremediation is a challenging task. In the present review, we have discussed the primary antibiotic sources like hospitals, dairy, and agriculture causing antibiotic pollution and their innovative detection methods. The strong commitment towards the resistance prevention and participation, nations through strict policies and their implementations now come to fight against the antibiotic resistance under WHO. The review also deciphers the bacterial evolution based strategies to overcome the effects of antibiotics, so the antibiotic degradation and elimination from the environment and its health benefits. The present review focuses on the environmental sources of antibiotics, it's possible degradation mechanisms, health effects, and bacterial antibiotics resistance mechanisms.
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•Antibiotic contamination is a global threat.•Ecotoxicity of antibiotics and impact on bacteria has described.•Inefficiency of WWTP's for antibiotic removal.•Bacterial strategies for resistance development is covered.•Antibiotic elimination from the environment is discussed.
In materials science, "green" synthesis has gained extensive attention as a reliable, sustainable, and eco-friendly protocol for synthesizing a wide range of materials/nanomaterials including ...metal/metal oxides nanomaterials, hybrid materials, and bioinspired materials. As such, green synthesis is regarded as an important tool to reduce the destructive effects associated with the traditional methods of synthesis for nanoparticles commonly utilized in laboratory and industry. In this review, we summarized the fundamental processes and mechanisms of "green" synthesis approaches, especially for metal and metal oxide e.g., gold (Au), silver (Ag), copper oxide (CuO), and zinc oxide (ZnO) nanoparticles using natural extracts. Importantly, we explored the role of biological components, essential phytochemicals (e.g., flavonoids, alkaloids, terpenoids, amides, and aldehydes) as reducing agents and solvent systems. The stability/toxicity of nanoparticles and the associated surface engineering techniques for achieving biocompatibility are also discussed. Finally, we covered applications of such synthesized products to environmental remediation in terms of antimicrobial activity, catalytic activity, removal of pollutants dyes, and heavy metal ion sensing.
Here we report the electrochemical performance of a interesting three-dimensional (3D) structures comprised of zero-dimensional (0D) cobalt oxide nanobeads, one-dimensional (1D) carbon nanotubes and ...two-dimensional (2D) graphene, stacked hierarchically. We have synthesized 3D self-assembled hierarchical nanostructure comprised of cobalt oxide nanobeads (Co-nb), carbon nanotubes (CNTs), and graphene nanosheets (GNSs) for high-performance supercapacitor electrode application. This 3D self-assembled hierarchical nanostructure Co3O4 nanobeads–CNTs–GNSs (3D:Co-nb@CG) is grown at a large scale (gram) through simple, facile, and ultrafast microwave irradiation (MWI). In 3D:Co-nb@CG nanostructure, Co3O4 nanobeads are attached to the CNT surfaces grown on GNSs. Our ultrafast, one-step approach not only renders simultaneous growth of cobalt oxide and CNTs on graphene nanosheets but also institutes the intrinsic dispersion of carbon nanotubes and cobalt oxide within a highly conductive scaffold. The 3D:Co-nb@CG electrode shows better electrochemical performance with a maximum specific capacitance of 600 F/g at the charge/discharge current density of 0.7A/g in KOH electrolyte, which is 1.56 times higher than that of Co3O4-decorated graphene (Co-np@G) nanostructure. This electrode also shows a long cyclic life, excellent rate capability, and high specific capacitance. It also shows high stability after few cycles (550 cycles) and exhibits high capacitance retention behavior. It was observed that the supercapacitor retained 94.5% of its initial capacitance even after 5000 cycles, indicating its excellent cyclic stability. The synergistic effect of the 3D:Co-nb@CG appears to contribute to the enhanced electrochemical performances.
We explore the dynamic and directional network connectedness between implied volatility measures of crude oil and the exchange rate of nine major currency pairs for a sample period from May 2007 to ...December 2016. We use the dynamic Cholesky-factor vector autoregression variance decomposition method. To ascertain the net directional spillovers and to determine the direction of the transmission of shocks, we employed the network graph connectedness method across a few select events characterized by the crude oil price volatility of 2008–09 and 2014–16. We found that the crude oil market has a dominant impact on the total connectedness of the crude oil currency-implied volatility relationship, suggesting that crude oil affects currencies more than currencies affect crude oil for the time frame of 2007–16. However, during the crude oil crisis periods, the dynamics are reversed, with crude having more of an effect. Additionally, the pairwise directional network connectedness between the currency pairs reveals that EURUSD is more sensitive to the crude price fluctuation than other major currency pairs and is one major currency that passes idiosyncratic shocks to other currency pairs.
•Dynamic and Directional connectedness between implied volatility of crude oil and major currency pairs is explored.•Used GEVD and Network graphs for pairwise and system-wide Connectedness.•Crude is most sensitive to USDCAD pair, followed by AUDUSD, EURUSD, NZDUSD.•EURUSD pair is most sensitive to implied volatility fluctuations in crude.•USDJPY remains most stable with respect to implied volatility fluctuations in crude.•EURUSD transfers significant risk spillovers to other currency pairs.
In machine learning and data science, feature selection is considered as a crucial step of data preprocessing. When we directly apply the raw data for classification or clustering purposes, sometimes ...we observe that the learning algorithms do not perform well. One possible reason for this is the presence of redundant, noisy, and non-informative features or attributes in the datasets. Hence, feature selection methods are used to identify the subset of relevant features that can maximize the model performance. Moreover, due to reduction in feature dimension, both training time and storage required by the model can be reduced as well. In this paper, we present a tri-stage wrapper-filter-based feature selection framework for the purpose of medical report-based disease detection. In the first stage, an ensemble was formed by four filter methods-Mutual Information, ReliefF, Chi Square, and Xvariance-and then each feature from the union set was assessed by three classification algorithms-support vector machine, naïve Bayes, and
-nearest neighbors-and an average accuracy was calculated. The features with higher accuracy were selected to obtain a preliminary subset of optimal features. In the second stage, Pearson correlation was used to discard highly correlated features. In these two stages, XGBoost classification algorithm was applied to obtain the most contributing features that, in turn, provide the best optimal subset. Then, in the final stage, we fed the obtained feature subset to a meta-heuristic algorithm, called whale optimization algorithm, in order to further reduce the feature set and to achieve higher accuracy. We evaluated the proposed feature selection framework on four publicly available disease datasets taken from the UCI machine learning repository, namely, arrhythmia, leukemia, DLBCL, and prostate cancer. Our obtained results confirm that the proposed method can perform better than many state-of-the-art methods and can detect important features as well. Less features ensure less medical tests for correct diagnosis, thus saving both time and cost.
Nowadays, in a world full of uncertainties and the threat of digital and cyber-attacks, blockchain technology is one of the major critical developments playing a vital role in the creative ...professional world. Along with energy, finance, governance, etc., the healthcare sector is one of the most prominent areas where blockchain technology is being used. We all are aware that data constitute our wealth and our currency; vulnerability and security become even more significant and a vital point of concern for healthcare. Recent cyberattacks have raised the questions of planning, requirement, and implementation to develop more cyber-secure models. This paper is based on a blockchain that classifies network participants into clusters and preserves a single copy of the blockchain for every cluster. The paper introduces a novel blockchain mechanism for secure healthcare sector data management, which reduces the communicational and computational overhead costs compared to the existing bitcoin network and the lightweight blockchain architecture. The paper also discusses how the proposed design can be utilized to address the recognized threats. The experimental results show that, as the number of nodes rises, the suggested architecture speeds up ledger updates by 63% and reduces network traffic by 10 times.
The paper utilizes MSCI energy indices of 21 major energy consumer economies as a full sample to serve as a proxy indicator of the energy sector of these economies and then studies the squared return ...spillover interlinkages among them for the pre- and post-global financial crisis of 2008 and during the crude oil crises of 2008–09 and 2014–15. The squared return spillover connectedness has been analyzed on intra- and inter-regional basis based on the segregation of indices into regions, viz., Europe, North and Latin America, and Asia Pacific and Africa. The connectedness interlinkage has been quantified deploying Generalized Error Variance Decomposition (GEVD), and the results have been visualized via network diagrams. The result shows that the return spillover connectedness is significant and is more prominent in some economies, primarily in Europe and North America, for the full sample. However, post-crisis, the squared return spillover connectedness of emerging economies of Asia Pacific has witnessed a boost. Surprisingly, Russia, as a major oil exporter, sends moderate shocks system-wide in the post-crisis era, implying economic sanctions downplay the influence it could make in the energy sector. During the crisis year, though spillover risk transmission is high during the 2008–09 crisis, it is more spread out during the 2014–15 crisis with the inclusion of the Asia Pacific emerging market energy indices.
•Interlinkages among the 21 MSCI energy equity indices are explored via GEVD and network graph applications.•Energy indices have been segregated into three regions: Europe, North and Latin America, and Asia Pacific and Africa.•The international isolation of Russia has restricted its influence on the energy sector worldwide.•Risk transmission is low and more concentrated in the energy indices of the west during the 2008-09 oil crisis.•Risk transmission intensifies and spreads out more during the 2014-15 oil crisis.
Human Action Recognition (HAR) is a popular area of research in computer vision due to its wide range of applications such as surveillance, health care, and gaming, etc. Action recognition based on ...3D skeleton data allows simplistic, cost-efficient models to be formed making it a widely used method. In this work, we propose DSwarm-Net, a framework that employs deep learning and swarm intelligence-based metaheuristic for HAR that uses 3D skeleton data for action classification. We extract four different types of features from the skeletal data namely: Distance, Distance Velocity, Angle, and Angle Velocity, which capture complementary information from the skeleton joints for encoding them into images. Encoding the skeleton data features into images is an alternative to the traditional video-processing approach and it helps in making the classification task less complex. The Distance and Distance Velocity encoded images have been stacked depth-wise and fed into a Convolutional Neural Network model which is a modified version of Inception-ResNet. Similarly, the Angle and Angle Velocity encoded images have been stacked depth-wise and fed into the same network. After training these models, deep features have been extracted from the pre-final layer of the networks, and the obtained feature representation is optimized by a nature-inspired metaheuristic, called Ant Lion Optimizer, to eliminate the non-informative or misleading features and to reduce the dimensionality of the feature set. DSwarm-Net has been evaluated on three publicly available HAR datasets, namely UTD-MHAD, HDM05, and NTU RGB+D 60 achieving competitive results, thus confirming the superiority of the proposed model compared to state-of-the-art models.
THPdb (http://crdd.osdd.net/raghava/thpdb/) is a manually curated repository of Food and Drug Administration (FDA) approved therapeutic peptides and proteins. The information in THPdb has been ...compiled from 985 research publications, 70 patents and other resources like DrugBank. The current version of the database holds a total of 852 entries, providing comprehensive information on 239 US-FDA approved therapeutic peptides and proteins and their 380 drug variants. The information on each peptide and protein includes their sequences, chemical properties, composition, disease area, mode of activity, physical appearance, category or pharmacological class, pharmacodynamics, route of administration, toxicity, target of activity, etc. In addition, we have annotated the structure of most of the protein and peptides. A number of user-friendly tools have been integrated to facilitate easy browsing and data analysis. To assist scientific community, a web interface and mobile App have also been developed.
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