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Durability Studies and Stress Strain Characteristics of hooked end steel fiber reinforced ambient cured geopolymer concrete
For conventional concrete, the use of fibers has proven to improve the strength properties of the material. However, in the case of ambient cured geopolymer concrete, there are limited studies that explore the application of fibers, in particular, the use of hooked end steel fibers. Further, it is important to study the durability properties of geopolymer concrete with fibers, since it will influence the service life of the structures in practice. Therefore, in the present study, fiber-reinforced geopolymer concrete was synthesized using fly ash, GGBS, hooked end steel fibers, and alkaline solution made with Na2SiO3 and NaOH. The percentage of steel fibers varied in the range of 0.5% to 2% with an increment of 0.5% by volume fraction of the binder. The precursor materials were characterized using techniques such as X-ray fluorescence (XRF), X-ray diffraction (XRD), and scanning electron microscope (SEM). Durability studies like water absorption, drying shrinkage, sulphate attack were studied. In addition, the elastic constants were determined through stress strain behaviour of geopolymer concrete in uniaxial compression. The results of the experimental study showed that the addition of hooked end steel fibers influences the strength of geopolymer concrete up to an optimal percentage, which was found to be 1%. Furthermore, in terms of durability properties, the addition of fibers exhibited better results in terms of resistance to water absorption and chemical attack, and this was validated by the microstructural studies, where the specimens with hooked end steel fibers revealed much denser hardened geopolymer matrix when compared to the mixes without fibers. Published under licence by IOP Publishing Ltd. -
Effect of fiber types, shape, aspect ratio and volume fraction on properties of geopolymer concrete A review
Researchers have emphasized on sustainable construction with utilization of industrial wastes or byproducts in production of concrete. Geopolymer concrete is one of the popular construction materials which has shown promising results and potential to substitute conventional energy intensive materials such as Portland cement concrete. Further, the use of fibers has shown potential to overcome various deficiencies of geopolymer concrete. However, there are limited studies which explore the benefits of fiber reinforced geopolymer concrete and its applications. The development of fiber reinforced geopolymer concrete is relatively new construction material and has to be experimentally validated in order to increase its usage in the construction industry. As a result, this review paper is an attempt to discuss the effect of shape, type, aspect ratio and volume fraction of fibers on strength and durability properties of geopolymer concrete. From this detailed review it can be concluded that fiber reinforced geopolymer concrete enhances ductile behavior, tensile strength, toughness & energy absorption capacities. 2022 -
Continuous emotion estimation for human machine interaction
Humans are able to interact and bond very efficiently with other species because every living organism has some form of emotion in them. Due to the advances in science and technology human life has become more dependent on machines for better living. The recent advances in technology enabled machines to become smarter but not efficient in terms of interaction with humans. Hence to address this issue and to bridge the gap between human machine interactions we propose a system to estimate human emotions from facial expressions. We believe that facial expressions are a form of nonverbal communication and primary means of conveying information. The system uses linear regression model to calculate emotional state of a facial expression which is mapped onto continuous 2-D coordinates with arousal and valence as axis from a captured digital image. Thus the proposed method estimates emotion continuously and predictively like humans rather than classifying the emotions because emotions are continuous and they have many dimensions. By estimating emotions continuously machines can better interact with humans. Experimental results showed that our system provides superior predictive performance. 2015 American Scientific Publishers. All rights reserved. -
Transformational leadership and organizational citizenship behavior: new mediating roles for trustworthiness and trust in team leaders
This study investigates the pivotal role of trust in bridging the effects of transformational leadership on organizational citizenship behavior (OCB). The study was conducted using a multilevel longitudinal approach with 276 employees in 71 teams from private medium-sized organizations in Kuala Lumpur, Malaysia. Transformational leadership was found to be positively related to: (1) three facets of trustworthiness (ability, benevolence, and integrity); (2) trust in the leader; and (3) OCB. All three facets of trustworthiness mediated the relationship between transformational leadership and trust in leaders. In addition, trust in the leader mediated only the relationship between the benevolence facet of trustworthiness and OCB. As OCB is inherently benevolent, these findings not only are consistent with the principle of compatibility, but they also contribute to theorizing about how trust plays an important role in the influence of transformational leadership on employees. The Author(s) 2023. -
Comprehensive transcriptomic and functional characterization of protoplast regeneration in Angelica gigas Nakai
Background: Protoplasts that are isolated from various plant sources, including leaf mesophyll tissue, callus, will continue to have the ability to take part in cell wall regeneration, division, and expression of totipotency. In model systems like Arabidopsis thaliana, it has been thoroughly established. Meanwhile, the fate of protoplasts isolated from both embryogenic (EC) and non-embryogenic callus (NEC) in other plants is unknown. Thus, we conducted transcriptome analyses of protoplasts produced from both EC and NEC in Angelica gigas in the present investigation. To achieve this, three stages of RNA sequencing were carried out: (1) EC, (2) freshly isolated protoplasts (Pt), and (3) cells undergoing cell division (CD) during A. gigas in vitro protoplast regeneration. Different gene expression programs were identified across stages through transcriptome profiling, which highlighted early stress responses, transcriptional changes, and the acquisition of stem cell identity following protoplast isolation. Results: A pre-existing stem cell-like condition was shown by the strong expression of WUSCHEL-RELATED HOMEOBOX5 (WOX5) and CUP-SHAPED COTYLEDON2 (CUC2) genes at the EC stage. Important genes linked to stress reactions and cellular transcriptional changes, as WOX13 and WOUND INDUCED DEDIFFERENTIATION1 (WIND1), were significantly elevated at the Pt stage. Additionally, during this phase, genes associated with the cell cycle, auxin and cytokinin signaling, and cell wall regeneration were also active, indicating a dynamic shift toward regaining stem cell identity. Key regulators of stem cell maintenance and proliferation, such as LATERAL ORGAN BOUNDARIES DOMAIN16 (LBD16) and ENHANCER OF SHOOT REGENERATION2 (ESR2), were then substantially expressed at the CD stage, encouraging the start of cell division. The dynamic regulation of embryogenesis-related genes, including SOMACTIC EMBRYOGENESIS RECEPTOR-LIKE KINASE 2 (SERK2), LBD29, ESR2, LBD16, D-TYPE CYCLINS (CYCD3-2), and WOX1, during protoplast culture was validated by real-time quantitative polymerase chain reaction (RT-qPCR). Conclusions: WOX5 and CUC2 genes were found to be significantly expressed at the EC stage, suggesting a pre-existing stem cell-like state. Important genes linked to stress reactions and cellular transcriptional changes, as WOX13 and WIND1, were clearly elevated at the Pt stage. The activation of genes linked to cell wall renewal, auxin and cytokinin signaling, and the cell cycle during this phase also suggested a dynamic transition toward regaining stem cell identity. Additionally, the findings previously reported show that the EC-derived protoplasts successfully underwent cell division and contributed to the development of somatic embryos. Although the NEC-generated protoplasts did not divide upon culture, these results show that embryogenic cells maintain their embryogenic potential even after protoplast isolation and culture. The Author(s) 2026. -
Implementation of digital signature using hybrid cryptosystem
Security is a major concern when it comes to electronic data transfer. Digital signature uses hash function and asymmetric algorithms to uniquely identify the sender of the data and it also ensures integrity of the data transferred. Hybrid encryption uses both symmetric and asymmetric cryptography to enhance the security of the data. Digital Signature is used to identify the owner of the document but it does not hide the information while transferring the document. Anyone can read the message. To avoid this, data sent along with the signature should be secured. In this paper, Digital signature is combined with hybrid encryption to enhance the security level. Security of the data or the document sent is achieved by using hybrid encryption technique along with digital signature. 2018 Authors. -
Need of integrated care model for positive childbirth experience in Indian maternity care services
BACKGROUND: Integrated care (IC) models are an emerging trend in healthcare reforms worldwide, especially in the maternal healthcare system. This research focuses on the scope of an integrated model for intrapartum care of women and explores the experience of birth under two intrapartum care modelsbiomedical and midwifery models, respectively. The term positive childbirth experience (PCE) is a concept defined by the World Health Organization (WHO) in the recommendations on intrapartum care for a PCE. MATERIALS AND METHOD: This study is convinced to employ a qualitative approach to explore how birth is experienced by women under maternity healthcare services in Kerala. A semi?structured interview was conducted to tap into the lived reality of birthing of sixteen first?time mothers (primipara) aged between 20 and 30 years under these two models. Furthermore, five participants have been specifically interviewed after their vaginal birth after a C?section (VBAC) experience. To achieve a systematic cross?case thematic analysis, systematic text condensation (STC) has been employed as a data analysis method. RESULTS: Four main categories were identified through the analysis as follows: (1) information and knowledge, (2) confidence, (3) quality of care, and (4) health?promoting perspective. These central themes evolved from 11 subthemes. CONCLUSION: The data analysis reveals both negative and positive experiences under two care models. It emphasizes the urgent need to reframe the biomedical?focused care model and adopt an integrated approach that aligns with the global intrapartum care model proposed by the World Health Organization (WHO) in 2018 and the definition of IC mentioned in the paper. 2024 Journal of Education and Health Promotion. -
ACCIDENT PREVENTION AND MANAGEMENT SYSTEM IN URBAN VANETS FOR IMPROVING SLIPPERY ROADS RIDE AFTER RAIN
Urban Vehicular Ad-hoc Networks (VANETs) face challenges in managing accidents and enhancing safety, particularly on slippery roads post rainfall. This study addresses this issue by proposing an Accident Prevention and Management System tailored for improving ride safety in such conditions. The problem statement identifies the increased risk of accidents and decreased road grip due to rain-induced slippery surfaces in urban areas. The proposed method integrates real-time data collection from vehicles and road infrastructure to predict and detect slippery road segments. Utilising this information, the system dynamically disseminates warnings to nearby vehicles, enabling them to adapt their driving behaviour and avoid potential accidents. The flow of the proposed system involves a multi-step process: (1) Real-time data collection using sensors installed in vehicles and roadside infrastructure, (2) Data analysis and prediction algorithms to identify slippery road segments, (3) Communication protocols for disseminating warnings to vehicles in the neighbourhood, and (4) Driver assistance mechanisms to aid in adapting to the road conditions. Results from simulations and real-world experiments demonstrate the efficacy of the system in significantly reducing the likelihood of accidents on slippery roads after rainfall. By leveraging VANET technology and real-time data analysis, this system enhances safety by providing timely warnings and promoting safer driving practices, ultimately mitigating the risks associated with adverse weather conditions in urban environments. 2024, Scibulcom Ltd.. All rights reserved. -
Prospects of CSR: An Overview of 500 Indian Companies
The IUP Journal of Corporate Governance, ISSN No. 0972-6853 -
Making a Difference: Social Responsibilities of Infosys
International Journal of Management, IT and Engineering Vol. 2, Issue 10, pp 336-350, ISSN No. 2249-0558 -
Sustainable Corporate Social Responsibility - An Analysis of 50 Definitions for a Period of 2000-2011
Zenith International Journal of Multidisciplinary Research Vol. 2, Issue 10, pp. 169-193, ISSN No. 2231-5780 -
Applying talent acquisition to the test: Assessing productivity in facilities organization /
Pramana Research Journal, Vol.9, Issue 2, pp.197-207, ISSN No: 2249-2976. -
Biomimicry : An approach to sustainable architecture and design /
International Journal of Life Sciences Research, Vol.7, Issue 1, pp.318-323, ISSN No: 2348-3148. -
Service delivery quality improvement models: A review /
Procedia Social Science And Behavioral Sciences, Vol.144, pp.510-527, ISSN No: 1877-0428. -
Problem-Based Learning for Critical Reflections on Skill-based Courses Using DEAL Model
Higher education institutions focus on i mpr ovi ng bot h sof t ski l l s and engi neer i ng proficiencies among students. The learning progress requires a systematic assessment to know the areas of improvement to meet global competitiveness. Self-reflections and critical reflections on knowledge, skill, and behavior are crucial for an industry-ready graduate. Our work deals with conceptualization, course design, and rubrics design to achieve critical reflections on the graduate outcomes of the students. We have designed the rubrics to assess the behavioral and engineering skills needed to solve complex engineering problems that can be solved better as a team for life-long learning and developing ethical interpersonal skills. Our assessment patterns also helped students achieve higher-order thinking skills through experiential learning. 2024, Rajarambapu Institute Of Technology. All rights reserved. -
Kubernetes for Fog Computing - Limitations and Research Scope
With the advances in communications, Internet of Everything has become the order of the day. Every application and its services are connected to the internet and the latency aware applications are greatly dependent on Fog Infrastructure with the cloud as a backbone. With these technologies, orchestration plays an important role in coordinating the services of an application. With multiple services contributing to a single application, the services may be deployed distributed in multiple server. Proper coordination with effective communication between the modules can improve the performance of the application. This paper deals with the need for orchestration, challenges, and tools with respect to edge/fog computing. Our proposed research solution in the area of intelligent pod scheduling is highlighted with the possible areas of research in Microservices for Fog infrastructure. 2022, The Author(s), under exclusive license to Springer Nature Switzerland AG. -
3D CNN-Based Classification of Severity in COVID-19 Using CT Images
With the pandemic worldwide due to COVID-19, several detections and diagnostic methods have been in place. One of the standard modes of detection is computed tomography imaging. With the availability of computing resources and powerful GPUs, the analyses of extensive image data have been possible. Our proposed work initially deals with the classification of CT images as normal and infected images, and later, from the infected data, the images are classified based on their severity. The proposed work uses a 3D convolution neural network model to extract all the relevant features from the CT scan images. The results are also compared with the existing state-of-the-art algorithms. The proposed work is evaluated in accuracy, precision, recall, kappa value, and Intersection over Union. The model achieved an overall accuracy of 94.234% and a kappa value of 0.894. 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. -
Transformative Trends in AI for Environmental Monitoring: Challenges, Applications
The integration of artificial intelligence (AI) and machine learning (ML) is reshaping environmental monitoring, responding to the escalating complexities of issues like climate change and pollution. This article presents a comprehensive overview of current trends, challenges, and applications in AI-driven environmental monitoring. While technologies like remote sensing and Internet of Things (IoT) have improved data resolution, the sheer volume necessitates AI for efficient processing. The review emphasizes the role of AI in real-time monitoring, providing timely insights critical for addressing natural disasters and pollution. Exploring various environmental monitoring verticals-air and water quality, climate change modeling, biodiversity, and disaster prediction-the article highlights AIs versatility in addressing diverse concerns. Challenges such as data quality, bias, interpretability, and privacy are examined, underlining ethical considerations in biased models impacting marginalized communities. This chapter discusses common environmental modeling methodologies, ranging from empirical to geospatial modeling, elucidating their advantages and challenges. 2025 Scrivener Publishing LLC. -
Urbanization, Carbon Emissions, and SDG Aligned Strategies for Sustainable Cities
With the rapid growth in urbanization, carbon emission has emerged as the major challenge. Urban areas are a major contributors of global carbon emissions with transportation, factories and construction sectors contributing to the majority of greenhouse gas (GHG) emissions. Urbanisation also demands excessive energy and creates pollution. Many prior studies have reported the direct relationship between rapid urbanization and an increase in carbon emissions. This chapter aims to highlight the carbon emission related challenges to o the urbanization and actions to address those challenges. Further, this chapter also outlines various SDGs aligned strategies to reduce the carbon emissions due to urbanization and suggests policy actions to adopt green technologies that resulted in the level of carbon footprint. It provides important insights into low-carbon transportation systems, smart waste management system and green digital infrastructure to reduce the carbon footprint in the urban area and help achieve SDG7, SDG9, SDG11, and SDG 15. The Author(s), under exclusive license to Springer Nature Switzerland AG 2026. -
Data Analysis on Hypothyroid Profiles using Machine Learning Algorithms
Machine learning algorithms enable computers to learn from data and continuously enhance performance without explicit programming. Machine learning algorithms have significantly improved the accuracy and efficacy of thyroid diagnosis. This study identified and analysed the usefulness of several machine-learning algorithms in predicting hypothyroid profiles. The main goal of this study was to see the extent to which the algorithms adequately assessed whether a patient had hypothyroidism. Age, sex, health, pregnancy, and other factors are among the many factors considered. Extreme Gradient Boosting Classifier, Logistic Regression, Random Forest, Long-Term Memory, and K-Nearest Neighbors are some of the machine learning methods used. For this work, two datasets were used and analysed. Data on hypothyroidism was gathered via DataHub and Kaggle. These algorithms were applied to the collected data based on metrics such as Precision, Accuracy, F1 score and Recall. The findings showed that the Extreme Gradient Boosting classification method outperformed the others regarding F1 score, accuracy, precision, and recall. The research demonstrated how machine learning algorithms might predict thyroid profiles and identify thyroid-related illnesses. 2023 IEEE.