arXiv subjects

Bharat Sharma

Publications and source records attributed to Bharat Sharma.

9 recordsLinked to original sources

arXiv2026

Modeling Disruptions to Urban Metabolism using Interconnected Networks

Representation of cities as organisms with metabolic processes is a useful analogy for urban design, development and sustainability. Urban metabolism can be modeled by representing urban systems as networks. The various networks included in a city's metabolism are interdependent in complex ways. Thus, understanding the interaction among these networks is essential to understanding how a healthy urban metabolism is sustained and how injuries to the metabolic system can "heal". It is particularly important to understand how disruptions to one system in an urban area affect the functioning of other systems. Using distribution-level data from a real U.S. city on the electricity distribution system and road geometry, we apply connected network modeling to two critical inter-connected urban infrastructure sectors: energy and transportation. We quantify the robustness of these interdependent networks by evaluating the connectivity disruptions that may occur due to natural or synthetic disruptive events, using both unweighted and weighted metrics.

physics.soc-ph↗

arXiv2025

Dynamic Disruption Resilience in Intermodal Transport Networks: Integrating Flow Weighting and Centrality Measures

Resilient intermodal freight networks are vital for sustaining supply chains amid increasing threats from natural hazards and cyberattacks. While transportation resilience has been widely studied, understanding how random and targeted disruptions affect both structural connectivity and functional performance remains a key challenge. To address this, our study evaluates the robustness of the U.S. intermodal freight network, comprising rail and water modes, using a simulation-based framework that integrates graph-theoretic metrics with flow-weighted centrality measures. We examine disruption scenarios including random failures as well as targeted node and edge removals based on static and dynamically updated degree and betweenness centrality. To reflect more realistic conditions, we also consider flow-weighted degree centralities and partial node degradation. Two resilience indicators are used: the size of the giant connected component (GCC) to measure structural connectivity, and flow-weighted network efficiency (NE) to assess freight mobility under disruption. Results show that progressively degrading nodes ranked by Weighted Degree Centrality to 60% of their original functionality causes a sharper decline in normalized NE, for up to approximately 45 affected nodes, than complete failure (100% loss of functionality) applied to nodes targeted by weighted betweenness centrality or selected at random. This highlights how partial degradation of high-tonnage hubs can produce disproportionately large functional losses. The findings emphasize the need for resilience strategies that go beyond network topology to incorporate freight flow dynamics.

physics.soc-ph↗

arXiv2025

Variational Autoencoders-based Detection of Extremes in Plant Productivity in an Earth System Model

Climate anomalies significantly impact terrestrial carbon cycle dynamics, necessitating robust methods for detecting and analyzing anomalous behavior in plant productivity. This study presents a novel application of variational autoencoders (VAE) for identifying extreme events in gross primary productivity (GPP) from Community Earth System Model version 2 simulations across four AR6 regions in the Continental United States. We compare VAE-based anomaly detection with traditional singular spectral analysis (SSA) methods across three time periods: 1850-80, 1950-80, and 2050-80 under the SSP585 scenario. The VAE architecture employs three dense layers and a latent space with an input sequence length of 12 months, trained on a normalized GPP time series to reconstruct the GPP and identifying anomalies based on reconstruction errors. Extreme events are defined using 5th percentile thresholds applied to both VAE and SSA anomalies. Results demonstrate strong regional agreement between VAE and SSA methods in spatial patterns of extreme event frequencies, despite VAE producing higher threshold values (179-756 GgC for VAE vs. 100-784 GgC for SSA across regions and periods). Both methods reveal increasing magnitudes and frequencies of negative carbon cycle extremes toward 2050-80, particularly in Western and Central North America. The VAE approach shows comparable performance to established SSA techniques, while offering computational advantages and enhanced capability for capturing non-linear temporal dependencies in carbon cycle variability. Unlike SSA, the VAE method does not require one to define the periodicity of the signals in the data; it discovers them from the data.

cs.LG↗

arXiv2025

Land Cover Changes Cause Increased Losses during Photosynthetic Extremes

Human-induced carbon dioxide (CO2) emissions, primarily from fossil fuel combustion and changes in land use and land cover (LULCC), are a key contributor to climate change. As the climate warms, extreme events such as heatwaves, droughts, and wildfires have become more frequent and are projected to intensify throughout the 21st century. These escalating extremes are likely to further disrupt vegetation productivity, known as gross primary production (GPP), and reduce the ecosystem's capacity to absorb carbon. In this study, we employ a global Earth system model to assess how (a) CO2 emissions alone and (b) CO2 combined with LULCC influence the severity, frequency, and duration of GPP extremes. Our results show that negative GPP extremes periods of unexpectedly low carbon uptake are increasing more rapidly than positive extremes, especially under LULCC scenarios. The primary climate factor driving these extremes is soil moisture variability, which is influenced by fluctuations in both precipitation and temperature. The delayed responses of GPP to different climate drivers depend on the specific driver and geographical region. Overall, the highest incidence of GPP extremes arises from the combined influence of water stress, temperature anomalies, and fire-related disturbances.

physics.ao-ph↗

arXiv2025

Investigating Resiliency of Transportation Network Under Targeted and Potential Climate Change Disruptions

Ensuring robustness and resilience in intermodal transportation systems is essential for the continuity and reliability of global logistics. These systems are vulnerable to various disruptions, including natural disasters and technical failures. Despite significant research on freight transportation resilience, investigating the robustness of the system after targeted and climate-change driven disruption remains a crucial challenge. Drawing on network science methodologies, this study models the interdependencies within the rail and water transport networks and simulates different disruption scenarios to evaluate system responses. We use the data from the US Department of Energy Volpe Center for network topology and tonnage projections. The proposed framework quantifies deliberate, stochastic, and climate driven infrastructure failure, using higher resolution downscaled multiple Earth System Models simulations from Coupled Model Intercomparison Project Phase version 6. We show that the disruptions of a few nodes could have a larger impact on the total tonnage of freight transport than on network topology. For example, the removal of targeted 20 nodes can bring the total tonnage carrying capacity to 30 percent with about 75 percent of the rail freight network intact. This research advances the theoretical understanding of transportation resilience and provides practical applications for infrastructure managers and policymakers. By implementing these strategies, stakeholders and policymakers can better prepare for and respond to unexpected disruptions, ensuring sustained operational efficiency in the transportation networks.

stat.AP↗

arXiv2025

Carbon Cycle Extremes Accelerate Weakening of the Land Carbon Sink in the Late 21st Century

Increasing surface temperature could lead to enhanced evaporation, reduced soil moisture availability, and more frequent droughts and heat waves. The spatiotemporal co-occurrence of such effects further drives extreme anomalies in vegetation productivity and net land carbon storage. However, the impacts of climate change on extremes in net biospheric production (NBP) over longer time periods are unknown. Using the percentile threshold on the probability distribution curve of NBP anomalies, we computed negative and positive extremes in NBP. Here we show that due to climate warming, about 88% of global regions will experience a larger magnitude of negative NBP extremes than positive NBP extremes toward the end of 2100, which accelerate the weakening of the land carbon sink. Our analysis indicates the frequency of negative extremes associated with declines in biospheric productivity was larger than positive extremes, especially in the tropics. While the overall impact of warming at high latitudes is expected to increase plant productivity and carbon uptake, high-temperature anomalies increasingly induce negative NBP extremes toward the end of the 21st century. Using regression analysis, we found soil moisture anomalies to be the most dominant individual driver of NBP extremes. The compound effect of hot, dry, and fire caused extremes at more than 50% of the total grid cells. The larger proportion of negative NBP extremes raises a concern about whether the Earth is capable of increasing vegetation production with growing human population and rising demand for plant material for food, fiber, fuel, and building materials. The increasing proportion of negative NBP extremes highlights the consequences of not only reduction in total carbon uptake capacity but also of conversion of land to a carbon source.

stat.AP↗

arXiv2025

Image Processing Techniques to Identify and Quantify Spatiotemporal Carbon Cycle Extremes

Rising atmospheric carbon dioxide due to human activities through fossil fuel emissions and land use changes have increased climate extremes such as heat waves and droughts that have led to and are expected to increase the occurrence of carbon cycle extremes. Carbon cycle extremes represent large anomalies in the carbon cycle that are associated with gains or losses in carbon uptake. Carbon cycle extremes could be continuous in space and time and cross political boundaries. Here, we present a methodology to identify large spatiotemporal extremes (STEs) in the terrestrial carbon cycle using image processing tools for feature detection. We characterized the STE events based on neighborhood structures that are three-dimensional adjacency matrices for the detection of spatiotemporal manifolds of carbon cycle extremes. We found that the area affected and carbon loss during negative carbon cycle extremes were consistent with continuous neighborhood structures. In the gross primary production data we used, 100 carbon cycle STEs accounted for more than 75\% of all the negative carbon cycle extremes. This paper presents a comparative analysis of the magnitude of carbon cycle STEs and attribution of those STEs to climate drivers as a function of neighborhood structures for two observational datasets and an Earth system model simulation.

stat.AP↗

arXiv2024

Molecular Beam Epitaxy of GaN Nanowires on Epitaxial Graphene

We demonstrate an all-epitaxial and scalable growth approach to fabricate single-crystalline GaN nanowires on graphene by plasma-assisted molecular beam epitaxy. As substrate, we explore several types of epitaxial graphene layer structures synthesized on SiC. The different structures differ mainly in their total number of graphene layers. Because graphene is found to be etched under active N exposure, the direct growth of GaN nanowires on graphene is only achieved on multilayer graphene structures. The analysis of the nanowire ensembles prepared on multilayer graphene by Raman spectroscopy and transmission electron microscopy reveals the presence of graphene underneath as well as in between nanowires, as desired for the use of this material as contact layer in nanowire-based devices. The nanowires nucleate preferentially at step edges, are vertical, well aligned, epitaxial, and of comparable structural quality as similar structures fabricated on conventional substrates.

cond-mat.mtrl-sci↗

arXiv2014

Word Sense Disambiguation using WSD specific Wordnet of Polysemy Words

This paper presents a new model of WordNet that is used to disambiguate the correct sense of polysemy word based on the clue words. The related words for each sense of a polysemy word as well as single sense word are referred to as the clue words. The conventional WordNet organizes nouns, verbs, adjectives and adverbs together into sets of synonyms called synsets each expressing a different concept. In contrast to the structure of WordNet, we developed a new model of WordNet that organizes the different senses of polysemy words as well as the single sense words based on the clue words. These clue words for each sense of a polysemy word as well as for single sense word are used to disambiguate the correct meaning of the polysemy word in the given context using knowledge based Word Sense Disambiguation (WSD) algorithms. The clue word can be a noun, verb, adjective or adverb.

cs.CL↗