Author: Sottong, H.
Hypermedievalizing and de-medievalizing Dante: Leopoldo Lugones’s and Jorge Luis Borges’s Rewritings of Inferno V
Publisher: ARC Humanities Press, 2023
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Author: Valadi, J.
Challenges in Eventing Horizontal Gene Transfer
Publisher: Springer, 2023
Links
https://doi.org/10.1007/978-981-19-9342-8_16
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Author: Patki, S.
A Cognitive and Socio-cultural Perspective on the Tendency to use Gmail’s Smart Reply-like AI-based Texting Features
2023
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Author: Patki, S.
Bridging the Digital Divide in Online Learning in Maharashtra, India: Learnings from the Pandemic
2023
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Author: Jain D K
Inclusive Finanace
2023
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Author: Jain, D.K.
How much big adjacent large economies can influence foreign exchange markets of small economies? : Evidence from the tri polar study of Fiji, Australia and New Zealand
2023
Links
https://drive.google.com/file/d/14D-Tgn0OWNwsmn3uoPTigA3d_sIKqVdm/view?usp=sharing
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Author: Ajith Abraham
Synergy of climate change with country success and city quality of life
Publisher: Scientific Reports, 2023
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Most people around the world have felt the effects of climate change on their quality of life. This study sought to achieve the maximum efficiency for climate change actions with the minimum negative impact on the well-being of countries and cities. The Climate Change and Country Success (C3S) and Climate Change and Cities’ Quality of Life (C3QL) models and maps of the world created as part of this research showed that as economic, social, political, cultural, and environmental metrics of countries and cities improve, so do their climate change indicators. For the 14 climate change indicators, the C3S and C3QL models indicated 68.8% average dispersion dimensions in the case of countries and 52.8% in the case of cities. Our research showed that increases in the success of 169 countries saw improvements in 9 climate change indicators out of the 12 considered. Improvements in country success indicators were accompanied by a 71% improvement in climate change metrics.
https://doi.org/10.1038/s41598-023-35133-4
Most people around the world have felt the effects of climate change on their quality of life. This study sought to achieve the maximum efficiency for climate change actions with the minimum negative impact on the well-being of countries and cities. The Climate Change and Country Success (C3S) and Climate Change and Cities’ Quality of Life (C3QL) models and maps of the world created as part of this research showed that as economic, social, political, cultural, and environmental metrics of countries and cities improve, so do their climate change indicators. For the 14 climate change indicators, the C3S and C3QL models indicated 68.8% average dispersion dimensions in the case of countries and 52.8% in the case of cities. Our research showed that increases in the success of 169 countries saw improvements in 9 climate change indicators out of the 12 considered. Improvements in country success indicators were accompanied by a 71% improvement in climate change metrics.
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Author: Hoshiar Mal
The Impact of IoT Characteristics, Cultural Factors and Safety Concerns on Consumer Purchase Intention of Green Electronic Products
Publisher: Sustainability, 2023
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This study examines the impact of IoT characteristics, key cultural dimensions, and safety concerns on consumer green purchase intentions. The research employed a survey approach, collecting responses from 278 consumers. Structural equation modeling (SEM) was used to test the hypotheses. The finding of the study indicates that IoT characteristics (IoTC), environmental concerns (EC), collectivism (GW), and individualism (ID) have a positive effect with attitude toward green purchasing (AGP), which further affect green purchase intentions (GPI). Attitude toward green purchasing (AGP) mediates the relationship between IoTC, safety concerns (SC), collectivism (GW), individualism (ID), and green purchase intentions (GPI). The study’s findings help us understand how IoTC, social concerns, and cultural factors affect consumer green purchase intention.
https://doi.org/10.3390/su15086597
This study examines the impact of IoT characteristics, key cultural dimensions, and safety concerns on consumer green purchase intentions. The research employed a survey approach, collecting responses from 278 consumers. Structural equation modeling (SEM) was used to test the hypotheses. The finding of the study indicates that IoT characteristics (IoTC), environmental concerns (EC), collectivism (GW), and individualism (ID) have a positive effect with attitude toward green purchasing (AGP), which further affect green purchase intentions (GPI). Attitude toward green purchasing (AGP) mediates the relationship between IoTC, safety concerns (SC), collectivism (GW), individualism (ID), and green purchase intentions (GPI). The study’s findings help us understand how IoTC, social concerns, and cultural factors affect consumer green purchase intention.
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Author: Garima Rajan
Investigating Linguistic Abilities and its Relationship with Empathy, Emotional Intelligence and Cognitive Flexibility
Publisher: Psycholinguistics, 2023
Abstract
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"Purpose. Languages play an important role in shaping our brain and personality. Numerous studies in the past have found that bilingual and trilingual individuals outperform monolinguals on certain cognitive assessments. In some studies, monolinguals have outperformed the other two groups on emotional tests. Most of the studies have reported mixed findings on this topic, making it difficult to draw conclusions.
Procedure. For the first time, the present study attempts to examine linguistic ability, empathy, emotional intelligence and cognitive flexibility in an Indian sample of 90 participants (Mage = 26.86 years, SD = 7.45) (28 monolingual, 30 bilingual and 32 trilingual). Each of the participants completed the Interpersonal Reactivity Index Questionnaire, Trait Emotional Intelligence Questionnaire – Short Form and Colour Stroop Test on PEBL (Psychology Experiment Building Language) software.
Results. One – Way ANOVA revealed statistically significant results for Empathy [F(2,87) = 218.84, p ir monolingual counterparts."
https://doi.org/10.31470/2309-1797-2023-33-2-57-89
"Purpose. Languages play an important role in shaping our brain and personality. Numerous studies in the past have found that bilingual and trilingual individuals outperform monolinguals on certain cognitive assessments. In some studies, monolinguals have outperformed the other two groups on emotional tests. Most of the studies have reported mixed findings on this topic, making it difficult to draw conclusions.
Procedure. For the first time, the present study attempts to examine linguistic ability, empathy, emotional intelligence and cognitive flexibility in an Indian sample of 90 participants (Mage = 26.86 years, SD = 7.45) (28 monolingual, 30 bilingual and 32 trilingual). Each of the participants completed the Interpersonal Reactivity Index Questionnaire, Trait Emotional Intelligence Questionnaire – Short Form and Colour Stroop Test on PEBL (Psychology Experiment Building Language) software.
Results. One – Way ANOVA revealed statistically significant results for Empathy [F(2,87) = 218.84, p ir monolingual counterparts."
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Author: Anuradha Batabyal
Prey populations with different predation histories show differences in behavioral and transcriptional effects under acute predation threat
Publisher: Neurobiology of Learning and Memory, 2023
Abstract
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Predator detection induces both behavioral and physiological responses in prey organisms. Our model organism, the pond snail Lymnaea stagnalis, shows multiple defensive behaviors in response to predator cues. In this study, we investigated and compared the transcriptional effects induced by the exposure to a predator scent (i.e., crayfish effluent - CE) in a strain of lab-inbred snails (i.e., W snails), which have been raised and maintained under standardized laboratory conditions for generations and a strain of freshly collected snails (i.e., Margo snails), which live in a crayfish-free pond. Neither the W- strain nor the Margo Lake snails used in this study have actually experienced crayfish. However, the W strain innately recognizes crayfish as a threat. We found that, following the exposure to CE, both strains showed significantly higher mRNA levels of serotonin-related genes. This is important, as the serotonergic system modulates predator detection and vigilance behaviors in pond snails. However, the expression levels of CREB1 and HSP70 were only upregulated in CE-exposed W snails but not in Margo ones. As CREB1 plays a key role in learning and memory formation, whereas HSP70 is involved in stress response, we investigated whether these differences in CREB1 and HSP70 mRNA levels would reflect differences in predator-induced learning (e.g., configural learning). We found that only W snails formed configural learning memory, whereas Margo snails did not. Thus, while both the strains molecularly respond to the CE by upregulating the serotoninergic system, only W snails behaviorally recognize CE as a threat and, therefore, form configural learning.
https://doi.org/10.1016/j.nlm.2023.107775
Predator detection induces both behavioral and physiological responses in prey organisms. Our model organism, the pond snail Lymnaea stagnalis, shows multiple defensive behaviors in response to predator cues. In this study, we investigated and compared the transcriptional effects induced by the exposure to a predator scent (i.e., crayfish effluent - CE) in a strain of lab-inbred snails (i.e., W snails), which have been raised and maintained under standardized laboratory conditions for generations and a strain of freshly collected snails (i.e., Margo snails), which live in a crayfish-free pond. Neither the W- strain nor the Margo Lake snails used in this study have actually experienced crayfish. However, the W strain innately recognizes crayfish as a threat. We found that, following the exposure to CE, both strains showed significantly higher mRNA levels of serotonin-related genes. This is important, as the serotonergic system modulates predator detection and vigilance behaviors in pond snails. However, the expression levels of CREB1 and HSP70 were only upregulated in CE-exposed W snails but not in Margo ones. As CREB1 plays a key role in learning and memory formation, whereas HSP70 is involved in stress response, we investigated whether these differences in CREB1 and HSP70 mRNA levels would reflect differences in predator-induced learning (e.g., configural learning). We found that only W snails formed configural learning memory, whereas Margo snails did not. Thus, while both the strains molecularly respond to the CE by upregulating the serotoninergic system, only W snails behaviorally recognize CE as a threat and, therefore, form configural learning.
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Author: Ajith Abraham
Exponential entropy-based multilevel thresholding using enhanced barnacle mating optimization
Publisher: Multimedia Tools and Applications, 2023
Abstract
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Links
Multilevel Thresholding (MLT) is a prominent image segmentation research field that can effectively handle problems encountered while collecting meaningful information from a digital image. Most of the existing entropy-based Multilevel thresholding approaches use the logarithmic behaviour of Shannon’s entropy, which does not exist for all possible points with appropriate bounded value. To evade this problem, an entropy-based on exponential information gain function is introduced as the fitness function in this paper to improve the thresholding accuracy. This research also proposes an enhanced Barnacle Mating optimization algorithm (EBMO) for obtaining appropriate threshold values by maximising the fitness function. The enhancement over basic Barnacle mating optimization is achieved by incorporating an additional Gaussian mutation strategy and a random flow towards the best solution steps with the original algorithm. The involvement of these additional steps helps the algorithm to prevent it to be stagnated at a local minimum by boosting its exploration capability. To validate the proposed optimization algorithm, it has been tested with a set of well-known benchmark functions and the CEC 2014 test suite. The results obtained in various tests are then compared with other standard and state-of-art algorithms with the help of quantitative analysis such as average, median, and standard deviation of the fitness values over several runs, qualitative analysis, such as search history, trajectory, and average fitness history and statistical analysis using Friedman Rank test and found superior to all. A more detailed analysis of the obtained results was also conducted using post hoc Bonferroni–Dunn and Holm test to observe how the proposed EBMO algorithm is significantly different from others. A comparison of the proposed exponential entropy (EE) based multilevel thresholding using EBMO (EBMO-EE) with other optimization algorithms also presented. Various performance measures such as peak signal-to-noise ratio (PSNR), structural similarity index (SSIM), feature similarity index (FSIM), and Uniformity Measures (UM) obtained from different standard benchmark images of varying dimension are considered. It has been observed that there is an improvement of the thresholding accuracy, using EBMO, about 2% to 4% over others.
https://doi.org/10.1007/s11042-023-15668-4
Multilevel Thresholding (MLT) is a prominent image segmentation research field that can effectively handle problems encountered while collecting meaningful information from a digital image. Most of the existing entropy-based Multilevel thresholding approaches use the logarithmic behaviour of Shannon’s entropy, which does not exist for all possible points with appropriate bounded value. To evade this problem, an entropy-based on exponential information gain function is introduced as the fitness function in this paper to improve the thresholding accuracy. This research also proposes an enhanced Barnacle Mating optimization algorithm (EBMO) for obtaining appropriate threshold values by maximising the fitness function. The enhancement over basic Barnacle mating optimization is achieved by incorporating an additional Gaussian mutation strategy and a random flow towards the best solution steps with the original algorithm. The involvement of these additional steps helps the algorithm to prevent it to be stagnated at a local minimum by boosting its exploration capability. To validate the proposed optimization algorithm, it has been tested with a set of well-known benchmark functions and the CEC 2014 test suite. The results obtained in various tests are then compared with other standard and state-of-art algorithms with the help of quantitative analysis such as average, median, and standard deviation of the fitness values over several runs, qualitative analysis, such as search history, trajectory, and average fitness history and statistical analysis using Friedman Rank test and found superior to all. A more detailed analysis of the obtained results was also conducted using post hoc Bonferroni–Dunn and Holm test to observe how the proposed EBMO algorithm is significantly different from others. A comparison of the proposed exponential entropy (EE) based multilevel thresholding using EBMO (EBMO-EE) with other optimization algorithms also presented. Various performance measures such as peak signal-to-noise ratio (PSNR), structural similarity index (SSIM), feature similarity index (FSIM), and Uniformity Measures (UM) obtained from different standard benchmark images of varying dimension are considered. It has been observed that there is an improvement of the thresholding accuracy, using EBMO, about 2% to 4% over others.
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Author: Ajith Abraham
A novel adaptive class weight adjustment-based multiclass segmentation error minimization technique for COVID-19 X-ray image analysis
Publisher: International Journal of Imaging Systems and Technology, 2023
Abstract
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Coronavirus outbreaks during the last couple of years created a huge health disaster for human lives. Diagnosis of COVID-19 infections is, thus, very important for the medical practitioners. For a quick detection, analysis of the COVID-19 chest X-ray images is inevitable. Therefore, there is a strong need for the development of a multiclass segmentation method for the purpose. Earlier techniques used for multiclass segmentation of images are mostly based on entropy measurements. Nonetheless, entropy methods are not efficient when the gray-level distribution of the image is nonuniform. To address this problem, a novel adaptive class weight adjustment-based multiclass segmentation error minimization technique for COVID-19 chest X-ray image analysis is investigated. Theoretical investigations on the first-hand objective functions are presented. The results on both the biclass and multiclass segmentation of medical images are enlightened. The key to our success is the adjustment of the pixel counts of different classes adaptively to reduce the error of segmentation. The COVID-19 chest X-ray images are taken from the Kaggle Radiography database for the experiments. The proposed method is compared with the state-of-the-art methods based on Tsallis, Kapur's, Masi, and Rényi entropy. The well-known segmentation metrics are used for an empirical analysis. Our method achieved a performance increase of around 8.03% in the case of PSNR values, 3.01% for FSIM, and 4.16% for SSIM. The proposed technique would be useful for extracting dots from micro-array images of DNA sequences and multiclass segmentation of the biomedical images such as MRI, CT, and PET.
https://doi.org/10.1002/ima.22916
Coronavirus outbreaks during the last couple of years created a huge health disaster for human lives. Diagnosis of COVID-19 infections is, thus, very important for the medical practitioners. For a quick detection, analysis of the COVID-19 chest X-ray images is inevitable. Therefore, there is a strong need for the development of a multiclass segmentation method for the purpose. Earlier techniques used for multiclass segmentation of images are mostly based on entropy measurements. Nonetheless, entropy methods are not efficient when the gray-level distribution of the image is nonuniform. To address this problem, a novel adaptive class weight adjustment-based multiclass segmentation error minimization technique for COVID-19 chest X-ray image analysis is investigated. Theoretical investigations on the first-hand objective functions are presented. The results on both the biclass and multiclass segmentation of medical images are enlightened. The key to our success is the adjustment of the pixel counts of different classes adaptively to reduce the error of segmentation. The COVID-19 chest X-ray images are taken from the Kaggle Radiography database for the experiments. The proposed method is compared with the state-of-the-art methods based on Tsallis, Kapur's, Masi, and Rényi entropy. The well-known segmentation metrics are used for an empirical analysis. Our method achieved a performance increase of around 8.03% in the case of PSNR values, 3.01% for FSIM, and 4.16% for SSIM. The proposed technique would be useful for extracting dots from micro-array images of DNA sequences and multiclass segmentation of the biomedical images such as MRI, CT, and PET.
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Author: Moulika Mandal
Computerized cognitive testing in patients with Parkinson’s disease: an Indian perspective
Publisher: Neurodegenerative Disease Management, 2023
Abstract
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This study investigates perspectives on computer-based assessments among elderly individuals with Parkinson’s disease (PD) in India for more efficient usability of digital assessments for this population. Materials & methods: Content analysis was conducted on 30 participants diagnosed with PD, who were interviewed for their preferences and perspectives on integrating technology in healthcare assessments. Results: Elderly individuals with PD in India favored paper–pencil assessments over computer-based alternatives due to limited technological familiarity, resistance to change, lack of trust in healthcare technology and PD-related motor challenges. Conclusion: Elderly patients with Parkinson’s in India indicated their discomfort with computer-based based cognitive assessment. Addressing barriers toward digital assessments in India is crucial for their successful implementation in healthcare.
https://doi.org/10.2217/nmt-2023-0002
This study investigates perspectives on computer-based assessments among elderly individuals with Parkinson’s disease (PD) in India for more efficient usability of digital assessments for this population. Materials & methods: Content analysis was conducted on 30 participants diagnosed with PD, who were interviewed for their preferences and perspectives on integrating technology in healthcare assessments. Results: Elderly individuals with PD in India favored paper–pencil assessments over computer-based alternatives due to limited technological familiarity, resistance to change, lack of trust in healthcare technology and PD-related motor challenges. Conclusion: Elderly patients with Parkinson’s in India indicated their discomfort with computer-based based cognitive assessment. Addressing barriers toward digital assessments in India is crucial for their successful implementation in healthcare.
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Author: Chiranjoy Chattopadhyay
SOLARNet: A single stage regression based framework for efficient and robust object recognition in aerial images
Publisher: Pattern Recognition Letters, 2023
Abstract
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Links
Object recognition and localization play a crucial role in aerial images and their applications. The aerial images are challenging due to the large aspect ratio, arbitrary orientation, variation in scales, and non-uniform and cluttered object distribution. To address these challenges, we propose an efficient and robust model called the Simultaneous Object Localization and Recognition Network (SOLARNet), which is a fusion network that integrates two different sub-networks: PixelAttentionDetector (PD) and RotationDetector (RD). The PD considers features from different scales and cluttered objects, while RD handles rotation invariance, giving horizontal and oriented object detection results. The state-of-the-art model fails to improve accuracy when images are adversarially attacked. SOLARNet is not only efficient in terms of accuracy but also robust concerning to Fast Gradient Sign Method (FGSM), Basic Iterative Method (BIM), Jacobian Based Saliency Map (JSM) adversarial attacks, which is the crucial factor for any mission-critical system. We have performed experiments and achieved the accuracy on the publicly available DOTA dataset (75.00% mAP, 66.40% mAP) and DIOR dataset (88.60% mAP, 81.50% mAP) for horizontal and oriented object recognition tasks respectively while having high inference speed. Qualitative and quantitative results reported in this paper substantiate the superiority of SOLARNet over other state-of-the-art methodologies.
https://doi.org/10.1016/j.patrec.2023.05.031
Object recognition and localization play a crucial role in aerial images and their applications. The aerial images are challenging due to the large aspect ratio, arbitrary orientation, variation in scales, and non-uniform and cluttered object distribution. To address these challenges, we propose an efficient and robust model called the Simultaneous Object Localization and Recognition Network (SOLARNet), which is a fusion network that integrates two different sub-networks: PixelAttentionDetector (PD) and RotationDetector (RD). The PD considers features from different scales and cluttered objects, while RD handles rotation invariance, giving horizontal and oriented object detection results. The state-of-the-art model fails to improve accuracy when images are adversarially attacked. SOLARNet is not only efficient in terms of accuracy but also robust concerning to Fast Gradient Sign Method (FGSM), Basic Iterative Method (BIM), Jacobian Based Saliency Map (JSM) adversarial attacks, which is the crucial factor for any mission-critical system. We have performed experiments and achieved the accuracy on the publicly available DOTA dataset (75.00% mAP, 66.40% mAP) and DIOR dataset (88.60% mAP, 81.50% mAP) for horizontal and oriented object recognition tasks respectively while having high inference speed. Qualitative and quantitative results reported in this paper substantiate the superiority of SOLARNet over other state-of-the-art methodologies.
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Author: Akash Kamble
Why can’t we help but love mobile banking chatbots? Perspective of stimulus-organism-response
Publisher: Journal of Financial Services Marketing, 2023
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In mobile banking, the relationship between consumers and conversational artificial intelligence chatbots (CAIC) is a growing interest. This study delves into consumers’ brand love for these chatbots and examines the factors that influence this love. The study tests the relationships between anthropomorphism, smart experience, and brand love based on 355 banking consumers. According to the findings, anthropomorphism, smart experience, and conversational AI chatbot engagement have a favorable effect on consumers’ interaction with CAIC, resulting in an increase in their brand love. This research offers valuable insights for mobile banking companies looking to increase their customers’ love for their conversational AI chatbots. The findings emphasize the importance of considering anthropomorphism, smart experience, and engagement in designing conversational AI chatbots, as these factors contribute to a meaningful and engaging customer experience.
https://doi.org/10.1057/s41264-023-00237-5
In mobile banking, the relationship between consumers and conversational artificial intelligence chatbots (CAIC) is a growing interest. This study delves into consumers’ brand love for these chatbots and examines the factors that influence this love. The study tests the relationships between anthropomorphism, smart experience, and brand love based on 355 banking consumers. According to the findings, anthropomorphism, smart experience, and conversational AI chatbot engagement have a favorable effect on consumers’ interaction with CAIC, resulting in an increase in their brand love. This research offers valuable insights for mobile banking companies looking to increase their customers’ love for their conversational AI chatbots. The findings emphasize the importance of considering anthropomorphism, smart experience, and engagement in designing conversational AI chatbots, as these factors contribute to a meaningful and engaging customer experience.
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Author: Yashobanta Parida and Joyita Roy Chowdhury
Land use changes and natural disaster fatalities: Empirical analysis for India
Publisher: Ecological Indicators, 2023
Abstract
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India’s land use pattern has witnessed significant changes over time. Various studies have pointed out that land use changes in India indicate a trend towards increasing urbanisation and deforestation (particularly for native forests). A priori, such changes in land use patterns may lead to a higher incidence of natural disasters. The study examines whether these land use changes have led to higher fatalities (and damages) due to floods and other natural disasters , controlling for various socio-economic factors. Our results indicate that land use changes, specifically deforestation, and urbanisation, are detrimental to environmental health, causing greater flood damages and natural disaster fatalities. Specifically, ‘forest cover’ is found to have a negative impact on ‘flood damages’, whereas ‘urbanisation’ has a positive impact, as per a priori expectations. Similarly, forest cover is found to be inversely related to total ‘natural disaster fatalities’ (which includes deaths due to floods, cyclones, landslides, heat waves, cold waves, and lightning), whereas ‘urbanisation’ is found to have a positive impact on disaster fatalities, according to a priori expectations. Our results confirm that land use changes in the direction of deforestation and urbanisation have increased fatalities and damages due to natural disasters. Another important finding of our study is that financial development has a mitigating impact on flood fatalities as well as overall natural disaster fatalities.
https://doi.org/10.1016/j.ecolind.2023.110525
India’s land use pattern has witnessed significant changes over time. Various studies have pointed out that land use changes in India indicate a trend towards increasing urbanisation and deforestation (particularly for native forests). A priori, such changes in land use patterns may lead to a higher incidence of natural disasters. The study examines whether these land use changes have led to higher fatalities (and damages) due to floods and other natural disasters , controlling for various socio-economic factors. Our results indicate that land use changes, specifically deforestation, and urbanisation, are detrimental to environmental health, causing greater flood damages and natural disaster fatalities. Specifically, ‘forest cover’ is found to have a negative impact on ‘flood damages’, whereas ‘urbanisation’ has a positive impact, as per a priori expectations. Similarly, forest cover is found to be inversely related to total ‘natural disaster fatalities’ (which includes deaths due to floods, cyclones, landslides, heat waves, cold waves, and lightning), whereas ‘urbanisation’ is found to have a positive impact on disaster fatalities, according to a priori expectations. Our results confirm that land use changes in the direction of deforestation and urbanisation have increased fatalities and damages due to natural disasters. Another important finding of our study is that financial development has a mitigating impact on flood fatalities as well as overall natural disaster fatalities.
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Author: Anuradha Batabyal
Comparison of behavioural and transcriptional responses to a heat stressor between freshly collected and an inbred strain of Lymnaea
Publisher: Canadian Journal of Zoology, 2023
Abstract
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Different populations of organisms occurring across varying thermal regimes show diversity in responses to heat stress. We use a “common garden experimental” approach designed to deal with phenotypic plasticity to study in Lymnaea stagnalis (Linnaeus, 1758) the behavioural and molecular responses to a heat shock in laboratory-inbred snails (W-strain) and freshly collected snails (Stony strain) from ponds. In the W-strain, which has been reared under standardized temperatures for generations, the exposure to 30 °C for 1 h (heat shock, HS) when experienced after a novel “taste” results in a taste-specific aversion known as the “Garcia effect”. This learned avoidance requires the upregulation of heat shock proteins (HSPs). In contrast, freshly collected Stony strain, which experiences temperature fluctuations regularly, does not exhibit a Garcia effect. Here, we found that (1) Stony-strain snails have higher basal mRNA levels of HSPs than W-strain ones; (2) in the W-strain, the training procedure to cause the Garcia effect upregulates the mRNA levels of HSPs and key neuroplasticity-related genes such as CREB1 and GRIN1; (3) in Stony-strain snails, the same training procedure fails to alter the mRNA levels of those targets. These data suggest that Stony-strain snails do not perceive the HS as a stressor because of the higher HSP basal mRNA levels, which may confer a higher thermal tolerance.
https://doi.org/10.1139/cjz-2023-0088
Different populations of organisms occurring across varying thermal regimes show diversity in responses to heat stress. We use a “common garden experimental” approach designed to deal with phenotypic plasticity to study in Lymnaea stagnalis (Linnaeus, 1758) the behavioural and molecular responses to a heat shock in laboratory-inbred snails (W-strain) and freshly collected snails (Stony strain) from ponds. In the W-strain, which has been reared under standardized temperatures for generations, the exposure to 30 °C for 1 h (heat shock, HS) when experienced after a novel “taste” results in a taste-specific aversion known as the “Garcia effect”. This learned avoidance requires the upregulation of heat shock proteins (HSPs). In contrast, freshly collected Stony strain, which experiences temperature fluctuations regularly, does not exhibit a Garcia effect. Here, we found that (1) Stony-strain snails have higher basal mRNA levels of HSPs than W-strain ones; (2) in the W-strain, the training procedure to cause the Garcia effect upregulates the mRNA levels of HSPs and key neuroplasticity-related genes such as CREB1 and GRIN1; (3) in Stony-strain snails, the same training procedure fails to alter the mRNA levels of those targets. These data suggest that Stony-strain snails do not perceive the HS as a stressor because of the higher HSP basal mRNA levels, which may confer a higher thermal tolerance.
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Author: Khyati Jagani
Social media influencer marketing: foundations, trends, and ways forward
Publisher: Electronic Commerce Research, 2023
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The increasing use and effectiveness of social media influencers in marketing have intrigued both academic scholars and industry professionals. To shed light on the foundations and trends of this contemporary phenomenon, this study undertakes a systematic literature review using a bibliometric-content analysis to map the extant literature where consumer behavior, social media, and influencer marketing are intertwined. Using 214 articles published in journals indexed by the Australian Business Deans Council (ABDC), Chartered Association of Business Schools (CABS), and Web of Science (WOS) from 2008 to 2021, this study unpacks the articles, journals, methods, theories, themes, and constructs (antecedents, moderators, mediators, and consequences) in extant research on social media influencer marketing. Noteworthily, the review highlighted that the major research streams in social media influencer marketing research involve parasocial interactions and relationships, sponsorship, authenticity, and engagement and influence. The review also revealed the prominent role of audience-, brand-, comparative-, content-, influencer-, social-, and technology-related factors in influencing how consumers react to social media influencer marketing. The insights derived from this one-stop, state-of-the-art review can help social media influencers and marketing scholars and professionals to recognize key characteristics and trends of social media influencer marketing, and thus, drive new research and social media marketing practices where social media influencers are employed and leveraged upon for marketing activities.
https://doi.org/10.1007/s10660-023-09719-z
The increasing use and effectiveness of social media influencers in marketing have intrigued both academic scholars and industry professionals. To shed light on the foundations and trends of this contemporary phenomenon, this study undertakes a systematic literature review using a bibliometric-content analysis to map the extant literature where consumer behavior, social media, and influencer marketing are intertwined. Using 214 articles published in journals indexed by the Australian Business Deans Council (ABDC), Chartered Association of Business Schools (CABS), and Web of Science (WOS) from 2008 to 2021, this study unpacks the articles, journals, methods, theories, themes, and constructs (antecedents, moderators, mediators, and consequences) in extant research on social media influencer marketing. Noteworthily, the review highlighted that the major research streams in social media influencer marketing research involve parasocial interactions and relationships, sponsorship, authenticity, and engagement and influence. The review also revealed the prominent role of audience-, brand-, comparative-, content-, influencer-, social-, and technology-related factors in influencing how consumers react to social media influencer marketing. The insights derived from this one-stop, state-of-the-art review can help social media influencers and marketing scholars and professionals to recognize key characteristics and trends of social media influencer marketing, and thus, drive new research and social media marketing practices where social media influencers are employed and leveraged upon for marketing activities.
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Author: Ajith Abraham
Adaptive variable sampling model for performance analysis in high cache-performance computing environments
Publisher: Heliyon, 2023
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High-performance computing provides computing power for a variety of scientific disciplines, supporting advancements by offering insights beyond metacognition. Maximizing computing performance without wasting resources is a major research issue. Predicting the performance of a computer's next state is effective for scheduling. However, hardware performance monitors representing the computer's state require high expert knowledge, and there is no standardized model. In this paper, we propose an adaptive variable sampling model for performance analysis in high-performance computing environments. Our method automatically classifies the optimal variables from numerous variables related to performance prediction and predicts performance using the sampled variables. The optimal variables for performance analysis do not require expert knowledge during the sampling process. We conducted experiments in various architectures and applications to validate this method. This model performed at least 24.25% and up to 58.75% faster without any loss in accuracy.
https://doi.org/10.1016/j.heliyon.2023.e16777
High-performance computing provides computing power for a variety of scientific disciplines, supporting advancements by offering insights beyond metacognition. Maximizing computing performance without wasting resources is a major research issue. Predicting the performance of a computer's next state is effective for scheduling. However, hardware performance monitors representing the computer's state require high expert knowledge, and there is no standardized model. In this paper, we propose an adaptive variable sampling model for performance analysis in high-performance computing environments. Our method automatically classifies the optimal variables from numerous variables related to performance prediction and predicts performance using the sampled variables. The optimal variables for performance analysis do not require expert knowledge during the sampling process. We conducted experiments in various architectures and applications to validate this method. This model performed at least 24.25% and up to 58.75% faster without any loss in accuracy.
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Author: Ajith Abraham
A Secure Big Data Storage Framework Based on Blockchain Consensus Mechanism With Flexible Finality
Publisher: IEEE Access, 2023
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Data security and integrity are becoming increasingly important as the volume of data being created and stored grows. A controlled third party that provides most of the existing big data security systems makes them susceptible to several security risks. By resolving current technology challenges, including scalability, non-tampering, trustworthiness, data governance, and transparency, blockchain technology plays a vital role and has a significant potential to safeguard personal information. Therefore, this work focuses on addressing real-time big data storage issues based on a transdisciplinary research approach. This study introduces a brand-new approach to big data storage security that leverages blockchain technology and applies highway protocol to generate new blocks. The proposed highway protocol works based on the flexible finality condition to overcome issues of baseline models. The highway allows blocks to run the consensus mechanism to configure security thresholds more freely. The proposed protocol also allows blocks with lower thresholds to reach finality more quickly than blocks requiring greater degrees of confidence. Therefore, the proposed big data framework can dynamically control data manipulation and continuously support individuals to participate in the data-sharing process. A comparison was performed with the number of data requests in terms of hit ratio, and a highway protocol provides better results than baseline models. The proposed model provides a data processing period of 13 to 30 ms and an energy consumption of 32 to 41 mJ.
DOI: 10.1109/ACCESS.2023.3282322
Data security and integrity are becoming increasingly important as the volume of data being created and stored grows. A controlled third party that provides most of the existing big data security systems makes them susceptible to several security risks. By resolving current technology challenges, including scalability, non-tampering, trustworthiness, data governance, and transparency, blockchain technology plays a vital role and has a significant potential to safeguard personal information. Therefore, this work focuses on addressing real-time big data storage issues based on a transdisciplinary research approach. This study introduces a brand-new approach to big data storage security that leverages blockchain technology and applies highway protocol to generate new blocks. The proposed highway protocol works based on the flexible finality condition to overcome issues of baseline models. The highway allows blocks to run the consensus mechanism to configure security thresholds more freely. The proposed protocol also allows blocks with lower thresholds to reach finality more quickly than blocks requiring greater degrees of confidence. Therefore, the proposed big data framework can dynamically control data manipulation and continuously support individuals to participate in the data-sharing process. A comparison was performed with the number of data requests in terms of hit ratio, and a highway protocol provides better results than baseline models. The proposed model provides a data processing period of 13 to 30 ms and an energy consumption of 32 to 41 mJ.
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