Author: Ajith Abraham
Ensemble Transfer Learning for Robust Human Activity Recognition from Images
Publisher: International Journal of Computer Information Systems and Industrial Management Applications, 2023
Abstract
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Links
"In recent years, the field of Human Activity
Recognition (HAR) has witnessed a significant growth owing to
the abundance of data and its practical applications in various
real-world scenarios. The recognition of human activities from
still images remains a challenging task due to the presence of
class imbalance and limited intra-class variability. To address
these issues, this work proposes an Ensemble Transfer Learning
approach for image-based HAR. The proposed model employs
an ensemble stacked averaging model consisting of well-known
transfer learning architectures such as ResNet50V2,
DenseNet169 and VGG19. The ensemble model can learn
different features from different architectures, thus providing a
robust recognition model. Additionally, data augmentation is
employed to increase the diversity of the images in the datasets.
The suggested model helps to mitigate the problems of classimbalance and the lack of intra-class variability by generating
new images with different variations of the original images. The
model is evaluated on two benchmark datasets for image based
HAR, namely, the PPMI action dataset and the Stanford 40
Actions dataset. The results demonstrate enhanced performance
compared to a few of the related research works."
https://www.softcomputing.net/aayush2023ijcisim.pdf
"In recent years, the field of Human Activity
Recognition (HAR) has witnessed a significant growth owing to
the abundance of data and its practical applications in various
real-world scenarios. The recognition of human activities from
still images remains a challenging task due to the presence of
class imbalance and limited intra-class variability. To address
these issues, this work proposes an Ensemble Transfer Learning
approach for image-based HAR. The proposed model employs
an ensemble stacked averaging model consisting of well-known
transfer learning architectures such as ResNet50V2,
DenseNet169 and VGG19. The ensemble model can learn
different features from different architectures, thus providing a
robust recognition model. Additionally, data augmentation is
employed to increase the diversity of the images in the datasets.
The suggested model helps to mitigate the problems of classimbalance and the lack of intra-class variability by generating
new images with different variations of the original images. The
model is evaluated on two benchmark datasets for image based
HAR, namely, the PPMI action dataset and the Stanford 40
Actions dataset. The results demonstrate enhanced performance
compared to a few of the related research works."
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Author: Ajith Abraham
Deep learning approaches for lyme disease detection: leveraging progressive resizing and self-supervised learning models
Publisher: Multimedia Tools and Applications, 2023
Abstract
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Links
Lyme disease diagnosis poses a significant challenge, with blood tests exhibiting an alarming inaccuracy rate of nearly 60% in detecting early-stage infections. As a result, there is an urgent need for improved diagnostic methods that can offer more accurate detection outcomes. To address this pressing issue, our study focuses on harnessing the potential of deep learning approaches, specifically by employing model pipelining through progressive resizing and multiple self-supervised learning models. In this paper, we present a comprehensive exploration of self-supervised learning models, including SimCLR, SwAV, MoCo, and BYOL, tailored to the context of Lyme disease detection using medical imaging. The effectiveness and performance of these models are evaluated using standard metrics such as F1 score, precision, recall, and accuracy. Furthermore, we emphasize the significance of progressive resizing and its implications when dealing with convolutional neural networks (CNNs) for medical image analysis. By leveraging deep learning approaches, progressive resizing, and self-supervised learning models, the challenges associated with Lyme disease detection are effectively addressed in this study. The application of our novel methodology and the execution of a comprehensive evaluation framework contribute invaluable insights, fostering the development of more efficient and accurate diagnostic methods for Lyme disease. It is firmly believed that our research will serve as a catalyst, inspiring interdisciplinary collaborations that accelerate progress at the convergence of medicine, computing, and technology, ultimately benefiting public health.
https://doi.org/10.1007/s11042-023-16306-9
Lyme disease diagnosis poses a significant challenge, with blood tests exhibiting an alarming inaccuracy rate of nearly 60% in detecting early-stage infections. As a result, there is an urgent need for improved diagnostic methods that can offer more accurate detection outcomes. To address this pressing issue, our study focuses on harnessing the potential of deep learning approaches, specifically by employing model pipelining through progressive resizing and multiple self-supervised learning models. In this paper, we present a comprehensive exploration of self-supervised learning models, including SimCLR, SwAV, MoCo, and BYOL, tailored to the context of Lyme disease detection using medical imaging. The effectiveness and performance of these models are evaluated using standard metrics such as F1 score, precision, recall, and accuracy. Furthermore, we emphasize the significance of progressive resizing and its implications when dealing with convolutional neural networks (CNNs) for medical image analysis. By leveraging deep learning approaches, progressive resizing, and self-supervised learning models, the challenges associated with Lyme disease detection are effectively addressed in this study. The application of our novel methodology and the execution of a comprehensive evaluation framework contribute invaluable insights, fostering the development of more efficient and accurate diagnostic methods for Lyme disease. It is firmly believed that our research will serve as a catalyst, inspiring interdisciplinary collaborations that accelerate progress at the convergence of medicine, computing, and technology, ultimately benefiting public health.
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Author: Ajith Abraham
A multi-attribute decision-making fusion model for stock trading with customizable investor personality traits in a picture fuzzy environment
Publisher: Applied Soft Computing, 2023
Abstract
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Links
In this paper, a fuzzy logic-based machine learning (ML) algorithm is introduced. This proposed ML algorithm accepts picture fuzzy sets (PFS) as the fuzzified input and incorporates genetic algorithm (GA) during the training process. The proposed ML algorithm is then incorporated into two well-known decision-making methods, namely the Technique for Order Preference by Similarity to Ideal Solution (TOPSIS) and Evaluation Based on Distance from Average Solution (EDAS). These two decision-making methods and the proposed ML algorithm are then applied to solve a multi-attribute decision-making (MADM) problem related to the evaluation and ranking of public listed companies based on their stock performance, in accordance with investors’ personalities. The actual daily closing stock price of five public listed companies from the big market capitalization (Big Cap) category traded in the Kuala Lumpur Stock Exchange (KLSE) for a period of 10 years is used as the datasets for this study. Monte Carlo simulation is used to verify the accuracy of the results. In addition, a comprehensive comparative study of some recent PFS-based decision-making methods in the existing literature and the proposed methods is conducted, and all the typical instances of the investors’ personalities are observed. The results obtained through this comparative study corroborates the results obtained via the proposed methods, and this proves the effectiveness of the proposed methods. The differences in the results obtained via the different methods are analyzed and discussed, and this again proves that the results obtained via the proposed methods are effective and consistent with the judgments of human experts.
https://doi.org/10.1016/j.asoc.2023.110715
In this paper, a fuzzy logic-based machine learning (ML) algorithm is introduced. This proposed ML algorithm accepts picture fuzzy sets (PFS) as the fuzzified input and incorporates genetic algorithm (GA) during the training process. The proposed ML algorithm is then incorporated into two well-known decision-making methods, namely the Technique for Order Preference by Similarity to Ideal Solution (TOPSIS) and Evaluation Based on Distance from Average Solution (EDAS). These two decision-making methods and the proposed ML algorithm are then applied to solve a multi-attribute decision-making (MADM) problem related to the evaluation and ranking of public listed companies based on their stock performance, in accordance with investors’ personalities. The actual daily closing stock price of five public listed companies from the big market capitalization (Big Cap) category traded in the Kuala Lumpur Stock Exchange (KLSE) for a period of 10 years is used as the datasets for this study. Monte Carlo simulation is used to verify the accuracy of the results. In addition, a comprehensive comparative study of some recent PFS-based decision-making methods in the existing literature and the proposed methods is conducted, and all the typical instances of the investors’ personalities are observed. The results obtained through this comparative study corroborates the results obtained via the proposed methods, and this proves the effectiveness of the proposed methods. The differences in the results obtained via the different methods are analyzed and discussed, and this again proves that the results obtained via the proposed methods are effective and consistent with the judgments of human experts.
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Author: Yashobanta Parida
Impacts of Training Rural Dairy Producers in India: Role of Dairy Vigyan Kendra
Publisher: International Journal of Rural Management, 2023
Abstract
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Links
The state of Gujarat, home to a vibrant network of dairy cooperatives, plays a significant role in milk production, accounting for 7.69% of the country’s total milk output. It ranked fifth in milk production among all Indian states and union territories in 2017–18. The state piloted a unique and specialised dairy extension program for dairy farmers through Dairy Vigyan Kendra (DVK) to promote dairy farming in its Panchmahal district. The DVK aimed to train rural dairy farmers and improve their socio-economic conditions. This study examines how DVK interventions increase the income from dairy, the herd size and milk production of the beneficiary farmers in the Panchmahal district. The result shows that farmers’ participation in DVK training increased their income from dairying. Further, the results highlighted that DVK intervention significantly increased milk production in the Panchmahal district. Our results conclude that the government can replicate the DVK training model in other districts of Gujarat, helping millions of dairy farmers enhance their skills and obtain more output and income from dairy farming.
https://doi.org/10.1177/09730052231157138
The state of Gujarat, home to a vibrant network of dairy cooperatives, plays a significant role in milk production, accounting for 7.69% of the country’s total milk output. It ranked fifth in milk production among all Indian states and union territories in 2017–18. The state piloted a unique and specialised dairy extension program for dairy farmers through Dairy Vigyan Kendra (DVK) to promote dairy farming in its Panchmahal district. The DVK aimed to train rural dairy farmers and improve their socio-economic conditions. This study examines how DVK interventions increase the income from dairy, the herd size and milk production of the beneficiary farmers in the Panchmahal district. The result shows that farmers’ participation in DVK training increased their income from dairying. Further, the results highlighted that DVK intervention significantly increased milk production in the Panchmahal district. Our results conclude that the government can replicate the DVK training model in other districts of Gujarat, helping millions of dairy farmers enhance their skills and obtain more output and income from dairy farming.
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Author: Tannistha Samanta
Leisure as social engagement: does it moderate the association between subjective wellbeing and depression in later life?
Publisher: Frontiers in Sociology, 2023
Abstract
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Links
"To investigate the role of leisure (as social engagement) in moderating the association between subjective wellbeing and depressive symptoms among older Indians.
Methods: The sample included data from 39,538 older adults (aged 55–80) from the Longitudinal Aging Study in India (LASI, Wave-1), 2017–2018. Individual level questionnaire was used to examine the relationship among social engagement, subjective wellbeing, and depressive symptoms. Moderating effects of leisure activities were estimated through interaction analysis and linear multivariable modeling.
Results: Low participation in social engagement activities (or leisure) was associated with greater likelihood of depressive symptoms. Leisure activities positively and significantly moderated the subjective wellbeing among older adults with depressive symptoms. Results suggest a significant wealth gradient where affluent older Indians having a clear advantage in heightened levels of social engagement and subsequently lower likelihood of depressive symptoms. Additionally, being in an urban area, co-residence in a “joint” household and belonging to the dominant social groups in terms of caste and religious categories are associated with gains in wellbeing.
Discussion: The direct and indirect effects of social engagement suggest that depressive symptoms can be mitigated while enhancing overall wellbeing of older adults. This holds promise for social policy in redirecting efforts to develop age-friendly initiatives and social infrastructure that enhance the link between engagement and wellbeing."
https://doi.org/10.3389/fsoc.2023.1185794
"To investigate the role of leisure (as social engagement) in moderating the association between subjective wellbeing and depressive symptoms among older Indians.
Methods: The sample included data from 39,538 older adults (aged 55–80) from the Longitudinal Aging Study in India (LASI, Wave-1), 2017–2018. Individual level questionnaire was used to examine the relationship among social engagement, subjective wellbeing, and depressive symptoms. Moderating effects of leisure activities were estimated through interaction analysis and linear multivariable modeling.
Results: Low participation in social engagement activities (or leisure) was associated with greater likelihood of depressive symptoms. Leisure activities positively and significantly moderated the subjective wellbeing among older adults with depressive symptoms. Results suggest a significant wealth gradient where affluent older Indians having a clear advantage in heightened levels of social engagement and subsequently lower likelihood of depressive symptoms. Additionally, being in an urban area, co-residence in a “joint” household and belonging to the dominant social groups in terms of caste and religious categories are associated with gains in wellbeing.
Discussion: The direct and indirect effects of social engagement suggest that depressive symptoms can be mitigated while enhancing overall wellbeing of older adults. This holds promise for social policy in redirecting efforts to develop age-friendly initiatives and social infrastructure that enhance the link between engagement and wellbeing."
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Author: Sairaj Patki
Effect of Fitspiration on State Self-Esteem Among Young Adults
Publisher: Identity, 2023
Abstract
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Links
Media trends are widely prevalent on social media sites, and have demonstrated the ability to influence people’s perceptions, beliefs, and attitudes. The present study examined the fitness and health-focused media trend of “fitspiration” and its effects on the state self-esteem of young adult males and females. The study is one of the first to examine fitspiration in an Indian context and amongst an Asian population and contributes to the growing literature concerning media trends, body image, and self-esteem in women and men. The sample consisted of 61 undergraduate and postgraduate students aged 18–25 years, recruited through convenience sampling. A pretest posttest control group design was used and data was collected using a personal demographic sheet and state self-esteem scale (SSES). Pretest group equivalence was statistically ascertained and then pre-post comparison analysis was done for male and female participants separately. Contrary to expectations, findings revealed that fitspiration caused a significant increase in the social state self-esteem of females. The findings have been discussed based on the Self-Discrepancy Theory. The study contributes to the limited literature on media trends in an Asian setting and has valuable implications for the fields of social, health, and sports psychology, as well as advertising.
https://doi.org/10.1080/15283488.2023.2253828
Media trends are widely prevalent on social media sites, and have demonstrated the ability to influence people’s perceptions, beliefs, and attitudes. The present study examined the fitness and health-focused media trend of “fitspiration” and its effects on the state self-esteem of young adult males and females. The study is one of the first to examine fitspiration in an Indian context and amongst an Asian population and contributes to the growing literature concerning media trends, body image, and self-esteem in women and men. The sample consisted of 61 undergraduate and postgraduate students aged 18–25 years, recruited through convenience sampling. A pretest posttest control group design was used and data was collected using a personal demographic sheet and state self-esteem scale (SSES). Pretest group equivalence was statistically ascertained and then pre-post comparison analysis was done for male and female participants separately. Contrary to expectations, findings revealed that fitspiration caused a significant increase in the social state self-esteem of females. The findings have been discussed based on the Self-Discrepancy Theory. The study contributes to the limited literature on media trends in an Asian setting and has valuable implications for the fields of social, health, and sports psychology, as well as advertising.
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Author: Shamsher Singh
The Impact of the Covid-19 Pandemic on Rural Sanitation Workers in Haryana
Publisher: Review of Agrarian Studies, 2023
Abstract
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Links
"The Covid-19 pandemic caused significant changes in the world of work. While
pandemic-induced lockdowns caused disruptions in the employment conditions and
earnings of wage earners in general, Covid-19 had a differentiated impact across
different groups of workers (Bonini, Mukherjee, and Roig 2020; Jaga and OllierMalaterre 2022; Kalhan, Singh, and Moghe 2020, 2023; Oxfam India 2021; and Sethu
and Thangaraj 2021).1 In a multi-country survey on the labour-market fallout of
Covid-19, Soares and Berg (2022) reported that workers who were vulnerable in the
labour market saw their vulnerability rise as a result of the pandemic. "
http://ras.org.in/c0b4e4ab1e9b4b985e5eaf2f2e50475a
"The Covid-19 pandemic caused significant changes in the world of work. While
pandemic-induced lockdowns caused disruptions in the employment conditions and
earnings of wage earners in general, Covid-19 had a differentiated impact across
different groups of workers (Bonini, Mukherjee, and Roig 2020; Jaga and OllierMalaterre 2022; Kalhan, Singh, and Moghe 2020, 2023; Oxfam India 2021; and Sethu
and Thangaraj 2021).1 In a multi-country survey on the labour-market fallout of
Covid-19, Soares and Berg (2022) reported that workers who were vulnerable in the
labour market saw their vulnerability rise as a result of the pandemic. "
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Author: Tannistha Samanta
Social egg freezing as ambivalent materialities of aging
Publisher: Journal of Aging Studies, 2023
Abstract
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Links
This commentary explores how the material-nonmaterial transactions around reproduction among women raise paradoxical questions of reproductive autonomy and commercialization of reproduction. Drawing from medical anthropological studies on human reproduction, the technology around social egg freezing has been conceived to proffer ambivalent possibilities of hope, despair, and repair as mature women recalibrate their reproductive identities, especially in pronatalist contexts. Building on the material-discursive critique of the ‘material turn’, I ask if social egg freezing offers an empowering biological reprieve for women who have ‘chosen’ a non-normative (i.e., a departure from heterosexual conjugality) life-course. Subsequently, how does one “do age” when material entanglements (here, reproductive technologies) disrupt the symbolic performance of the life-course? Or, does this reproductive autonomy actualized through social egg freezing align well with the neoliberal prerogatives of “successful aging,” thereby intensifying the specter of the “Third Age”? Overall, through an analysis of (reproductive) technologies, as well as the question of choice and social bodies, I argue how new materialities and anxieties of growing old can undergird the material-cultural link in gerontology.
https://doi.org/10.1016/j.jaging.2023.101183
This commentary explores how the material-nonmaterial transactions around reproduction among women raise paradoxical questions of reproductive autonomy and commercialization of reproduction. Drawing from medical anthropological studies on human reproduction, the technology around social egg freezing has been conceived to proffer ambivalent possibilities of hope, despair, and repair as mature women recalibrate their reproductive identities, especially in pronatalist contexts. Building on the material-discursive critique of the ‘material turn’, I ask if social egg freezing offers an empowering biological reprieve for women who have ‘chosen’ a non-normative (i.e., a departure from heterosexual conjugality) life-course. Subsequently, how does one “do age” when material entanglements (here, reproductive technologies) disrupt the symbolic performance of the life-course? Or, does this reproductive autonomy actualized through social egg freezing align well with the neoliberal prerogatives of “successful aging,” thereby intensifying the specter of the “Third Age”? Overall, through an analysis of (reproductive) technologies, as well as the question of choice and social bodies, I argue how new materialities and anxieties of growing old can undergird the material-cultural link in gerontology.
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Author: Dnyaneshwar Jadhav and Dinesh Shenoy
Measurement model for dynamic capabilities of an academic library (DCAL)
Publisher: Library & Information Science Research, 2023
Abstract
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Links
Environmental changes such as the evolving needs of society, advancements in technologies, and shifting learning paradigms have impacted the way academic libraries operate. Academic libraries must respond to environmental changes by acquiring new capabilities. Using the strategic management concept of dynamic capabilities, this study investigates the relationship between the dynamic capabilities of an academic library and its overall performance. This study uses the structural equation modelling (SEM) technique and develops a new model, the DCAL model, for academic libraries to build these new capabilities. The analysis of the DCAL model indicates a positive and significant relationship between the three core capabilities, searching, seizing, and transforming, and the overall performance of the academic library. This study also identifies activities, scouting for new technologies that can be adapted by academic libraries, piloting projects, analysing large amounts of data, balancing routine work with innovative work, and the need for academic librarians to build capabilities to perform those activities to succeed in their future roles.
https://doi.org/10.1016/j.lisr.2023.101266
Environmental changes such as the evolving needs of society, advancements in technologies, and shifting learning paradigms have impacted the way academic libraries operate. Academic libraries must respond to environmental changes by acquiring new capabilities. Using the strategic management concept of dynamic capabilities, this study investigates the relationship between the dynamic capabilities of an academic library and its overall performance. This study uses the structural equation modelling (SEM) technique and develops a new model, the DCAL model, for academic libraries to build these new capabilities. The analysis of the DCAL model indicates a positive and significant relationship between the three core capabilities, searching, seizing, and transforming, and the overall performance of the academic library. This study also identifies activities, scouting for new technologies that can be adapted by academic libraries, piloting projects, analysing large amounts of data, balancing routine work with innovative work, and the need for academic librarians to build capabilities to perform those activities to succeed in their future roles.
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Author: Dinesh Shenoy
How do megaprojects build dynamic capabilities?
Publisher: Innovation and Development, 2023
Abstract
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Links
Governments around the world are increasingly using megaprojects to deliver services to communities. However, most megaprojects fail; they are delivered late, with significant cost overruns and/or diluted benefits. Researchers have suggested implementing open innovation (OI) to improve megaproject performance. However, implementing OI requires an organization to build new capabilities. This study contributes to the literature on megaprojects by adapting the dynamic capabilities concept to develop a theoretical framework for implementing OI. A combination of a literature review and expert judgement was used to extract twenty-nine elements that are the micro-foundations of the capability to implement OI in megaprojects. A grey number theory-based mathematical model was developed to measure and objectively monitor the OI implementation index (O3I) for megaprojects. This study presents a case study that indicates the ease of implementation of the developed model and its transferability to other megaprojects across domains. This study intersects three areas of inquiry: megaproject management, OI, and dynamic capabilities and makes two key contributions to the growing body of knowledge in megaproject management: (1) we adapt the dynamic capabilities concept to develop a framework to implement OI in megaprojects and (2) we develop a scale to qualitatively assess the level of OI implementation in an organization.
https://doi.org/10.1080/2157930X.2023.2263674
Governments around the world are increasingly using megaprojects to deliver services to communities. However, most megaprojects fail; they are delivered late, with significant cost overruns and/or diluted benefits. Researchers have suggested implementing open innovation (OI) to improve megaproject performance. However, implementing OI requires an organization to build new capabilities. This study contributes to the literature on megaprojects by adapting the dynamic capabilities concept to develop a theoretical framework for implementing OI. A combination of a literature review and expert judgement was used to extract twenty-nine elements that are the micro-foundations of the capability to implement OI in megaprojects. A grey number theory-based mathematical model was developed to measure and objectively monitor the OI implementation index (O3I) for megaprojects. This study presents a case study that indicates the ease of implementation of the developed model and its transferability to other megaprojects across domains. This study intersects three areas of inquiry: megaproject management, OI, and dynamic capabilities and makes two key contributions to the growing body of knowledge in megaproject management: (1) we adapt the dynamic capabilities concept to develop a framework to implement OI in megaprojects and (2) we develop a scale to qualitatively assess the level of OI implementation in an organization.
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Author: Anuradha Batabyal
Predator–prey systems as models for integrative research in biology: the value of a non-consumptive effects framework
Publisher: Journal of Experimental Biology, 2023
Abstract
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Links
Predator-prey interactions are a cornerstone of many ecological and evolutionary processes that influence various levels of biological organization, from individuals to ecosystems. Predators play a crucial role in shaping ecosystems through the consumption of prey species and non-consumptive effects. Non-consumptive effects (NCEs) can induce changes in prey behavior, including altered foraging strategies, habitat selection, life history and anti-predator responses. These defensive strategies have physiological consequences for prey, affecting their growth, reproduction and immune function to name a few. Numerous experimental studies have incorporated NCEs in investigating predator-prey dynamics in the past decade. Interestingly, predator-prey systems can also be used as experimental models to answer physiology, cognition and adaptability questions. In this Commentary, I highlight research that uses NCEs in predator-prey systems to provide novel insights into cognition, adaptation, epigenetic inheritance and aging. I discuss the evolution of instinct, anxiety and other cognitive disorders, the shaping of brain connectomes, stress-induced aging and the development of behavioral coping styles. I outline how studies can integrate the investigation of NCEs with advanced behavioral, genomic and neurological tools to provide novel insights into physiological and cognitive health.
https://doi.org/10.1242/jeb.245851
Predator-prey interactions are a cornerstone of many ecological and evolutionary processes that influence various levels of biological organization, from individuals to ecosystems. Predators play a crucial role in shaping ecosystems through the consumption of prey species and non-consumptive effects. Non-consumptive effects (NCEs) can induce changes in prey behavior, including altered foraging strategies, habitat selection, life history and anti-predator responses. These defensive strategies have physiological consequences for prey, affecting their growth, reproduction and immune function to name a few. Numerous experimental studies have incorporated NCEs in investigating predator-prey dynamics in the past decade. Interestingly, predator-prey systems can also be used as experimental models to answer physiology, cognition and adaptability questions. In this Commentary, I highlight research that uses NCEs in predator-prey systems to provide novel insights into cognition, adaptation, epigenetic inheritance and aging. I discuss the evolution of instinct, anxiety and other cognitive disorders, the shaping of brain connectomes, stress-induced aging and the development of behavioral coping styles. I outline how studies can integrate the investigation of NCEs with advanced behavioral, genomic and neurological tools to provide novel insights into physiological and cognitive health.
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Author: Sasi Kiran R. M.
Cultural Appropriation and Aesthetic Transformation of Telangana Dhoom Dham
Publisher: Summerhill, 2023
Abstract
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Links
Cultural Appropriation and Aesthetic Transformation of Telangana Dhoom Dham
http://14.139.58.200/ojs/index.php/summerhill/article/view/1537
Cultural Appropriation and Aesthetic Transformation of Telangana Dhoom Dham
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Author: Andrea Phillott
Tried and tested: the role of evidence-based practices in sea turtle conservation
Publisher: Current Conservation, 2023
Abstract
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Links
Tried and tested: the role of evidence-based practices in sea turtle conservation
https://www.currentconservation.org/tried-and-tested-the-role-of-evidence-based-practices-in-sea-turtle-conservation/#:~:text=Examples%20for%20evidence%2Dbased%20practices,in%20sea%20turtle%20hatchery%20management!
Tried and tested: the role of evidence-based practices in sea turtle conservation
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Author: Andrea Phillott
Tried and tested: the role of evidence-based practices in sea turtle conservation
Publisher: Current Conservation, 2023
Abstract
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Links
Tried and tested: the role of evidence-based practices in sea turtle conservation
https://www.currentconservation.org/tried-and-tested-the-role-of-evidence-based-practices-in-sea-turtle-conservation/#:~:text=Examples%20for%20evidence%2Dbased%20practices,in%20sea%20turtle%20hatchery%20management!
Tried and tested: the role of evidence-based practices in sea turtle conservation
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Author: Shweta Rana
Unravelling blood-based epigenetic mechanisms: the impact of hsa-miR-146a and histone H3 acetylation in lead-induced inflammation among occupational workers
Publisher: International Archives of Occupational and Environmental Health, 2023
Abstract
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Links
"Occupational and environmental exposure to lead (Pb) is a persistent health problem majorly in developing countries and has been implied to cause epigenetic alterations. Its effect on histone post-translational modifications is not explored in human population. MicroRNAs are epigenetic modulators reported to be differentially expressed under Pb exposure. The present study was targeted to find plausible association between the role of hsa-miR-146a and global histone (H3) acetylation in Pb-induced inflammation in occupationally exposed workers.
Materials and methods
A total of 100 occupationally exposed individuals working in different industries were recruited for the study and divided into 2 groups based on the median Pb levels [low Pb group (Pb 5 μg/dL)]. The Pb levels were measured in whole blood using atomic absorption spectrometry to confirm Pb exposure. Histone H3 acetylation and serum interleukin-6 (IL-6) levels were measured using colorimetric methods and enzyme-linked immunosorbent assay (ELISA), respectively. MicroRNA-146a expression was quantified using TaqMan assay.
Results
The median BLL of the study population was 5 μg/dL. BLL, IL-6, and Histone (H3) acetylation increased significantly with the duration of exposure. BLL level showed a significant positive correlation with IL-6 and histone H3 acetylation level. We also found that hsa-miR-146a exhibited significantly increased expression in the high Pb group compared to the low Pb group (Fold change: 2.56; P = 0.014). The linear regression model suggested that BLL has significantly predicted histone H3 acetylation, hsa-miR-146a, and IL-6 in the study subjects.
Conclusion
The finding that hsa-miR146a was significantly upregulated in individuals with high BLL and had a significant negative correlation with serum IL-6 suggests that Pb-induced oxidative stress likely activates H3 acetylation, which then releases inflammatory cytokines like IL-6."
https://doi.org/10.1007/s00420-023-02004-4
"Occupational and environmental exposure to lead (Pb) is a persistent health problem majorly in developing countries and has been implied to cause epigenetic alterations. Its effect on histone post-translational modifications is not explored in human population. MicroRNAs are epigenetic modulators reported to be differentially expressed under Pb exposure. The present study was targeted to find plausible association between the role of hsa-miR-146a and global histone (H3) acetylation in Pb-induced inflammation in occupationally exposed workers.
Materials and methods
A total of 100 occupationally exposed individuals working in different industries were recruited for the study and divided into 2 groups based on the median Pb levels [low Pb group (Pb 5 μg/dL)]. The Pb levels were measured in whole blood using atomic absorption spectrometry to confirm Pb exposure. Histone H3 acetylation and serum interleukin-6 (IL-6) levels were measured using colorimetric methods and enzyme-linked immunosorbent assay (ELISA), respectively. MicroRNA-146a expression was quantified using TaqMan assay.
Results
The median BLL of the study population was 5 μg/dL. BLL, IL-6, and Histone (H3) acetylation increased significantly with the duration of exposure. BLL level showed a significant positive correlation with IL-6 and histone H3 acetylation level. We also found that hsa-miR-146a exhibited significantly increased expression in the high Pb group compared to the low Pb group (Fold change: 2.56; P = 0.014). The linear regression model suggested that BLL has significantly predicted histone H3 acetylation, hsa-miR-146a, and IL-6 in the study subjects.
Conclusion
The finding that hsa-miR146a was significantly upregulated in individuals with high BLL and had a significant negative correlation with serum IL-6 suggests that Pb-induced oxidative stress likely activates H3 acetylation, which then releases inflammatory cytokines like IL-6."
Journal |
SCOPUS®
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Q2
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Q2
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Author: Anuradha Batabyal
The Multifaceted Effects of Flavonoids on Neuroplasticity
Publisher: Brain Plasticity, 2023
Abstract
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Links
"There has been a significant increase in the incidence of multiple neurodegenerative and terminal diseases in the human population with life expectancy increasing in the current times.
This highlights the urgent need for a more comprehensive understanding of how different aspects of lifestyle, in particular diet, may affect neural functioning and consequently cognitive performance as well as in enhancing overall health. Flavonoids, found in a variety of fruits, vegetables, and derived beverages, provide a new avenue of research that shows a promising influence on different aspects of brain function. However, despite the promising evidence, most bioactive compounds lack strong clinical research efficacy. In the current scoping review, we highlight the effects of Flavonoids on cognition and neural plasticity across vertebrates and invertebrates with special emphasis on the studies conducted in the pond snail, Lymnaea stagnalis, which has emerged to be a functionally dynamic model for studies on learning and memory. In conclusion, we suggest future research directions and discuss the social, cultural, and ethnic dependencies of bioactive compounds that influence how these compounds are used and accepted globally. Bridging the gap between preclinical and clinical studies about the effects of bioactive natural compounds on brain health will surely lead to lifestyle choices such as dietary Flavonoids being used complementarily rather than as replacements to classical drugs bringing about a healthier future."
https://doi.org/10.3233/BPL-230150
"There has been a significant increase in the incidence of multiple neurodegenerative and terminal diseases in the human population with life expectancy increasing in the current times.
This highlights the urgent need for a more comprehensive understanding of how different aspects of lifestyle, in particular diet, may affect neural functioning and consequently cognitive performance as well as in enhancing overall health. Flavonoids, found in a variety of fruits, vegetables, and derived beverages, provide a new avenue of research that shows a promising influence on different aspects of brain function. However, despite the promising evidence, most bioactive compounds lack strong clinical research efficacy. In the current scoping review, we highlight the effects of Flavonoids on cognition and neural plasticity across vertebrates and invertebrates with special emphasis on the studies conducted in the pond snail, Lymnaea stagnalis, which has emerged to be a functionally dynamic model for studies on learning and memory. In conclusion, we suggest future research directions and discuss the social, cultural, and ethnic dependencies of bioactive compounds that influence how these compounds are used and accepted globally. Bridging the gap between preclinical and clinical studies about the effects of bioactive natural compounds on brain health will surely lead to lifestyle choices such as dietary Flavonoids being used complementarily rather than as replacements to classical drugs bringing about a healthier future."
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Author: Rakesh Chaturvedi
Cost of efficiency in trading perfect complements
Publisher: Economics Letters, 2023
Abstract
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For trade involving perfect complements and fragmented ownership, lower bounds on the deficit cost of supporting efficiency is provided by first providing informative estimates for the generalized VCG mechanism, then doing so for all incentive-feasible direct mechanisms, and finally for all voluntary trading mechanisms.
https://doi.org/10.1016/j.econlet.2023.111236
For trade involving perfect complements and fragmented ownership, lower bounds on the deficit cost of supporting efficiency is provided by first providing informative estimates for the generalized VCG mechanism, then doing so for all incentive-feasible direct mechanisms, and finally for all voluntary trading mechanisms.
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Author: Chiranjoy Chattopadhyay
Development of image-based fast defect recognition and localization network (FDRLNet) for steel surfaces
Publisher: Manufacturing Letters, 2023
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The utilization of vision-based systems for automated inspection and quality control tasks increased in recent years due to the adaptation of Industry 4.0 initiatives in manufacturing. Continuous steel production units extensively employ vision-based solutions to classify and localize surface defects. The inspection system must provide quick and reliable feedback about surface defect type (classification) and location (localization) utilizing images acquired from the camera. This paper presents a novel surface defect detector, Fast Defect Recognition and Localization Network (FDRLNet), for achieving accurate prediction abilities at higher inference speeds. The robustness of the proposed approach is validated by utilizing publicly available Northeastern University (NEU-DET) surface defect dataset. The prediction abilities are corroborated by comparing the mean Average Precision (mAP) and inference speed with other competitive approaches presented in the literature. It has been shown that the proposed approach has robust prediction abilities at higher inference speeds and can be implemented in real-time defect detection tasks.
https://doi.org/10.1016/j.mfglet.2023.08.036
The utilization of vision-based systems for automated inspection and quality control tasks increased in recent years due to the adaptation of Industry 4.0 initiatives in manufacturing. Continuous steel production units extensively employ vision-based solutions to classify and localize surface defects. The inspection system must provide quick and reliable feedback about surface defect type (classification) and location (localization) utilizing images acquired from the camera. This paper presents a novel surface defect detector, Fast Defect Recognition and Localization Network (FDRLNet), for achieving accurate prediction abilities at higher inference speeds. The robustness of the proposed approach is validated by utilizing publicly available Northeastern University (NEU-DET) surface defect dataset. The prediction abilities are corroborated by comparing the mean Average Precision (mAP) and inference speed with other competitive approaches presented in the literature. It has been shown that the proposed approach has robust prediction abilities at higher inference speeds and can be implemented in real-time defect detection tasks.
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Author: Ragini Mohite
Yeats 2015 and the Crowdsourced Audio Archive: Pedagogical Possibilities for Poetry
Publisher: International Yeast Studies, 2023
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Yeats 2015 and the Crowdsourced Audio Archive: Pedagogical Possibilities for Poetry
https://tigerprints.clemson.edu/iys/vol7/iss1/9?utm_source=tigerprints.clemson.edu%2Fiys%2Fvol7%2Fiss1%2F9&utm_medium=PDF&utm_campaign=PDFCoverPages
Yeats 2015 and the Crowdsourced Audio Archive: Pedagogical Possibilities for Poetry
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Author: Debasis Rooj and Rituparna Kaushik
Impact of technological change on growth trajectory of India: a multivariate-BVAR analysis
Publisher: Economics of Innovation and New Technology, 2023
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This paper examines the impact of technological change on Indian economic growth using the Bayesian Vector Auto-Regressive (BVAR) methodology. We use a comprehensive annual time series dataset covering the period of 1980 to 2019 on real economic activity, gross fixed capital formation, and employment. Technological innovation is measured by the number of patents filed by resident Indians. Technological innovation positively impacts both economic growth and gross fixed capital formation. Our findings indicate that increasing the number of patents leads to higher investment, which drives India's economic growth. However, our results also point towards the possible negative influence of technological innovation on the aggregate employment scenario in India. Our main findings are robust to alternative identification strategies and variable transformation. The asymmetric analysis also corroborates the positive influence of patents on driving investment and economic growth in India.
https://doi.org/10.1080/10438599.2023.2267994
This paper examines the impact of technological change on Indian economic growth using the Bayesian Vector Auto-Regressive (BVAR) methodology. We use a comprehensive annual time series dataset covering the period of 1980 to 2019 on real economic activity, gross fixed capital formation, and employment. Technological innovation is measured by the number of patents filed by resident Indians. Technological innovation positively impacts both economic growth and gross fixed capital formation. Our findings indicate that increasing the number of patents leads to higher investment, which drives India's economic growth. However, our results also point towards the possible negative influence of technological innovation on the aggregate employment scenario in India. Our main findings are robust to alternative identification strategies and variable transformation. The asymmetric analysis also corroborates the positive influence of patents on driving investment and economic growth in India.
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