Integration of ecological innovation, institutional governance, and human capital development for a sustainable environment in Asian Countries

The study evaluates the dynamic influence of institutional quality, green innovation, and human capital on the ecological footprint in South Asian countries from 1990 to 2018. For empirical estimation of panel data, the study applied the cross-section autoregressive distributed lag (CS-ARDL) estimator to address the issues of crosssection dependency and slope heterogeneity. The long-run findings reveal that institutional governance and ecological innovation reduce the ecological footprint. Likewise, human development decreases the ecological footprint. The short-run outcomes are identical to the long-run; however, the short-run estimates’ magnitude is smaller than the long-run. The results also support the Environmental Kuznets Curve Hypothesis in the long run. The error correction term (ECT) with a significant negative value endorsed the conversion towards the long-run equilibrium position with a 26.5% annual adjustment rate in case of short-run deviation. The augmented mean group estimator ensures the robustness of estimates. The findings recommend that South Asian economies should promote green technology and human capital through R&D allocations in industrial and academic sectors

Reduced expression of cardiac ryanodine receptor protects against stress-induced ventricular tachyarrhythmia, but increases the susceptibility to cardiac alternans

Peer ReviewedPostprint (author's final draft

1st Place Solution of The Robust Vision Challenge 2022 Semantic Segmentation Track

This report describes the winning solution to the Robust Vision Challenge (RVC) semantic segmentation track at ECCV 2022. Our method adopts the FAN-B-Hybrid model as the encoder and uses SegFormer as the segmentation framework. The model is trained on a composite dataset consisting of images from 9 datasets (ADE20K, Cityscapes, Mapillary Vistas, ScanNet, VIPER, WildDash 2, IDD, BDD, and COCO) with a simple dataset balancing strategy. All the original labels are projected to a 256-class unified label space, and the model is trained using a cross-entropy loss. Without significant hyperparameter tuning or any specific loss weighting, our solution ranks the first place on all the testing semantic segmentation benchmarks from multiple domains (ADE20K, Cityscapes, Mapillary Vistas, ScanNet, VIPER, and WildDash 2). The proposed method can serve as a strong baseline for the multi-domain segmentation task and benefit future works. Code will be available at https://github.com/lambert-x/RVC_Segmentation.Comment: The Winning Solution to The Robust Vision Challenge 2022 Semantic Segmentation Trac