Author: Sasikiran, R.M.
The PhD Odyssey: Retrospective Reflections on the Notion of “Care”
Publisher: Journal of Autoethnography, 2023
Abstract
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"[Neoliberalism] has pervasive effects on ways of thought to the point where it has become incorporated into the common-sense way many of us interpret, live in, and understand the world.1
My critical autoethnographic account reflects on the lived experience of being a PhD scholar inhabiting a neoliberal public education space. It is written from a position of caste and class privilege, mindful of the trials and tribulations of less privileged fellow research scholars, who face unimaginable obstacles in the pursuit of their doctoral degrees. The attempt is not just to highlight the inconvenience caused to me but to reflect on the structures erected by neoliberalism that make pursuing a PhD a traumatic experience prone with vulnerabilities. I reflect on the precarity of doing a PhD in a public university in India after the normative age, the resulting anxiety, and notions of care.
When I discussed my plans of pursuing..."
https://doi.org/10.1525/joae.2023.4.2.283
"[Neoliberalism] has pervasive effects on ways of thought to the point where it has become incorporated into the common-sense way many of us interpret, live in, and understand the world.1
My critical autoethnographic account reflects on the lived experience of being a PhD scholar inhabiting a neoliberal public education space. It is written from a position of caste and class privilege, mindful of the trials and tribulations of less privileged fellow research scholars, who face unimaginable obstacles in the pursuit of their doctoral degrees. The attempt is not just to highlight the inconvenience caused to me but to reflect on the structures erected by neoliberalism that make pursuing a PhD a traumatic experience prone with vulnerabilities. I reflect on the precarity of doing a PhD in a public university in India after the normative age, the resulting anxiety, and notions of care.
When I discussed my plans of pursuing..."
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Author: Yugank Goyal
A theory of legal apparitions: regulation and escape in Indian divorces
Publisher: International Journal of Law in Context, 2023
Abstract
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When people do not approach a formal court of law to settle their disputes, and cannot enter into out-of-court settlements either, what do they do? I find that people install court-like processes which mimetically follow the court procedures, executing the settlement as if the decision were rendered officially. By examining such practices in the case of divorce-related disputes in India, I advance a theory of legal apparitions, a phenomenon in which cosmetic mimicry of legal processes creates a new form of extra-legal resolution. This is likely to prevail in societies where access to justice is hindered due to socio-institutional factors and customary forms of adjudication are not possible (sometimes because of state law’s design). This idea can be used to explain a range of practices observed in South Asian societies, where people’s imagination of, and interaction with, legal apparatuses creates new forms of institutions.
https://doi.org/10.1017/S1744552323000095
When people do not approach a formal court of law to settle their disputes, and cannot enter into out-of-court settlements either, what do they do? I find that people install court-like processes which mimetically follow the court procedures, executing the settlement as if the decision were rendered officially. By examining such practices in the case of divorce-related disputes in India, I advance a theory of legal apparitions, a phenomenon in which cosmetic mimicry of legal processes creates a new form of extra-legal resolution. This is likely to prevail in societies where access to justice is hindered due to socio-institutional factors and customary forms of adjudication are not possible (sometimes because of state law’s design). This idea can be used to explain a range of practices observed in South Asian societies, where people’s imagination of, and interaction with, legal apparatuses creates new forms of institutions.
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Author: Gayatri Kotbagi
Analyse des causes psychologiques des premières consommations de substances psychoactives chez les adolescents en contexte scolaire et universitaire
Publisher: La Revue de l'Infirmiere, 2023
Abstract
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Experimentation with psychoactive substances (PAS), such as alcohol, tobacco or cannabis, is common in adolescence, and continues to pose a public health issue that can lead to failure at school and university. Most of the work on these issues focuses on addiction-related aspects, and little on the underlying causes of addiction. This article sheds psycho-social theoretical light on the causes of first-time use of APS, and cannabis in particular. It is particularly aimed at school nurses and university preventive medicine nurses.
http://dx.doi.org/10.1016/j.revinf.2023.05.010
Experimentation with psychoactive substances (PAS), such as alcohol, tobacco or cannabis, is common in adolescence, and continues to pose a public health issue that can lead to failure at school and university. Most of the work on these issues focuses on addiction-related aspects, and little on the underlying causes of addiction. This article sheds psycho-social theoretical light on the causes of first-time use of APS, and cannabis in particular. It is particularly aimed at school nurses and university preventive medicine nurses.
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Author: Shweta Rana
Recent Advances and Perspectives of Nanomaterials in Agricultural Management and Associated Environmental Risk: A Review
Publisher: Nanomaterials, 2023
Abstract
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The advancement in nanotechnology has enabled a significant expansion in agricultural production. Agri-nanotechnology is an emerging discipline where nanotechnological methods provide diverse nanomaterials (NMs) such as nanopesticides, nanoherbicides, nanofertilizers and different nanoforms of agrochemicals for agricultural management. Applications of nanofabricated products can potentially improve the shelf life, stability, bioavailability, safety and environmental sustainability of active ingredients for sustained release. Nanoscale modification of bulk or surface properties bears tremendous potential for effective enhancement of agricultural productivity. As NMs improve the tolerance mechanisms of the plants under stressful conditions, they are considered as effective and promising tools to overcome the constraints in sustainable agricultural production. For their exceptional qualities and usages, nano-enabled products are developed and enforced, along with agriculture, in diverse sectors. The rampant usage of NMs increases their release into the environment. Once incorporated into the environment, NMs may threaten the stability and function of biological systems. Nanotechnology is a newly emerging technology, so the evaluation of the associated environmental risk is pivotal. This review emphasizes the current approach to NMs synthesis, their application in agriculture, interaction with plant-soil microbes and environmental challenges to address future applications in maintaining a sustainable environment.
https://doi.org/10.3390/nano13101604
The advancement in nanotechnology has enabled a significant expansion in agricultural production. Agri-nanotechnology is an emerging discipline where nanotechnological methods provide diverse nanomaterials (NMs) such as nanopesticides, nanoherbicides, nanofertilizers and different nanoforms of agrochemicals for agricultural management. Applications of nanofabricated products can potentially improve the shelf life, stability, bioavailability, safety and environmental sustainability of active ingredients for sustained release. Nanoscale modification of bulk or surface properties bears tremendous potential for effective enhancement of agricultural productivity. As NMs improve the tolerance mechanisms of the plants under stressful conditions, they are considered as effective and promising tools to overcome the constraints in sustainable agricultural production. For their exceptional qualities and usages, nano-enabled products are developed and enforced, along with agriculture, in diverse sectors. The rampant usage of NMs increases their release into the environment. Once incorporated into the environment, NMs may threaten the stability and function of biological systems. Nanotechnology is a newly emerging technology, so the evaluation of the associated environmental risk is pivotal. This review emphasizes the current approach to NMs synthesis, their application in agriculture, interaction with plant-soil microbes and environmental challenges to address future applications in maintaining a sustainable environment.
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Author: Barun Kumar Thakur
Interrelationship between Share of Women in Parliament and Gender and Development: A Critical Analysis
Publisher: Administrative Sciences, 2023
Abstract
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Links
Gender and development are among the two most important components of any economy to sustain its perpetual and sustainable economic growth in both the long as well as short run. The role of women in parliament and the interrelationship between gender and development is critically analysed. Women’s representation in parliament is the dependent variable and the predictor variables considered are gender development index, female access to assets, female labour force, and country GDP per capita. Data were collected from the UNDP human development report for the period 2015 to 2021–2022 and World Bank for 188 countries of which finally 159 were considered to develop the model based on data availability. We have used the theoretical lens of social stratification theory and gender role theory to frame the hypothesis. A random effects model-based panel regression analysis of the data indicated a strong positive relationship between gender development index and the dependent variable, but no relationship between female labour force, and access to assets. The study addresses a critical gap in policy and development of the literature on gender, politics, and development using a global data set, establishing the importance of indicators such as gender development index, and laying down the path for future research on the subject.
https://doi.org/10.3390/admsci13040106
Gender and development are among the two most important components of any economy to sustain its perpetual and sustainable economic growth in both the long as well as short run. The role of women in parliament and the interrelationship between gender and development is critically analysed. Women’s representation in parliament is the dependent variable and the predictor variables considered are gender development index, female access to assets, female labour force, and country GDP per capita. Data were collected from the UNDP human development report for the period 2015 to 2021–2022 and World Bank for 188 countries of which finally 159 were considered to develop the model based on data availability. We have used the theoretical lens of social stratification theory and gender role theory to frame the hypothesis. A random effects model-based panel regression analysis of the data indicated a strong positive relationship between gender development index and the dependent variable, but no relationship between female labour force, and access to assets. The study addresses a critical gap in policy and development of the literature on gender, politics, and development using a global data set, establishing the importance of indicators such as gender development index, and laying down the path for future research on the subject.
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Author: Sunil Rajpal
Progress on Sustainable Development Goal indicators in 707 districts of India: a quantitative mid-line assessment using the National Family Health Surveys, 2016 and 2021
Publisher: The Lancet Regional Health – Southeast Asia, 2023
Abstract
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"India has committed itself to accomplishing the Sustainable Development Goals (SDGs) by 2030. Meeting these goals would require prioritizing and targeting specific areas within India. We provide a mid-line assessment of the progress across 707 districts of India for 33 SDG indicators related to health and social determinants of health.
Methods
We used data collected on children and adults from two rounds of the National Family Health Survey (NFHS) conducted in 2016 and 2021. We identified 33 indicators that cover 9 of the 17 official SDGs. We used the goals and targets outlined by the Global Indicator Framework, Government of India and World Health Organization (WHO) to determine SDG targets to be met by 2030. Using precision-weighted multilevel models, we estimated district mean for 2016 and 2021, and using these values, computed the Annual Absolute Change (AAC) for each indicator. Using the AAC and targets, we classified India and each district as: Achieved-I, Achieved-II, On-Target and Off-Target. Further, when a district was Off-Target on a given indicator, we further identified the calendar year in which the target will be met post-2030.
Findings
India is not On-Target for 19 of the 33 SDGs indicators. The critical Off-Target indicators include Access to Basic Services, Wasting and Overweight Children, Anaemia, Child Marriage, Partner Violence, Tobacco Use, and Modern Contraceptive Use. For these indicators, more than 75% of the districts were Off-Target. Because of a worsening trend observed between 2016 and 2021, and assuming no course correction occurs, many districts will never meet the targets on the SDGs even well after 2030. These Off-Target districts are concentrated in the states of Madhya Pradesh, Chhattisgarh, Jharkhand, Bihar, and Odisha. Finally, it does not appear that Aspirational Districts, on average, are performing better in meeting the SDG targets than other districts on majority of the indicators.
Interpretation
A mid-line assessment of districts' progress on SDGs suggests an urgent need to increase the pace and momentum on four SDG goals: No Poverty (SDG 1), Zero Hunger (SDG 2), Good Health and Well-Being (SDG 3) and Gender Equality (SDG 5). Developing a strategic roadmap at this time will help India ensure success with regards to meeting the SDGs. India's emergence and sustenance as a leading economic power depends on meeting some of the more basic health and social determinants of health-related SDGs in an immediate and equitable manner."
https://doi.org/10.1016/j.lansea.2023.100155
"India has committed itself to accomplishing the Sustainable Development Goals (SDGs) by 2030. Meeting these goals would require prioritizing and targeting specific areas within India. We provide a mid-line assessment of the progress across 707 districts of India for 33 SDG indicators related to health and social determinants of health.
Methods
We used data collected on children and adults from two rounds of the National Family Health Survey (NFHS) conducted in 2016 and 2021. We identified 33 indicators that cover 9 of the 17 official SDGs. We used the goals and targets outlined by the Global Indicator Framework, Government of India and World Health Organization (WHO) to determine SDG targets to be met by 2030. Using precision-weighted multilevel models, we estimated district mean for 2016 and 2021, and using these values, computed the Annual Absolute Change (AAC) for each indicator. Using the AAC and targets, we classified India and each district as: Achieved-I, Achieved-II, On-Target and Off-Target. Further, when a district was Off-Target on a given indicator, we further identified the calendar year in which the target will be met post-2030.
Findings
India is not On-Target for 19 of the 33 SDGs indicators. The critical Off-Target indicators include Access to Basic Services, Wasting and Overweight Children, Anaemia, Child Marriage, Partner Violence, Tobacco Use, and Modern Contraceptive Use. For these indicators, more than 75% of the districts were Off-Target. Because of a worsening trend observed between 2016 and 2021, and assuming no course correction occurs, many districts will never meet the targets on the SDGs even well after 2030. These Off-Target districts are concentrated in the states of Madhya Pradesh, Chhattisgarh, Jharkhand, Bihar, and Odisha. Finally, it does not appear that Aspirational Districts, on average, are performing better in meeting the SDG targets than other districts on majority of the indicators.
Interpretation
A mid-line assessment of districts' progress on SDGs suggests an urgent need to increase the pace and momentum on four SDG goals: No Poverty (SDG 1), Zero Hunger (SDG 2), Good Health and Well-Being (SDG 3) and Gender Equality (SDG 5). Developing a strategic roadmap at this time will help India ensure success with regards to meeting the SDGs. India's emergence and sustenance as a leading economic power depends on meeting some of the more basic health and social determinants of health-related SDGs in an immediate and equitable manner."
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Author: Lily Kelting
Sorry Not Sorry: Monster Truck’s Postcolonial Anti-Authenticity Spectacular!
Publisher: TDR: The Drama Review, 2023
Abstract
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Links
Monster Truck’s performances reproduce power dynamics that are at once painful and hurtful. By staging the representational process of dehumanizing black bodies, Monster Truck applies a different strategy than other Freie Szene groups: shining a bright light on dark discourse, selling the audience’s own willing consumption of neocolonial power relationships back to them as art.
https://doi.org/10.1017/S1054204323000060
Monster Truck’s performances reproduce power dynamics that are at once painful and hurtful. By staging the representational process of dehumanizing black bodies, Monster Truck applies a different strategy than other Freie Szene groups: shining a bright light on dark discourse, selling the audience’s own willing consumption of neocolonial power relationships back to them as art.
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Author: Ajith Abraham
An Improved Boykov’s Graph Cut-based Segmentation Technique for the Efficient Detection of Cervical Cancer
Publisher: IEEE Access, 2023
Abstract
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Links
The accurate and reliable derivation of the pap smear cell, which contains cytoplasm and nucleus regions, depends on the segmentation process employed in the cervical cancer detection mechanism. In this paper, an Improved Boykov’s Graph Cut-based Conditional Random Fields and Superpixel imposed Semantic Segmentation Technique (IBGC-CRF-SPSST) is proposed for efficient cervical cancer detection. This proposed IBGC-CRF-SPSST embeds the complete benefits of constraint association among pixels and superpixel edge data for accurate determination of the nuclei and cytoplasmic boundaries so as to ensure efficient differentiation of the healthy and unhealthy cancer cells. Finally, the pixel-level forecasting potential of Conditional Random Fields is included for enhancing the degree of semantic-based segmentation accuracy to a predominant level. The experimental evaluated results of the proposed IBGC-CRF-SPSST aim to produce an accuracy of 99.78%, a mean processing time of 2.18sec, a precision of 96%, a sensitivity of 98.92%, and a specificity of 99.32% value which is determined to be excellent and on par with the existing detection techniques used for investigating cervical cancer.
https://doi.org/10.1109/ACCESS.2023.3295833
The accurate and reliable derivation of the pap smear cell, which contains cytoplasm and nucleus regions, depends on the segmentation process employed in the cervical cancer detection mechanism. In this paper, an Improved Boykov’s Graph Cut-based Conditional Random Fields and Superpixel imposed Semantic Segmentation Technique (IBGC-CRF-SPSST) is proposed for efficient cervical cancer detection. This proposed IBGC-CRF-SPSST embeds the complete benefits of constraint association among pixels and superpixel edge data for accurate determination of the nuclei and cytoplasmic boundaries so as to ensure efficient differentiation of the healthy and unhealthy cancer cells. Finally, the pixel-level forecasting potential of Conditional Random Fields is included for enhancing the degree of semantic-based segmentation accuracy to a predominant level. The experimental evaluated results of the proposed IBGC-CRF-SPSST aim to produce an accuracy of 99.78%, a mean processing time of 2.18sec, a precision of 96%, a sensitivity of 98.92%, and a specificity of 99.32% value which is determined to be excellent and on par with the existing detection techniques used for investigating cervical cancer.
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Author: Aamod Sane, Renu Dhadwal and Jayaraman Valadi
Interpretation of Drop Size Predictions from a Random Forest Model Using Local Interpretable Model-Agnostic Explanations (LIME) in a Rotating Disc Contactor
Publisher: Industrial & Engineering Chemistry Research, 2023
Abstract
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Drop size is a crucial parameter for the efficient design and operation of the rotating disc contactor (RDC) in liquid–liquid extraction. The current work focuses on providing local and global explanations for the prediction of the drop size in a rotating disc contactor (RDC). The Random Forest (RF) regression model is a robust machine learning algorithm that can accurately capture complex relationships in the data. However, the interpretability of the model is limited. In order to address the issue of interpretability of the developed RF model, in the current work, we employed Local Interpretable Model-Agnostic Explanations (LIME) of the predictions of the RF model. This provides both local and global views of the model and thereby helps one to gain insights into the factors influencing predictions. We have provided local explanations depicting the impact of different attributes on the prediction of the output for any given input example. We have also obtained global feature importance, providing the top subset of informative attributes. We have also developed local surrogate models incorporating second order attribute interactions. This has provided important information about the effect of interactions on the drop size prediction. By augmenting the random forest model with LIME, it is possible to develop a more accurate and interpretable model for estimating the drop size in RDCs, ultimately leading to improved performance and efficiency.
https://doi.org/10.1021/acs.iecr.3c00808
Drop size is a crucial parameter for the efficient design and operation of the rotating disc contactor (RDC) in liquid–liquid extraction. The current work focuses on providing local and global explanations for the prediction of the drop size in a rotating disc contactor (RDC). The Random Forest (RF) regression model is a robust machine learning algorithm that can accurately capture complex relationships in the data. However, the interpretability of the model is limited. In order to address the issue of interpretability of the developed RF model, in the current work, we employed Local Interpretable Model-Agnostic Explanations (LIME) of the predictions of the RF model. This provides both local and global views of the model and thereby helps one to gain insights into the factors influencing predictions. We have provided local explanations depicting the impact of different attributes on the prediction of the output for any given input example. We have also obtained global feature importance, providing the top subset of informative attributes. We have also developed local surrogate models incorporating second order attribute interactions. This has provided important information about the effect of interactions on the drop size prediction. By augmenting the random forest model with LIME, it is possible to develop a more accurate and interpretable model for estimating the drop size in RDCs, ultimately leading to improved performance and efficiency.
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Author: Anuradha Batabyal
LPS-Induced Garcia Effect and Its Pharmacological Regulation Mediated by Acetylsalicylic Acid: Behavioral and Transcriptional Evidence
Publisher: Biology, 2023
Abstract
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Links
Lymnaea stagnalis learns and remembers to avoid certain foods when their ingestion is followed by sickness. This rapid, taste-specific, and long-lasting aversion—known as the Garcia effect—can be formed by exposing snails to a novel taste and 1 h later injecting them with lipopolysaccharide (LPS). However, the exposure of snails to acetylsalicylic acid (ASA) for 1 h before the LPS injection, prevents both the LPS-induced sickness state and the Garcia effect. Here, we investigated novel aspects of this unique form of conditioned taste aversion and its pharmacological regulation. We first explored the transcriptional effects in the snails’ central nervous system induced by the injection with LPS (25 mg), the exposure to ASA (900 nM), as well as their combined presentation in untrained snails. Then, we investigated the behavioral and molecular mechanisms underlying the LPS-induced Garcia effect and its pharmacological regulation by ASA. LPS injection, both alone and during the Garcia effect procedure, upregulated the expression levels of immune- and stress-related targets. This upregulation was prevented by pre-exposure to ASA. While LPS alone did not affect the expression levels of neuroplasticity genes, its combination with the conditioning procedure resulted in their significant upregulation and memory formation for the Garcia effect.
https://doi.org/10.3390/biology12081100
Lymnaea stagnalis learns and remembers to avoid certain foods when their ingestion is followed by sickness. This rapid, taste-specific, and long-lasting aversion—known as the Garcia effect—can be formed by exposing snails to a novel taste and 1 h later injecting them with lipopolysaccharide (LPS). However, the exposure of snails to acetylsalicylic acid (ASA) for 1 h before the LPS injection, prevents both the LPS-induced sickness state and the Garcia effect. Here, we investigated novel aspects of this unique form of conditioned taste aversion and its pharmacological regulation. We first explored the transcriptional effects in the snails’ central nervous system induced by the injection with LPS (25 mg), the exposure to ASA (900 nM), as well as their combined presentation in untrained snails. Then, we investigated the behavioral and molecular mechanisms underlying the LPS-induced Garcia effect and its pharmacological regulation by ASA. LPS injection, both alone and during the Garcia effect procedure, upregulated the expression levels of immune- and stress-related targets. This upregulation was prevented by pre-exposure to ASA. While LPS alone did not affect the expression levels of neuroplasticity genes, its combination with the conditioning procedure resulted in their significant upregulation and memory formation for the Garcia effect.
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Author: Anuradha Batabyal
A Novel Behavioral Display in Lymnaea Induced by Quercetin and Hypoxia
Publisher: The Biological Bulletin, 2023
Abstract
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Links
The pond snail Lymnaea stagnalis employs aerial respiration under hypoxia and can be operantly conditioned to reduce this behavior. When applied individually, a heat shock (30 °C for 1 h) and the flavonoid quercetin enhance long-term memory formation for the operant conditioning of aerial respiration. However, when snails are exposed to quercetin before the heat shock, long-term memory is no longer enhanced. This is because quercetin prevents the heat-induced upregulation of heat-shock proteins 70 and 40. When we tested the memory outcome of operant conditioning due to the simultaneous exposure to quercetin and 30 °C, we found that Lymnaea entered a quiescent survival state. The same behavioral response occurred when snails were simultaneously exposed to quercetin and pond water made hypoxic by bubbling nitrogen through it. Thus, in this study, we performed six experiments to propose a physiological explanation for that curious behavioral response. Our results suggest that bubbling nitrogen in pond water, heating pond water to 30 °C, and bubbling nitrogen in 30 °C pond water create a hypoxic environment, to which organisms may respond by upregulating the heat-shock protein system. On the other hand, when snails experience quercetin together with these hypoxic conditions, they can no longer express the physiological stress response evoked by heat or hypoxia. Thus, the quiescent survival state could be an emergency response to survive the hypoxic condition when the heat-shock proteins cannot be activated.
https://doi.org/10.1086/725689
The pond snail Lymnaea stagnalis employs aerial respiration under hypoxia and can be operantly conditioned to reduce this behavior. When applied individually, a heat shock (30 °C for 1 h) and the flavonoid quercetin enhance long-term memory formation for the operant conditioning of aerial respiration. However, when snails are exposed to quercetin before the heat shock, long-term memory is no longer enhanced. This is because quercetin prevents the heat-induced upregulation of heat-shock proteins 70 and 40. When we tested the memory outcome of operant conditioning due to the simultaneous exposure to quercetin and 30 °C, we found that Lymnaea entered a quiescent survival state. The same behavioral response occurred when snails were simultaneously exposed to quercetin and pond water made hypoxic by bubbling nitrogen through it. Thus, in this study, we performed six experiments to propose a physiological explanation for that curious behavioral response. Our results suggest that bubbling nitrogen in pond water, heating pond water to 30 °C, and bubbling nitrogen in 30 °C pond water create a hypoxic environment, to which organisms may respond by upregulating the heat-shock protein system. On the other hand, when snails experience quercetin together with these hypoxic conditions, they can no longer express the physiological stress response evoked by heat or hypoxia. Thus, the quiescent survival state could be an emergency response to survive the hypoxic condition when the heat-shock proteins cannot be activated.
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Author: Anuradha Batabyal
Agricultural Use of Insecticides Alters Homeostatic Behaviors and Cognitive Ability in Lymnaea stagnalis
Publisher: Environmental Toxicology and Chemistry, 2023
Abstract
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Links
Lymnaea stagnalis is an ecologically important, stress-sensitive, freshwater mollusk that is at risk for exposure to insecticides via agricultural practices. We provide insight into the impact insecticides have on L. stagnalis by comparing specific behaviors including feeding, locomotion, shell regeneration, and cognition between snails collected at two different sites: one contaminated by insecticides and one not. We hypothesized that each of the behaviors would be altered in the insecticide-exposed snails and that similar alterations would be induced when control snails were exposed to the contaminated environment. We found no significant differences in locomotion, feeding, and shell regeneration of insecticide-exposed L. stagnalis compared with nonexposed individuals. Significant changes in feeding and shell repair were observed in nonexposed snails inhabiting insecticide-contaminated pond water. Most importantly, snails maintained and trained in insecticide-contaminated pond water did not form configural learning, but this cognitive deficit was reversed when these snails were maintained in insecticide-free pond water. Our findings conclude that insecticides have a primarily negative impact on this higher form of cognition in L. stagnalis.
https://doi.org/10.1002/etc.5728
Lymnaea stagnalis is an ecologically important, stress-sensitive, freshwater mollusk that is at risk for exposure to insecticides via agricultural practices. We provide insight into the impact insecticides have on L. stagnalis by comparing specific behaviors including feeding, locomotion, shell regeneration, and cognition between snails collected at two different sites: one contaminated by insecticides and one not. We hypothesized that each of the behaviors would be altered in the insecticide-exposed snails and that similar alterations would be induced when control snails were exposed to the contaminated environment. We found no significant differences in locomotion, feeding, and shell regeneration of insecticide-exposed L. stagnalis compared with nonexposed individuals. Significant changes in feeding and shell repair were observed in nonexposed snails inhabiting insecticide-contaminated pond water. Most importantly, snails maintained and trained in insecticide-contaminated pond water did not form configural learning, but this cognitive deficit was reversed when these snails were maintained in insecticide-free pond water. Our findings conclude that insecticides have a primarily negative impact on this higher form of cognition in L. stagnalis.
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Author: Ajith Abraham
Modeling IoT based Forest Fire Detection System with IoTsec
Publisher: International Journal of Computer Information Systems and Industrial Management Applications, 2023
Abstract
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Links
"The Internet of Things (IoT) has become a real
technological revolution in different sectors starting from body
sensors to professional eras. The current growth of the IoT field
and its use in multiple domains attracts the attention of attackers.
However, this technology creates new security issues. Security is
frequently critical and demands cybersecurity specialists and
the IT community for looking for a reliable solution. Nowadays,
forest fires have become the most widespread around the world
targeting the ecosystem (trees, plants, animals, and people).
Therefore, designing and modeling an IoT-Forest Fires
Detection System is a real challenge. To overcome this challenge,
UML is a resource for representing IoT systems in different
views. In this context, the IoT has become a real technological
revolution that is increasingly used in several fields. However,
security, fault tolerance, real-time are the specific problems of
an IoT based Forest Fire Detection System. The Forest Fires
Detection System is another important service that IoT offers
several opportunities to monitor, control and collect data. Forest
fires can undoubtedly destroy the ecosystem. Despite its rapid
spread, security of forests faces many issues, like the
confidentiality and integrity of data, and the functionality and
availability of equipment (such as sensors). The goal is to focus
more on extensions rather than languages. It is rather
imperative to compare these extensions in order to choose the
best and most effective UML extension for IoT security
modeling. We used a UML extension called IoTsec to model an
IoT based Forest Fire Detection System through a use case
diagram. This work aims to ensure the security and safety of the
proposed system against attacks exploiting the vulnerability of
the system."
https://www.softcomputing.net/hind2023ijcisim.pdf
"The Internet of Things (IoT) has become a real
technological revolution in different sectors starting from body
sensors to professional eras. The current growth of the IoT field
and its use in multiple domains attracts the attention of attackers.
However, this technology creates new security issues. Security is
frequently critical and demands cybersecurity specialists and
the IT community for looking for a reliable solution. Nowadays,
forest fires have become the most widespread around the world
targeting the ecosystem (trees, plants, animals, and people).
Therefore, designing and modeling an IoT-Forest Fires
Detection System is a real challenge. To overcome this challenge,
UML is a resource for representing IoT systems in different
views. In this context, the IoT has become a real technological
revolution that is increasingly used in several fields. However,
security, fault tolerance, real-time are the specific problems of
an IoT based Forest Fire Detection System. The Forest Fires
Detection System is another important service that IoT offers
several opportunities to monitor, control and collect data. Forest
fires can undoubtedly destroy the ecosystem. Despite its rapid
spread, security of forests faces many issues, like the
confidentiality and integrity of data, and the functionality and
availability of equipment (such as sensors). The goal is to focus
more on extensions rather than languages. It is rather
imperative to compare these extensions in order to choose the
best and most effective UML extension for IoT security
modeling. We used a UML extension called IoTsec to model an
IoT based Forest Fire Detection System through a use case
diagram. This work aims to ensure the security and safety of the
proposed system against attacks exploiting the vulnerability of
the system."
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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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