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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. -
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. -
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. -
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. -
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. -
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. -
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. -
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. -
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. -
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. -
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. -
Classification and Retrieval of Research Classification and Retrieval of Research Papers: A Semantic Hierarchical Approach
"Classification and Retrieval of Research papers: A Semantic Hierarchical Approach" demonstrates an effective and efficient technique for classification of Research documents pertaining to Computer Science. The explosion in the number of documents and research publications in electronic form and the need to perform a semantic search for their retrieval has been the incentive for this research. The popularity and the widespread use of electronic documents and publications, has necessitated the development of an efficient document archival and retrieval mechanism. Categorizing journal papers by assigning them relevant and meaningful classes, predicting the latent concept or the topic of research, based on the relevant terms and assigning the appropriate Classification labels is the objective of this thesis. This thesis takes a semantic approach and applies the text mining techniques in a hierarchical manner in order to classify the documents. The use of a lexicon containing domain specific terms (DSL) adds a semantic dimension to classification and document retrieval. The Concept Prediction based on Term Relevance (CPTR) technique demonstrates a semantic model for assigning concepts or topics to papers. This Thesis proposes a conceptual framework for organizing and classifying the research papers pertaining to Computer Science. The efficacy of the proposed concepts is demonstrated with the help of Classification experiments. Classification experiments reveal that the DSL technique of training works efficiently when categorization is based on keywords. The CPTR technique, on the other hand, shows very high accuracy; when the classification is based on the contents of the document. Both these techniques lend a semantic dimension to classification. Narrowing down the scope of search at each level of hierarchy enables time efficient retrieval and access of the goal documents. The hierarchical interface for Document Retrieval enables retrieval of the target documents by gradually restricting the scope of search at each level of hierarchy This work comprises of two main components. 1. The Framework for Hierarchical Classification. 2. The Hierarchical Interface for Document Retrieval. Two distinct techniques for classification are proposed in this thesis. These include 1. The use of Domain Specific Lexicon (DSL) which is comparable to a Domain Specific Ontology. 2. The Concept Prediction Based on Term Relevance (CPTR) technique These techniques lend a semantic dimension to classification. Keywords: Text Mining, Classification, Document Retrieval, Hierarchical, Domain specific lexicon (DSL), Probabilistic Latent Semantic Analysis (PLSA) , Concept Prediction based on Term frequency (CPTR) -
Preparation and Application of Nanoparticles and Core-Shell Nanoparticles of Transition Metals
There is an increasing need for the development of environmentally viable, economically effective, highly active and renewable catalytic systems for the various applications in the industrial field. The demand for the decolorisaion of synthetic dyes using bioremediation methods has been in a decreased mode due to its lower decomposition rates. Hence in recent years the decomposition of these organic colorants considered to be a worldwide need. The term nanocatalysis has gained huge importance in recent years due to its selectivity, higher activity, and productivity compared to their bulk materials. The nanosize, shape, and large surface to volume ratio provide unique properties to the nanomaterials. The principles of green chemistry are mainly relies on the development of catalytic systems that work similar to nature. Nanocatalyst combines both homogeneous and heterogeneous catalysis and provides rapid and selective chemical transformations with high yield and easier separation of catalyst at the end of reaction. In this work we have synthesized various metal doped magnetite nanoparticles (Ferrite nanoparticles-NdMxFe3-xO4 where M (Mn, Co, Cu, Ni)) by precipitation and hydrothermal method. The one objective of this work was to check the photocatalytic application of prepared ferrite nanoparticles for the heterogeneous photo-Fenton degradation of MB and Rh B dyes. Each dye was degraded separately under visible light and dark with the assistance of neutral pH and H2O2, in order to shows the improved activity of catalysts under visible light. The degradation experiments using the photo-Fenton systems (Fe2+/H2O2/Visible light) suggested that, the highest degradation rate was 97% for MB and 81% for Rh B within 4h and the used catalyst was NdFe3-xCuxO4, a good photo-Fenton catalyst. . We have also tried the synthesis of core- shell nanoparticles using NdFe3O4 nanoparticles with the help of polyethylene glycol as dispersing agent. The synthesized samples were characterized by various techniques like, XRD, XPS, SEM and TEM. -
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. -
Prognosis of Kidney Disease on Ultrasound Images Using Machine Learning
Kidney diseases can affect the ability to clean the blood, filter extra water out of your blood. The kidneys failure will affect the control over blood pressure and sugar level. It can also affect red blood cell production and vitamin D metabolism which is very important for bone health. When your kidneys are damaged, waste products and fluid can build up in the body. This is harmful to the health. This damages the kidney function, can get worse over time, and when the kidneys stop working completely, this is called kidney failure or end-stage renal disease. Not all patients with kidney disease progress to kidney failure. This disease has emerged as one of the most prominent reasons of death and suffering in this century. Recent studies states that, kidney disease affects most of the population and over two million people require kidney replacement. To help prevent Chronic Kidney Diseases and lower the risk for kidney failure, control risk factors for CKD, get tested yearly, make lifestyle changes, take medicine as needed. The detection of kidney abnormalities at their early stages helps to avoid the impairment of newlinekidney. The US imaging is considered as preliminary diagnostic tool in finding various kidney diseases in the clinical imaging field. This is one of the commonly used imaging modalities due to the inexpensiveness and non-ionization nature. The presence of noise in US images, degrade newlinethe quality and clarity of the images. Also, the heterogeneous structure of kidney, makes it very difficult to detect and measure the size of stones and cysts. Hence, an automatic kidney disease detection system is highly in demand. The proposed model can assist the radiologist in accurate abnormality detection. The proposed model includes different phases such as, pre-processing, features extraction, classification and newlinesegmentation. The pre-processing phase include cropping and noise removal. Further, the GLCM and intensity-based features are extracted for the classification of abnormal kidney images. -
Prognosis of kidney disease on ultrasound images using machine learning
Kidney diseases can affect the ability to clean the blood, filter extra water out of your blood. The kidneys failure will affect the control over blood pressure and sugar level. It can also affect red blood cell production and vitamin D metabolism which is very important for bone health. When your kidneys are damaged, waste products and fluid can build up in the body. This is harmful to the health. This damages the kidney function, can get worse over time, and when the kidneys stop working completely, this is called kidney failure or end-stage renal disease. Not all patients with kidney disease progress to kidney failure. This disease has emerged as one of the most prominent reasons of death and suffering in this century. Recent studies states that, kidney disease affects most of the population and over two million people require kidney replacement. To help prevent Chronic Kidney Diseases and lower the risk for kidney failure, control risk factors for CKD, get tested yearly, make lifestyle changes, take medicine as needed. The detection of kidney abnormalities at their early stages helps to avoid the impairment of newlinekidney. The US imaging is considered as preliminary diagnostic tool in finding various kidney diseases in the clinical imaging field. This is one of the commonly used imaging modalities due to the inexpensiveness and non-ionization nature. The presence of noise in US images, degrade newlinethe quality and clarity of the images. Also, the heterogeneous structure of kidney, makes it very difficult to detect and measure the size of stones and cysts. Hence, an automatic kidney disease detection system is highly in demand. The proposed model can assist the radiologist in accurate abnormality detection. The proposed model includes different phases such as, pre-processing, features extraction, classification and newlinesegmentation. The pre-processing phase include cropping and noise removal. Further, the GLCM and intensity-based features are extracted for the classification of abnormal kidney images.
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Why Employees Need Both Recognition and Appreciation
We often use the words “recognition” and “appreciation” interchangeably, but there’s a big difference between them. The former is about giving positive feedback based on results or performance. The latter, on the other hand, is about acknowledging a person’s inherent value. This distinction matters because recognition and appreciation are given for different reasons. Even when people succeed, inevitably there will be failures and challenges along the way; depending on the project, there may not even be tangible results to point to. If you focus solely on praising positive outcomes, on recognition, you miss out on lots of opportunities to connect with and support your team members — to appreciate them. Managers should make sure they’re doing both. -
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. -
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.