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Effects of Processing Parameters on Microstructure Evolution of Al-7Si-Mg Alloy by Cooling Slope Casting
This work investigates the effects of pouring temperature, slope length, and slope temperature in cooling slope casting on the formation of globular microstructure of Al-7Si-Mg alloy. The remnant alloy on the slope during casting was quenched and characterized at different stages of flow to evaluate the microstructure features developed in cooling slope casting. The primary ?-Al dendritic phase found in conventional cast alloy was transformed into globular shape in slope-processed cast alloy. Finer and more homogenous primary ?-Al phase was formed at lower pouring temperature (625C). The effect of slope length on microstructure of Al-7Si-Mg alloy was significant at high pouring temperatures (640 and 660C) but was not visible at low pouring temperature (625C). The microstructure of alloy became coarser with increasing slope temperature. 2015, ASM International. -
An updated review on advancement in fermentative production strategies for biobutanol using Clostridium spp.
A significant concern of our fuel-dependent era is the unceasing exhaustion of petroleum fuel supplies. In parallel to this, environmental issues such as the greenhouse effect, change in global climate, and increasing global temperature must be addressed on a priority basis. Biobutanol, which has fuel characteristics comparable to gasoline, has attracted global attention as a viable green fuel alternative among the many biofuel alternatives. Renewable biomass could be used for the sustainable production of biobutanol by the acetone-butanol-ethanol (ABE) pathway. Non-extinguishable resources, such as algal and lignocellulosic biomass, and starch are some of the most commonly used feedstock for fermentative production of biobutanol, and each has its particular set of advantages. Clostridium, a gram-positive endospore-forming bacterium that can produce a range of compounds, along with n-butanol is traditionally known for its biobutanol production capabilities. Clostridium fermentation produces biobased n-butanol through ABE fermentation. However, low butanol titer, a lack of suitable feedstock, and product inhibition are the primary difficulties in biobutanol synthesis. Critical issues that are essential for sustainable production of biobutanol include (i) developing high butanol titer producing strains utilizing genetic and metabolic engineering approaches, (ii) renewable biomass that could be used for biobutanol production at a larger scale, and (iii) addressing the limits of traditional batch fermentation by integrated bioprocessing technologies with effective product recovery procedures that have increased the efficiency of biobutanol synthesis. Our paper reviews the current progress in all three aspects of butanol production and presents recent data on current practices in fermentative biobutanol production technology. 2022, The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature. -
Sensitivity and tolerance analysis of 2D Profilometer for TMT primary mirror segments
The primary mirror (M1) of Thirty Meter Telescope (TMT) consists of 492 segments of which, 86 are ground and polished by India-TMT. These segments are off-Axis and aspheric in nature and one of the effective methods to polish such segments is through Stressed Mirror Polishing (SMP). During SMP, consistent in-situ metrology of the surface is needed to achieve the required profile. A 2D Profilometer (2DP) will be used by India-TMT for the low frequency profile metrology. The 2DP is a contact-Approach metrology, consisting of probes positioned in a spiral pattern, measuring the sag of segment surface. Initial section of this paper deals with the sensitivity and tolerance analysis of the 2DP. This is followed by the study on position and rotational errors of the 2DP as a whole. Simulation of these analysis is carried out initially on a sphere and then on different segments of the TMT, in order to study the induced measurement errors. COPYRIGHT SPIE. Downloading of the abstract is permitted for personal use only. -
Development and Efficacy of Parenting Skill Training for Mothers of Adolescents in Kerala
The primary objective of this research is to develop and assess the effectiveness of an intervention program tailored for mothers of adolescents in Kerala to strengthen their parenting skills. The digital age and unique socio-cultural context present new challenges in child-rearing, and existing parenting programs fall short of addressing these evolving issues. The study employed a mixed-method framework with specific objectives to fill this research gap. The research unfolded in three phases. The initial stage encompassed comprehensive interviews with ten mothers and their adolescents, utilizing a constructionist model for thematic analysis. It unveiled five main and 22 sub-themes, shedding light on mothers' and adolescents' needs and challenges in Kerala. The second phase focused on designing an intervention module specifically suited to address the needs and challenges identified in the qualitative phase. The study used a pre-test, post-test, and experimental design with a control group for the third phase. The researcher used the Alabama Parenting Questionnaire, the Family Environmental Scale, and the Parental Satisfaction Scale to measure the efficacy of the training. The results presented significant improvements in parenting practices in the experimental group, particularly in positive parenting and mothers' involvement with their children. Corporal punishment and inconsistent discipline decreased, while family environment and parenting satisfaction increased. This study contributes substantially to the mental health field by offering an evidence-based program to assist mothers in navigating parenting challenges during adolescence. This intervention aims to improve family dynamics and adolescent well-being. It is a valuable resource for trainers seeking to facilitate behavioral changes within the target groups. -
Multifunctional biosensor activities in food technology, microbes and toxins A systematic mini review
Biosensors have its significant applications in various fields, its use in food processing, food safety and food technology has helped to enhance the overall health of the society as it can successfully determine the presence and concentration of different microorganisms including Escheichia coli, Vibrio cholera, Clostridium spp. etc., and also determination of various toxins present in food like acrylamides, benzene, ethylbenzene, toluene, xylene, nitrosamines, Benzo[a]pyrene (BaP) which are carcinogenic. The preface of biosensors has assisted food industries for monitoring and verification of raw materials, food processing, and composition of the food and assessment of product freshness. Symbolic biosensors have been developed in recent years and yet there is much immediate need for the development of more reliable, cost-effective, sensitive and novel biosensors for rapid detection and identification of food borne pathogens and toxins. Extensive review recapitulates overall food-pathogen testing research market trends, as well as commercialization of biosensors for the food safety industry as legislation creates novel standards for microbial monitoring. Furthermore, the world's concern about the food safety and human's healthcare, the one and only biosensor's exclusive demand is nothing but an alternative in real time diagnosis of diseases causing pathogens. 2022 Elsevier Ltd -
The Role of Big Data and Predictive Analytics in Financial Decision-Making
Big data refers to enormous amounts of structured and unstructured data that arise daily. All the finance sector records transactions, market data, social media sentiments, customer behavior, and macroeconomic indicators. The big three dimensions of big data that are commonly called the "3 Vs" include Volume, Velocity, and Variety. It means the data came from sources such as social media, IoT devices, transactions, and sensors. This process involves large amounts of data hence requiring scalable storages, complex processing tools, and resources for effective data management and analytics. Velocity means the rate or speed at which data is originated and processed. Lastly, Variety denotes the different kinds of data, which include the structured ones such as databases and the unstructured kinds, such as images, video, social media post, and audio. Collectively, the 3 Vs capture both the richness and the potentiality of big data, demanding the need for innovation in terms of technologies and strategies to leverage this value. 2026, IGI Global Scientific Publishing. All rights reserved. -
Bridging Academia and Communities Through Service-Learning Praxis at Christ University
The Service Learning (SL) experiences of students in higher education institutions play a pivotal role in shaping future generations to be more inclusive and responsible. This chapter delves into the institutionalisation of SL, community partnerships, and specific departmental SL experiences, focusing on the initiatives at Christ University. At the core of these efforts is the Centre for Service Learning at Christ University, which actively monitors, recommends policies, and establishes frameworks for departments. The chapter introduces the ADORED model, elucidating the phases of SL at the institution: Assess, Develop, Organise, Reflect, Execute, and Demonstrate. This chapter describes SL practices in the School of Commerce, Education, and Psychology, offering tangible examples. By examining these specific cases, the chapter provides insights into the successful integration and sustainability of SL within a university setting. 2025 The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Switzerland AG. -
Effective and Meaningful Student Engagement Through Service Learning
A paradigm shift is underway in education, challenging traditional teaching methods and calling for a more engaging and purposeful approach. It is necessary to explore how service learning empowers students to address real-world issues, fostering critical thinking, creativity, collaboration, and communication skills essential for the 21st century. Effective and Meaningful Student Engagement Through Service Learning is a comprehensive exploration of the transformative power of service learning in contemporary education. Within this text, seasoned researchers and practitioners delve into the intricacies of student engagement, emphasizing the importance of active involvement in the learning process. This book opens with a reflection on education, where traditional practices give way to innovative pedagogies. This includes a new pedagogical approach that not only imparts knowledge but also cultivates socially responsible citizens. The book provides a rich tapestry of theoretical foundations, curriculum development strategies, and innovative pedagogical approaches that move beyond passive learning. From evaluating the impact of service learning to incorporating technology and measuring learning outcomes, each chapter offers theoretical frameworks, practical experiments, and real-life examples for educators, administrators, and policymakers. The book addresses the challenges and barriers to achieving meaningful student engagement, proposing practical solutions and recommendations. It emphasizes the role of service learning in building reciprocal relationships with communities and fostering inclusivity. Case studies and best practices from diverse educational settings showcase the effectiveness of different approaches to student engagement. The diverse audience within and beyond the education sector, including students, faculty members, parents, policymakers, NGOs, and community organizations, will find within the pages of this book valuable insights and tools to create more effective and meaningful learning experiences. The book covers a broad spectrum of topics, from the institutionalization of service learning to motivations for sustainable engagement, making it an indispensable resource for anyone passionate about shaping the future of education. 2024 by IGI Global. All rights reserved. -
Disclosure of University Social Responsibility A Review of Select Higher Educational Institutions
This paper explores the disclosure of university social responsibility by higher educational institutions. Based on the disclosure of information on institutional websites, 39 universities were selected for the study. The data, which was assessed on the criteria used by regulatory authorities for grading institutions, revealed that while 12 institutions performed above average in most of the criteria, 17 were in the medium range, and 10 performed below average. The study proposes that disclosure of social responsibility activities with adequate evidence from institutional websites can attract more viewers and prospective students. 2023 Tata Institute of Social Sciences. All rights reserved. -
Enhancing curricula with service learning models
In today's digital age, technological advancements permeate every sector, especially higher education. However, higher education must go beyond merely integrating AI into the curriculum. Additionally, it needs to prioritize educating students about societal issues. Integrating service learning into higher education curriculums, however, is a significant challenge facing schools today. There is a need for comprehensive research on its effectiveness and guidance on institutionalizing it effectively. This hampers its potential to foster civic engagement and social responsibility among students. With clear strategies and best practices, institutions can implement service learning programs that benefit all stakeholders. Enhancing Curricula with Service Learning Models provides a comprehensive blend of theoretical frameworks, practical experimentation, and real-world examples to guide educators, administrators, and policymakers in fostering profound student engagement. It emphasizes the role of emerging educational paradigms, like service-learning, in instilling a sense of civic duty and purpose in students. By enriching the educational dialogue with an emphasis on the pivotal role of student engagement in creating transformative and purposeful learning experiences, this book empowers educators and institutions to create impactful and sustainable programs. To ensure that educators and stakeholders are equipped with the knowledge and tools necessary to cultivate environments that encourage active student participation, Enhancing Curricula with Service Learning Models provides practical guidance on building effective tri-party relationships between community partners, academia, and students. By offering a meta-analysis of service learning practices, this book is a valuable resource for institutions looking to enhance their academic quality and community engagement. 2024 by IGI Global. All rights reserved. -
Exploring student perspectives on service learning: Their expectations, challenges, and perceived benef
Many students foresee that a service-learning course would help them to participate in the development of the community, addressing its real concerns. On the other hand, students anticipate individual growth in terms of personal, interpersonal, and societal skills. However, service-learning also throws up some concerns and challenges. Thus, it is very important for educational institutions to understand the challenges in the planning and execution phase and reap a positive impact from the service-learning course. A qualitative research technique is adopted and the responses are grouped into themes for better evaluation of the factors. The current research surveyed 290 respondents to understand students' expectations and benefits from service learning. It throws light on the challenges that need attention to harness its benefits. Addressing these issues and expectations would enhance students' engagement and elevate the benefits for all the stakeholders. 2024, IGI Global. All rights reserved. -
Socially responsible universities and student satisfaction: Case analysis
Universities play the dual role of providing new knowledge and inculcating a sense of social responsibility in student citizens to contribute to community development. Higher Education Institutions (HEIs) are often expected to be socially responsible. The principal focus of this chapter is to determine the dimensions of University Social Responsibility (USR) and examine its impact on student satisfaction. A case study research was conducted with 299 students from a private university in India. Exploratory and Confirmatory Factor Analysis were used to identify the dimensions of USR. A structural equation model was used to analyze the impact of USR on student satisfaction, with gender and volunteerism in USR activities as moderators. The results show that student satisfaction is influenced by their perception of USR activities undertaken by the university. Findings indicate that the degree of influence of USR on satisfaction is more among female than male students. Contrastingly, the degree of influence of USR on satisfaction remained the same for volunteers and non-volunteers, indicating that the university is transparent in its USR activities. The findings highlight the importance of USR actions and how these activities lead to increased student satisfaction. The study also discusses the model adopted by the university to achieve higher standards of USR that other HEIs can adapt. 2024 Nova Science Publishers, Inc. -
An Understanding of Knowledge Management Perception and Implementation in Higher Education
Global Journal of Arts and Management, Vol. 2, No.3, pp 204-206, ISSN No. 2249-2658 -
Organization is an Incubator to Develop Intrapreneurship
Global Journal of Arts and Management, Vol. 2, No.3, pp 216-218, ISSN No. 2249-2658 -
Revolutionizing legal services with blockchain and artificial intelligence
[No abstract available] -
An improved web caching system with locally normalized user intervals
Caching is one of the most promising areas in the field of future internet architecture like Information-centric Networking, Software Defined Networking, and IoT. In Web caching, most of the web content is readily available across the network, even if the webserver is not reachable. Several existing traditional caching methods and cache replacement strategies are evaluated based on the metrics like hit ratio and byte hit Ratio. However, these metrics have not been improved over the period because of the traditional caching policies. So, in this paper, we have used an intelligent function like locally normalized intervals of page visit, website duration, users' interest between user groups is proposed. These intervals are combined with multiple distance metrics like Manhattan, squared Euclidean, and 3-,4-,5-norm Minkowski. In order to obtain significant common user navigation patterns, the clustering relation between the users using different intervals and distances is thoroughly analyzed. These patterns are successfully coupled with greedy web cache replacement strategies to improve the efficiency of the proposed web cache system. Particularly for improving the caching metrics more, we used an AI-based intelligent approach like Random Forest classifier to boost the prefetch buffer performance and achieves the maximum hit rate of 0.89, 0.90, and byte hit rate of 0.87, 0.89 for Greedy Dual Size Frequency and Weighted Greedy Dual Size Frequency algorithms, respectively. Our experiments show good hit/byte hit rates than the frequently used algorithms like least recently used and least frequently used. 2013 IEEE. -
Pearson correlation-based clustering with collaborative task allocation in 5G Industrial Internet of Things divergent health networks
Simultaneous task allocation is crucial for enhancing service quality in Industrial Internet of Things (IIoT) environments. The distribution and management of tasks remain among the biggest challenges in the IIoT era. Efficient allocation strategies are needed to enable transparent network configurations and maximize task throughput. Although recent methods address the dynamic management of objects, they often overlook the correlations between tasks and their associated functionalities. This paper introduces a novel Connected Harmonical Adaptive Task Allocation (CHATA) model for IIoT health networks to ensure fair task distribution. CHATA leverages similarity measures of object functionalities to identify the most suitable object to perform each task. Simulations conducted in NS-3 demonstrate that CHATA achieves up to 90% allocation efficiency in 5G Radio Access Technologies IIoT health environments and significantly outperforms recent approaches in task assignment performance. The Author(s) 2025. -
Normalized group activations based feature extraction technique using heterogeneous data for Alzheimers disease classification
Several deep learning networks are developed to identify the complex atrophic patterns of Alzheimers disease (AD). Among various activation functions used in deep neural networks, the rectifier linear unit is the most used one. Even though these functions are analyzed individually, group activations and their interpretations are still not explored for neuroimaging analysis. In this study, a unique feature extraction technique based on normalized group activations that can be applied to both structural MRI and resting-state-fMRI (rs-fMRI) is proposed. This method is split into two phases: multi-trait condensed feature extraction networks and regional association networks. The initial phase involves extracting features from various brain regions using different multi-layered convolutional networks. Then, multiple regional association networks with normalized group activations for all the regional pairs are trained and the output of these networks is given as input to a classifier. To provide an unbiased estimate, an automated diagnosis system equipped with the proposed feature extraction is designed and analyzed on multi-cohort Alzheimers Disease Neuroimaging Initiative (ADNI) data to predict multi-stages of AD. This system is also trained/tested on heterogeneous features such as non-transformed features, curvelets, wavelets, shearlets, textures, and scattering operators. Baseline scans of 185 rs-fMRIs and 1442 MRIs from ADNI-1, ADNI-2, and ADNI-GO datasets are used for validation. For MCI (mild cognitive impairment) classifications, there is an increase of 14% in performance. The outcome demonstrates the good discriminatory behaviour of the proposed features and its efficiency on rs-fMRI time-series and MRI data to classify multiple stages of AD. 2024 Vaithianathan et al.