Month: April 2024

Abstract

Wadia Institute of Himalayan Geology (WIHG), Dehradun is a premier Geological institute involved in both basic and applied research to unravel the Geodynamics of the mighty Himalaya, which covers a wide spectrum of Geoscientific disciplines: petrology, geochemistry, structural geology, geophysics, sedimentology, biostratigraphy, earthquake geology, geomorphology, environment & engineering geology, quaternary geology, hydrology, glaciology, etc. The state-of-the-art sophisticated analytical laboratories strongly substantiate the field data for understanding the geodynamic evolution of the Himalaya, seismogenesis of the region, studying landslides and avalanches, characterization and mitigation of geohazards related to earthquakes, landslides, snow/ice avalanches, glacier/landslide lakes outbursts, exploration of natural resources (minerals/ore bodies, hydrocarbons, springs, geothermal, etc.), comprehending glacier dynamics and fluvial systems, etc. Additionally, sub-surface features such as crustal heterogeneities, accumulation of elastic strain and convergence rate, crust-mantle interaction, and shallow/deep earth processes are also being probed. Besides investigating basic scientific issues, the Institute provides geoscience support to other government agencies/bodies in understanding and mitigating several hazards-related programs like landslides, avalanches, earthquakes, and floods in the Himalaya. Research activities during 2020–2023 are centered on the major thrust area of “Characterization and Assessment of Surface and Subsurface Processes in Himalaya (CAP-Himalaya): Implications on Geodynamics, Seismogenesis, Bioevents, Paleo-climates, Natural Hazards, and Natural Resources for Sustainable Development”. The research program of the CAP Himalaya is accomplished through different activities. The major achievements in each activity are highlighted here.

Abstract

Wadia Institute of Himalayan Geology (WIHG), Dehradun is a premier Geological institute involved in both basic and applied research to unravel the Geodynamics of the mighty Himalaya, which covers a wide spectrum of Geoscientific disciplines: petrology, geochemistry, structural geology, geophysics, sedimentology, biostratigraphy, earthquake geology, geomorphology, environment & engineering geology, quaternary geology, hydrology, glaciology, etc. The state-of-the-art sophisticated analytical laboratories strongly substantiate the field data for understanding the geodynamic evolution of the Himalaya, seismogenesis of the region, studying landslides and avalanches, characterization and mitigation of geohazards related to earthquakes, landslides, snow/ice avalanches, glacier/landslide lakes outbursts, exploration of natural resources (minerals/ore bodies, hydrocarbons, springs, geothermal, etc.), comprehending glacier dynamics and fluvial systems, etc. Additionally, sub-surface features such as crustal heterogeneities, accumulation of elastic strain and convergence rate, crust-mantle interaction, and shallow/deep earth processes are also being probed. Besides investigating basic scientific issues, the Institute provides geoscience support to other government agencies/bodies in understanding and mitigating several hazards-related programs like landslides, avalanches, earthquakes, and floods in the Himalaya. Research activities during 2020–2023 are centered on the major thrust area of “Characterization and Assessment of Surface and Subsurface Processes in Himalaya (CAP-Himalaya): Implications on Geodynamics, Seismogenesis, Bioevents, Paleo-climates, Natural Hazards, and Natural Resources for Sustainable Development”. The research program of the CAP Himalaya is accomplished through different activities. The major achievements in each activity are highlighted here.

Abstract

The aging of operational reactors leads to increased mechanical vibrations in the reactor interior. The vibration of the in-core sensors near their nominal locations is a new problem for neutronic field reconstruction. Current field-reconstruction methods fail to handle spatially moving sensors. In this study, we propose a Voronoi tessellation technique in combination with convolutional neural networks to handle this challenge. Observations from movable in-core sensors were projected onto the same global field structure using Voronoi tessellation, holding the magnitude and location information of the sensors. General convolutional neural networks were used to learn maps from observations to the global field. The proposed method reconstructed multi-physics fields (including fast flux, thermal flux, and power rate) using observations from a single field (such as thermal flux). Numerical tests based on the IAEA benchmark demonstrated the potential of the proposed method in practical engineering applications, particularly within an amplitude of 5 cm around the nominal locations, which led to average relative errors below 5% and 10% in the \(L_2\) and \(L_{\infty }\) norms, respectively.

Abstract

The aging of operational reactors leads to increased mechanical vibrations in the reactor interior. The vibration of the in-core sensors near their nominal locations is a new problem for neutronic field reconstruction. Current field-reconstruction methods fail to handle spatially moving sensors. In this study, we propose a Voronoi tessellation technique in combination with convolutional neural networks to handle this challenge. Observations from movable in-core sensors were projected onto the same global field structure using Voronoi tessellation, holding the magnitude and location information of the sensors. General convolutional neural networks were used to learn maps from observations to the global field. The proposed method reconstructed multi-physics fields (including fast flux, thermal flux, and power rate) using observations from a single field (such as thermal flux). Numerical tests based on the IAEA benchmark demonstrated the potential of the proposed method in practical engineering applications, particularly within an amplitude of 5 cm around the nominal locations, which led to average relative errors below 5% and 10% in the \(L_2\) and \(L_{\infty }\) norms, respectively.

Abstract

This research paper delves into the intricate dynamics of Chinese firms’ foreign direct investment (FDI) in Japan, shedding light on the “China model” of foreign investment. While previous studies have extensively explored Chinese FDI in resource-rich sectors, this paper focuses on the unique challenges and opportunities in a developed country like Japan, which shares similar technological capabilities with China. In particular, we investigate the role of corporate social responsibility (CSR) in mediating the efficiency of Chinese companies’ investments in Japan. As China’s economy continues to grow globally, understanding the effectiveness of its FDI activities has become increasingly important. This study employs a comprehensive approach, drawing insights from various academic perspectives, including digital finance, product market competition, education systems, energy intensity, logistical capability, and green credit policies, to provide a holistic view of Chinese FDI in Japan. Our findings emphasize the pivotal role of CSR in enhancing investment efficiency and mitigating challenges faced by Chinese firms in Japan. We highlight how CSR positively influences organizational trust, credibility, and risk management, ultimately improving investment effectiveness. Additionally, resource-based theory studies reveal the connection between CSR, technological innovation, and corporate growth, further enhancing investment efficiency. This research, grounded in stakeholder and information asymmetry theories, utilizes robust statistical methods and a rich dataset to establish and validate hypotheses. The results underscore the significance of CSR as a mediating factor and its positive impact on Chinese companies’ FDI efficiency in Japan. This study aligns with the principles of the knowledge economy and provides practical insights for policymakers in China and Japan. Both nations can foster a conducive environment for sustainable and mutually beneficial international collaborations in the global marketplace by promoting CSR practices and recognizing their influence on investment efficiency.

Abstract

This research paper delves into the intricate dynamics of Chinese firms’ foreign direct investment (FDI) in Japan, shedding light on the “China model” of foreign investment. While previous studies have extensively explored Chinese FDI in resource-rich sectors, this paper focuses on the unique challenges and opportunities in a developed country like Japan, which shares similar technological capabilities with China. In particular, we investigate the role of corporate social responsibility (CSR) in mediating the efficiency of Chinese companies’ investments in Japan. As China’s economy continues to grow globally, understanding the effectiveness of its FDI activities has become increasingly important. This study employs a comprehensive approach, drawing insights from various academic perspectives, including digital finance, product market competition, education systems, energy intensity, logistical capability, and green credit policies, to provide a holistic view of Chinese FDI in Japan. Our findings emphasize the pivotal role of CSR in enhancing investment efficiency and mitigating challenges faced by Chinese firms in Japan. We highlight how CSR positively influences organizational trust, credibility, and risk management, ultimately improving investment effectiveness. Additionally, resource-based theory studies reveal the connection between CSR, technological innovation, and corporate growth, further enhancing investment efficiency. This research, grounded in stakeholder and information asymmetry theories, utilizes robust statistical methods and a rich dataset to establish and validate hypotheses. The results underscore the significance of CSR as a mediating factor and its positive impact on Chinese companies’ FDI efficiency in Japan. This study aligns with the principles of the knowledge economy and provides practical insights for policymakers in China and Japan. Both nations can foster a conducive environment for sustainable and mutually beneficial international collaborations in the global marketplace by promoting CSR practices and recognizing their influence on investment efficiency.

Abstract

Malaria is a potentially fatal plasmodium parasite injected by female anopheles mosquitoes that infect red blood cells and cause millions of lifelong disability worldwide yearly. However, specialists’ manual screening in clinical practice is laborious and prone to error. Therefore, a novel Deep Boosted and Ensemble Learning (DBEL) framework, comprising the stacking of new Boosted-BR-STM convolutional neural networks (CNN) and the ensemble ML classifiers, is developed to screen malaria parasite images. The proposed Boosted-BR-STM is based on a new dilated-convolutional block-based Split Transform Merge (STM) and feature-map Squeezing–Boosting (SB) ideas. Moreover, the new STM block uses regional and boundary operations to learn the malaria parasite’s homogeneity, heterogeneity, and boundary with patterns. Furthermore, the diverse boosted channels are attained by employing Transfer Learning-based new feature-map SB in STM blocks at the abstract, medium, and conclusion levels to learn minute intensity and texture variation of the parasitic pattern. Additionally, to enhance the learning capacity of Boosted-BR-STM and foster a more diverse representation of features, boosting at the final stage is achieved through TL by utilizing multipath residual learning. The proposed DBEL framework implicates the stacking of prominent and diverse boosted channels and provides the generated discriminative features of the developed Boosted-BR-STM to the ensemble of ML classifiers. The proposed framework improves the discrimination ability and generalization of ensemble learning. Moreover, the deep feature spaces of the developed Boosted-BR-STM and customized CNNs are fed into ML classifiers for comparative analysis. The proposed DBEL framework outperforms the existing techniques on the NIH malaria dataset that are enhanced using discrete wavelet transform to enrich feature space. The proposed DBEL framework achieved Accuracy (98.50%), Sensitivity (0.9920), F-score (0.9850), and AUC (0.9960), which suggests it to be utilized for malaria parasite screening.

Abstract

Malaria is a potentially fatal plasmodium parasite injected by female anopheles mosquitoes that infect red blood cells and cause millions of lifelong disability worldwide yearly. However, specialists’ manual screening in clinical practice is laborious and prone to error. Therefore, a novel Deep Boosted and Ensemble Learning (DBEL) framework, comprising the stacking of new Boosted-BR-STM convolutional neural networks (CNN) and the ensemble ML classifiers, is developed to screen malaria parasite images. The proposed Boosted-BR-STM is based on a new dilated-convolutional block-based Split Transform Merge (STM) and feature-map Squeezing–Boosting (SB) ideas. Moreover, the new STM block uses regional and boundary operations to learn the malaria parasite’s homogeneity, heterogeneity, and boundary with patterns. Furthermore, the diverse boosted channels are attained by employing Transfer Learning-based new feature-map SB in STM blocks at the abstract, medium, and conclusion levels to learn minute intensity and texture variation of the parasitic pattern. Additionally, to enhance the learning capacity of Boosted-BR-STM and foster a more diverse representation of features, boosting at the final stage is achieved through TL by utilizing multipath residual learning. The proposed DBEL framework implicates the stacking of prominent and diverse boosted channels and provides the generated discriminative features of the developed Boosted-BR-STM to the ensemble of ML classifiers. The proposed framework improves the discrimination ability and generalization of ensemble learning. Moreover, the deep feature spaces of the developed Boosted-BR-STM and customized CNNs are fed into ML classifiers for comparative analysis. The proposed DBEL framework outperforms the existing techniques on the NIH malaria dataset that are enhanced using discrete wavelet transform to enrich feature space. The proposed DBEL framework achieved Accuracy (98.50%), Sensitivity (0.9920), F-score (0.9850), and AUC (0.9960), which suggests it to be utilized for malaria parasite screening.

Abstract

Oncogenic microRNA (miRNA), especially miRNA-21 upregulation in triple-negative breast cancer (TNBC), suggests a new class of therapeutic targets. In this study, we aimed to create GE11 peptide-conjugated small interfering RNA-loaded chitosan nanoparticles (GE11-siRNA-CSNPs) for the targeting of EGFR overexpressed TNBC and selectively inhibit miRNA-21 expression. A variety of in-silico and in vitro cellular and molecular studies were conducted to investigate the binding affinities of specific targets used as well as the anticancer efficacies and mechanisms of GE11-siRNA-CSNPs in TNBC cells. An in-silico assessment reveals a distinct binding affinity of miRNA-21 with siRNA as well as between the extracellular domain of EGFR and synthesized peptides. Notably, the in vitro results showed that GE11-siRNA-CSNPs were revealed to have better cytotoxicity against TNBC cells. It significantly inhibits miRNA-21 expression, cell migration, and colony formation. The results also indicated that GE11-siRNA-CSNPs impeded cell cycle progression. It induces cell death by reducing the expression of the antiapoptotic gene Bcl-2 and increasing the expression of the proapoptotic genes Bax, Caspase 3, and Caspase 9. Additionally, the docking analysis and immunoblot investigations verified that GE1-siRNA-CSNPs, which specifically target TNBC cells and suppress miRNA-21, can prevent the effects of miRNA-21 on the proliferation of TNBC cells via controlling EGFR and subsequently inhibiting the PI3K/AKT and ERK1/2 signaling axis. The GE11-siRNA-CSNPs design, which specifically targets TNBC cells, offers a novel approach for the treatment of breast cancer with improved effectiveness. This study suggests that GE11-siRNA-CSNPs could be a promising candidate for further assessment as an additional strategy in the treatment of TNBC.

Graphical Abstract

Abstract

Oncogenic microRNA (miRNA), especially miRNA-21 upregulation in triple-negative breast cancer (TNBC), suggests a new class of therapeutic targets. In this study, we aimed to create GE11 peptide-conjugated small interfering RNA-loaded chitosan nanoparticles (GE11-siRNA-CSNPs) for the targeting of EGFR overexpressed TNBC and selectively inhibit miRNA-21 expression. A variety of in-silico and in vitro cellular and molecular studies were conducted to investigate the binding affinities of specific targets used as well as the anticancer efficacies and mechanisms of GE11-siRNA-CSNPs in TNBC cells. An in-silico assessment reveals a distinct binding affinity of miRNA-21 with siRNA as well as between the extracellular domain of EGFR and synthesized peptides. Notably, the in vitro results showed that GE11-siRNA-CSNPs were revealed to have better cytotoxicity against TNBC cells. It significantly inhibits miRNA-21 expression, cell migration, and colony formation. The results also indicated that GE11-siRNA-CSNPs impeded cell cycle progression. It induces cell death by reducing the expression of the antiapoptotic gene Bcl-2 and increasing the expression of the proapoptotic genes Bax, Caspase 3, and Caspase 9. Additionally, the docking analysis and immunoblot investigations verified that GE1-siRNA-CSNPs, which specifically target TNBC cells and suppress miRNA-21, can prevent the effects of miRNA-21 on the proliferation of TNBC cells via controlling EGFR and subsequently inhibiting the PI3K/AKT and ERK1/2 signaling axis. The GE11-siRNA-CSNPs design, which specifically targets TNBC cells, offers a novel approach for the treatment of breast cancer with improved effectiveness. This study suggests that GE11-siRNA-CSNPs could be a promising candidate for further assessment as an additional strategy in the treatment of TNBC.

Graphical Abstract