China and India are tremendously expediting progress towards technological innovation in line with SDG-9, which could be a measure to tackle climate change (SDG-13) and attain net zero emissions. Thus, our study intends to scrutinize the environmental influence of technological innovation in India and China under the cubic Environmental Kuznets curve framework during 1984–2021. The ARDL model’s results validate the significantly negative influence of technological innovation on carbon emissions in India. Further, our results satisfy the validity criterion i.e., necessary as well as sufficient condition and therefore confirm a valid inverted N-pattern EKC with two real tipping points for China and India. China’s estimated two turning points fall within the sample period at a threshold of economic growth of 517.68 and 4813.2 USD in the years 1983 and 2008. However, for India, the first estimated turning point falls within the study’s time period, whereas the predicted second turning point towards environmental sustainability is at an income threshold of 6305 USD, which highlights that economic growth would be a remedy for CO2 emissions only after 2048. The findings suggest the significance of investing in technical innovation for policy formulation to address climate change. Further, nations should achieve the threshold income, beyond which further rises will reduce CO2 emissions. Nonetheless, it is imperative for policymakers to ensure that increasing economic growth should not damage the environment.
This research presents a high-fidelity Digital Twin-driven framework for the sustainable operation of hybrid power systems, balancing economic efficiency with environmental mandates through the integration of real-world spatial-temporal wind and solar data from Gujarat, India. By modeling Plug-in Electric Vehicles (PEVs) as a coordinated Virtual Power Plant (VPP) with bidirectional Vehicle-to-Grid (V2G) and Grid-to-Vehicle (G2V) capabilities, the study addresses the inherent stochasticity of renewable-heavy grids. The resulting non-convex optimization problem is solved using the Sine Cosine algorithm (SCA), which effectively navigates complex search landscapes, such as those induced by turbine valve-point loading effects. Validated on 10-unit and 20-unit thermal systems, the proposed approach significantly reduces operational costs and achieves a net annual emission decrease of over 1.6 million tons in the larger test case. Comparative benchmarking against state-of-the-art metaheuristics confirms the SCA's superior convergence stability and technical proficiency in managing the intricacies of modern, sustainable energy infrastructures.
The Jammu and Kashmir Himalaya, located between the rupture zones of the 1905 Kangra and 2005 Kashmir earthquakes, represent a prominent "seismic gap" where understanding the subsurface structure is critical for seismic hazard assessment. This study presents new insights into the crustal structure of this region using teleseismic P-wave coda autocorrelation, applied for the first time in this region. We observe sediment thicknesses reaching similar to 7 km in the foreland basin, thinning progressively to the north. Moho depths reveal a flat structure in the south, localized shallowing between the MBT and MCT, and deepening north of the MCT, consistent with crustal thickening from continental collision and underthrusting. Zones of Moho thinning correlate with elevated Vp/Vs ratios (up to 1.85), suggesting higher temperatures or partial melts. These findings align with regional topography and are consistent with isostatic compensation. The MHT exhibits lateral ramps beneath the Riasi Thrust (RT) and Mandli-Kishanpur Thrust (MKT), which may act as rupture-segment boundaries. We image a ramp-flat-ramp geometry of the MHT, improving earlier models of a single frontal ramp deepening toward the MCT. The first ramp lies within the locked MHT segment, introducing internal segmentation in the seismogenic-zone. This geometry may concentrate strain, enhance stress buildup, and limit rupture propagation, supporting the 1555 earthquake as a deep blind thrust. It also promotes slip deficit accumulation, explaining persistence of the seismic gap despite similar to 11 mm/yr arc-normal convergence. This revised MHT geometry implies potential for future large blind earthquakes and calls for reassessing regional seismic hazard models.
The penetration of distributed generation to the grid and microgrid has been increasing immensely due to the growing electricity demand. However, there is a crucial problem of islanding detection associated with it, a condition when a section of the grid keeps running even after being cut off from the main utility. The accurate and instant detection is generally a matter of concern, as delay or failure of detection can endanger working personnel and threaten grid stability. To address this, the paper introduces a novel technique, the Complex Voltage Unbalance Factor (CVUF), which is the ratio of negative sequence voltage to the positive sequence voltage at the rate of angle. The magnitude of this ratio and the associated angle are both used to detect the islanding instantly. This extra information of angle in CVUF signifies the orientation of unbalance or the direction of unbalance, which is very useful for the diagnosis of faults and system analysis, especially in a rotating machine. The performance of the technique is illustrated utilizing a test system of four Distributed Generation units with three wind farms and one emergency diesel generator connected into a radial distribution network, and thus the obtained results are compared to the existing methods, like Rate of Change of Phase Angle Difference and Rate of Change of Frequency. The suggested technique has also been validated with many standard literature and examined under various scenarios, notably symmetrical and unsymmetrical faults and load shedding.
In the present situation, a lot of research has been directed towards the potency of plants. These natural resources contain characteristics valuable in combat against a number of diseases. But due to lack of familiarity of these plants among human beings, an appropriate advantage of their significance cannot be drawn away. Plants also shares the certain similar characteristics of leaves like color, texture, shape or size, making them hard to classify them among others. So, to eradicate this problem, a deep learning model has been used for the purpose for classification of different plants species captured in real-time using internet of things practice. Six different plants namely Ashwagandha, Black Pepper, Garlic, Ginger, Basil, and Turmeric has been selected for this purpose. Our proposed convolutional neural network (CNN) model achieved higher performance with an accuracy of 99
