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Photoaligned Liquid Crystalline Structures for Photonic Applications
With the advancement of information display technologies, research on liquid crystals is undergoing a tremendous shift to photonic devices. For example, devices and configurations based on liquid crystal materials are being developed for various applications, such as spectroscopy, imaging, and fiber optics. One of the problems behind the development of photonic devices lies in the preparation of patterned surfaces that can provide high resolution. Among all liquid crystal alignment techniques, photoalignment represents a promising non-contact method for the fabrication of patterned surfaces. In this review, we discuss the original research findings on electro-optic effects, which were mainly achieved at the Department of Electronic and Computer Engineering of the Hong Kong University of Science and Technology and the collaborating research laboratories. 2023 by the authors. -
An ensemble deep learning model for automatic classification of cotton leaves diseases
Cotton plant (Gossypium herbaceum), is one of the significant fiber crop grown worldwide. However, the crop is quite prone to leaves diseases, for which deep learning (DL) techniques can be utilized for early disease prediction and prevent stakeholders from losing the harvest. The objective of this paper is to develop a novel ensemble based deep convolutional neural network (DCNN) model developed on two base pretrained models named: VGG16 and InceptionV3 for early detection of cotton leaves diseases. The proposed ensemble model trained on cotton leaves dataset reports higher training and testing prediction accuracies as compared to the base pretrained models. Given that, deep learning architectures have hyper-parameters, this paper presents exhaustive experimental evaluations on ensemble model to tune hyper-parameters named learning rate, optimizer and no of epochs. The suggested hyper-parameter settings can be directly utilized while employing the ensemble model for cotton plant leaves disease detection and prediction. With suggested hyper-parameters settings of learning rate 0.0001, 20 epochs and stochastic gradient descent (SGD) optimizer, ensemble model reported training and testing accuracies of 98% and 95% respectively, which was higher than the training and testing accuracies of VGG16 and InceptionV3 pretrained DCNN models. 2024 Institute of Advanced Engineering and Science. All rights reserved. -
Effective Groundnut Crop Management by Early Prediction of Leaf Diseases through Convolutional Neural Networks
Groundnut (Arachis hypogaea L.), is the sixth-most significant leguminous oilseed crop grown all over worldwide. Groundnut, due to its high content of various dietary fibers, is classified as a valuable cash, staple and a feed crop for millions of households around the world. However, due to varied environmental factors, the crop is quite prone to many kinds of diseases, identifiable through its leaves, for which Groundnut producers have to suffer major losses every year. An early detection of such diseases is essential in order to save this significant crop and avoid huge losses. This paper presents a novel Machine Learning based Deep Convolution Neural Network (CNN) model CNN8GN. The model uses transfer learning technique for detection of such diseases in Groundnuts at an early stage of crop production. A Groundnut real image data set containing a total of 5322 real images for six different classes of Groundnut leaf diseases, captured in the fields of Gujarat state (India) during September 2022 to February 2023, is generated for training, testing and evaluation of the proposed model. The proposed deep learning model architecture is designed on eight different layers and can be used on varied sized images using simple ReLu and Softmax activation functions. The performance of the proposed CNN8GN model on Groundnut real image dataset is examined using a detailed experimental analysis with other six pre-trained models: VGG16, InceptionV3, Resnet50, ResNet152V2, VGG19, and MobileNetV2. CNN8GN results are also examined in detail using different sets of input parameters values. The proposed model has shown significant improvements for disease detection in comparative analysis with 99.11% training and 91.25% testing accuracy. The Author(s) 2024. -
Purification and Biochemical Characterization of Beta-Hexosaminidase B from Freshwater CnidarianHydra vulgaris Ind-Pune
Beta-N-acetylhexosaminidase (Hex) is a vital lysosomal hydrolase found in all living organisms, playing a crucial role in cellular homeostasis. Dysfunctions in this enzyme are implicated in severe pathological conditions such as Tay-Sachs and Sandhoff diseases in humans. In this paper, we report the purification and biochemical characterization of hexosaminidase from the soluble extracts obtained from the polyps of Hydra vulgaris Ind Pune. The Hydra Hex was purified by two-step sequential chromatography (hydrophobic interaction and gel filtration). Our results suggested that the enzyme isoform purified from Hydra is HexB, most likely to be a homodimer with a subunit mass of 65 kD. The pH optimum was in the range of 5.0 to 6.0 and the temperature optimum in the range of 50 C to 60 C. pH stability and temperature stability were found to be 5.0 and 40C respectively. The homology modelling studies corroborated the homodimeric nature of Hydra HexB, and indicated its structural resemblance to human HexB. This study offers new insights into the biochemical characteristics of Hydra HexB, providing a foundational framework for extensive investigations on this and other lysosomal hydrolases in Hydra. In a broader context, our results significantly contribute to establishing Hydra as a potential model organism to study the lysosomal biogenesis pathway. (2024), (Association of Carbohydrate Chemists and Technologists). All Rights Reserved. -
Rational design of bifunctional catalyst from KF and ZnO combination on alumina for cyclic urea synthesis from CO2 and diamine
This study is mainly focused on the design of stable, active and selective catalyst for direct synthesis of 2-imidazolidinone (cyclic urea) from ethylenediamine and CO2. Based on the rationale for the catalyst properties needed for this reaction, KF, ZnO and Al2O3 combination was selected to design the catalyst. ZnO/KF/Al2O3 catalyst was prepared by stepwise wet-impregnation followed by the removal of physisorbed KF from the surface. High product yield could be achieved by tuning acid-base sites by varying the composition and calcination temperature. The catalysts were characterized by various techniques like XRD, N2-sorption, NH3-TPD, CO2-TPD, TEM, XPS and FT-IR measurements. It is shown that acidic and basic properties of the solvent can influence the activity and product selectivity for this reaction. Under optimized condition; 180 C, 10 bar and 10 wt.% catalyst in batch mode, 96.3 % conversion and 89.6 % selectivity towards the 2-imidazolidinone were achieved. 2020 Elsevier B.V. -
Mindfulness-based strengths practice: a conceptual framework and empirical review of the literature
This review set out to provide empirical literature on mindfulness-based strengths practice (MBSP), a new approach in positive psychology that integrates mindfulness with character strengths, two positive predictors of well-being. First, the conceptualization of integrating character strengths and mindfulness into MBSP is discussed. The literature on the interrelatedness of character strengths and mindfulness is then described, along with ways that the intervention of MBSP encourages positive outcomes at various levels. The literature search returned 7 (10 samples, N = 3,851) studies supporting a positive association between character strengths and mindfulness (r = 0.30.4) and the mediating role of character strengths/virtues in mindfulness and mindfulnesss role in enhancing character strengths toward psychological well-being. The nine MBSP intervention studies (9 samples, N = 354) conducted in diverse contexts provide evidence of a significant improvement in well-being, engagement, life satisfaction, mindfulness, positive affect, character strengths, work-related outcomes, heightened birthing parents well-being during pregnancy and childbirth, increased academic performance, and enhanced mental health among students. The intervention studies also reported the fostering of mindful positive parenting and contributions to a significant reduction in negative psychological states, such as stress, depression, anxiety, and negative affect. This comprehensive review provides empirical support for the MBSP framework and its positive impact on well-being across various domains, including organizations, education, healthcare, and family. However, it underscores the need for more extensive research, as the current literature on MBSP is limited. The review encourages future studies to explore MBSP applications in diverse domains, thereby paving the way for a deeper understanding of its potential benefits. 2024 Taylor & Francis Group, LLC. -
CHARACTER STRENGTHS INTERVENTIONS IN HIGHER EDUCATION STUDENTS: A LITERATURE REVIEW
This review provides a comprehensive overview of interventions on character strengths in college and university students. Both qualitative and quantitative studies were reviewed. The review showed that focusing on character strengths leads to improved well-being, stronger interpersonal relationships, and reduced levels of stress, depression, anxiety, and academic pressure among students. The review also suggests that such interventions can be integrated into elective courses, first-year programs, and short-term training sessions tailored to address the specific needs of students. The interventions can offer a cost-effective alternative to traditional mental health strategies and could be implemented within college counseling centers. The limitations and practical implications of character strengths intervention modules designed specifically for college students are pointed out. By highlighting positive attributes and nurturing personal growth, character strengths interventions emerge as a valuable tool in bolstering the overall well-being of college students. The Author(s). All articles are licensed under the terms and conditions of the Creative Commons Attribution 4.0 International License (CC-BY 4.0 ). -
SmartHealth: Personalized Diet and Exercise Plans Using Similarity Modeling
Due to the growing prevalence of chronic diseases stemming from unhealthy lifestyles, a personalized approach to patient care is crucial. This paper delves into a system that utilizes cosine similarity and Pearson correlation to generate tailored diet and exercise plans, effectively managing chronic diseases. The system focuses on common chronic conditions like diabetes, hypertension, and thyroid disorders. Through sophisticated similarity modeling for diet and exercise, the proposed system provides integrated and personalized lifestyle recommendations, outperforming non-personalized or basic rule-based systems. 2024 IEEE. -
HULA: Dynamic and Scalable Load Balancing Mechanism for Data Plane of SDN
Multi-rooted topologies are used in large-scale networks to provide greater bisectional bandwidth. These topologies efficiently use a higher degree of multipathing, probing, and link utilization. An end-to-end load balancing strategy is required to use the bisection bandwidth effectively. HULA (Hop-by-hop Utilization-aware Load balancing Architecture) monitors congestion to determine the best path to the destination but, needs to be evaluated in terms of scalability. The authors of this paper through artifact research methodologies, stretch the scalability up to 1000 nodes and further evaluate the performance of HULA on software defined network platform over ONOS controller. A detailed investigation on HULA algorithm is analysed and compared with four proficient large-scale load balancing mechanisms including: connection hash, weighted round-robin, Data Plane Devlopment Kit (DPDK) technique, and a Stateless Application-Aware Load-Balancer (SHELL). 2023 IEEE. -
P4 based Load Balancing Strategies for Large Scale Software-Defined Networks
To meet the large demands of future networks, several large-scale Software Defined Networking (SDN) test-beds have been designed. The increasing complexity of networks has resulted in convoluted methods for managing and orchestrating efficiently across a wide range of network environments. The load balance function is impaired when the controller fails to connect with the switches. A traditional Load Balancer (LB) must decapsulate layers one by one and get the information needed to run load balancing algorithms. For instance, OpenFlow, NetConf, Programming Protocol-independent Packet Processors (P4), and Data Plane Developement Kit (DPDK) provide network programmability at both the control and data plane levels. In this paper, authors implement load balancing using the P4 programming language without the need of a controller, the P4 load balancer can operate on its own. Controller's support is used to keep track on the health of the web servers. In this situation, the controller can identify a server failure and notify the P4 load balancer, which will restrict requests to the malfunctioning server, lowering the dispatching failure rate. A detailed investigation of various load balancing mechanisms is analysed in this paper followed by the identification of four potential approaches to large-scale SDN tests, including connection hash, weighted round-robin, DPDK technique, a Stateless Application-Aware Load-Balancer (SHELL). 2022 IEEE. -
Application of Artificial Intelligence on Smart Tourism Eco Space: An Integrated Approach in Post-COVID-19 Era
The AI-integrated approach in recent times has evolved with innovative techniques and gained much importance in the post-COVID-19 scenario. This chapter extends contemporary and exponential research findings for Smart Tourism Practices and the Application of AI-enabled systems for the Tourism Ecosystem. It highlights for various service segments like hotels, motels, resorts, restaurants, cafes, airlines, and destinations under this large umbrella known as the hospitality sector. Smart tourism eco space capacitates an ICT-enabled system consolidates tourism resources and information technologies. Perhaps, with multiple challenges, a successful implementation of smart tourism approaches empowers and supports a smart system in place. The tourism eco space is highly vulnerable, and this situation in the service sector creates an intense requirement of a comprehensive view of digitally enabled smart tourism eco space with innovative mechanisms to remain contact-free with less human intervention. The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024. -
Impact of Rupee Volatility on the Financials of the Indian IT Companies
International Journal of Advanced Research in Economics and Commerce, Vol-1 (1), pp. 1-8. ISSN-2320-7248 -
Analysis on techniques used to recognize and identifying the Human emotions
Facial expression is a major area for non-verbal language in day to day life communication. As the statistical analysis shows only 7 percent of the message in communication was covered in verbal communication while 55 percent transmitted by facial expression. Emotional expression has been a research subject of physiology since Darwins work on emotional expression in the 19th century. According to Psychological theory the classification of human emotion is classified majorly into six emotions: happiness, fear, anger, surprise, disgust, and sadness. Facial expressions which involve the emotions and the nature of speech play a foremost role in expressing these emotions. Thereafter, researchers developed a system based on Anatomic of face named Facial Action Coding System (FACS) in 1970. Ever since the development of FACS there is a rapid progress in the domain of emotion recognition. This work is intended to give a thorough comparative analysis of the various techniques and methods that were applied to recognize and identify human emotions. This analysis results will help to identify proper and suitable techniques, algorithms and the methodologies for future research directions. In this paper extensive analysis on various recognition techniques used to identify the complexity in recognizing the facial expression is presented. Copyright 2020 Institute of Advanced Engineering and Science. All rights reserved. -
Product specific determinants of electronic gadget purchase intention - a case of the purchase behaviour of Indian youth
This study investigated the impact of product specific features of electronic gadgets on the purchase intention on the Indian youth. The study was quantitative in nature and data was collected from 650 young electronic gadget consumers in Bengaluru, India using structured questionnaires. Descriptive statistics and structural equation modelling (SEM) were used for data analysis. Brand image, product design, and country of origin are referred as product evaluation attributes; and corporate identity were identified as the determinants of purchase intention. Respondents were neutral regarding the role of product evaluation attributes and corporate identity in their purchases, but acknowledged these factors' importance. Findings implied a positive and significant influence of product evaluation attributes on the corporate identity of companies, and purchase intention of the youth. However, corporate identity did not influence purchase intention, clearly indicating that only product specific features, such as brand, design and country of origin are considered when youngsters purchase gadgets. Copyright 2022 Inderscience Enterprises Ltd. -
IOT based application to detect fall with a measured force
Fall of patients and aged individuals may end up deadly if unnoticed in time. A fall detection framework has been developed which sends caution notification to the concerned individuals or to the specialist, at the time of occurrence. To limit the consequences of associated wounds/damage caused by the fall, such a device has been developed. The model in this study, detects the fall and measures the force of the fall without using the force sensor and the direction of the fall. In this study, the body posture is obtained from change of increasing speed in three axes, which is measured with a triaxial accelerometer (ADXL335). The sensor is set on the lumbar area to interpret the tilt point. The value obtained from the sensor is compared with the threshold given to diminish the false cautions and furthermore provides the force by which the individual has fallen and the direction in which the person has fallen. The threshold value is computed by the execution of various trials on subjects in different directions of fall. The sensor data is collected on the fall is computed and analyzed in the Audrino microcontroller. The location of fall is detected by GPS beneficiary, which is customized to trace the subject persistently. On detecting the fall, the gadget sends an instant message through GSM module to the emergency contact. The developed model is tested on 7 volunteers who replicated falls in different direction with varying forces. Out of 28 trials, 80% of exactness is accomplished with zero false cautions for dayto-day activities like sitting, lying down on bed and grabbing objects. IAEME Publication. -
Optimized Load Balancing Technique for Software Defined Network
Software-defined networking is one of the progressive and prominent innovations in Information and Communications Technology. It mitigates the issues that our conventional network was experiencing. However, traffic data generated by various applications is increasing day by day. In addition, as an organization's digital transformation is accelerated, the amount of information to be processed inside the organization has increased explosively. It might be possible that a Software-Defined Network becomes a bottleneck and unavailable. Various models have been proposed in the literature to balance the load. However, most of the works consider only limited parameters and do not consider controller and transmission media loads. These loads also contribute to decreasing the performance of Software- Defined Networks. This work illustrates how a software-defined network can tackle the load at its software layer and give excellent results to distribute the load. We proposed a deep learning-dependent convolutional neural networkbased load balancing technique to handle a software-defined network load. The simulation results show that the proposed model requires fewer resources as compared to existing machine learning-based load balancing techniques. 2022 Tech Science Press. All rights reserved. -
Magnetohydro-convective instability in a saturated DarcyBrinkman medium with viscous dissipation
The influence of dissipation with viscosity on magnetohydro-convective instability in a saturated DarcyBrinkman medium is examined. The bottom boundary is designated as adiabatic, whereas the top boundary is isothermal. Numerical linear stability analysis investigates normal modes that disturb the horizontal base flow at different inclinations. The case study shows that the most unstable disturbances are horizontal rolls, normal modes characterized by a wave vector perpendicular to the main flow direction. The horizontal rolls are the favored instability mode. Barletta et al. also showed that horizontal rolls are more unstable than any other oblique roll mode in the hydromagnetic scenario. This finding provides insights into the behavior of MHD fluid flow and heat transfer in porous media, with implications for applications in geoscience, engineering, and environmental science. Graphical abstract: (Figure presented.) The Author(s), under exclusive licence to EDP Sciences, SIF and Springer-Verlag GmbH Germany, part of Springer Nature 2024. -
Evaluation of forecasting accuracy of an equity valuation model: a case of ZEE
Investing can prove to be a very enriching and enjoyable experience if one sticks to certain principles and guidelines. The research is based on secondary data pulled out from Money Control website for ZEE Entertainment Enterprises Limited (ZEEL). The identification of target prices is important and involves precision in the price points that are forecasted. The expected growth rate for the next year is figured out to forecast the financial statement for the next year. Regression analysis has been used to estimate growth rate. Regression analysis was done on the income data for the past years for the media entertainment company, and the target prices have been identified. By taking a careful look at the forecasted prices and the prevailing prices, an investor can figure out whether the stock is under-priced or over-priced. 2023 Inderscience Enterprises Ltd. -
Impact of Abuse on Mental Health and Happiness Among Students: Mediating Role of Family Environment
Background: Child abuse and neglect is an issue of concern for public health professionals. The impact of abuse may lead to poor physical and mental health conditions. Family environment may impact coping and recovery among victims of abuse. The association between child abuse, mental health, happiness, and family environment is complex. The study examines the association and pathways between child abuse exposure, mental health and happiness, while exploring the potentially mediating effect of the family environment. Methods: Data were collected from 571 high school students from Kerala, India, by using various tools, including a semi-structured questionnaire, Depression and Anxiety Youth Scale, and happiness scale. A mediation analysis using structural equation modeling (SEM) was carried out to test the objectives of the study. Results: The analysis shows that mental health, happiness, and family environment are correlated with abuse experience. The mediation analysis further shows that the indirect effect of abuse on mental health via the family environment was significant (? = 0.013, 95% CI [0.002, 0.033]). The indirect effect of abuse on happiness via the family environment was significant (? = 0.019, 95% CI [0.044, 0.003]). Furthermore, the total effect of abuse on mental health (? = 0.266, 95% CI [0.164, 0.354]) and abuse on happiness (? = 0.152, 95% CI [0.259, 0.050]) was significant. Conclusion: The study reveals that abuse experiences impact happiness and mental health outcomes among students. The family environment mediates the relationship between child abuse and mental health, and between child abuse and happiness. 2023 The Author(s). -
Experimenting with resilience and scalability of wifi mininet on small to large SDN networks
Today everything is getting digitized where people want to be wireless by all aspects. There is a high demand of WiFi in every sector. Highest influence on network planning of newly developed network infrastructure is of SDN to meet the futuristic needs of upcoming technology. As a result, newly developed networks have become more adaptive to dynamic circumstances along with enhanced flexibility. Being globally connected, it is inevitable to obtain adequate services from data centers through Wi-Fi support on SDN Networks, which is still a dream. Thus, the target of the experiment performed and presented by the authors of this paper is to implement WiFi support on SDN. Further, authors have also demonstrated the scalability and resilience of SDN based WiFi Network on Mininet by testing performance parameters in various dynamic scenarios. This paper will have a high impact on the end users as SDN technology can be implemented as last mile technology using WiFi SDN. BEIESP.