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WEAR AND FRICTION BEHAVIOUR OF ALUMINIUM METAL MATRIX COMPOSITE REINFORCED WITH GRAPHITE NANOPARTICLES
In the current research work, AA7050 a marine aluminium alloy was reinforced with the nano-graphite particles, processed through the stir casting technique. The scanning electron microstructure reveals, that the nanoparticles were uniformly distributed over the matrix material and the hardness of the composites increased with a rise in the weight percentage of Gr particles owing to the Hall patch effect. The wear experiments were conducted by varying reinforcement, load, velocity, distance, and temperature, and the experimental runs were designed using the L25 orthogonal array, in which wear, coefficient of friction and worn surface hardness were recorded as a response. The wear resistance of the composites increases with a rise in the graphite content attributed to the formation of a mechanically mixed layer, the wear rate transfers from mild to severe, when there is shift in temperature from 100C to 150C. The worn surface hardness of the composites was higher than those of as-cast composites owing to the presence of Fe on the surface confirmed through the EDAX mapping. The composites were optimized using the modified PROMETHEE optimization technique and the results revealed that AA7050 reinforced with 8% Gr particles showed the best result and was recommended for the marine sector. 2024, Scibulcom Ltd.. All rights reserved. -
Wear characterization of hnt filled glass-epoxy composites using taguchis design of experiments and study of wear morphology
Glass-epoxy composites are increasingly being used in several industrial applications, viz. automobile, marine, aerospace, electrical and electronics components, especially in tribological components, viz. bearings, impellers, cams, driving wheels, bolts, nuts, seals, bushes and gears, which are used extensively in machinery because their lower weight, exceptional strength, resistance to corrosion capabilities, and cost effectiveness. The work focuses on optimization of the process parameters of the dry sliding wear test, viz. the applied load, disc rotation speed, weight percentage (wt.%) of the Halloysite nanotube (HNT) filler, time as well as the track diameter to minimize the wear rate of the glass fabric reinforced epoxy composite against EN-32 steel. In this research, the specimens are fabricated in accordance with the ASTM G-99 standard and the experiment is carried out with various combinations of parameters using a pin-on-disc tribometer, while keeping the time and track diameter constant. To proceed further, trial runs are conducted using MINITAB 19 software to optimize the process parameters for minimum wear by developing Taguchis design of experiments (DOE) based on the L45 orthogonal array (OA), and subse-quent analysis of the signal-to-noise (S/N) ratio. The results of the optimization clearly indicate that the wt.% of HNT is the most significant parameter that has a significant effect on minimizing the applied load, speed and sliding wear rate. In over-view, the experiment results showed that the combined parameters influenced the wear. In addition, scanning electron microscopy (SEM) is performed to study the surface morphologies of the worn specimens and determine the wear mechanism in accordance with the test results. The wear mechanism clearly indicates that there is a larger amount of matrix debris, fiber breakage and fiber-matrix debonding in the neat composites as compared to the HNT filled glass-epoxy composites since a distinct pattern of micro coring and segregation of the filler along the peripheries of the glass fiber-epoxy interstitial sites, leading to strong bonding between the fibers and matrix are observed in the HNT filled composites. The strong bonding thus resists the wear to a certain extent, and the wear debris is relatively less in the HNT filled composites as compared to the neat composites. 2020, Polish Society of Composite Materials. All rights reserved. -
Wearable Leaf-Shaped Slotted Antenna Including Human Phantom for WBAN Applications
A 5.8 GHz leaf-shaped slotted antenna for Wireless Body Area Network (WBAN) applications is presented in this piece of content. The leaf structure includes tri leaves, having a complete ground plane at the lowest floor and a central circle slot. The suggested antenna is 60 mm by 60 mm by 1.16 mm in total dimensions. The ISM (Industrial Science and Medical) band frequency of 5.8 GHz is covered by this antenna's radiation range of 5.5 to 6.4 GHz. The radiated pattern, efficiency, S11 magnitude and gain were the different attributes of the leaf-patterned slot antenna. The creation of a stylish leaf-shaped antenna that can be incorporated into clothing designs is the main goal of this project. This antenna may be used in difficult situations because of its flexible base and conductive fabric. The method considers the needs of wearable antennas, such as the impact of human interactions on this antenna, as well as the opposite. 2023 IEEE. -
Wearable Sensors for Pervasive and Personalized Health Care
Healthcare systems are designed to provide commendable services to cater health needs of individuals with minimum expenditure and limited use of human resources. Pervasive health care can be considered as a major development in the healthcare system which aims to treat patients with minimal human resources. This provides a solution to several existing healthcare problems which might change the future of the healthcare systems in a positive way. Pervasive health care is defined as a system which is available to anyone at any point of time and at any place without any location constraints. At a broader definition, it helps in monitoring the health-related issues at a home-based environment by medical stakeholders which is very beneficial in case of emergency situations. This chapter elaborates architecture of IoT, how wearable sensors can be used to help people to get personalized and pervasive healthcare systems, and it also gives a detailed working of different types of IoT-enabled wearable devices for pervasive health care. 2021, The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. -
Wearable Smart Technologies: Changing the Future of Healthcare
Wearable smart technologies are the innovative solutions for the issues of healthcare services. In this chapter, a review of the innovative wearable healthcare devices and applications has been done. Wearable devices are used for supervision and illness control. These innovative wearable technologies can straightforwardly affect the medical dynamic, can upgrade the quality of treatment for patients, and can reduce the expenses incurred in it. The large health record generated by the wearable devices provides an opportunity for data analysts to apply machine learning techniques for prediction on the data generated by sensors. Today's wearable smart technologies are capable of being integrated into eyeglasses, cloths, shoes, belts, watches, etc. Sensors can be inserted in these objects to be worn. The advanced forms of wearable technologies can be attached to the skin of the wearer. A smartphone is mainly utilized to collect data and communicate it to a server situated at a remote area for greater capacity and investigation. Maximum innovations related to wearable technologies are still in the prototyping phase. The study covers almost every aspect of wearable technologies, which could be helpful in the future for innovation and research in this area. 2024 selection and editorial matter, Ankur Beohar, Ribu Mathew, Abhishek Kumar Upadhyay, and Santosh Kumar Vishvakarma -individual chapters, the contributors. All rights reserved. -
Weather Forecasting Accuracy Enhancement Using Random Forests Algorithm
In today's world, weather forecasting is essential for decision-making in a variety of fields, including agriculture, transportation, and disaster preparedness. It's not simple to make weather predictions. Today, both in business and academia, data analytics is growing in importance as a tool for decision-making. The adoption of data-driven concepts is for our graduates, enhancing their marketability. Data Analytics us a study belonging to science that analyses gathered raw data, which makes conclusions about the particular information. Data analytics has been used by many sectors recently, such as hospitality, where this industry can collect data, find out where the problem is, and manage to fix the problem. Nominal, ordinal, interval, and ratio data levels are the four types of data measurement. Applications of data analytics can be found in many industries, including shipping and logistics, manufacturing, security, education, healthcare, and web development. Any business that wants to succeed in the modern digital economy should make analytics a core focus. To make such data meaningful, a transformation engine was used with types from several sources. Ironically, this has made analytics harder for businesses. As businesses employ more platforms and applications, the amount of data available has grown tremendously. This article focuses on different applications of data analytics in the modern world. Weather forecasting is a highly intricate and multifaceted process that draws upon data from various sources. It relies on a combination of scientific studies and sophisticated weather models to decipher the vast amount of information available. 2023 IEEE. -
Web mining patterns discovery and analysis using custombuilt Apriori Algorithm
International Journal of Engineering Inventions Vol.2, Issue 5,pp.16-21 ISSN No. 2278-7461 -
Web Platforms for Fintech Products
Internet marketing and digital marketing are not synonymous in the minds of the majority of the population, yet this may not be true. Given the rise in popularity of digital marketing as a marketing tactic, it is critical to comprehend the distinctions between the two methods. Even while it should be evident that they might be connected, there is very little difference between them. Internet marketing is merely a subclass of digital marketing, as well as the extent of digital marketing encompasses much more than internet marketing. This paper discussed digital marketing technologies, as well as the advantages and disadvantages of employing digital marketing and digital finance tools in general. In order to remain competitive, businesses must overcome obstacles and seize possibilities presented by digital marketing technologies. Lastly, it's critical to prioritise digital marketing and make use of digital finance techniques in order to maintain a good performance without wasting time or money. 2022 IEEE. -
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Web User Access Log Analytics Using Neural Learning, Regression and Logit Boost Clustering Techniques for Accurate User Behavioural Pattern Identification
Web Usage Mining (WUM), is the process of mining user behaviour patterns from huge log fles. Weblogs provide substantial input to learning the identity of an online user. Analysis of these patterns extracted from the weblog datasets is currently being explored by various researchers. Due to the recent advent of automation, mining patterns from weblogs are automated. These automated mining processes focus on browsing habits and usage patterns. To make this process of gathering better, there are many ways to look at how users act and put them into relevant groups.Identifying, detecting, and classifying features that demarcate specifc traits that are related is an important task. Conventional research is designed to discover web usage mining strategies through clustering and classifcation methods. However, there is a need to focus on and improve the accuracy of the prediction systems that classify acquired features to fgure out the patterns of web users. Deep learning methods are used to mine weblog data to improve accuracy and precision. To improve user behaviour pattern mining, a two-level clustering process is introduced as Ensemble Fuzzy K-Means with Logit Boost Clustering (EFK-LBC) technique to extract the weblog. In this technique, a preprocessing step is included to remove redundant data and choose reliable log fles. The Fuzzy-K means clustering technique is used to identify behavioural patterns exhibited by recurrent users. Finally, the Logit Boost Clustering method is introduced to the data,that help in generating a strong cluster. Clustering of web users frequent behavioural patterns using the Logit Boost ensemble technique helps the proposed EFK-LBC method to improve newlinethe accuracy up to 88% and reduce the clustering time by 20% compared with existing approaches. Though the proposed EFK-LBC technique performs better for user identifcation, the different initialization of clusters provides various fnal clustering results. -
Web-based single session therapy training for mental health support providers: a mixed-methods evaluation study protocol
The growing mental health needs and constrained resources in low- and middle-income countries necessitate scalable solutions. Single Session Therapy (SST) is a global trend in brief and cost-effective options for mental health interventions. It involves a single planned session between mental health service provider and client. This study aims to present a protocol to develop and evaluate a culture specific web-based training program to equip mental health support providers with the skills and confidence to deliver SST. The study protocol uses a mixed-methods evaluation design through three phasesneed assessment where psychologists and social workers collaborate to identify training needs and co-create the program; development and expert validation of the web-based training program; and randomized control trial to evaluate the training, followed by in-depth discussions with participants. This study breaks new ground by empirically designing and evaluating a training program for SST. It uniquely co-designs and validates a culturally sensitive SST training program, leveraging the expertise of a renowned international panel. This protocol goes beyond a blueprint for replicating this study, it serves as a foundational guide for nations seeking to implement effective SST training for their mental health professionals, preventing duplication of efforts. The Author(s) 2024. -
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Weighted Mask Recurrent-Convolutional Neural Network based Plant Disease Detection using Leaf Images
Large losses in output, money, and quality/quantity of agricultural goods are incurred due to plant diseases. Seventy percent of India's GDP is tied to the agricultural sector, thus protecting plants from diseases is crucial. For this reason, it is important to keep an eye on plants from the moment they sprout. The usual approach for this omission is naked eye inspection, which is more time-consuming, costly, and requires significant skill. Thus, automating the method for detecting diseases is necessary to speed up this process. It is imperative that image processing methods be used in the creation of the illness detection system. Disease detection involves a number of processes, including Weighted Mask R-CNN, GLCM feature extraction, Multi-thresholding image pre-processing, and K means image segmentation classification. The weighted Mask R-CNN outperforms the standard RNN, the Mask R-CNN, and the CNN in terms of accuracy and recall in analytical trials by a significant margin. 2023 IEEE. -
Well-being and Career Decision-making Difficulties Among Masters Students: A Simultaneous Multi-Equation Modeling
There is a stellar upsurge in the number of persons pursuing a masters level of education as well as the institutions offering it in the current generation. Nevertheless, an explicit theoretical and empirical implication of how the tutelages, at this level, shape the well-being of learnerssuch that it could help individuals overcome career decision-making difficulties remains to be elucidated. The present study addressed two major objectives. Firstly, we investigated the well-being of masters degree students along with career decision-making difficulties in India. Secondly, apart from exploring the possible influence of nationality of the respondents on career decision difficulty, the study expanded the literature on career decision-making difficulties to under-researched populations in developing countries. Through a cross-sectional research design, we recruited a sample of 136 masters degree respondents. The result reveals that while the composite well-being resources significantly influenced Career Decision Difficulties, the nationality of respondents appeared not as a germane factor in this context. Following the evaluation of the direct effect of individual well-being resources; Self-acceptance and Personal growth proved to have a statistically significant effect on career decision-making difficulties. Also, among the constituents of career decision-making difficulties studied, lack of readiness appears to be the major concern among the respondents. The findings expand the literature on cognitive, vocational, and organization science vis-a-vis career decision-making difficulties and provide useful insights for educational institutions and practitioners. 2021 The Author(s). This open access article is distributed under a Creative Commons Attribution (CC-BY) 4.0 license. -
WELL-BEING AND PROSPERITY: Multidirectional Disciplinary Interactions with Religion
Despite significant advancements in science and technology, religion continues to influence human lives. The twentieth-century perspectives from social sciences, influenced by the secular hypothesis, mainly highlight the negative influence of religion on human progress and practically ignore its influential and positive impact on various fields of knowledge/disciplines. In this paper, we have examined literature from politics, economics, and psychology to understand religions impact on these disciplines and vice versa. We find that religions contribution to human society in the 20th and 21st centuries has been mostly positive, especially in education, healthcare, social justice, economic growth, ethics, and initiatives for eradicating inequality and injustice. For instance, religion provides effective coping measures and strategies when humans face uncertainties and catastrophes and facilitate comfort, confidence, and emotional wellness. Further, we realised that (i) the contemporary research literature in social sciences generally highlights the interaction between religion and various fields of knowledge in a unidirectional way i.e., religion influencing disciplines and not how disciplines influence religion, and (ii) that it fails to reveal a more complex multidirectional and circular relationship between religion and social sciences. This paper proposes ways to bring together social scientists and religious scholars to facilitate the much-needed discussion on the multidirectional relationship between religion and social sciences, thereby paving the way toward the well-being of individuals and social transformation. 2022 Journal of Dharma: Dharmaram Journal of Religions and Philosophies (DVK, Bangalore),. -
Well-being of North Eastern Migrant Workers in Bangalore
This paper explores the quality of life and subjective well-being of north-east migrant workers engaged in various formal and informal jobs in Bangalore. The composite well-being index reveals moderate well-being for the majority of workers. The disaggregated analysis, however, shows poor material conditions of life. Using the Day Reconstruction Method, we also find positive emotions associated with activities such as socialising but negative emotions for work and commuting. With respect to interacting partners, the negative emotions were highest while dealing with clients and customers. We also found positive correlations between life satisfaction and quality of life indicators, most strongly, with job quality. Lower quality of jobs, reported by women in comparison to men, suggests that organisations should aim to create more equal and enabling work spaces for all genders. 2020 Institute for Human Development. -
Were the recent air pollution and landfill fires in Brahmapuram at odds with Kerala's vision of sustainable development?
Air pollution is a global issue, as is commercial, and industrial waste disposal. Industrialized cities have poor air quality. Emissions from fossil fuel, solid household resources and industry, uncontrolled construction, and human and natural activity pollution are the main sources. The purpose of the study is to investigate answers to the question: Were the recent air pollution and landfill fire in Brahmapuram at odds with Kerala's vision of sustainable development? The study consists of a content analysis of prominent newspaper reports on the Prisma model of sorting articles on Brahmapuram issues to investigate the issue and assess the acceptance of sustainable development in Kochi. The reports cover the period from March 3, 2023, to April 3, 2023. The content analysis revealed that the contractor's failure to meet their obligations was the immediate cause. However, the ineffectiveness of the State's solid waste management policies, from a general failure of waste segregation at source, posed a threat to sustainable development. The researcher classified the causes of the Kochi waste fire under the following reasons, namely, environmental, economic, social, and political. The researcher concluded that the recent landfill fire and air pollution at Brahmapuram were contrary to Keralas vision of sustainable development. 2024 by the authors. -
Whale Optimization Based Approach toCompress andFasten CNN forCrop Disease andSpecies Identification
In recent years deep learning and machine learning have been widely researched for image based recognition. This research proposes a simplified CNN with 3 layers for classification from 39 classes of crops and their diseases. It also evaluates the performance of pre-trained models such as VGG16 and ResNet50 using transfer learning. Similarly traditional Machine Learning algorithms have been trained and tested on the same dataset. The best accuracy using proposed CNN was 87.67% whereas VGG16 gave best accuracy of 91.51% among Convolution Neural Network models. Similarly Random Forest machine learning method gave best accuracy of 93.02% among Machine Learning models. Since the pre-trained models are having huge size hence in order to deploy these solutions on tiny edge devices compression is done using Whale Optimization. The maximum compesssion was obtained with VGG16 of 88.19% without loss in any performance. It also helped betterment of inference time of 44.13% for proposed CNN, 56.76% for VGG16 and 63.23% for ResNet50. 2023, Springer Nature Switzerland AG. -
What drives the wheels of evolution in NGC 1512?: A UVIT study
Context. Environmental and secular processes play a pivotal role in the evolution of galaxies. These can be external processes such as interactions or internal processes linked to the action of bar, bulge, and spiral structures. Ongoing star formation in spiral galaxies can be affected by these processes. By studying the star formation progression in the galaxy, we can gain insights into the role of different processes that regulate the overall evolution of a galaxy. Aims. The ongoing interaction between the barred-spiral galaxy NGC 1512 and its satellite NGC 1510 offers an opportunity to inves- tigate how galactic interactions and the presence of a galactic bar influence the evolution of NGC 1512. We aim to understand the recent star formation activity in the galaxy pair and thus gain insight into the evolution of NGC 1512. Methods. The UltraViolet Imaging Telescope (UVIT) on board AstroSat enables us to characterise the star-forming regions in the galaxy with a superior spatial resolution of ?85 pc in the galaxy rest frame. We identified and characterised 175 star-forming regions in the UVIT far-ultraviolet (FUV) image of NGC 1512 and correlated with the neutral hydrogen (Hi) distribution. Extinction correc- tion was applied to the estimated photometric magnitude. We traced the star-forming spiral arms of the galaxy and studied the star formation properties across the galaxy in detail. Results. We detect localised regions of star-formation enhancement and distortions in the galactic disc. We find this to be consistent with the distribution of Hi in the galaxy. This is evidence of past and ongoing interactions affecting the star formation properties of the galaxy. We studied the properties of the inner ring. We find that the regions of the inner ring show maximum star-formation-rate density (log(SFRDmean[M yr?1 kpc?2]) ? ?1.7) near the major axis of the bar, hinting at a possible crowding effect in these regions. The region of the bar in the galaxy is also depleted of UV emission. This absence suggests that the galactic bar may have played an active role in the redistribution of gas and quenching of star formation inside the identified bar region. We therefore suggest that both secular and environmental factors might be influencing the evolution of NGC 1512. The Authors 2023. -
What fuels the employees in startups?: Data on hybrid/colocated/virtual working environment towards efficiency
Purpose: This article examines the concepts of workplace satisfaction and productivity using data. The data will be used to investigate the variables contributing to employee satisfaction to achieve optimum efficiency through various startup working environments. Design/ Methodology/ Approach: Descriptive causal investigation. A structured instrument scale questionnaire via the internet to 256 employees working for highly organized organizations in Bangalore, India, using Qualtrics. The researcher adopted a simple random sampling method. Findings: The respondents in the data believed that the pre-covid workplace was advantageous. The hybrid model's prevalence of autonomy and flexibility increases work productivity. When employees are given more responsibility, their job satisfaction and productivity increase. Research Limitations/ Implications: Collecting data in a startup was extremely difficult due to the difficulty of obtaining permission, and through the analysis, it was determined that businesses have a responsibility to provide supplemental benefits to remote employees, which may increase the level of job satisfaction and enjoyment experienced by these individuals. 2023 The Author(s)