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Fluorescent detection of Pb2+ pollutant in water samples with the help of Delonix regia leaf-derived CQDs /
Synthetic Metals, Vol.291, ISSN No: 0379-6779.
Heavy metals released from different sources into water bodies are a major concern in the view of environmental protection. Their non-biodegradability and the numerous health hazards add to the issue. Scientists worldwide have emphasized the issue and are trying to resolve it by different means. Among all the methods, the fluorescent method stands out for its simplicity and rapid results. Here, the study focuses on the development of an efficient and sustainable method for the detection of lead in waste-water effluents. Carbon quantum dots (GCDs), a highly non-toxic substance developed from <em>Delonix regia</em> leaves for the purpose via a simple hydrothermal method. -
Fluorescent imidazole derived sensor for selective in vitro and in vivo Fe2+ detection and bioimaging in zebrafish with DFT studies
Herein, we have developed imidazole derivatized fluorescent probes IM-1 and IM-2 for extremely selective detection of Fe2+ with rapid response (LOD: 3.245 ?M for IM-1 and 0.297 ?M for IM-2) and excellent binding constants (0.214 105 M?1 and 1.004 105 M?1). Aqueous ethanol system was employed to assess the sensing potency of the probes both in vitro and in vivo in zebrafish is the main highlight of this work. The synthesized fluorophores possess admirable quantum yields of 0.61 and 0.78. The 1:1 binding mechanism of ligands with Fe2+ ions is supported by Job's plot and ESI-Mass spectrum. The synthesized probes demonstrated limited cytotoxicity both in vitro (MDA-MB-231 cells) and in vivo (zebrafish, Danio Rerio) studies. These results prompted us to employ the probes IM-1 and IM-2 to trace out intra cellular Fe2+ ions in zebrafish embryos. 2024 Elsevier B.V. -
Fluorescent PVDF dots: from synthesis to biocidal activity
Infection by microorganisms is a serious concern in food storage, water purification, drugs, and particularly in biomedical devices. Long-term use of permanent implants often leads to its contamination due to pathogens. Timely tracking of bacterial activity and its interaction with antibodies are crucial for overcoming these infections. In this work, fluorescent polymeric biocides are obtained from a non-conjugated polymer polyvinylidene fluoride (PVDF), which is neither emissive nor known for its antibacterial activity. PVDF dot was synthesized via hydrothermal treatment eliminating the need for complicated and toxic preparation strategies. PVDF-based dot exhibits high fluorescence aroused from the carbogenic core due to the carbonization of the hydrocarbon chain. It is found that the dots were semiconducting contrary to the bulk form of PVDF. The photoluminescent polymer dots also exhibited an excellent antibacterial activity toward Escherichia coli (E.coli) and Streptococcus bacteria. This luminescence and biocidal activity of PVDF-derived dots have attractive applications in the field of fluorescent diagnostics and therapeutics. Graphical abstract: [Figure not available: see fulltext.] 2022, The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature. -
FO-DPSO Algorithm for Segmentation and Detection of Diabetic Mellitus for Ulcers
In recent days, the major concern for diabetic patients is foot ulcers. According to the survey, among 15 people among 100 are suffering from this foot ulcer. The wound or ulcer found which is found in diabetic patients consumes more time to heal, also required more conscious treatment. Foot ulcers may lead to deleterious danger condition and also may be the cause for loss of limb. By understanding this grim condition, this paper proposes Fractional-Order Darwinian Particle Swarm Optimization (FO-DPSO) technique for analyzing foot ulcer 2D color images. This paper deals with standard image processing, i.e. efficient segmentation using FO-DPSO algorithm and extracting textural features using Gray Level Co-occurrence Matrix (GLCM) technique. The whole effort projected results as accuracy of 91.2%, sensitivity of 100% and specificity as 96.7% for Nae Bayes classifier and accuracy of 91.2%, sensitivity of 100% and sensitivity of 79.6% for Hoeffding tree classifier. 2023 World Scientific Publishing Company. -
Foetal brain extraction using mathematically modelled local foetal minima
This paper proposes segmentation techniques to separate brain parcel from the MRI of the human embryo and also determines the abnormality of the foetal brain at various gestational weeks. These strategies mean to characterise areas of the premium of various granularities: brain, tissue types, or constructions that are more limited. Various philosophies have been applied for this division task and can be grouped into the solo, parametric, characterisation, atlas combination, and deformable models. Brain atlases are usually used as preparing information in the division interaction. Difficulties identifying using pictures secured, the quick mental health, and the restricted accessibility of imaging information thwart this division task. This paper discusses foetal brain segmentation using mathematically modelled foetal brain minima by using a curve fitting segmentation technique. Broad tests show that the proposed approach beats the ebb and flow of various segmentation techniques and the results gained are significant. Copyright 2023 Inderscience Enterprises Ltd. -
Folic Acid-Modified B-Type Y2O3:Eu3+ Quantum Dots: A Bright Approach to Fluorescence Imaging of Cancer Cells
Clinical applications of nanophosphors have gained extensive interest in research areas such as bioimaging and targeted drug delivery. The development of nontoxic semiconductor quantum dots (QDs), which can replace the conventional fluorescent probes, can bring significant developments in the bioimaging industry. This work reports the synthesis of monoclinic Y2O3:Eu QDs, without and with surface functionalization using PEG/folic acid at low temperature and its application in live cancer cell imaging. The synthesized quantum dots show sharp absorption in the short UV region and an intense red emission at 614 nm. Concentration-dependent optical properties are studied in detail, and color purity is measured. Transmission electron microscopy substantiates the monoclinic structure, crystalline nature, and the lower particle dimensions essential for the biological applications. The surface-modified sample is characterized for its structural and luminescence properties. Biocompatibility was ensured by performing MTT Assay on L6 skeletal muscle cell lines (normal) and MCF 7 cell lines (cancer) for the samples without and with surface modification, respectively. Fluorescence detection experiments on SKMEL cells using an uncapped sample prove the suitability of the material as a fluorescent probe. The effect of surface functionalization on imaging results was established by carrying out fluorescence detection experiments on MCF 7 cells using PEG-folic acid-functionalized sample, which resulted in enhanced cell uptake, specific binding, and bright fluorescence emission. Thus, this work authenticates the suitability of the material to be used as a reliable nanophosphor and an efficient fluorescent probe for imaging cancer cells. 2024 American Chemical Society. -
Folksonomy-based fuzzy user profiling for improved recommendations
Genre is a major factor influencing user decisions to peruse an item in domains such as movies, books etc. Recommender systems, generally have, at their disposal, information regarding genres/categories that a movie/book belongs to. However, the degree of membership of the objects in these categories is typically unavailable. Such information, if available, would provide a better description of items and consequently lead to quality recommendations. In this paper, we propose an approach to infer the degree of genre presence in a movie by examining the various tags conferred on them by various users. Tags are user-defined metadata for items and embed abundant information about various facets of user likes, their opinion on the quality and the type of object tagged. Leveraging on tags to guide the genre degree determination exploits crowd sourcing to enrich item content description. Fuzzy logic naturally models human logic allowing for the nuanced representation of features of objects and thus is utilized to derive such gradual representation as well as for modeling user profiles. To the best of our knowledge ours is one of the first approaches to utilize such folksonomy information to infer genre degrees subsequently used for recommendations. The proposed method has the twin advantages of utilizing enriched content information for recommendation as well as squeezing the information from the user-item-tag and user-item ratings spaces and condensing them into fuzzy user profiles. The fuzzy user and object representations are leveraged both for the design of content-based as well as collaborative recommender systems. Experimental evaluations establish the effectiveness of the proposed approaches as compared to other baselines. 2013 Elsevier Ltd. All rights reserved. -
Food and communities in post-COVID-19 cities: Case of India
While Covid-19 pandemic has affected countries across the world, the burden has been shared disproportionately by urban poor from the cities in Global South. In much of Global South, while cities have emerged as growth centers, they are mostly driven by informalities, belying the image of cities, visualized in the mainstream development economics literature as a place of secured formal jobs that free one from the drudgery of rural life. Covid-19 pandemic has exposed these fault-lines in the cities. India serves as a typical case of such urban-centric growth, with informal workers, predominated by disadvantaged social and religious categories, accounting for 81% of workers in urban space. In cities, migrant in general and seasonal migrants increasingly account for bulk of informal workforce. The lockdown imposed in the wake of Covid-19 pandemic left the community of households reliant on informal works for livelihoods, without any rights and entitlements, which affect their access to food. The review of evidence collected in both primary surveys and macro level data points towards sluggishness in recovery of jobs, which coupled with high food inflation, suggests that access to food continues to be an issue in urban governance. The paper calls for a roadmap entailing both short-term and long-term measures to build sustainable urban livelihoods for ensuring food secure urban space in India. 2023 The Author(s) -
Food innovation adoption and organic food consumerism-a cross national study between Malaysia and Hungary
In order to meet the rising global demand for food and to ensure food security in line with the United Nations Sustainable Development Goal 2, technological advances have been introduced in the food production industry. The organic food industry has benefitted from advances in food technology and innovation. However, there remains skepticism regarding organic foods on the part of consumers, specifically on consumers acceptance of food innovation technologies used in the production of organic foods. This study measured factors that influence consumers food innovation adoption and subsequently their intention to purchase organic foods. We compared the organic foods purchase behavior of Malaysian and Hungarian consumers to examine differences between Asian and European consumers. The findings show food innovation adoption as the most crucial predictor for the intention to purchase organic foods in Hungary, while social lifestyle factor was the most influential in Malaysia. Other factors such as environmental concerns and health consciousness were also examined in relation to food innovation adoption and organic food consumerism. This paper discusses differences between European and Asian organic foods consumers and provides recommendations for stakeholders. 2021 by the authors. Licensee MDPI, Basel, Switzerland. -
Food quality traceability prototype for restaurants using blockchain and food quality data index
As competition between organizations are evolving into competition between supply chains, to survive and indeed grow, it is necessary to deliver added value to customers. Traceability has emerged as one of the key measures of operational efficiencies within supply chains and ultimately, customer service. Over the years, organizations have deployed number of methods in delivering food traceability. This paper examines major methods of food traceability currently in existence and proposes a restaurant prototype for implementing more reliable food traceability using Blockchain and product identifiers. The prototype captures data from various stakeholders across the food supply chain, segregates it and finally, applies the Food Quality Index (FQI) algorithm to generate an FQI value. The FQI value helps in identifying whether the food is good for consumption on specified parameters. FQI value is generated based on extant standard storage and handling regulations specified by food safety authorities, and checks whether value so derived, is within the permissible range. The prototype helps in grading food quality for human consumption besides strengthening food (product) traceability. This prototype can be customized to address future requirements of traceability triggered through new information emanating from any stakeholder or the node in the supply chain. 2019 Elsevier Ltd -
Forbidden Cravings: Exploring socio-cultural ramifications of food practices in Aamis
Food choices represent conscious affirmation and expression of personal, group, ethnic or national identity. Due to its multidimensional role, food that we rely on sustenance is often politicised and used as a tool to create conflict amongst and within diverse social groups. Assamese cuisine includes a rich platter of authentic food varieties, often limited to the north-eastern region. Although food consumption is a subjective experience, cultural taboos within a community might be acceptable practices in another culture, creating conflicting notions of food practices. The balance between the twin axis of culture and politics regarding food is disrupted when heterogeneous cultural patterns and opposing political notions are in discord. Similarly, the solidarity within a cultural group becomes hostile when the authority of the individual concerning food choices is not aligned with the authority of the social structure. This discord from a political and cultural standpoint is evident in the Assamese socio-cultural scenario. Taking Bhaskar Hazarika's Ravening/Aamis (2019) as a case study, this paper proposes to analyse the representational troupe of food, through a structuralist anthropological lens, with respect to food politics to understand socio-cultural ramifications of Assamese food patterns. 2022 Aesthetics Media Services. All rights reserved. -
Forced Labour, Global Supply Chain and TNCs: Recent Trends and Practices
The abolition of forced labour is a fundamental element of contemporary international human rights law, but the idea has undergone a protracted and complex history, and the scope of the various international mechanisms that handle different aspects of it is not always precisely defined. Slavery, forced labour, and related practices are strictly prohibited under international law. Forced labour is a longstanding and complex obstacle in global supply chains, frequently associated with the desire for inexpensive products and the outsourcing of manufacturing processes to nations with lax labour regulations. The growing power of transnational corporations (TNCs) poses significant challenges to workers at the bottom of supply chains. However, disagreements have made it unclear how to deal with new forms of forced labour, or modern forms of slavery. This confusion highlights the need for a comprehensive approach to combating these issues. Efforts to stop or restrict forced labour will be made easier with a clear legal definition at both the national and international levels, particularly with an emphasis on the human rights perspective. 2024 Kluwer Law International BV, The Netherlands -
Forecasting gold prices based on extreme learning machine
In recent years, the investors pay major attention to invest in gold market because of huge profits in the future. Gold is the only commodity which maintains its value even in the economic and financial crisis. Also, the gold prices are closely related with other commodities. The future gold price prediction becomes the warning system for the investors due to unforeseen risk in the market. Hence, an accurate gold price forecasting is required to foresee the business trends. This paper concentrates on forecasting the future gold prices from four commodities like historical data's of gold prices, silver prices, Crude oil prices, Standard and Poor's 500 stock index (S & P500) index and foreign exchange rate. The period used for the study is from 1st January 2000 to 31st April 2014. In this paper, a learning algorithm for single hidden layered Feed forward neural networks called Extreme Learning Machine (ELM) is used which has good learning ability. Also, this study compares the five models namely Feed forward networks without feedback, Feed forward back propagation networks, Radial basis function, ELMAN networks and ELM learning model. The results prove that the ELM learning performs better than the other methods. 2006-2016 by CCC Publications. -
Forecasting gold prices based on extreme learning machine /
International Journal Of Computers Communications & Control, Vol.11, Issue 3, pp.372-380, ISSN: 1841-9836. -
Forecasting intraday stock price using ANFIS and bio-inspired algorithms
The main focus of this study is to explore the predictability of stock price with variants of adaptive neuro-fuzzy inference system (ANFIS) and suggests a hybrid model to enhance the prediction accuracy. Two variants of ANFIS model are designed which includes genetic algorithm-ANFIS (GA-ANFIS) and particle swarm optimisation-ANFIS (PSO-ANFIS) to forecast stock price more accurately. The standard ANFIS is tuned employing GA and PSO algorithm. The experimental data used in this investigation are stocks traded per minute price of four companies from NSE. Sixteen technical indicators are calculated from the historical prices and used as inputs to the developed models. Prediction ability of the developed models is analysed by varying number of input samples. Numerical results obtained from the simulation confirmed that the PSO-ANFIS model has the potential to predict the future stock price more precisely than GA-ANFIS as well as other earlier methods. Copyright 2021 Inderscience Enterprises Ltd. -
Forecasting NIFTY 50 in Volatile Markets Using RNNLSTM: A Study on the Performance of Neural Network Models During the COVID-19 Pandemic
The COVID-19 pandemic has shown us how the market can be highly uncertain and volatile at certain times. This brings a new level of challenges to all the investors and active traders in the market, as they have not seen such a movement in the past. However, as technology is evolving, highly sophisticated tools and techniques are being used by hedge funds and other investment banks to track down these movements and turn this into an opportunity. In this paper, we try to analyse how recurrent neural network (RNN) with long- and short-term memory architecture performs under volatile market conditions. For this study, we tried to perform a comparative analysis between two models within two successive time periods, where one is trained in a volatile market condition and the other in a relatively low volatile market condition. The results showed that the RNN model is less accurate in predicting the prices in a volatile market compared to a relatively low volatile market. We also compared these two models to a separate model where we trained using the combined data from the two successive time periods. Even though the addition in data points for the neural network produced a better result compared to the model trained under volatile conditions, it did not significantly perform better than the model, which was trained in the low volatile period. 2022 Management Development Institute. -
Forecasting of foreign currency exchange rate using neural network
Foreign exchange market is the largest and the most important one in the world. Foreign exchange transaction is the simultaneous selling of one currency and buying of another currency. It is essential for currency trading in the international market. In this paper, we have investigated Artificial Neural Networks based prediction modelling of foreign exchange rates using five different training algorithms. The model was trained using historical data to predict four foreign currency exchange rates against Indian Rupee. The forecasting performance of the proposed system is evaluated by using statistical metric and compared. From the results, it is confirmed that the new approach provided an improve technique to forecast foreign exchange rate. It is also an effective tool and significantly close prediction can be made using simple structure. Among the five models, Levenberg-Marquardt based model outperforms than other models and attains comparable results. It also demonstrates the power of the proposed approach and produces more accurate prediction. In conclusion, the proposed scheme can improve the forecasting performance significantly when measured on three commonly used metrics. -
Forecasting stock market volatility in India - Using linear and non - Linear models
Volatility models and their forecasting performance attracted the interest of many economic agents, especially for financial risk management. The role of economic agents is to decide which one will be best model for forecasting volatility. This paper examines the modeling and forecasting performance of BSE Sensex daily stock market returns over the period from 1 July 1997 to 31 October 2008, by using simple Random Walk, GARCH, EGARCH and TGARCH models. The out-of-sample forecasts are evaluated by using MAE, RMSE, MAPE and Theil - U Statistics. The result suggests the standardized residual of white noise series strongly rejects the null hypothesis for GARCH model and capture the serial dependence and inherent nonlinearity series. Moreover, Random walk model dominates the forecasting performance and it is considered as the best model followed by the TGARCH model. International Economic Society. -
Forecasting the Volatility of Indian Forex Market: An Evidence from GARCH Model
Forecasting the volatility of forex market will create more trading opportunities to investors, despite of ups and downs in the forex market. The present study attempted to examine how the volatility in the exchange rate between Indian rupee and selected four foreign currencies, such as US dollar, euro, Japanese yen and British pound, can influence the market return. The data, used in the present study, covered the daily price observation of four foreign currencies, for a period of 5 years, from 2019-2023. The GARCH (1, 1) (generalized autoregressive conditional hetero skedasticity) was used for develop the model for foreign exchange (FX) rates volatility. Mean equation model confirmed that the series had attained stationary and previous price did influence the current price. It was also supported by co-efficient values in the variance equation. The co-efficient value, in the variance equation, was around one, which showed that the forex market was efficient. Further, it was validated that the volatility shocks in forex market were quite persistent. The active investors in the market may use this opportunity immediately. The policy maker may correct this deviation through timely intervention in the currency market. 2024, Iquz Galaxy Publisher. All rights reserved. -
Foreground algorithms for detection and extraction of an object in multimedia
Background Subtraction of a foreground object in multimedia is one of the major preprocessing steps involved in many vision-based applications. The main logic for detecting moving objects from the video is difference of the current frame and a reference frame which is called "background image" and this method is known as frame differencing method. Background Subtraction is widely used for real-time motion gesture recognition to be used in gesture enabled items like vehicles or automated gadgets. It is also used in content-based video coding, traffic monitoring, object tracking, digital forensics and human-computer interaction. Now-a-days due to advent in technology it is noticed that most of the conferences, meetings and interviews are done on video calls. It's quite obvious that a conference room like atmosphere is not always readily available at any point of time. To eradicate this issue, an efficient algorithm for foreground extraction in a multimedia on video calls is very much needed. This paper is not to just build Background Subtraction application for Mobile Platform but to optimize the existing OpenCV algorithm to work on limited resources on mobile platform without reducing the performance. In this paper, comparison of various foreground detection, extraction and feature detection algorithms are done on mobile platform using OpenCV. The set of experiments were conducted to appraise the efficiency of each algorithm over the other. The overall performances of these algorithms were compared on the basis of execution time, resolution and resources required. 2020 Institute of Advanced Engineering and Science.