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
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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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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
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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
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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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Author: Singh, T., Malik, G., Someshwar,S., Le, H.T.T., Polavarapu, R., Chavali, L.N., Melethadathil, N., Sundararajan, V.S., Valadi, J., Kavi Kishor, P.B., Suravajhala, P.,Jayaraman Valadi,
Machine Learning Heuristics on Gingivobuccal Cancer Gene Datasets Reveals Key Candidate Attributes for Prognosis
Publisher: Genes, 2022
Abstract
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Delayed cancer detection is one of the common causes of poor prognosis in the case of many cancers, including cancers of the oral cavity. Despite the improvement and development of new and efficient gene therapy treatments, very little has been carried out to algorithmically assess the impedance of these carcinomas. In this work, from attributes or NCBI’s oral cancer datasets, viz. (i) name, (ii) gene(s), (iii) protein change, (iv) condition(s), clinical significance (last reviewed). We sought to train the number of instances emerging from them. Further, we attempt to annotate viable attributes in oral cancer gene datasets for the identification of gingivobuccal cancer (GBC). We further apply supervised and unsupervised machine learning methods to the gene datasets, revealing key candidate attributes for GBC prognosis. Our work highlights the importance of automated identification of key genes responsible for GBC that could perhaps be easily replicated in other forms of oral cancer detection.
https://doi.org/10.3390/genes13122379
Delayed cancer detection is one of the common causes of poor prognosis in the case of many cancers, including cancers of the oral cavity. Despite the improvement and development of new and efficient gene therapy treatments, very little has been carried out to algorithmically assess the impedance of these carcinomas. In this work, from attributes or NCBI’s oral cancer datasets, viz. (i) name, (ii) gene(s), (iii) protein change, (iv) condition(s), clinical significance (last reviewed). We sought to train the number of instances emerging from them. Further, we attempt to annotate viable attributes in oral cancer gene datasets for the identification of gingivobuccal cancer (GBC). We further apply supervised and unsupervised machine learning methods to the gene datasets, revealing key candidate attributes for GBC prognosis. Our work highlights the importance of automated identification of key genes responsible for GBC that could perhaps be easily replicated in other forms of oral cancer detection.
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SCOPUS®
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Q2
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Q1
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Author: Mobaraki, A., Phillott, A., Erfani, M., Ghasemi, M., Jafari, H.
Inferred Impacts of Extreme Environments on Hawksbill Turtle (Eretmochelys imbricata) Body Size and Reproductive Output
Publisher: Chelonian Conservation and Biology, 2022
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This study presents the first published data on the morphometrics of nesting hawksbill turtles (Eretmochelys imbricata) and their eggs and counts of yolked and yolkless eggs per clutch from Iranian Islands in the northern Persian Gulf. We found significant variation in some variables among 4 main nesting islands (e.g., weight of yolked eggs, p = 0.000; number of yolkless eggs per clutch, p = 0.017), but effect size was moderate or less (r vealed significant variation in CCL (p = 0.000) and number of yolked eggs (p s (g2 = 0.880 and 0.616, respectively). Hawksbill turtles from the Gulf were smaller than populations from the Gulf of Oman (p he Red Sea (p = 0.104), and laid fewer yolked eggs than populations in the Caribbean (p o hawksbill turtles nesting in the Gulf remaining within its waters postnesting so adult body size is restricted by the relatively poor foraging habitat and/or success and/or the extreme environments, subsequently limiting clutch size. More information on home range and foraging habitat is required to draw similar conclusions about hawksbill turtles nesting in the Red Sea. Hawksbill turtles nesting in the Gulf, Red Sea, and Arabian Sea also appear to lay larger number of yolkless eggs per clutch than other populations worldwide (mean = 17.6 ± 10.8 SD [range = 0–59]). The functional role of yolkless eggs, potentially in the maintenance of thermal and hydric conditions within tolerable conditions in shallow nests laid in extreme environments, requires further investigation. Similarly, the potential for yolkless eggs to be an adaptation to extreme nest environments in other sea turtle species also nesting in the northern Indian Ocean also warrants examination.
https://doi.org/10.2744/CCB-1546.1
This study presents the first published data on the morphometrics of nesting hawksbill turtles (Eretmochelys imbricata) and their eggs and counts of yolked and yolkless eggs per clutch from Iranian Islands in the northern Persian Gulf. We found significant variation in some variables among 4 main nesting islands (e.g., weight of yolked eggs, p = 0.000; number of yolkless eggs per clutch, p = 0.017), but effect size was moderate or less (r vealed significant variation in CCL (p = 0.000) and number of yolked eggs (p s (g2 = 0.880 and 0.616, respectively). Hawksbill turtles from the Gulf were smaller than populations from the Gulf of Oman (p he Red Sea (p = 0.104), and laid fewer yolked eggs than populations in the Caribbean (p o hawksbill turtles nesting in the Gulf remaining within its waters postnesting so adult body size is restricted by the relatively poor foraging habitat and/or success and/or the extreme environments, subsequently limiting clutch size. More information on home range and foraging habitat is required to draw similar conclusions about hawksbill turtles nesting in the Red Sea. Hawksbill turtles nesting in the Gulf, Red Sea, and Arabian Sea also appear to lay larger number of yolkless eggs per clutch than other populations worldwide (mean = 17.6 ± 10.8 SD [range = 0–59]). The functional role of yolkless eggs, potentially in the maintenance of thermal and hydric conditions within tolerable conditions in shallow nests laid in extreme environments, requires further investigation. Similarly, the potential for yolkless eggs to be an adaptation to extreme nest environments in other sea turtle species also nesting in the northern Indian Ocean also warrants examination.
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Author: Kumari, G., Phillott, A., Cruz-Rivera, E.
Sediment processing by two estuarine crabs in Bangladesh: small efficient consumers alongside big inefficient ones
Publisher: Journal of Crustacean Biology, 2022
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Due to their foraging and burrowing behaviors, many crabs in the brachyuran superfamily Ocypodoidea are considered important bioturbators that enhance sediment turnover, energy and matter flow, and nutrient cycling in sandy beaches, mangrove forests, and estuaries. There is surprisingly little comparative work assessing the relative effects of sympatric species, even though many co-occur over broad ranges. We compared uptake of sediment water, organic matter, and carbonate by two sympatric deposit-feeding crabs from Chittagong, Bangladesh, the small sand-bubbler Dotilla intermedia De Man, 1888 and the much larger ghost crab Ocypode macrocera H. Milne Edwards, 1837. Comparisons between feeding pellets and surrounding surface sediments showed that D. intermedia reduced sediment water content by 66% and organic matter content by 67%. In contrast, feeding by O. macrocera had no significant effects on these two parameters. Although sediments excavated by O. macrocera were twice as rich in organic matter compared to surface sediments, variance was high, rendering the difference not statistically significant. Inorganic carbonate distribution was affected by both crabs in different ways. Dotilla feeding pellets had significantly less carbonate than unprocessed surface sediments. In contrast, Ocypode feeding pellets had similar carbonate concentrations to surface sediments, but sediments excavated by those crabs were significantly lower in carbonates (~28%) than those at the surface. While both crab species have the potential to affect sediment properties, they do so through different mechanisms.
https://doi.org/10.1093/jcbiol/ruac051
Due to their foraging and burrowing behaviors, many crabs in the brachyuran superfamily Ocypodoidea are considered important bioturbators that enhance sediment turnover, energy and matter flow, and nutrient cycling in sandy beaches, mangrove forests, and estuaries. There is surprisingly little comparative work assessing the relative effects of sympatric species, even though many co-occur over broad ranges. We compared uptake of sediment water, organic matter, and carbonate by two sympatric deposit-feeding crabs from Chittagong, Bangladesh, the small sand-bubbler Dotilla intermedia De Man, 1888 and the much larger ghost crab Ocypode macrocera H. Milne Edwards, 1837. Comparisons between feeding pellets and surrounding surface sediments showed that D. intermedia reduced sediment water content by 66% and organic matter content by 67%. In contrast, feeding by O. macrocera had no significant effects on these two parameters. Although sediments excavated by O. macrocera were twice as rich in organic matter compared to surface sediments, variance was high, rendering the difference not statistically significant. Inorganic carbonate distribution was affected by both crabs in different ways. Dotilla feeding pellets had significantly less carbonate than unprocessed surface sediments. In contrast, Ocypode feeding pellets had similar carbonate concentrations to surface sediments, but sediments excavated by those crabs were significantly lower in carbonates (~28%) than those at the surface. While both crab species have the potential to affect sediment properties, they do so through different mechanisms.
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Author: Ghura, A.S., Sharma, G.D., Pareira, V., Islam, N., Chopra, R.
Corporate Entrepreneurship Champions: Mapping the past and present state of the field for future advancements
Publisher: International Journal of Entrepreneurial Behavior & Research, 2022
Abstract
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The purpose of this study is to critically examine and review the extant research on corporate entrepreneurship champions in the broader area of corporate entrepreneurship and to uncover the avenues for advancement of the scholarship with the purpose of engaging CE champions towards the upliftment of organisations in particular, and younger workforce in general.
https://doi.org/10.1108/IJEBR-09-2021-0758
The purpose of this study is to critically examine and review the extant research on corporate entrepreneurship champions in the broader area of corporate entrepreneurship and to uncover the avenues for advancement of the scholarship with the purpose of engaging CE champions towards the upliftment of organisations in particular, and younger workforce in general.
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Author: Ghura, A.S., Damani, B.
Sarvaay Solutions – creating value innovation for farmers
Publisher: Emerald Emerging Markets Case Studies, 2022
Links
https://doi.org/10.1108/EEMCS-01-2022-0018
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Author: Ghura, A.S.
Unicepts Technologies: Need for Growth Strategy
Publisher: Asian Journal of Management Cases, 2022
Links
https://doi.org/10.1177/09728201221120326
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Author: Ghura, A.S., DeNoble, A., Flores, R.M.
Prodensa Consulting Services: in search of corporate entrepreneurs
Publisher: Emerald Emerging Markets Case Studies, 2022
Links
https://doi.org/10.1108/EEMCS-06-2022-0207
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Author: Jain, P., Bhasin, N.,Pankaj Jain
The Objectification of Women in V. Shantaram’s Films
Publisher: Visual Anthropology, 2022
Abstract
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This essay looks at Jhanak Jhanak Payal Baaje (Citation1955; hereafter JJPB), and Geet Gaya Pattharon Ne (Citation1964; hereafter GGPN), both made by the legendary Indian filmmaker, Rajaram Vankudre Shantaram (1901–90; generally known as V. Shantaram). These two commercial films are both based on the male protagonist’s artistic talents, supported by their female counterparts’ heroic sacrifices. We introduce and then analyze their plots, using Nussbaum’s concept of objectification—personal relationships involving a constant struggle over freedom as one either treats others as objects (so undermining their freedom) or allows them to be treated by others as an object (undermining their own freedom). Either way, someone’s freedom is compromised. The films demonstrate how the female protagonists are expected to sacrifice their careers and lives for their partners—a theme first broached with the role of Sīta in the Rāmāyana more than two millennia ago.
https://doi.org/10.1080/08949468.2022.2063676
This essay looks at Jhanak Jhanak Payal Baaje (Citation1955; hereafter JJPB), and Geet Gaya Pattharon Ne (Citation1964; hereafter GGPN), both made by the legendary Indian filmmaker, Rajaram Vankudre Shantaram (1901–90; generally known as V. Shantaram). These two commercial films are both based on the male protagonist’s artistic talents, supported by their female counterparts’ heroic sacrifices. We introduce and then analyze their plots, using Nussbaum’s concept of objectification—personal relationships involving a constant struggle over freedom as one either treats others as objects (so undermining their freedom) or allows them to be treated by others as an object (undermining their own freedom). Either way, someone’s freedom is compromised. The films demonstrate how the female protagonists are expected to sacrifice their careers and lives for their partners—a theme first broached with the role of Sīta in the Rāmāyana more than two millennia ago.
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Author: Popa, E.
Causation without the causal theory of action
Publisher: Human Affairs, 2022
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This paper takes a critical stance on Tallis’s separation of causation and agency. While his critique of the causal theory of action and the assumptions about causation underlying different versions of determinism, including the one based on neuroscience is right, his rejection of causation (of all sorts) has implausible consequences. Denying the link between action and causation amounts to overlooking the role action plays in causal inference and in the origin of causal concepts. I suggest that a weaker version of Tallis’ claim, compatible with causation understood as agency, would work better.
https://doi.org/10.1515/humaff-2022-0032
This paper takes a critical stance on Tallis’s separation of causation and agency. While his critique of the causal theory of action and the assumptions about causation underlying different versions of determinism, including the one based on neuroscience is right, his rejection of causation (of all sorts) has implausible consequences. Denying the link between action and causation amounts to overlooking the role action plays in causal inference and in the origin of causal concepts. I suggest that a weaker version of Tallis’ claim, compatible with causation understood as agency, would work better.
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Author: Wadhwaniya S., Korde R., Varkkey B.
Examining Decent Work During COVID-19: With Reference to Female Migrant Workers in South Asia. In: Gonçalves M..C..S., Gutwald R., Kleibl T., Lutz R., Noyoo N., Twikirize J. (eds) The Coronavirus Crisis and Challenges to Social Development.
Publisher: Springer, Cham., 2022
Links
https://doi.org/10.1007/978-3-030-84678-7_2
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Author: Long, J.D., Sharma, R.D., Jain, P., and Khanna, M.
Hinduism & Tribal Religions
Publisher: Springer, 2022
Links
https://doi.org/10.1007/978-94-024-1188-1
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Author: Rooj, D., and Sengupta, R.
Forecasting Private Consumption with Digital Payment Data: A Mixed-Frequency Analysis In: John Beirne and David G. Fernandez (Eds): Harnessing Digitalization for Sustainable Economic Development: Insights for Asia
Publisher: Asian Development Bank, 2022
Links
https://www.adb.org/publications/harnessing-digitalization-sustainable-economic-development
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Author: Jain, Pankaj
Jain Dharma as a Virtue Ethics for Sustainability in: Kawall, J (Ed) The Virtues of Sustainability,
Publisher: Oxford University Press, 2022
Links
https://doi.org/10.1093/oso/9780190919818.003.0005
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