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Strength and durability properties of geopolymer paver blocks made with fly ash and brick kiln rice husk ash
In India the generation of agro waste rice husk ash is abundant. The utilization of rice husk ash in development of geopolymer binders can be suitable to alleviate the environmental problems associated with disposal of rice husk ash. Further, the utilization of rice husk ash generated from the stacks of brick kilns has not been addressed in past, particularly in development of geopolymer binders. This study proposes development of geopolymer paver (GEOPAV) blocks utilizing brick kiln rice husk ash (BKRHA). It presents fresh, mechanical and durability properties of GEOPAV blocks blended with fly ash, BKRHA, natural aggregates, NaOH and Na2SiO3 solution, and cured in both sundry and room temperature conditions. Microstructural analysis using scanning electron microscope (SEM) and X-ray diffraction (XRD) was adopted to study the influence of BKRHA on hardened properties of GEOPAV blocks. The results show that addition of BKRHA reduce the workability of GEOPAV mixes due to micro porous surface with honeycombed structure of BKRHA particles. The addition of BKRHA showed negligible improvement in compressive strength of GEOPAV blocks. However, the major advantage was observed with improved split tensile strength and flexural strength for GEOPAV blocks with BKRHA. Further, the durability properties in terms of resistance to acid and frost attack was significantly improved with the addition of BKRHA in GEOPAV blocks. Such improvements can be attributed to high amounts of amorphous silica in BKRHA which contribute towards dissolution and formation of polymeric gel, and thereby serve as a binder to enhance the geopolymer matrix making it dense. Finally, all the developed GEOPAV blocks satisfy the IS 156582021 specification requirements and perform much better when compared to commercially available paver blocks. 2021 The Authors -
Performance analysis of different classifier for remote sensing application
The classification of remotely sensed data on thematic map is a challenging task from very long time and it is also a goal of todays remote sensing because of complexity level of earth surface and selection of suitable classification technique. Hence selection of best classification technique in remote sensing will give better result. Classification of remotely sensed data is an important task within the domain of remote sensing and it is outlined as processing technique that uses a systematic approach to group the pixels into different classes. In this study, we have classified the multispectral data of Udupi district, Karnataka, India using different classifier including Support Vector Machine (SVM), Maximum Likelihood, Minimum Distance and Mahalanobis Distance classifier. The data of dimension 3980x3201 pixels are collected from a Landsat-3 satellite. Performance of the each classifier is compared by conducting accuracy assessment test and Kappa analysis. The obtained results shows that SVM will give accuracy of 95.35% and kappa value of 0.9408 respectively when compared other classifier, hence effectiveness of SVM is a good choice for classifying remotely sensed data. BEIESP. -
Optimized uplink scheduling model through novel feedback architecture for wimax network
Broadband Wireless Access has drawn the fine attention due to the wide range of data requirement and user mobility all the time. Moreover, WiMAX provides the best QoE (Quality of Experience) which is based on the IEEE 802.16 standards; this includes several services such as data, video and audio. However, in order to provide the effective and smooth experience i.e. QoS scheduling plays one of the critical part. In past several mechanism has been proposed for effective scheduling however, through the research it is observed that it can be furthermore improvised hence in this we propose a mechanism named as OUS (Optimized Uplink Scheduling) which helps in improvising the QoS. In here, we have proposed a novel feedback architecture and proposed optimized scheduling which helps in computing the bandwidth request this in terms helps in reducing the delay as well as jitter. Moreover, the performance evaluation is performed through extensive simulation by varying the different SS and frequency and the results analysis confirms that our mechanism performs way better than the existing algorithm. BEIESP. -
Adaptive uplink scheduling model for WiMAX network using evolutionary computing model
The increased usage of smart phones has led to increase usage an internet based application services. These application requires different quality of service (QoS) and bandwidth requirement. WiMAX is an efficient network to provision high bandwidth connectivity and coverage to end user. To meet QoS requirement the exiting model used adaptive model selection scheme. However, these model induce bandwidth wastage as it does not considers any feedback information for scheduling. This work present an Adaptive Uplink Scheduling (AUS) by optimizing MAC layer using Multi-Objective Genetic Algorithm (MOGA). The MAC scheduler use feedback information from both physical layer and application layer. Further, to meet QoS requirement of application and utilize bandwidth efficiently this paper presented an adaptive modulation selection scheme based on user application requirement using MOGA. Our model provides application level based QoS provisioning for WiMAX network. Experiment are conducted to evaluate performance of AUS over exiting model. The overall result attained shows AUS model attain good performance in term of throughput, successful packet transmission and packet collision. 2019 Institute of Advanced Engineering and Science. All rights reserved. -
A new facile synthesis of (2S,5S)-5-hydroxypipecolic acid hydrochloride
A simple and efficient synthesis of (2S,5S)-5-Hydroxypipecolic acid hydrochloride is reported. The key features of the synthesis involve the asymmetric reduction of ketone using (S)-CBS oxazaborolidine and the use of commercially available methyl pyroglutamate as a starting material.. 2022 Taylor & Francis Group, LLC. -
Stakeholders' pedagogical preferences for teaching 'marketing' in management education
This study has been realized that there is a dire need for re-thinking, particularly obvious for matters of assessment and its relation to the current focus on teaching marketing. A descriptive design of the research was used where convenient sampling has been followed for data collection. In order to achieve the purpose, it was decided to collect independent opinions of students, teachers, and professionals. Analysis has been done through descriptive statistics and Spearman's rank correlation. As a result, a significant difference between the stakeholders' perceptions about the pedagogy for teaching marketing in management education was identified. 2021 Ecological Society of India. All rights reserved. -
PEVRM: Probabilistic Evolution Based Version Recommendation Model for Mobile Applications
Traditional recommendation approaches for the mobile Apps basically depend on the Apps related features. Now a days many users are in quench of Apps recommendation based on the version description. Earlier mobile Apps recommendation system do not handle the cold start problem and also lacks in time for recommending the related and latest version of Apps. To overcome this issues, a hybrid Apps recommendation framework which is considering the version of the mobile Apps is proposed. This novel framework named 'Probabilistic Evolution based Version Recommendation Model (PEVRM)' integrates the principles of Probabilistic Matrix Factorization (PMF) with Version Evolution Progress Model (VEPM). With the help this novel recommendation algorithm, the mobile users easily identify the specific Apps for particular task based on its version progression. At same time, this framework helps in resolving cold start problems of new users. Evaluations of this framework utilize a benchmark dataset, i.e., Apple's iTunes App Store3, for revealing its promising performance. 2013 IEEE. -
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 -
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. -
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 in India: Questions of social exclusion /
International Research Journal Of Social Sciences, Vol.4, Issue 4, pp.87-91, ISSN No: 2319-3565. -
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). -
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. -
Novel approach for nonlinear time-fractional Sharma-Tasso-Olever equation using Elzaki transform
In this article, we demonstrated the study of the time-fractional nonlinear Sharma-Tasso-Olever (STO) equation with different initial conditions. The novel technique, which is the mixture of the q-homotopy analysis method and the new integral transform known as Elzaki transform called, q-homotopy analysis Elzaki transform method (q-HAETM) implemented to find the adequate approximated solution of the considered problems. The wave solutions of the STO equation play a vital role in the nonlinear wave model for coastal and harbor designs. The demonstration of the considered scheme is done by carrying out some examples of time-fractional STO equations with different initial approximations. q-HAETM offers us to modulate the range of convergence of the series solution using ?, called the auxiliary parameter or convergence control parameter. By performing appropriate numerical simulations, the effectiveness and reliability of the considered technique are validated. The implementation of the new integral transform called the Elzaki transform along with the reliable analytical technique called the q-homotopy analysis method to examine the time-fractional nonlinear STO equation displays the novelty of the presented work. The obtained findings show that the proposed method is very gratifying and examines the complex nonlinear challenges that arise in science and innovation. 2023 Balikesir University. All rights reserved. -
A new computational technique for the analytic treatment of time-fractional EmdenFowler equations
This paper presents the study of fractional EmdenFowler (FEF) equations by utilizinga new adequate procedure, specifically the q-homotopy analysis transform method (q-HATM). The EF equation has got greater significance in both physical and mathematical investigation of capillary and nonlinear dispersive gravity waves. The projected technique is tested by considering four illustrations of the time-fractional EF equations. The q-HATM furnish ?, known as an auxiliary parameter, by the support of ? we can modulate the various stages of convergence of the series solution. Additionally, to certify the resolution and accurateness of the proposed method we fitted the suitable numerical simulations. The redeem results guarantee that the proposed process is more convincing and scrutinizes the extremely nonlinear issues emerging in the field of science and engineering. 2021 International Association for Mathematics and Computers in Simulation (IMACS) -
Emotional intelligence, job satisfaction and psychological well-being among nurses in a tertiary care hospital
Background: Emotional intelligence helps in preservation of mental health because of their effective emotional regulation skills. Objectives: We aimed to evaluate the impact of emotional intelligence on nurses job satisfaction and psychological well-being. Methods: This cross-sectional study was conducted in a tertiary hospital and included 120 nurses. Wong and Law Emotional Intelligence Scale, Psychological General Well-being scale and Job Satisfaction Survey questionnaires were used. Results: The study showed a low positive correlation between emotional intelligence and psychological wellbeing (r=0.313) and a low correlation between emotional intelligence and job satisfaction (r= 0.122). The emotional intelligence was significantly correlated to their psychological well-being (9.8%). Conclusion: Nurses with higher emotional intelligence experience greater psychological well-being. We did not find a link between emotional intelligence and job satisfaction. Implementing interventions to enhance emotional intelligence in nurses is crucial for improving psychological well-being and reducing burnout risk. The Author(s). 2024.