Author: Sethiya, A., Thenmozhi, M.,
Does product market competition moderate the impact of promoter ownership on firm value?
Publisher: Managerial Finance, 2022
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This study explores whether product market competition is a substitute for or complementary to good internal governance through promoter holdings. Specifically, it examines the impact of product market competition on the linkage between promoter ownership and firm value and investigates whether this impact varies with the type of blockholders and level of ownership.
https://doi.org/10.1108/MF-05-2020-0244
This study explores whether product market competition is a substitute for or complementary to good internal governance through promoter holdings. Specifically, it examines the impact of product market competition on the linkage between promoter ownership and firm value and investigates whether this impact varies with the type of blockholders and level of ownership.
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Author: Mochish, K.S.
Democratise Radio News in India
Publisher: Economic and Political Weekly, 2022
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Author: Mabon, S., Kapur, S.
The complexity of (super)complexes: Pakistan, India and West Asia
Publisher: Global Discourse, 2022
Abstract
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Despite areas of synergy, international relations theory has typically considered South and West Asia as analytically distinct. Following the work of Barry Buzan, whose work on regional security complexes is formative in shaping the intellectual debate, the Gulf is considered a subregion of a larger Middle Eastern regional security complex, while South Asia is regarded as its own regional security complex. This article argues that the analytical distinction between these different (sub)regional security complexes has become blurred, reflecting the emergence of a supercomplex. We contend that strong patterns of amity, enmity and securitisation that link the two regional security complexes suggest a thinning boundary between them, with the potential for them to merge. We distinguish between a supercomplex and a merger using the concepts of amity, enmity and securitisation provided by regional security complex theory. We add the English School’s ideas of order, justice and regional society to enhance our understanding. We focus on three issues in which the two regions interact: the Abraham Accords; the Iran nuclear crisis, and Jammu and Kashmir. We argue that increasing relations between the two regional security complexes have resulted in a supercomplex, with powerful states in both regional security complexes seeking to project their power into the adjacent regional security complex. We further note the strengthening patterns of amity, enmity and securitisation connecting the two regions, leading to a thinning of the boundary separating South and West Asia. We contribute to the literature on regional security complex theory by clarifying the distinction between a supercomplex and a merger through the South-West Asian case.
https://doi.org/10.1332/204378921X16588505305495
Despite areas of synergy, international relations theory has typically considered South and West Asia as analytically distinct. Following the work of Barry Buzan, whose work on regional security complexes is formative in shaping the intellectual debate, the Gulf is considered a subregion of a larger Middle Eastern regional security complex, while South Asia is regarded as its own regional security complex. This article argues that the analytical distinction between these different (sub)regional security complexes has become blurred, reflecting the emergence of a supercomplex. We contend that strong patterns of amity, enmity and securitisation that link the two regional security complexes suggest a thinning boundary between them, with the potential for them to merge. We distinguish between a supercomplex and a merger using the concepts of amity, enmity and securitisation provided by regional security complex theory. We add the English School’s ideas of order, justice and regional society to enhance our understanding. We focus on three issues in which the two regions interact: the Abraham Accords; the Iran nuclear crisis, and Jammu and Kashmir. We argue that increasing relations between the two regional security complexes have resulted in a supercomplex, with powerful states in both regional security complexes seeking to project their power into the adjacent regional security complex. We further note the strengthening patterns of amity, enmity and securitisation connecting the two regions, leading to a thinning of the boundary separating South and West Asia. We contribute to the literature on regional security complex theory by clarifying the distinction between a supercomplex and a merger through the South-West Asian case.
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Author: Luthra, A., Cunningham, K., Fraser, A.M., Pandey, A., Rana, S., Singh, V.,Shweta Rana,
Ecological livelihoods of farmers and pollinators in the Himalayas: Doing critical physical geography using citizen science
Publisher: The Canadian Geographer, 2022
Abstract
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An ecological livelihoods framework can be productively deployed in an investigation of inter dependant farmer pollinater relationship
https://doi.org/10.1111/cag.12799
An ecological livelihoods framework can be productively deployed in an investigation of inter dependant farmer pollinater relationship
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Author: Jain, P.
Ecocritical Analysis of Classics by Three Indian Film Maestros: An Extended Film Review
Publisher: Worldviews: Global Religions, Culture, and Ecology, 2022
Abstract
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"Satyajit Ray is the only Indian filmmaker who has received the prestigious
Academy award and India’s highest civilian honor of Bharat Ratna for his life-
time achievements and contributions to the cinema. Although he made dozens
of films, we review only those with natural elements playing essential roles in
shaping the plot. After a few of Ray’s films, we look at another maestro, Ritwik
Ghatak’s one film, which prominently features a drying river and its impact on
people. Finally, we complete our trinity by reviewing a few movies by Mrinal
Sen that show the conflicts between the people of the natural world in villages
and those from the modern world. Each film is rooted in Indian settings, yet
the themes they evoke are universal and universally accoladed with awards and
recognition worldwide."
https://doi.org/10.1163/15685357-02603001
"Satyajit Ray is the only Indian filmmaker who has received the prestigious
Academy award and India’s highest civilian honor of Bharat Ratna for his life-
time achievements and contributions to the cinema. Although he made dozens
of films, we review only those with natural elements playing essential roles in
shaping the plot. After a few of Ray’s films, we look at another maestro, Ritwik
Ghatak’s one film, which prominently features a drying river and its impact on
people. Finally, we complete our trinity by reviewing a few movies by Mrinal
Sen that show the conflicts between the people of the natural world in villages
and those from the modern world. Each film is rooted in Indian settings, yet
the themes they evoke are universal and universally accoladed with awards and
recognition worldwide."
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Author: Abbasi, B.A., Dharan, A., Mishra, A., Saraf, D., Ahamad, I., Suravajhala, P., Valadi, J.,Jayaraman Valadi,
In Silico Characterization of Uncharacterized Proteins From Multiple Strains of Clostridium Difficile
Publisher: Frontiers in Genetics, 2022
Abstract
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Clostridium difficile is a multi-strain, spore-forming, Gram-positive anaerobic bacterium posing a global threat to post-operative individuals. Infamously known for antibiotic-associated diarrhoea, it is one of the most important causes of healthcare-associated infections worldwide, leading to a quarter of reported cases of infectious diarrhoea and a broad spectrum of gastrointestinal complications, including sepsis and pseudomembranous colitis (Barbut and Petit, 2001). Studies have suggested that it is a crucial part of healthy human gut flora as it overgrows and imbalances intestinal microflora with unnecessary antibiotic therapies (Abt et al., 2016). With the progression of antibiotic-based therapeutics accompanied by sub-standard hygiene in hospitals, the incidence of C. difficile infection (CDI) has significantly increased since the 20th century (Czepiel et al., 2019). Being a major causative pathogen, C. difficile contributes to almost half a million cases with 29,000 deaths per annum in the United States alone and impacting Latin America, Europe, and the Asian regions (Goudarzi et al., 2014; Lessa et al., 2015). Whereas in India, the incidence and prevalence rates of CDI-associated diarrhoea in hospitalised patients ranges from 3 to 29% and 7.1–26.6%, respectively (Segar et al., 2017).
https://doi.org/10.3389/fgene.2022.878012
Clostridium difficile is a multi-strain, spore-forming, Gram-positive anaerobic bacterium posing a global threat to post-operative individuals. Infamously known for antibiotic-associated diarrhoea, it is one of the most important causes of healthcare-associated infections worldwide, leading to a quarter of reported cases of infectious diarrhoea and a broad spectrum of gastrointestinal complications, including sepsis and pseudomembranous colitis (Barbut and Petit, 2001). Studies have suggested that it is a crucial part of healthy human gut flora as it overgrows and imbalances intestinal microflora with unnecessary antibiotic therapies (Abt et al., 2016). With the progression of antibiotic-based therapeutics accompanied by sub-standard hygiene in hospitals, the incidence of C. difficile infection (CDI) has significantly increased since the 20th century (Czepiel et al., 2019). Being a major causative pathogen, C. difficile contributes to almost half a million cases with 29,000 deaths per annum in the United States alone and impacting Latin America, Europe, and the Asian regions (Goudarzi et al., 2014; Lessa et al., 2015). Whereas in India, the incidence and prevalence rates of CDI-associated diarrhoea in hospitalised patients ranges from 3 to 29% and 7.1–26.6%, respectively (Segar et al., 2017).
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Author: Parida Y., Chowdhury, J.R., Saini, S., Dash, D.P.
Role of income and government responsiveness in reducing the death toll from floods in Indian states
Publisher: Scientific Reports, 2022
Abstract
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Floods are the most commonly occurring natural disasters in India due to India’s unique geographical location and socioeconomic conditions. Frequent flooding causes enormous loss of human lives and damage crops and public utilities. Furthermore, floods adversely affect economic development and increase the government's financial burden by increasing spending on various disaster mitigation measures. Recent empirical literature based on cross-national comparisons shows that disaster fatalities and damages are monotonically decreasing in per capita income. We challenge this view on the monotonic negative relationship between income and flood damages. We examine the non-monotonic (inverted U-shaped) relationship between per capita income and flood impact in terms of deaths, people affected, and damages due to floods in 19 major Indian states from 1980 to 2011, using Poisson and Tobit estimation methods. In particular, deaths and the population affected by floods increase with a turning point of income up to 882 US$ and 578 US$, respectively, and diminishes thereafter. Our results confirm an inverted U-shaped relationship between income and fatalities and the population affected by floods. In addition to income, we argue that government responsiveness plays an essential role in mitigating the risk of floods. We employ the fixed-effect Poisson estimation method to examine the government's role in protecting people against disaster risk, focusing on regional differences in India. Deaths from floods remain non-linear and follow the inverted U-pattern with respect to government responsiveness. However, the effect of government responsiveness on flood fatalities and flood damages is statistically insignificant. Our results further suggest that high-income states experience a lower death toll from floods. The high-income (rich) states are capable of incurring a higher threshold level of income and higher natural calamity expenditure to reduce flood fatalities and protect the population affected by floods than the low-income (poor) states. The poor states have minimal resources and face severe financial constraints to reduce the death toll from floods. From the perspective of public policy, the poor states, in particular, require an increase in income, better governance, and effective disaster management policies to mitigate flood impact.
https://doi.org/10.1038/s41598-022-21334-w
Floods are the most commonly occurring natural disasters in India due to India’s unique geographical location and socioeconomic conditions. Frequent flooding causes enormous loss of human lives and damage crops and public utilities. Furthermore, floods adversely affect economic development and increase the government's financial burden by increasing spending on various disaster mitigation measures. Recent empirical literature based on cross-national comparisons shows that disaster fatalities and damages are monotonically decreasing in per capita income. We challenge this view on the monotonic negative relationship between income and flood damages. We examine the non-monotonic (inverted U-shaped) relationship between per capita income and flood impact in terms of deaths, people affected, and damages due to floods in 19 major Indian states from 1980 to 2011, using Poisson and Tobit estimation methods. In particular, deaths and the population affected by floods increase with a turning point of income up to 882 US$ and 578 US$, respectively, and diminishes thereafter. Our results confirm an inverted U-shaped relationship between income and fatalities and the population affected by floods. In addition to income, we argue that government responsiveness plays an essential role in mitigating the risk of floods. We employ the fixed-effect Poisson estimation method to examine the government's role in protecting people against disaster risk, focusing on regional differences in India. Deaths from floods remain non-linear and follow the inverted U-pattern with respect to government responsiveness. However, the effect of government responsiveness on flood fatalities and flood damages is statistically insignificant. Our results further suggest that high-income states experience a lower death toll from floods. The high-income (rich) states are capable of incurring a higher threshold level of income and higher natural calamity expenditure to reduce flood fatalities and protect the population affected by floods than the low-income (poor) states. The poor states have minimal resources and face severe financial constraints to reduce the death toll from floods. From the perspective of public policy, the poor states, in particular, require an increase in income, better governance, and effective disaster management policies to mitigate flood impact.
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Author: Samanta, T., Gopalan, K., Devi, T.
Blocked by Gender: Disparities in COVID19 infection detection in Tamil Nadu, India
Publisher: Frontiers in Public Health, 2022
Abstract
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That gender is an important axis of inequality is well-documented in the public health scholarship globally (1). To be sure, health scholarship has consistently shown that women and girls make comparatively fewer gains in health care than men and boys across similar age and social registers in most societies. However, this empirical narrative was shifted in the early months of the COVID-19 pandemic when global data revealed that men were 2.5 times more likely to be infected and are also 2.4 times more at risk of dying from COVID-19 than women (2). For example, the Global Health 50/50 repository demonstrated significant gender gaps in infections and deaths, where men seemed to fare worse in both counts. Since then, a considerable body of scholarship has attempted to explain this variation by privileging lifestyle and socio-economic factors (e.g., labor) and critiquing the biologically deterministic way of explaining disease risk (3, 4). Put simply, in societies that are governed by pervasive gender norms, the social realities of men and women are vastly different affecting their social and health outcomes. Or as a noted medical anthropologist, Paul Farmer, explains with the notion “the social production of disease” (p. 261) emphasizing how social and economic positioning produce gendered risk in epidemics and infectious disease outbreaks (4). Of all factors, the gender inequality in the labor force, finds particular attention among experts attempting to explain the variation in infection and mortality due to COVID-19 exposure. For instance, Adams showed that the percentage of female deaths due to COVID-19 were higher in countries that also have a higher proportion of women in the full-time workforce (5). In another study, Lewandowski and colleagues argue that when women work, they are largely concentrated in sectors where workplace interactions are higher (e.g., care, hospitality and education) and so is the exposure to the contagion (6). This study is significant since it undergirds the importance of labor market segregation in explaining disease risk.
https://doi.org/10.3389/fpubh.2022.966490
That gender is an important axis of inequality is well-documented in the public health scholarship globally (1). To be sure, health scholarship has consistently shown that women and girls make comparatively fewer gains in health care than men and boys across similar age and social registers in most societies. However, this empirical narrative was shifted in the early months of the COVID-19 pandemic when global data revealed that men were 2.5 times more likely to be infected and are also 2.4 times more at risk of dying from COVID-19 than women (2). For example, the Global Health 50/50 repository demonstrated significant gender gaps in infections and deaths, where men seemed to fare worse in both counts. Since then, a considerable body of scholarship has attempted to explain this variation by privileging lifestyle and socio-economic factors (e.g., labor) and critiquing the biologically deterministic way of explaining disease risk (3, 4). Put simply, in societies that are governed by pervasive gender norms, the social realities of men and women are vastly different affecting their social and health outcomes. Or as a noted medical anthropologist, Paul Farmer, explains with the notion “the social production of disease” (p. 261) emphasizing how social and economic positioning produce gendered risk in epidemics and infectious disease outbreaks (4). Of all factors, the gender inequality in the labor force, finds particular attention among experts attempting to explain the variation in infection and mortality due to COVID-19 exposure. For instance, Adams showed that the percentage of female deaths due to COVID-19 were higher in countries that also have a higher proportion of women in the full-time workforce (5). In another study, Lewandowski and colleagues argue that when women work, they are largely concentrated in sectors where workplace interactions are higher (e.g., care, hospitality and education) and so is the exposure to the contagion (6). This study is significant since it undergirds the importance of labor market segregation in explaining disease risk.
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Author: Gopalan, K., Devi, T.
A Statistical Model of COVID-19 Infection Incidence in the Southern Indian State of Tamil Nadu
Publisher: International Journal of Environmental Research and Public Health, 2022
Abstract
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In this manuscript, we present an analysis of COVID-19 infection incidence in the Indian state of Tamil Nadu. We used seroprevalence survey data along with COVID-19 fatality reports from a six-month period (1 June 2020 to 30 November 2020) to estimate age- and sex-specific COVID-19 infection fatality rates (IFR) for Tamil Nadu. We used these IFRs to estimate new infections occurring daily using the daily COVID-19 fatality reports published by the Government of Tamil Nadu. We found that these infection incidence estimates for the second COVID wave in Tamil Nadu were broadly consistent with the infection estimates from seroprevalence surveys. Further, we propose a composite statistical model that pairs a k-nearest neighbours model with a power-law characterisation for “out-of-range” extrapolation to estimate the COVID-19 infection incidence based on observed cases and test positivity ratio. We found that this model matched closely with the IFR-based infection incidence estimates for the first two COVID-19 waves for both Tamil Nadu as well as the neighbouring state of Karnataka. Finally, we used this statistical model to estimate the infection incidence during the recent “Omicron wave” in Tamil Nadu and Karnataka.
https://doi.org/10.3390/ijerph191711137
In this manuscript, we present an analysis of COVID-19 infection incidence in the Indian state of Tamil Nadu. We used seroprevalence survey data along with COVID-19 fatality reports from a six-month period (1 June 2020 to 30 November 2020) to estimate age- and sex-specific COVID-19 infection fatality rates (IFR) for Tamil Nadu. We used these IFRs to estimate new infections occurring daily using the daily COVID-19 fatality reports published by the Government of Tamil Nadu. We found that these infection incidence estimates for the second COVID wave in Tamil Nadu were broadly consistent with the infection estimates from seroprevalence surveys. Further, we propose a composite statistical model that pairs a k-nearest neighbours model with a power-law characterisation for “out-of-range” extrapolation to estimate the COVID-19 infection incidence based on observed cases and test positivity ratio. We found that this model matched closely with the IFR-based infection incidence estimates for the first two COVID-19 waves for both Tamil Nadu as well as the neighbouring state of Karnataka. Finally, we used this statistical model to estimate the infection incidence during the recent “Omicron wave” in Tamil Nadu and Karnataka.
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Author: Thakur, B.K., Gupta, V., Bhattacharya, P., Chakraborty, T.
Impact of socioeconomic factors on households’ willingness to pay for arsenic-free safe drinking water - A case study of Bihar, India
Publisher: Groundwater for Sustainable Development, 2022
Abstract
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The paper studies the impact of socioeconomic factors on households' willingness-to-pay (WTP) for drinking water in Bihar, India. The data were collected from 420 households through a primary survey from two arsenic affected districts. A linear regression model was used to examine the causal relationship among various socioeconomic factors and their influence on household decision to pay for water services. Around 84% of the surveyed respondents were willing to pay for better-quality water sources. Households value the good water quality, which resulted in good health, and, therefore, influenced the household decision to pay and opt for the services. Average As concentration level were found to be 130.4 μg/L and 115.2 μg/L in 2013 and 2019, respectively. The results found that socioeconomic factors that influence overall WTP are: households' income, age, education, awareness, arsenic concentration levels, and visit to the doctor. Education of the household's head and awareness are interlinked, and more education causes a shift in water sources from poor quality to safer one. WTP for better-quality drinking water were estimated through Contingent Valuation Method (CVM). The annual WTP for the individual households and for the study area were estimated at INR 240 ($4.13) and INR 17.1 ($0.29) million, respectively. The inference of the results may be used in implementing of new policies for providing arsenic free drinking water in the affected areas.
https://doi.org/10.1016/j.gsd.2022.100837
The paper studies the impact of socioeconomic factors on households' willingness-to-pay (WTP) for drinking water in Bihar, India. The data were collected from 420 households through a primary survey from two arsenic affected districts. A linear regression model was used to examine the causal relationship among various socioeconomic factors and their influence on household decision to pay for water services. Around 84% of the surveyed respondents were willing to pay for better-quality water sources. Households value the good water quality, which resulted in good health, and, therefore, influenced the household decision to pay and opt for the services. Average As concentration level were found to be 130.4 μg/L and 115.2 μg/L in 2013 and 2019, respectively. The results found that socioeconomic factors that influence overall WTP are: households' income, age, education, awareness, arsenic concentration levels, and visit to the doctor. Education of the household's head and awareness are interlinked, and more education causes a shift in water sources from poor quality to safer one. WTP for better-quality drinking water were estimated through Contingent Valuation Method (CVM). The annual WTP for the individual households and for the study area were estimated at INR 240 ($4.13) and INR 17.1 ($0.29) million, respectively. The inference of the results may be used in implementing of new policies for providing arsenic free drinking water in the affected areas.
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Author: Bhuiyan, M.A., Hu, P., Khare, V., Hamaguchi, Y., Thakur, B.K., Rahman, M.K.
Economic feasibility of marine renewable energy: Review
Publisher: Frontiers in Marine Science, 2022
Abstract
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This paper aims to comprehensively review the economic feasibility of Marine Renewable Energy. Five major continents are at different development stages of implementing MREs commercialization; Europe is in the most advanced, while Africa is at the initial stage. The Levelized Cost of Energy is usually used to make decisions and measure the plant’s economic feasibility. Literature suggests that MRE technology is still costly, and many emerging countries are sensitive to the income and use of MRE. Among various types of MREs, wind energy is the most feasible for many countries. Offshore wave energy is still at the pre-commercialization stage for many developing countries. Tidal energy plants can be economically viable depending on a reduction in investment cost and high capacity factors. Most of the world’s tidal flows have too low a speed to operate a turbine of commercial size for ocean thermal energy. In conclusion, the factors hindering MRE development are pointed out, and future challenges are discussed.
https://doi.org/10.3389/fmars.2022.988513
This paper aims to comprehensively review the economic feasibility of Marine Renewable Energy. Five major continents are at different development stages of implementing MREs commercialization; Europe is in the most advanced, while Africa is at the initial stage. The Levelized Cost of Energy is usually used to make decisions and measure the plant’s economic feasibility. Literature suggests that MRE technology is still costly, and many emerging countries are sensitive to the income and use of MRE. Among various types of MREs, wind energy is the most feasible for many countries. Offshore wave energy is still at the pre-commercialization stage for many developing countries. Tidal energy plants can be economically viable depending on a reduction in investment cost and high capacity factors. Most of the world’s tidal flows have too low a speed to operate a turbine of commercial size for ocean thermal energy. In conclusion, the factors hindering MRE development are pointed out, and future challenges are discussed.
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Author: Mitra, P., Goyal, T., Sharma, P., Kiran, G.S., Rana, S., Sharma, S.
Plasma microRNA expression and immunoregulatory cytokines in an Indian population occupationally exposed to cadmium
Publisher: Journal of Biochemical and Molecular Toxicology, 2022
Abstract
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following its accumulation in the body, cadmium (Cd) exposure is associated with devastating effects on multiple organ system of the human body. The immune system is one of the sensitive targets for Cd-induced toxicity. Recently, studies have demonstrated a significant role of Cd in inducing epigenetic alterations. With this background, the present study was planned to study the changes in candidate microRNA (miRNA) expression associated with immune regulation in occupationally Cd-exposed workers. One hundred individuals involved in welding and metal handicraft manufacturing, while 80 apparently healthy subjects without any prior history of occupational exposure were recruited for the study. Blood Cd level was determined by atomic absorption spectrometry. Serum cytokine levels were measured using an enzyme-linked immunosorbent assay and serum miRNA expression of candidate miRNAs (miR-146a, miR-210, and miR-222) were determined by real-time polymerase chain reaction. The median Cd level (2.40 μg/L) in the occupationally exposed workers was significantly higher than the nonexposed subjects (0.90 μg/L). Among the cytokines, interleukin-4 (IL-4), and tumor necrosis factor-alpha (TNF-α) were significantly higher while IL-2 and IL-10 were significantly lower in the exposed. The expression level of miR-146a and miR-222 were significantly different between the groups with the former showing downregulation and later showing upregulation. Correlation analysis revealed a positive and negative association of miR-222 and miR-146a with blood cadmium level, IL-17 as well as TNF-α, respectively. Furthermore, the in-silico analysis revealed a significant role of the studied miRNAs in various cellular and genetic pathways. The findings of the present study demonstrate significant involvement of Cd-induced alteration in miRNAs in varied immune regulatory changes in exposed individuals.
http://dx.doi.org/10.1002/jbt.23221
following its accumulation in the body, cadmium (Cd) exposure is associated with devastating effects on multiple organ system of the human body. The immune system is one of the sensitive targets for Cd-induced toxicity. Recently, studies have demonstrated a significant role of Cd in inducing epigenetic alterations. With this background, the present study was planned to study the changes in candidate microRNA (miRNA) expression associated with immune regulation in occupationally Cd-exposed workers. One hundred individuals involved in welding and metal handicraft manufacturing, while 80 apparently healthy subjects without any prior history of occupational exposure were recruited for the study. Blood Cd level was determined by atomic absorption spectrometry. Serum cytokine levels were measured using an enzyme-linked immunosorbent assay and serum miRNA expression of candidate miRNAs (miR-146a, miR-210, and miR-222) were determined by real-time polymerase chain reaction. The median Cd level (2.40 μg/L) in the occupationally exposed workers was significantly higher than the nonexposed subjects (0.90 μg/L). Among the cytokines, interleukin-4 (IL-4), and tumor necrosis factor-alpha (TNF-α) were significantly higher while IL-2 and IL-10 were significantly lower in the exposed. The expression level of miR-146a and miR-222 were significantly different between the groups with the former showing downregulation and later showing upregulation. Correlation analysis revealed a positive and negative association of miR-222 and miR-146a with blood cadmium level, IL-17 as well as TNF-α, respectively. Furthermore, the in-silico analysis revealed a significant role of the studied miRNAs in various cellular and genetic pathways. The findings of the present study demonstrate significant involvement of Cd-induced alteration in miRNAs in varied immune regulatory changes in exposed individuals.
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Author: Sangwan, J., Singh, S.
"Women’s Participation in Protests against the Three Farm Laws in India Perspectives from the Ground"
Publisher: Economic and Political Weekly, 2022
Abstract
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The farmers’ movement in India against the three farm laws has been considered historic in many ways. The movement built unity and solidarity between different sections of society and at the same time it led to a churning in the agrarian social structure. An important feature of the movement was a large-scale active participation of women at different levels. This paper provides descriptions and insights into the forms in which women participated in the protests and factors, which were working behind this participation with specific reference to Haryana. Certain possible progressive changes in the gender relations as a result of the women’s participation in the movement are noted and tasks for future to sustain and strengthen these changes are identified.
https://www.epw.in/journal/2022/43/special-articles/womens-participation-protests-against-three-farm.html
The farmers’ movement in India against the three farm laws has been considered historic in many ways. The movement built unity and solidarity between different sections of society and at the same time it led to a churning in the agrarian social structure. An important feature of the movement was a large-scale active participation of women at different levels. This paper provides descriptions and insights into the forms in which women participated in the protests and factors, which were working behind this participation with specific reference to Haryana. Certain possible progressive changes in the gender relations as a result of the women’s participation in the movement are noted and tasks for future to sustain and strengthen these changes are identified.
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Author: Nath, V., Chattopadhyay, C., Desai, K.A.
NSLNet: An Improved Deep Learning Model for Steel Surface Defect Classification utilizing Small Training Datasets
Publisher: Manufacturing Letters, 2022
Abstract
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Manufacturing industries contemplate integrating computer vision and artificial intelligence into shop floor operations, such as steel surface defect identification, to realize smart manufacturing goals. However, inadequate annotated training datasets and reduced prediction abilities with image perturbations restrict the practical implementation. This paper introduces NSLNet framework utilizing ImageNet as a feature-extractor combined with adversarial training in the extracted feature space through Neural Structure Learning to address these barriers. The experiments on public (NEU) and synthetically generated datasets (ENEU) showed that the NSLNet could learn with few training samples maintaining resilience against image perturbations outperforming conventional models significantly and nearest deep learning competitors marginally.
https://doi.org/10.1016/j.mfglet.2022.10.001
Manufacturing industries contemplate integrating computer vision and artificial intelligence into shop floor operations, such as steel surface defect identification, to realize smart manufacturing goals. However, inadequate annotated training datasets and reduced prediction abilities with image perturbations restrict the practical implementation. This paper introduces NSLNet framework utilizing ImageNet as a feature-extractor combined with adversarial training in the extracted feature space through Neural Structure Learning to address these barriers. The experiments on public (NEU) and synthetically generated datasets (ENEU) showed that the NSLNet could learn with few training samples maintaining resilience against image perturbations outperforming conventional models significantly and nearest deep learning competitors marginally.
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Author: Nath, V., Chattopadhyay, C., Desai, K.A.,
On enhancing prediction abilities of vision-based metallic surface defect classification through adversarial training
Publisher: Engineering Applications of Artificial Intelligence, 2022
Abstract
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Links
Computer vision is augmented in various manufacturing industries to perform automated inspection operations accurately and efficiently. It has been observed that the performance of vision-based inspection approaches degrades considerably upon utilizing images captured under shop-floor conditions. This work proposes utilizing Histogram Equalization and adversarial training through Neural Structure Learning (NSL) for developing a robust vision-based Surface Defect Classification framework. A novel deep neural network architecture obtains adversarial samples in the extracted feature space instead of obtaining the same in the original input image space. The architecture can be easily integrated and employed with various machine learning models. A commonly employed steel surface defect dataset (NEU) with practical relevance to industrial cases is selected for the model training and experimental studies. The robustness of the proposed approach is evaluated over the Extended Diversity Enhanced (ENEU) dataset derived by simulating image acquisition variations similar to shop floor conditions. The results reveal that the proposed approach enhances the recognition accuracy of the baseline method from 87.7% to 92.4% over ENEU. The prediction accuracy of the proposed approach is considerably better than the traditional methods and deep learning competitors over ENEU. The qualitative and quantitative comparison of results obtained using the present approach with methods reported in the literature demonstrates the effectiveness of adversarial training in improving the generalization abilities of machine learning models.
https://doi.org/10.1016/j.engappai.2022.105553
Computer vision is augmented in various manufacturing industries to perform automated inspection operations accurately and efficiently. It has been observed that the performance of vision-based inspection approaches degrades considerably upon utilizing images captured under shop-floor conditions. This work proposes utilizing Histogram Equalization and adversarial training through Neural Structure Learning (NSL) for developing a robust vision-based Surface Defect Classification framework. A novel deep neural network architecture obtains adversarial samples in the extracted feature space instead of obtaining the same in the original input image space. The architecture can be easily integrated and employed with various machine learning models. A commonly employed steel surface defect dataset (NEU) with practical relevance to industrial cases is selected for the model training and experimental studies. The robustness of the proposed approach is evaluated over the Extended Diversity Enhanced (ENEU) dataset derived by simulating image acquisition variations similar to shop floor conditions. The results reveal that the proposed approach enhances the recognition accuracy of the baseline method from 87.7% to 92.4% over ENEU. The prediction accuracy of the proposed approach is considerably better than the traditional methods and deep learning competitors over ENEU. The qualitative and quantitative comparison of results obtained using the present approach with methods reported in the literature demonstrates the effectiveness of adversarial training in improving the generalization abilities of machine learning models.
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Author: Dash, G., Sharma, K., Yadav, N.
The diffusion of mobile payments: Profiling the adopters and non-adopters, Roger's way
Publisher: Journal of Retailing and Consumer Services, 2022
Abstract
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This study aims to establish various market segments based on consumers' attitudinal elements influencing the adoption of mobile payments (m-payments). It also examines the best discriminators between identified clusters of m-payment users. Three hundred and sixty m-payment users participated in the study. Cluster and discriminant analyses were employed for the analysis. The findings suggest that two segments are established, and strategies can be developed accordingly. The study provides marketers with numerous touchpoints to propagate the adoption of m-payment apps. It also comprehensively explains m-payment users in developing economies by superimposing the cohorts to Roger's diffusion of innovation model.
https://doi.org/10.1016/j.jretconser.2022.103219
This study aims to establish various market segments based on consumers' attitudinal elements influencing the adoption of mobile payments (m-payments). It also examines the best discriminators between identified clusters of m-payment users. Three hundred and sixty m-payment users participated in the study. Cluster and discriminant analyses were employed for the analysis. The findings suggest that two segments are established, and strategies can be developed accordingly. The study provides marketers with numerous touchpoints to propagate the adoption of m-payment apps. It also comprehensively explains m-payment users in developing economies by superimposing the cohorts to Roger's diffusion of innovation model.
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Author: Hajoary, P.K., Negi, T., Akhilesh, K.B.
Electric Vehicle Mobility in India: Challenges and Opportunities
Publisher: Economic & Political Weekly, 2022
Abstract
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Considering the challenges emerging from extreme levels of urban pollution, high population density, and economic growth rates, the Indian government has shown keen interest in launching multiple initiatives to achieve 100% electric mobility by 2030. This article analyses the opportunities and challenges in their adoption.
https://www.epw.in/journal/2022/47/commentary/electric-vehicle-mobility-india.html
Considering the challenges emerging from extreme levels of urban pollution, high population density, and economic growth rates, the Indian government has shown keen interest in launching multiple initiatives to achieve 100% electric mobility by 2030. This article analyses the opportunities and challenges in their adoption.
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Author: Pathak, P., Pandya, P., Shukla, S., Sane, A., Sengupta, R.
A Sensor Placement Strategy for Comprehensive Urban Heat Island Monitoring
Publisher: ISPRS International Journal of Geo-Information, 2022
Abstract
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Urban heat islands (UHIs) increase the energy consumption of cities and impact the health of its residents. In light of the correlation between energy consumption and health and UHI variations observed at a local level within the canopy layer, satellite-derived land surface temperatures (LSTs) may be insufficient to provide comprehensive information about these deleterious effects. For both LST and air temperatures to be collected in a spatially representative and continuous manner, and for the process to be affordable, on-ground temperature and humidity sensors must be strategically placed. This study proposes a strategy for placing on-ground sensors that utilizes the spatial variation of measurable factors linked to UHI (i.e., seasonal variation in LSTs, wind speed, wind direction, bareness, and local climate zones), allowing for the continuous measurement of UHI within the canopy layer. As a representative city, Pune, India, was used to demonstrate how to distribute sensors based on the spatial variability of UHI-related variables. The proposed method may be helpful for any city requiring local-level observations of UHI, regardless of the climate zone. Further, we evaluate the placement of low-cost technology sensors that use LoRaWAN technology for this purpose, in order to overcome the problem of high costs associated with traditional in-situ weather stations.
https://doi.org/10.3390/ijgi12010011
Urban heat islands (UHIs) increase the energy consumption of cities and impact the health of its residents. In light of the correlation between energy consumption and health and UHI variations observed at a local level within the canopy layer, satellite-derived land surface temperatures (LSTs) may be insufficient to provide comprehensive information about these deleterious effects. For both LST and air temperatures to be collected in a spatially representative and continuous manner, and for the process to be affordable, on-ground temperature and humidity sensors must be strategically placed. This study proposes a strategy for placing on-ground sensors that utilizes the spatial variation of measurable factors linked to UHI (i.e., seasonal variation in LSTs, wind speed, wind direction, bareness, and local climate zones), allowing for the continuous measurement of UHI within the canopy layer. As a representative city, Pune, India, was used to demonstrate how to distribute sensors based on the spatial variability of UHI-related variables. The proposed method may be helpful for any city requiring local-level observations of UHI, regardless of the climate zone. Further, we evaluate the placement of low-cost technology sensors that use LoRaWAN technology for this purpose, in order to overcome the problem of high costs associated with traditional in-situ weather stations.
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Author: Chakrabarti, A., Chakrabarti, A.S.
Sparsistent filtering of comovement networks from high-dimensional data
Publisher: Journal of Computational Science, 2022
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Network filtering is a technique to isolate core subnetworks of large and complex interconnected systems, which has recently found many applications in financial, biological, physical and technological networks among others. We introduce a new technique to filter large dimensional networks arising out of dynamical behavior of the constituent nodes, exploiting their spectral properties. As opposed to the well known network filters that rely on preserving key topological properties of the realized network, our method treats the spectrum as the fundamental object and preserves spectral properties. Applying asymptotic theory of high-dimensional covariance matrix estimation, we show that the proposed filter can be tuned to interpolate between zero filtering to maximal filtering that induces sparsity via thresholding, while having the least spectral distance from a consistent (non-)linear shrinkage estimator. We demonstrate the application of our proposed filter by applying it to covariance networks constructed from financial data, to extract core subnetworks embedded in full networks.
https://doi.org/10.1016/j.jocs.2022.101902
Network filtering is a technique to isolate core subnetworks of large and complex interconnected systems, which has recently found many applications in financial, biological, physical and technological networks among others. We introduce a new technique to filter large dimensional networks arising out of dynamical behavior of the constituent nodes, exploiting their spectral properties. As opposed to the well known network filters that rely on preserving key topological properties of the realized network, our method treats the spectrum as the fundamental object and preserves spectral properties. Applying asymptotic theory of high-dimensional covariance matrix estimation, we show that the proposed filter can be tuned to interpolate between zero filtering to maximal filtering that induces sparsity via thresholding, while having the least spectral distance from a consistent (non-)linear shrinkage estimator. We demonstrate the application of our proposed filter by applying it to covariance networks constructed from financial data, to extract core subnetworks embedded in full networks.
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Author: Batabyal, A., Lukowiak, K.
Tracking the path of predator recognition in a predator-naive population of the pond snail
Publisher: Behavioral Ecology, 2022
Abstract
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Organisms evolve adaptive strategies to adjust to rapidly changing environmental stressors. Predation pressure is one of the strongest selective forces and organisms respond to predatory threats via innate and learned responses. We utilized a natural, experimental set-up, where two lakes Stoney and Margo in Canada containing natural populations of the prey Lymnaea stagnalis differed in the presence and absence of an invasive, predatory Northern crayfish, Faxonius virilis. We exploited the contrast in the predation backgrounds of the snail populations from the two lakes to test, 1) predator recognition in predator-experienced snails is innate, (2) predator-naive snails learn to detect a novel invasive predator, and 3) learning about a novel predator gets transmitted to the successive generations. We quantified predator fear memory formation using a higher-order learning paradigm called configural learning. We found that 1) predator recognition in predator-experienced snails is innate, 2) predator-naive snails learned to recognize the novel predator even after a brief exposure to predator cues highlighting the role of learning in combating invasive predators and the critical time-window during development that accounts for predator recognition, and 3) the learning and predator detection mechanism in predator-naive snails are not transmitted to successive generations. The population variation observed in the predator-detection mechanism may be due to the past and current experience of predators in one population over the other. We find an interesting study system to address how fear learning occurs and prospective future directions to understand the mechanism of innate fear recognition from a learned fear recognition.
https://doi.org/10.1093/beheco/arac107
Organisms evolve adaptive strategies to adjust to rapidly changing environmental stressors. Predation pressure is one of the strongest selective forces and organisms respond to predatory threats via innate and learned responses. We utilized a natural, experimental set-up, where two lakes Stoney and Margo in Canada containing natural populations of the prey Lymnaea stagnalis differed in the presence and absence of an invasive, predatory Northern crayfish, Faxonius virilis. We exploited the contrast in the predation backgrounds of the snail populations from the two lakes to test, 1) predator recognition in predator-experienced snails is innate, (2) predator-naive snails learn to detect a novel invasive predator, and 3) learning about a novel predator gets transmitted to the successive generations. We quantified predator fear memory formation using a higher-order learning paradigm called configural learning. We found that 1) predator recognition in predator-experienced snails is innate, 2) predator-naive snails learned to recognize the novel predator even after a brief exposure to predator cues highlighting the role of learning in combating invasive predators and the critical time-window during development that accounts for predator recognition, and 3) the learning and predator detection mechanism in predator-naive snails are not transmitted to successive generations. The population variation observed in the predator-detection mechanism may be due to the past and current experience of predators in one population over the other. We find an interesting study system to address how fear learning occurs and prospective future directions to understand the mechanism of innate fear recognition from a learned fear recognition.
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