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Polynomial time algorithm for inferring subclasses of parallel internal column contextual array languages
In [2,16] a new method of description of pictures of digitized rectangular arrays is introduced based on contextual grammars, called parallel internal contextual array grammars. In this paper, we pay our attention on parallel internal column contextual array grammars and observe that the languages generated by these grammars are not inferable from positive data only. We define two subclasses of parallel internal column contextual array languages, namely, k-uniform and strictly parallel internal column contextual languages which are incomparable and not disjoint classes and provide identification algorithms to learn these classes. Springer International Publishing AG 2017. -
Shadowing the image archive: In medias res: Inside Nalini Malani's shadow plays, Mieke Bal (2016) /
Moving Image Review & Art Journal (MIRAJ), Vol.7, Issue 2, pp.325-335, ISSN No: 2045-6298. -
A finger print recognition using CNN Model
The fundamental goal of this research is to improve the new identification accuracy for fingerprint acknowledgment by contrasting Convolutional Neural Networks (CNN) model frameworks for biometric safety in the cloud with Conventional inception models (TIM). Accuracy was computed and compared using a CNN model and standard Inception Models (N=10). The statistical significance was calculated using SPSS. Average and standard deviation for a 95% confidence interval, 0.05% G-power cutoff. The TIM and Convolutional Neural Networks performed an autonomous T-Test on the samples. CNN is more successful (93%) than TIM (61%). Based on a significant value of 0.048 for the comparison ratio (p0.05), there is a statistically significant difference between the CNN and the TIM transformation. According to the findings, the suggested CNN model is 93% accurate on the dataset, with no rejected samples. 2023 IEEE. -
Positive side effects of the Covid-19 pandemic on environmental sustainability: evidence from the quadrilateral security dialogue countries
Purpose: The eruption of coronavirus disease 2019 (COVID-19) has pointedly subdued global economic growth and producing significant impact on environment. As a medicine or a treatment is yet available at mass level, social distancing and lockdown is expected the key way to avert it. Some outcome advocates that lockdown strategies considered to reduce air pollution by curtailing the carbon emission. Current investigation strives to affirm the impact of lockdown and social distancing policy due to covid-19 outbreak on environmental pollution in the QUAD nations. Design/methodology/approach: To calibrate the social movement of public, six indicators such residential mobility, transit mobility, workplace mobility, grocery and pharmacy mobility, retail and recreation mobility and park mobility have been deliberated. The data of human mobility have been gathered from the Google mobility database. To achieve the relevant objectives, current pragmatic analysis exerts a panel autoregressive distributed lag model (ARDL)-based framework using the pooled mean-group (PMG) estimator, proposed by Pesaran and Shin (1999), Pesaran and Smith (1995). Findings: The outcome reveals that in the long-run public mobility change significantly impact the pollutants such as PM2.5 and nitrogen dioxide; however, it does not lead to any changes on ozone level. As per as short run outcome is concerned, the consequence unearths country wise heterogeneous impact of different indicators of public mobility on the air pollution. Research limitations/implications: The ultimate inferences of the above findings have been made merely on the basis of examination of QUAD economies; however, comprehensive studies can be performed by considering modern economies simultaneously. Additionally, finding could be constraint in terms of data; for instance, Google data used may not suitably signify real public mobility changes. Originality/value: A considerable amount of investigation explores the impact of covid-19 on environmental consequences by taking carbon emission as a relevant indicator of environmental pollution. Hence, the present pragmatic investigation attempts to advance the present discernment of the above subject in two inventive ways. Primarily, by investigating other components of environmental pollution such as nitrogen dioxide, PM2.5 and ozone, to reveal the impact of covid-19 outbreak on environmental pollution, as disregarded by the all preceding studies. Additionally, it makes a methodological contribution before integrating supplementary variables accompanying with ecological air pollution. Finally, the current research article provides an alternative and creative approach of modeling the impact of public mobility on environmental sustainability. 2021, Emerald Publishing Limited. -
Tracking the transmission channels of fiscal deficit and food inflation linkages: A structural var approach
This empirical analysis aspired to unearth the transmission channels of fiscal deficit and food inflation linkages in the Indian perspective by reasonably exerting the data for 1991 to 2017. The precise results of structural vector autoregressive (SVAR) analysis proffered that there were three different mechanisms of transmission such as consumption, general inflation, and import channels that led to food inflation in response to the high fiscal deficit. The first channel revealed that government deficit spending had a positive impact on income which further led to food inflation through surging the household consumption expenditure. It was concluded that fiscal deficit passed through general inflation finally leading to a food price surge in the economy and seemed to work as cost-push inflation for the food and agricultural industry. The outcome also revealed that the impact of fiscal deficit passed to food inflation through external linkages such as import and export. 2020 The Society of Economics and Development, except certain content provided by third parties. -
Foreign Exchange, Gold, and Real Estate Markets in India: An Analysis of Return Volatility and Transmission
This empirical analysis endeavored to investigate the return volatility, covolatility, and the spillover impact of gold, real estate, and U.S. dollar in India. The generalized autoregressive conditional heteroskedasticity dynamic conditional correlation (GARCH -DCC) was used to reveal the return volatility and conditional correlation. The volatility spillover was examined by using the variance decomposition technique. The empirical outcome clearly revealed the presence of ARCH and GARCH effect on gold, realty, and U.S. dollar. Additionally, the results also manifested that the returns of these variables were not moving away from their means in the long run. On the other hand, the consequences of volatility spillover reported that real estate was the most dominating among all markets. This is so because returns on real estate had a significant contribution to the return volatility of the other markets. Finally, it was also found that return volatility of U.S. dollar was most affected as it was the net receiver of volatility, while return volatility of gold seemed to be neutral in the Indian financial market. -
The Himalayan Ecosystem and Development Paradigm: A Sustainability Perspective
The Himalaya is physically and biologically very complex and diverse. It is one of the youngest and loftiest of the mountain systems of the world. It is a biodiversity rich area having a distinctive climatic impact on lives of people in Asia. Major rivers of the region originate from the Himalayan Mountains providing the source of water for a large mass of population. Himalaya is a house to many crops of the world, natural wealth as well as indigenous societies and knowledge system. It is a rich repository of plant and animal wealth providing a source of livelihood to millions of people in the Asian region. Its significance lies in the fact that it has been recognised as among the 34 global biodiversity hotspots. Due to anthropogenic activitiesdriven by the economic development modellike mining, road construction, dam construction, tourism, infrastructure development et al. and climate change there has been wide spread damage to fragile Himalayan ecosystem. The consequences of such developmental activities has far reaching consequences on the future of Himalayan ecosystem altering the course of nature and impacting human lives and societies depending on Himalaya and hence unsustainable for the earth. It is predicted that the anthropogenic activities in and around Himalayas shall have significant consequences regionally and globally raising questions about natural resources, ecology, sustainability, loss of habitat by species, human rights, livelihood of people in the region. The present model of development solely premised on the anthropocentricism has caused enormous harm to Himalayan ecology. It has ignored the traditional conservation model adopted by traditional inhabitants bringing great destruction to the Himalaya which has global implications. In the light of the above this chapter will discuss the consequences of anthropogenic activities on the Himalayan ecosystem. Moreover, it will explore the sustainability of anthropogenic activities and how that can be helpful to the planet. Moreover, it will offer few suggestions for the improvement of the Himalayan ecosystem which will be advantageous to the present as well as future generations. The Author(s), under exclusive license to Springer Nature Switzerland AG 2024. -
Food Security and Global Institutions: A Global Justice Perspective
Food security refers to a condition where all people have physical and economic access, at all times, to sufficient, safe and nutritious food that meets their needs and food preferences to lead an active and healthy life. Universal Declaration of Human Rights, 1948 (UDHR) declares the right to food as a basic human rights. International Covenant on Economic, Social and Cultural Rights, 1976 (ICESR) explicitly recognises the right of everyone to food and mandates all state parties for its realization; also it recognizes everyones right to be free from hunger as a fundamental right. Further, it instructs the state parties to ensure equitable distribution of world food supplies to achieve the right of everyone to be free from hunger. Rome Declaration on World Food Security, 1996 reaffirmed the right of everyone to access to safe and nutritious food compatible with right to adequate food and also right to be free from hunger. United Nations Millennium Declaration set the goal for fighting hunger and resolved to reduce the proportion of people suffering from hunger to half by 2015, then Sustainable Development Goals were floated, inter alia, to end extreme poverty and achieve the target of zero hunger and food security by 2030. Regardless of its being a universal human rights, food security scenario across the globe is far from satisfactory and fair. Post COVID 19 scenario has seen a surge in undernourishment and food insecurity. According to The State of Food Security and Nutrition in the World, 2022, 3.1 billion people across the globe are unable to afford a healthy diet. At this juncture we are living in a deeply connected and globalized world run not by national institutions but by global institutions. The role of global institutions assume significance in a globalized world. Justice demands that policy planning and legal framework on food security should be fair and equitable; they should be based on the idea of entitlement and obligation. To achieve the goal of zero hunger and food security, what is required is an equitable and unified global governance approach premised upon the idea of global justice which shall fix obligations on global institutions. This chapter aims at examining the issue of food security from a global justice perspective and how it can be sustainably achieved. It will explain the concept of global justice and obligations of global institutions by relying upon few legal and political theories. Further, the chapter will explain the human rights perspective of the food security and the challenges involved with it. The Author(s), under exclusive license to Springer Nature Switzerland AG 2024. -
Assessing Climate Change through Artificial Intelligence An Ethico-Legal Study
IPCC (The Intergovernmental Panel on climate change) [1], in the 6th Assessment Report released in 2022, reports that the net anthropogenic GHGs (greenhouse gases) continued to rise during the period 2010-19. It shows that GHG emission in the last decade is the highest in human history. According to the World Inequality Report, 2022, carbon dioxide concentration level in the atmosphere across the globe is the highest in millions of years. Consistent rise in the global emission level leading to alarming rise in atmospheric temperature has been a cause of concern for mankind. Rising atmospheric temperature leading to climate change has severely affected weather patterns; led to melting of glaciers; caused natural disaster and extinction of species, and severely impacted the ground water table. It has put the human race at a crossroads and thrown open an existential question for the world. Attempts have been made, both international and national, to reverse the impact of the rising scenario concerning climate change but have yet to be successful. The technological revolutions arising in recent times, especially in the domain of Artificial Intelligence (AI), offer hope to give a new shape to human civilization. With the aid of human intelligence, AI can perform assessment and predictive work as well which may help in mitigating the effect of adversely affecting climate change and help improve the environment. As per UNESCO (United Nations Educational, Scientific and Cultural Organisation), AI can perform assessment and prediction of climate change, which may assist in the protection of the environment. The Council identifies three priority areas relating to use of AI which includes improved understanding and predictions of climate change and geohazards [2]. This chapter aims at exploring the contribution of AI in assessing the behavioral pattern of climate change and the ethico-legal challenges involved therein. 2024 Sachi Nandan Mohanty, Preethi Nanjundan and Tejaswini Kar. -
Return volatility transmission among Asian stock exchanges: Evidence from a heterogeneous market outlook
. This pragmatic research strives to reveal the return volatility transmission throughout Asian stock exchanges, by employing variance decomposition technique of Vector autoregressive (VAR) based framework. Additionally, the current examination exerts a Granger causality approach to detect short-term cause and effect among the stock exchanges. The consequence of volatility spill-over exhibits the dominancy of Indian, Chinese and Japanese exchanges in terms of net volatility transmitter. Further, it is found that Korean, Thai, and Malaysian stock exchanges seem to be net receiver of volatility in Asia. Additionally, the outcome of current investigation reveals neutrality of Bangladeshi and Pakistani stock exchange, as the returns volatility of these stock exchange are not influenced by any other Asian stock exchanges. Furthermore, the result of Granger causality analysis signifies the existence of unidirectional causality among the Asian stock exchanges. In terms of policy implication, it is imperative for investors and policymakers to closely monitor the behaviour of the Japanese stock exchange, as it plays a significant role as a net transmitter of volatility to other stock exchanges in Asia. By keeping a vigilant eye on the Japanese stock exchange, investors can better assess and manage potential risks and opportunities in the region. 2023 IOS Press. All rights reserved. -
Phytochemical analysis and antioxidant activities of Artemisia stelleriana Besser leaf extracts
The present study aims to report the proximate and mineral composition, phenolic contents, and antioxidant potential of Artemisia stelleriana leaves. The leaf extracts were prepared using various solvents like distilled water, methanol, ethanol and acetone and analyzed for their phenolic and flavo-noid contents and antioxidant activity. The methanolic extracts showed the highest total phenolic and flavonoid contents (10.09 0.24 mg GAE/g and 225.04 0.38 mg QE/g respectively). The methanolic extracts showed signifi-cantly higher 1,1-Diphenyl-2-picrylhydrazyl radical scavenging assay (DPPH-RSA), Reducing power assay and total antioxidant capacity compared to distilled water, ethanol and acetone extracts. Gas Chromatography-Mass Spectroscopy revealed that the methanolic extracts of leaves to be a good source of bioactive compounds like 2,4-di-tert-butylphenol (2,4-DTBP), neo-phytadiene, octacosane and eucalyptol. 2022 Horizon e-Publishing Group. All rights reserved. -
Zirconia Supported on Rice Husk Silica from Biowaste: A Novel, Efficient, and Recoverable Nanocatalyst for the Green Synthesis of Tetrahydro-1-benzopyrans
Abstract: Zirconia supported silica from rice husk (an agricultural waste) has been utilized as a novel and efficient heterogeneous catalyst for the synthesis of bioactive tetrahydro-1-benzopyran derivatives via multicomponent condensation of various aldehydes with dimedone and malononitrile. This protocol offers various advantages such as high yields, simple experimental work-up procedure, short reaction time, no by-products, economic availability, easy purification, and reusability of the catalyst. 2020, Pleiades Publishing, Ltd. -
Masked Face Recognition and Liveness Detection Using Deep Learning Technique
Face recognition has been the most successful image processing application in recent times. Most work involving image analysis uses face recognition to automate attendance management systems. Face recognition is an identification process to verify and authenticate the person using their facial features. In this study, an intelligent attendance management system is built to automate the process of attendance. Here, while entering, a persons image will get captured. The model will detect the face; then the liveness model will verify whether there is any spoofing attack, then the masked detection model will check whether the person has worn the mask or not. In the end, face recognition will extract the facial features. If the persons features match the database, their attendance will be marked. In the face of the COVID-19 pandemic, wearing a face mask is mandatory for safety measures. The current face recognition system is not able to extract the features properly. The Multi-task Cascaded Convolutional Networks (MTCNN) model detects the face in the proposed method. Then a classification model based on the architecture of MobileNet V2 is used for liveness and mask detection. Then the FaceNet model is used for extracting the facial features. In this study, two different models for the recognition have been built, one for people with masks another one for people without masks. 2022, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. -
Switchable surface activity of Bi2Al4O9 nano particles: A contemporary approach in heterocyclic synthesis
Ferroelectric catalysis is emerging as an efficient chemical transformation strategy, especially in the field of clean energy production, wastewater treatment and degradation of pollutants. The core of ferroelectric catalysis is the dynamically switchable electrical polarization on their surface. It enables them to switch their surface activity, more precisely due to binding strength with the substrate. Even though a plethora of reports are available, the introduction of ferroelectric catalytic surfaces for the generation of heterocyclic compounds is a novel aspect. Here, we introduce ferroelectric Bismuthaluminate nanoparticles as catalysts for generating derivatives of azalactone, tetrahydro-benzopyran and pyranopyrazole with improved catalytic efficiency. This can be achieved by switching the direction of polarization of the catalyst which indeed alters the surface electronic states and stimulates the reaction followed by the excellent yield. Here the switchable property is due to the thermally induced polarization of water. Graphical Abstract: [Figure not available: see fulltext.]. 2023, The Author(s), under exclusive licence to Springer Nature B.V. -
[18-C-6H3O+]: an in-situ generated macrocyclic complex and an efficient, novel catalyst for synthesis of pyrano[2,3-c]pyrazole derivatives
Synthesis of small aromatic heterocycles is of greater importance in the organic chemistry due to their vibrant applications in pharmaceuticals, agrochemicals and veterinary products. Pyranopyrazoles are one such class of heterocycles associated with numerous applications. Hence herein we report a multicomponent crown ether catalyzed, ultrasound irradiated methodology to make different functionalized pyranopyrazoles in a single step. This technique involves the in-situ generation of [18-C-6H3O+][OH?] complex, which in turn activates the aromatic aldehyde and aids in the facile nucleophilic addition. 2020, The Author(s). -
A New Facile Ultrasound-Assisted Magnetic Nano-[CoFe2O4]-Catalyzed One-Pot Synthesis of Pyrano[2,3-c]pyrazoles
Pyrano[2,3-c]pyrazole derivatives have been synthesized through a one-pot multicomponent condensation of various aldehydes, dialdehydes, and ketones with malononitrile, ethyl acetoacetate, hydrazine hydrate (or phenylhydrazine) in the presence of magnetic nano-[CoFe2O4] catalyst under ultrasonic irradiation. The catalyst can be recovered using an external magnet and used repeatedly. 2019, Pleiades Publishing, Ltd. -
A hybrid scheme of image compression employing wavelets and 2D-PCA
In this paper, we have presented a method of compressing 2D grey-scale images employing wavelets and two-dimensional principal component analysis (2D-PCA). Principal component analysis (PCA) is an already established technique for image compression which primarily aims at exploiting inter pixel redundancies present in the image, while wavelet is a tool widely used in multi-resolution image processing. In the proposed method the image is subjected to a multi-resolution decomposition using wavelet. Subsequently, 2D-PCA is applied on the set of detail images at each level of resolution. The compressed form of the image is constituted by representative pairs of principal components and projection vectors from each level of resolution along with the approximate image at the coarsest resolution. The proposed method requires relatively few number of principal components (of varied dimension) to produce improved compression ratio with acceptable peak signal to noise ratio (PSNR). The method has been implemented and tested on a set of real 2D grey-scale images and the results have been assessed on both qualitative and quantitative basis by measuring parameters like compression ratio (CR), PSNR, structural similarity index measurement (SSIM) and the overall performance is found to be satisfactory. Copyright 2017 Inderscience Enterprises Ltd. -
Encryption of motion vector based compressed video data
Enormous size of video data for natural scene and objects is a burden, threat for practical applications and thus there is a strong requirement of compression and encryption of video data. The proposed encryption technique considers motion vector components of the compressed video data and conceals them for their protection. Since the motion vectors exhibit redundancies, further reduction of these redundancies are removed through run-length coding prior to the application of encryption operation. For this, the motion vectors are represented in terms of ordered pair (val, run) corresponding to the motion components along the row and column dimensions, where val represents value of the motion vector while run represents the length of repetition of val. However, an adjustment for having maximal run is made by merging the smaller run value. Eventually we encrypted the val components using knapsack algorithm before sending them to the receiver. The method has been formulated, implemented and executed on real video data. The proposed method has also been evaluated on the basis of some performance measures namely PSNR, MSE, SSIM and the results are found to be satisfactory. Springer International Publishing Switzerland 2016. -
Symbiotic cyanobacteria in gymnosperms
Cyanobacteria are a widespread group of phototrophic bacteria that are morphologically diverse and present on almost every environment on earth. Many cyanobacteria are able to fix atmospheric nitrogen and thus are able to form symbiotic association with a wide range of eukaryotic hosts such as plants, fungi, sponges, and protists. Cyanobacteria are able to provide carbon to nonphotosynthetic hosts such as fungi, but their primary role is to supply fixed nitrogen to enable the host to flourish in nitrogen poor environments. In turn, cyanobionts get the benefits of protection from competition, predation, and environmental extremes. Of all the cyanobacterial symbiotic associations, this chapter focuses on understanding the symbiotic association between gymnosperm and cyanobacteria. Species belonging to phylum cycadophyta are associated with nitrogen-fixing cyanobacteria (Nostoc species) through small specialized roots called coralloid roots. The cyanobionts are expected to have a heterotrophic mode of carbon nutrition, due to their location in coralloid roots (complete darkness). 2023 Elsevier Inc. All rights reserved. -
Blockchain Using Wireless Technology
In today's dynamic digital landscape, blockchain technology emerges as a pioneering force with the potential to redefine industries and transform the way we conduct business, share information, and establish trust. This chapter explores the foundational concepts of blockchain technology, its versatile applications, and the profound impacts it can have on various sectors. While blockchain holds immense promise, challenges like scalability, energy consumption, and regulatory frameworks must be addressed. Decentralized apps and smart contracts introduce new vulnerabilities that demand vigilant management. The integration of blockchain with wireless technology expands opportunities and streamlines processes. Wireless connectivity enhances accessibility, reach, and interaction with blockchain applications, benefiting finance, supply chain, and healthcare sectors. Real-time data sharing and reduced infrastructure reliance boost productivity. Environmental concerns, including blockchain's energy consumption and e-waste from wireless devices, need mitigation. In conclusion, the fusion of blockchain and wireless technology offers tremendous potential but demands a delicate balance between technological progress and environmental stewardship. Addressing reliability, security, scalability, and environmental impacts through innovative solutions and ethical practices is vital for a connected and sustainable future. 2024 CRC Press.