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This work investigates controllability issues under the leader–follower framework of consensus problem on multi-agent networks where its agents exhibit the heterogeneous dynamics involving first-, second-, and third-order integrator agents on a continuous time-scale. The communication topology is directed, time-invariant, and the reliability of all interactions are unity. We employ two kinds of consensus protocols that are linear and distributed. The necessary and sufficient criteria for the controllability of followers’ dynamics through the selected leaders are obtained in terms of matrix-rank conditions. We also obtain some necessary conditions of controllability involving eigenvectors of system matrices. It is verified that, if either of the first-, second-, or third-order integrator agents are absent, then the controllability of the network becomes equivalent to the controllability of the communication topology just like those of the networks with homogeneous dynamic agents. Obtained results are verified with illustrative examples.

Temperature trends over the high-altitude mountains depict an increase with elevation during recent years. These stratified warming trends observed over the Himalayan-Tibetan (HT) regions are higher than the mean warming trends observed over low-elevation regions of South and East Asia, which is attributed to several factors including snow albedo feedback, clouds and water vapor feedback. In this study, we demonstrate the effects of deposition of absorbing aerosols like black carbon and dust on snow albedo and its implications for elevation-dependent warming (EDW). Though the aerosol concentration decreases with elevation, warming due to aerosol-induced snow darkening increases with elevation. Further, surface cooling due to the direct radiative effects (DRE) of aerosols is found to decrease with elevation, which also favors higher warming at high altitudes. The effects of both the deposition of absorbing aerosols on snow albedo and the surface cooling due to the DRE of atmospheric aerosols could strengthen EDW. This study clearly shows the potential of albedo feedback due to aerosol-cryosphere interaction as one of the physical mechanisms contributing to the observed EDW over the HT region.

The recent trend in global mean temperature is expected to increase the frequency, duration, and intensity of heat waves and heat stress all around the world, especially over the Indian subcontinent. To investigate the plausible mechanisms and implications of meteorological feedback of aerosol forcing on heat stress over South Asia, simulations using a regional climate model (RegCM4) coupled with aerosols were carried out from 2015 to 2019. Generally, extreme heat days are observed mainly over the central and western Indo-Gangetic plains (IGPs) during May. The two heat-humidity indices, wet-bulb temperature (WBT) and heat index (HI), showed moderate effects due to aerosol forcing despite the high aerosol loading during the pre-monsoon. The inclusion of aerosols leads to surface cooling (a reduction in maximum surface temperature), with the highest impact over western India (WI) and the IGP. However, this surface cooling due to aerosols is compensated by an increase in near-surface relative humidity (RH), leading to an increase in WBT and HI. This compensating effect of RH on WBT and HI is greater in WI than that in eastern India. Regional heterogeneity in aerosol-induced changes in heat stress has a strong dependence on land-atmosphere interactions. This study clearly shows that aerosol-induced changes in RH play a decisive role in the assessment of heat stress, which answers the link between the prevalence of high heat stress conditions despite high aerosol loading (surface cooling) over the Indian region.

Natural language processing (NLP) developments have made it possible for robots to read and analyze human language with astounding precision, revolutionizing the field of text understanding. An overview of current advancements in NLP approaches and their effects on text comprehension are provided in this abstract. It examines significant developments in fields including named entity identification, sentiment analysis, semantic analysis, and question answering, highlighting the difficulties encountered and creative solutions put forth. To sum up, recent developments in natural language processing have raised the bar for text comprehension. Deep learning models and extensive pre-training have changed methods including semantic analysis, sentiment analysis, named entity identification, and question answering. These developments have produced text comprehension systems that are increasingly precise and complex. However, issues with prejudice, coreference resolution, and contextual comprehension still need to be resolved. The future of NLP for text understanding has considerable potential with continuing study and innovation, opening the door for increasingly sophisticated applications in numerous sectors.