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Dual-mode chemosensor for the fluorescence detection of zinc and hypochlorite on a fluorescein backbone and its cell-imaging applications
Fluorescein coupled with 3-(aminomethyl)-4,6-dimethylpyridin-2(1H)-one (FAD) was synthesized for the selective recognition of Zn2+ over other interfering metal ions in acetonitrile/aqueous buffer (1 : 1). Interestingly, there was a significant fluorescence enhancement of FAD in association with Zn2+ at 426 nm by strong chelation-induced fluorescence enhancement (CHEF) without interrupting the cyclic spirolactam ring. A binding stoichiometric ratio of 1 : 2 for the ligand FAD with metal Zn2+ was proven by a Jobs plot. However, the cyclic spirolactam ring was opened by hypochlorite (OCl?) as well as oxidative cleavage of the imine bond, which resulted in the emission enhancement of the wavelength at 520 nm. The binding constant and detection limit of FAD towards Zn2+ were determined to be 1 104 M?1 and 1.79 ?M, respectively, and the detection limit for OCl? was determined as 2.24 ?M. We introduced here a dual-mode chemosensor FAD having both the reactive functionalities for the simultaneous detection of Zn2+ and OCl? by employing a metal coordination (Zn2+) and analytes (OCl?) induced chemodosimetric approach, respectively. Furthermore, for the practical application, we studied the fluorescence imaging inside HeLa cells by using FAD, which demonstrated it can be very useful as a selective and sensitive fluorescent probe for zinc. 2022 The Royal Society of Chemistry. -
Colorimetric and theoretical investigation of coumarin based chemosensor for selective detection of fluoride
Coumarin based Sensor 1 has been designed and synthesized to recognize fluoride ion visually with high selectivity and sensitivity over other anionic analytes through color change from very faint yellow to pink in acetonitrile. The probable binding phenomenon in solution phase has been explained by 1H NMR study of sensor 1 with different concentration of fluoride ions. The binding constant of the sensor 1 with fluoride has been determined as 3.9 104 M?1 and the lower detection limit 6.5 M of the sensor 1 towards fluoride, which has made the sensor 1 as a promising backbone for selective detection of fluoride. For the practical application, test strips based on sensor 1 were fabricated, which could act as a convenient and efficient naked eye F?test kits. The experimentally observed absorption maxima along with its binding nature with fluoride ions also have been supported through theoretical calculations using density functional theory (DFT) calculations. 2022 -
Sustainable Technologies for Recycling Process of Batteries in Electric Vehicles
The effective management of batteries has always been a key concern for people because of the imposing challenges posed by battery waste on the environment. This paper explores strategic perspectives on the sustainable management of batteries incorporating modern techniques and scientific methodologies giving batteries a second-life application. A paradigm shift towards the legitimate use of the batteries by the introduction of round economy for battery materials and simultaneously checking the biological impression of this fundamental innovation area. 2023 IEEE. -
A Hybrid Approach for Predictive Maintenance Monitoring of Aircraft Engines
The realm of aircraft maintenance involves predictive maintenance, which utilizes historical data and machine parts' performance to anticipate the need for maintenance activities. The primary focus of this paper is to delve into the application of predictive maintenance of aircraft gas turbine engines. Our methodology involves assigning a randomly chosen deterioration value and monitoring the change in flow and efficiency over time. By carefully analyzing these factors, we can deduce whether the engines are at fault and whether their condition will deteriorate further. The ultimate objective is to identify potential engine malfunctions early to prevent future accidents. Recent years have witnessed the emergence of multiple machine learning and deep learning algorithms to predict the Remaining Useful Life (RUL) of engines. The precision and accuracy of these algorithms in assessing the performance of aircraft engines are pretty promising. We have incorporated a hybrid model on various time series cycles to enhance their efficacy further. Employing data collected from 21 sensors, we can predict the remaining useful life of the turbine engines with greater precision and accuracy. 2024 IEEE. -
A novel AI model for the extraction and prediction of Alzheimer disease from electronic health record
Dark data is an emerging concept, with its existence, identification, and utilization being key areas of research. This study examines various aspects and impacts of dark data in the healthcare domain and designs a model to extract essential clinical parameters for Alzheimer's from electronic health records (EHR). The novelty of dark data lies in its significant impact across sectors. In healthcare, even the smallest data points are crucial for diagnosis, prediction, and treatment. Thus, identifying and extracting dark data from medical data corpora enhances decision-making. In this research, a natural language processing (NLP) model is employed to extract clinical information related to Alzheimer's disease, and a machine learning algorithm is used for prediction. Named entity recognition (NER) with SpaCy is utilized to extract clinical departments from doctors' descriptions stored in EHRs. This NER model is trained on custom data containing processed EHR text and associated entity annotations. The extracted clinical departments can then be used for future Alzheimer's diagnosis via support vector machine (SVM) algorithms. Results show improved accuracy with the use of extracted dark data, highlighting its importance in predicting Alzheimer's disease. This research also explores the presence of dark data in various domains and proposes a dark data extraction model for the clinical domain using NLP. 2025 Institute of Advanced Engineering and Science. All rights reserved. -
Design of a Decision Making Model for Integrating Dark Data from Hybrid Sectors
The research on Dark data, from its definition to identification and utilization is a widely identified and encountered research problem since 2012 when Gartner defined Dark data as every possible information that an organization collects, process, analyze and store throughout regular business activities, but usually fails to make use of the stored information for other suitable purposes. The presence of dark data and its impact has been experienced by every sector, these data occupy large storage and remain unused. In this paper, we analyze Dark Data and proposed a design model to utilize dark data from multiple sectors and providing a solution to any critical situation a person might be in. For eg: Multiple cash transactions from an organizational bank account in a hospital successively over a period of 2-3 days may indicate a health emergency of any particular employee from that organization. Thus we are considering institutional data, medical data, and banking data in which machine learning algorithms can contribute huge changes in the current system and can help the decision-makers to make better decisions. The paper also proposes a few techniques and methods for the conversion of unstructured dark data to structured one and some extraction techniques for data using NLP and Machine Learning. Grenze Scientific Society, 2022. -
A novel two-tier feature selection model for Alzheimers disease prediction
The interdisciplinary research studies of artificial intelligence in health sector is bringing drastic life saving changes in the healthcare domain. One such aspect is the early disease prediction using machine learning and regression algorithms. The purpose of this research is to improve the prediction accuracy of Alzheimers disease by analysing the correlation of unexplored Alzheimer causing diseases. The work proposes Chi square-lasso ridge linear (Chi-LRL) model, a new two-tier feature ranking model which recognizes the significance of including diabetes, blood pressure and body mass index as potential Alzhiemer predictive parameters. The newly added predictive parameters of Alzheimers disease were statistically verified along with the conventional prediction parameters using chi-square method (Chi) as Tier 1 and an embedded model of lasso, ridge and linear (LRL) Regression for feature ranking as Tier 2. The performance of the proposed Chi-LRL model with selected features were then analysed using machine learning algorithms for performance analysis. The result shows a noticeable performance by selecting eleven significant features and a 4.5% increase in the prediction accuracy of Alzheirmer disease. 2024 Institute of Advanced Engineering and Science. All rights reserved. -
Identifying Social-Cognitive Factors Influencing Aggression in Adolescents: A Cross-Sectional Indian Study
Adolescence is a critical period during which the likelihood of experiencing self-regulation failures like aggressive outbursts is increased. Recent Indian studies on adolescents have reported an increasing incidence of aggressive acts during this time of transition, which is a threat to the adolescent, the victim and society in general. This study focuses on the social-cognitive perspective, implying that aggression is a social behaviour that is largely affected by ones beliefs about the acceptability of aggression and the degree of cognitive and effortful control they have over their emotions. Such beliefs are likely to be influenced by emotion socialisation, wherein parents and peers act as key agents. With this perspective, the current study, through a mediational model, explains the social-cognitive factors predicting aggressive behaviour in adolescents. This is a cross-sectional, descriptive study carried out on a sample of 475 adolescent students from the Delhi-NCR region recruited through purposive sampling. The data were collected through self-report questionnaires from schools and colleges. The model was tested using SPSS AMOS and was found to be a good fit for the data. The findings of this study are crucial from a risk and intervention perspective. It emphasises the need to build socially and emotionally competent students who not only have the skills needed to succeed but also nurture healthy social relationships and maintain positive mental health through adaptive emotion regulation skills. 2024 Department of Psychology, University of Allahabad. -
Migration to bangalore : A study of return migration of IT professionals
Bengaluru, Karnataka, is widely known as India s Silicon Valley and an important centre for Information Technology (IT). It is also one of the fastest growing cities in India. Migration to the city has contributed to its growth and this study has focussed on the phenomenon of return migration with specific reference to the return migration of IT professionals to Bengaluru. The main newlineobjectives of the study were to explore some of the factors underlying return migration in general and return migration to Bengaluru in particular, to examine the reasons underlying the decision to migrate, to examine the process of return migration, to describe transnationalism in the context of this return migration and to observe the experience of being back home This study used mixed methods and adopted concurrent triangulation to analyse and interpret the data in the study of the research problem. The framework adopted by Lee was also used to understand the factors that influence the decision to migrate within a broader theoretical framework to study return migration. The researcher explored the factors that influenced the decision to return which were an outcome of the push factors operating from the place of origin and the pull factors operating in the place of destination, India and Bengaluru in this case, both sets of which relate to the socio-cultural, economic and political realms. The newlineresearcher examined the factors that operated as push factors in the host country as well as those that operated as pull factors upon professionals working in the IT sector in the context of return migration to Bengaluru.According to the study, the pull factors being exercised from the city of newlineBengaluru were more relevant in terms of the forces underlying the decision to move. Bengaluru s reputation as a technopole or a high tech cluster has contributed largely to the way in which the migrants have been drawn to the city. -
Migration in India: Questions of social exclusion /
International Research Journal Of Social Sciences, Vol.4, Issue 4, pp.87-91, ISSN No: 2319-3565. -
Changing representation of women in recent bollywood cinema /
For almost a century, popular Hindi films rarely dealt with women issues and if it all it attempted to do so, the role of a women was restricted to be either that of nurturer or the epitome of sacrifice and forgiveness. Films like Mother India or Bandini glorified woman’s ability to give up her choices and even life for the sake of her family and dear ones. But post LPG (Liberalization, Privatization and Globalization) this scenario has gradually changed. Today, we come across films where a woman not only expresses her choice but she is also unapologetic about herself. Cinema very interesting deals with gender representation in a consensually problematic manner where the provider of the image as well as the receiver of the medium believes that the space is designed to give women her place only in a particular way. -
Chloroform fraction of Chaetomorpha brachygona, a marine green alga from Indian Sundarbans inducing autophagy in cervical cancer cells in vitro
Sundarbans Mangrove Ecosystem (SME) is a rich repository of bioactive natural compounds, with immense nutraceutical and therapeutic potential. Till date, the algal population of SME was not explored fully for their anticancer activities. Our aim is to explore the potential of these algal phytochemicals against the proliferation of cervical cancer cells (in vitro) and identify the mode of cell death induced in them. In the present work, the chloroform fraction of marine green alga, Chaetomorpha brachygona was used on SiHa cell line. The algal phytochemicals were identified by GCMS, LCMS and column chromatography and some of the identified compounds, known for significant anticancer activities, have shown strong Bcl-2 binding capacity, as analyzed through molecular docking study. The extract showed cytostatic and cytotoxic activity on SiHa cells. Absence of fragmented DNA, and presence of increased number of acidic vacuoles in the treated cells indicate nonapoptotic cell death. The mode of cell death was likely to be autophagic, as indicated by the enhanced expression of Beclin 1 and LC3BII (considered as autophagic markers) observed by Western blotting. The study indicates that, C. brachygona can successfully inhibit the proliferation of cervical cancer cells in vitro. 2020, The Author(s). -
Reliability analysis of cement manufacturing technique in computerized clinker processing method
Cement production will face severe resource constraints in the future, as they rely on natural resources. Therefore, the industry focuses on raising natural resource requirements at both the development and operational levels. One of the situations left unattended in cement production is modelling reliability on a clinker production device with a defect in its three main components. Bridging this gap, this paper provides a reliability model on the manufacturing method of clinkers. The manufacturing of clinkers is the first step in the cement production process. The clinker manufacturing process comprises three main components: crusher, roller mill, and rotary kiln. Three reliability models are developed in this paper, with failures in its three important components considering three situations. All three components are operative, the first two components are operative, and only the first component is operative. In this paper, the transition probabilities and mean sojourn times and also MTSF are measured. 2023 Author(s). -
Artificial intelligence its growing role in human resource management
In this competitive era, the impact of artificial intelligence in human resource management is increasing quickly. Collecting the relevant data and then analyzing it accurately can accelerate the efficient working process of the organization. Artificial intelligence has paved its way in the working of various departments like IT, finance, marketing, and HRM. In this research, the researcher focused on the emerging role of artificial intelligence in HR department and how it is acting as a stimulator in enhancing the efficiency and accuracy of various HR functions like recruitment, hiring, work- life balance, performance appraisal, etc. The chapter is based on descriptive research, in which the researcher has collected secondary data from various research papers, articles, blogs, and websites. The chapter focuses on the growing role of artificial intelligence in the human resource function of the organization. 2024, IGI Global. All rights reserved. -
Transforming Financial Sector Through Financial Literacy and Fintech Revolution
The current research focuses on the financial literacy among the women, youth and impact of fin-tech on the socioeconomic well-being of the economy. The researcher has incorporated extensive data mining on financial well-being and assesses the financial literacy along with attitude and behavior for financial knowledge mainly focusing on youth and usage of AI in financial services. It has been observed during research that the benefits of the technology are limited only to the urban areas, but the rural places are not much benefited and therefore more focus is needed for the financial technology to get amalgamated with rural lifestyle for convenience and transparency of financial transactions. This research paper contributes to the awareness of financial literacy and importance of financial reliance for the youth of today through knowledge sharing. The research also highlights the importance of government involvement in financial literacy and creating awareness at a global level for well-being of youth and women. Financial literacy can also improve the rate of entrepreneurs growth and financial decisions. Financial knowledge and fin-tech services can help in better financial decisions and saving culture for self-reliant youth. The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2021. -
Effect of nonlinear thermal radiation on MHD boundary layer flow and melting heat transfer of micro-polar fluid over a stretching surface with fluid particles suspension
A comprehensive numerical study is conducted to investigate effect of nonlinear thermal radiation on MHD boundary layer flow and melting heat transfer of micro-polar fluid over a stretching surface with fluid particles suspension. Using suitable transformations, the governing equations of the problem are transformed in to a set of coupled nonlinear ordinary differential equations and then they are solved numerically using the Runge-Kutta-Fehlberg method with the help of shooting technique. Authentication of the current method is proved by having compared with established results with limiting solution. The impact of the various stimulating parameters on the flow and heat transfer is analyzed and deliberated through plotted graphs in detail. We found that the velocity, angular velocity and temperature fields increase with an increase in the melting process of the stretching sheet. Also it is visualize that the shear stress factor is lower for micropolar fluids as compared to Newtonian fluids, which may be beneficial in flow and heat control of polymeric processing. 2017 Trans Tech Publications, Switzerland. -
MHD nanofluid flow past a rotating disk with thermal radiation in the presence of aluminum and titanium alloy nanoparticles
The effects of thermal and exponential space dependent heat sources (THS and ESHS) on magneto-nanoliquid flow across a rotating disk with uniform stretching rate along radial direction are scrutinized in this communication. H2O based nanoliquids containing aluminium (AA 7075) and titanium (Ti6Al4V) alloy nanoparticles are considered. The AA7075 is made up of 90% Al, 5-6% Zn, 2-3% Mg, 1-2% Cu with additives such as Fe, Mn and Si etc. The flow is driven due to rotating disk with uniform stretching of the disk. Impacts of Joule and viscous heating are also deployed. The multidegree ordinary differential equations are formed via Von Karman transformations. The obtained non-linear BVP is solved by Runge-Kutta-Fehlberg based shooting approach (RKFS). Graphical illustrations depict the impacts of influential parameters on flow fields. The skin friction and Nusselt number are also calculated. Results pointed out that the thermal boundary layer growth stabilizes due to the influence of ESHS aspect. Velocities of AA7075 H2O nanofluid are superior than that of Ti6 Al4V H2O nanoliquid. Furthermore, the thermal performance of base liquid is outstanding when we added titanium alloy nanoparticles in comparison with aluminium alloy nanoparticles. 2018 Trans Tech Publications, Switzerland. -
Significance of buoyancy, velocity index and thickness of an upper horizontal surface of a paraboloid of revolution: The case of non-Newtonian carreau fluid
The problem of fluid flow on air-jet weaving machine (i.e. mechanical engineering and chemical engineering) is deliberated upon in this report using the case of non-Newtonian Carreau fluid flow. In this report, the boundary layer flow of the fluid over an upper horizontal surface of a paraboloid of revolution is presented. The dimensional governing equations were nondimensionalized, parameterized, solved numerically and discussed. Maximum horizontal velocity is ascertained at smaller values of thickness parameter, a larger value of buoyancy related parameter and the flow is characterized as shear-thickening. Local skin friction coefficient is an increasing and a decreasing property of Deborah number for Shear thinning and Shear-thickening cases of the flow respectively. The velocity of the flow parallel to the surface (uhspr) is a decreasing property of thickness parameter and increasing function of velocity index parameter. 2018 Trans Tech Publications, Switzerland. -
IDS for Internet of things (IoT) and Industrial IoT Network
The Internet of Things (IoT) is a swiftly increasing domain of interconnected gadgets, technologies, and structures that may be achieved in a small, tightly associated environment or can travel across big geographic areas, including Smart Cities. IoT devices are increasingly deployed for numerous goals inclusive of records sensing, accumulating, and controlling. The IoT enhances user affairs by permitting a huge variety of smart gadgets to link and possible information. IoT gadgets are hastily evolving universally while IoT offerings have become pervasive. IoT devices include a big assortment of devices, along with small, embedded sensors, AI assistants, digital cameras, and so on, which can be found in various backgrounds, i.e., Smart Homes, Smart Communities, and Smart Cities. Smart Cities have developed into intriguing areas with technologies consisting of traffic-conscious streetlights which dynamically react to emergencies by editing site visitors styles. Moreover, with the adoption of 5G networks, technologies and techniques throughout towns have become blended. This persevered improvement of IoT advocated the expansion of sophisticated and complicated systems which appreciably adjust the community. However, these technologies have guided to a brand new threat to the security of grids. Many present-day malware assaults, targeted at classic computer systems linked to the Internet, will also be required for IoT gadgets. With those enhancements, malicious actors have found new methods to control their weaknesses. One of the biggest cyber-attacks in instances of terabits in step with 2d operated, infected IoT gadgets harmonized within a botnet provides a massive DDoS assault which disrupts the Internet range for large geographic regions. This attack underlines the increasing hazard posed via uncertain IoT devices. Moreover, attacks that include those are evolving as greater threats as a larger quantity of exposed gadgets is introduced to networks throughout the globe. Their actions are anomalous and higher are the numbers of hazards and assaults toward IoT devices. Cyber-attacks arent new to the IoT, however as the IoT may be deeply interwoven in our lives and societies, traditional protection resolutions are inadequate when managing these dangers. Oftentimes, safety answers are created to run locally on host appliances, i.e., antivirus software, or as standalone machines (i.e. community firewalls and intrusion detection structures (IDSs). However, the IoT has obtained a clean set of community protocols, together with Zigbee, Ant+, and 6LoWPAN, that traditional safety solutions, such as rule-primarily based firewalls and host-based total antivirus software programs, had been not equipped with or have no longer been revised to account for. Moreover, many IoT gadgets suffer from computational, storehouse, or network situations. Due to those constraints, IoT safety answers, especially an IoT IDS, must be lightweight enough, in phrases of the computational, garage, and networking resources, to be living on the devices but sturdy enough to accurately hit upon potential intrusions. Therefore, a holistic method needs to be regular while coming to IoT intrusion detection. IoT devices cant be considered in a vacuum as self-contained machines due to the fact a totally fledged, modern protection answer is just too aid-annoying for constructing on those gadgets. The normal safety of the network necessitates IoT gadgets to be included as associates within a security answer rather than as man or woman nodes. Therefore, green protection of IoT devices could keep millions of net customers away from malicious moves. However, present malware detection techniques are afflicted by excessive computational complexity. Hence, theres a real necessity to protect the IoT, which has therefore resulted in a requirement to completely recognize the threats and assaults in an IoT infrastructure. 2024 selection and editorial matter, Mayank Swarnkar and Shyam Singh Rajput; individual chapters, the contributors. -
Fractional ReactionDiffusion Model: An Efficient Computational Technique for Nonlinear Time-Fractional Schnakenberg Model
In this article, the q-homotopy analysis transform method (q-HATM) is committed to finding the solutions and analyzing the gathered results for the nonlinear fractional-order reactiondiffusion systems such as the fractional Schnakenberg model. These models are well known for the modelling of morphogen in developmental biology. The efficiency and reliability of the q-HATM, which is the proper mixture of Laplace transform and q-HAM, always keep it in a better position in comparison with many other analytical techniques. By choosing a precise value for the auxiliary parameter ?, one can modify the region of convergence of the series solution. In the current framework, the investigation of the Schnakenberg models is implemented with exciting results. The acquired results guarantee that the considered method is very satisfying and scrutinizes the complex nonlinear issues that arise in the arena of science and technology. 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.