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We propose a novel algorithm called Unique Brain Network Identification Number (UBNIN) for encoding the brain networks of individual subjects. To realize this objective, we employed structural MRI on 180 Parkinson's disease (PD) patients and 70 healthy controls (HC) from the National Institute of Mental Health and Neurosciences, India. We parcellated each subject's brain volume and constructed an individual adjacency matrix using the correlation between the gray matter volumes of every pair of regions. The unique code is derived from values representing connections for every node (i), weighted by a factor of 2−(i−1). The numerical representation (UBNIN) was observed to be distinct for each individual brain network, which may also be applied to other neuroimaging modalities. UBNIN ranges observed for PD were 15,360 to 17,768,936,615,460,608, and HC ranges were 12,288 to 17,733,751,438,064,640. This model may be implemented as a neural signature of a person's unique brain connectivity, thereby making it useful for brainprinting applications. Additionally, we segregated the above datasets into five age cohorts: A: ≤32 years (n1 = 4, n2 = 5), B: 33–42 years (n1 = 18, n2 = 14), C: 43–52 years (n1 = 42, n2 = 23), D: 53–62 years (n1 = 69, n2 = 22), and E: ≥63 years (n1 = 46, n2 = 6), where n1 and n2 are the number of individuals in PD and HC, respectively, to study the variation in network topology over age. Sparsity was adopted as the threshold estimate to binarize each age-based correlation matrix. Connectivity metrics were obtained using Brain Connectivity toolbox (Version 2019-03-03)-based MATLAB (R2020a) functions. For each age cohort, a decreasing trend was observed in the mean clustering coefficient with increasing sparsity. Significantly different clustering coefficients were noted in PD between age-cohort B and C (sparsity: 0.63, 0.66), C and E (sparsity: 0.66, 0.69), and in HC between E and B (sparsity: 0.75 and above 0.81), E and C (sparsity above 0.78), E and D (sparsity above 0.84), and C and D (sparsity: 0.9). Our findings suggest network connectivity patterns change with age, indicating network disruption may be due to the underlying neuropathology. Varying clustering coefficients for different cohorts indicate that information transfer between neighboring nodes changes with age. This provides evidence of age-related brain shrinkage and network degeneration. We also discuss limitations and provide an open-access link to software codes and a help file for the entire study. [ABSTRACT FROM AUTHOR]

This paper seeks to design the least-cost delivery routes relating to the distribution of geotowns through the shortest distance matrix estimated by the multiple traveling salesman problems. We construct the initial distance matrix based on road distance considering all the nodes. Based on the adjacency matrix, we use the R package "sna" to calculate the different measures of centrality in the network. Next, we apply the traveling salesman problem to connect all the nodes at once for planning the trip in the proposed circuit. Finally, we use the Louvain community detection algorithm to cluster geotowns for better tourism planning and ease of travel. Based on the proposed geoheritage sites in Northeast India, the tourist spots may be organized and interconnected. This paper promotes and develops an approach to incorporating national geosites with national geoheritages and moves towards geoparks, keeping in view conservation measures of appropriate international impartial organizations. [ABSTRACT FROM AUTHOR]

Severe thunderstorms are a manifestation of deep convection. Conditional instability is known to be the mechanism by which thunderstorms are formed. The energy that drives conditional instability is convective available potential energy (CAPE), which is computed with radio sonde data at each pressure level. The purpose of the present paper is to identify the pattern or shape of CAPE required for the genesis of severe thunderstorms over Kolkata (22°32′N, 88°20′E) confined within the northeastern part (20°N to 24°N latitude, 85°E to 93°E longitude) of India. The method of chaotic graph theory is adopted for this purpose. Chaotic graphs of pressure levels and CAPE are formed for thunderstorm and non-thunderstorm days. Ranks of the adjacency matrices constituted with the union of chaotic graphs of pressure levels and CAPE are computed for thunderstorm and non-thunderstorm days. The results reveal that the rank of the adjacency matrix is maximum for non-thunderstorm days and a column with all zeros occurs very quickly on severe thunderstorms days. This indicates that CAPE loses connectivity with pressure levels very early on severe thunderstorm days, showing that for the genesis of severe thunderstorms over Kolkata short, and therefore broad, CAPE is preferred. [ABSTRACT FROM AUTHOR]

Studies the Indian economic structure using the concept of important coefficients (ICs) within a graph-theoretic formulation. Results from the aggregated input-output tables of the Indian economy for 1983/1984 and 1989/1990; Adjacency matrices for the Indian economy; Changes in the construction sector; Significant increase in the number of ICs in 1989/1990.

The rapid urbanization trend in most developing countries including India is creating a plethora of civic concerns such as loss of green space, degradation of environmental health, scarcity of clean water, rise in air pollution, and exacerbated traffic congestion resulting in significant delays in vehicular transportation. To address the intricate nature of transportation issues, many researchers and planners have analyzed the complexities of urban and regional road systems using transportation models by employing transportation indices such as road length, network density, accessibility, and connectivity metrics. This study addresses the complexities of predicting road network density for small and medium-sized Indian cities that come under the Integrated Development of Small and Medium Towns (IDSMT) project at a national level. A hybrid framework based on Kernel Ridge Regression (KRR) and the CityGAN model is introduced to predict network density using spatial indicators of human settlements. The major goal of this study is to generate hyper-realistic urban patterns of small and medium-sized Indian cities using an unsupervised CityGAN model and to study the causal relationship between human settlement indices (HSIs) and transportation index (network density) using supervised KRR for the real cities. The synthetic urban universes mimic Indian urban patterns and evaluating their landscape structures through the settlement indices can aid in comprehending urban landscape, thereby enhancing sustainable urban planning. We analyzed 503 real cities to find the actual relationship between the urban settlements and their road density. The nonlinear KRR model may help urban planners in deriving the network density for GAN-generated futuristic urban patterns through the settlement indicators. The proposed hybrid process, termed as RidgeGAN model, can gauge the sustainability of urban sprawl tied to infrastructure and transportation systems in sprawling cities. Analysis results clearly demonstrate the utility of RidgeGAN in predicting network density for different kinds of human settlements, particularly for small and medium Indian cities. By predicting future urban patterns, this study can help in the creation of more livable and sustainable areas, particularly by improving transportation infrastructure in developing cities. [ABSTRACT FROM AUTHOR]

The increasing amount of air pollution on a global scale is a major source of worry. The elevated levels of pollution are affecting the health of urban dwellers and motorists to a great extent. Since the situation has become so dire, it has become an absolute necessity to find effective ways to reduce air pollution and mitigate the negative effects of air pollution on human health. In this paper, the task of determining the least polluted route is addressed and how this can be used to curb pollution is discussed. The proposed work uses Dijkstra's algorithm, suggests a weight selection metric for air pollution parameters and analyzes the efficacy of the proposed algorithm in determining the least polluted route. The algorithm is tested on real-time data extracted from the Central Pollution Control Board, a statutory organization under the Ministry of Environment, Forest and Climate Change, Government of India. From the results obtained, it is found that the least polluted routes between a given source location and other different locations of a city can be determined using Dijkstra's algorithm accurately and the algorithm has a time complexity of O(V2). Further, using a least polluted route map will help motorists take less polluted routes, diverting air pollution off congested streets and enabling national governments to create policies like constructing flyovers or widening roads in increasingly polluted areas. [ABSTRACT FROM AUTHOR]

Concurrent extreme rainfall events, or synchronous extremes, during Indian Summer Monsoon Rainfall (ISMR), cause significant damage, but their spatiotemporal evolution remains unclear. Using the event synchronization approach to examine the synchronicity of extreme rainfall events from 1901 to 2019, we find that Central India consistently hosts strongly connected synchronous extreme hubs with localized connections, indicating the geographical trapping of these concurrent events in the region. We observe a moderate positive correlation between network cohesiveness and El Niño Southern Oscillations (ENSO), and a negative correlation between ENSO and link lengths, suggesting localized synchronicity during El Niño dominant decades and opposite patterns in La Niña periods. Despite increasing ISMR variability and spatial nonuniformity, the persistence of hubs and network attributes could offer insights for predicting synchronous extremes, informing effective adaptation and risk management strategies during the monsoon season. Plain Language Summary: Synchronous extreme rainfall events during the India monsoon season have been persistent over the past century, tending to concentrate in Central India and remaining highly localized. These events are correlated with El Niño Southern Oscillations (ENSO), with more synchronization during strong El Niño periods and less during La Niña conditions. The networks representing these synchronous extremes at various levels of severity exhibit key attributes that are time‐invariant with minimal divergence. These properties demonstrate a significant correlation with recurring climate patterns such as ENSO and Tropical Sea‐Surface Temperature Anomalies. Understanding these time‐invariant characteristics and their links to larger climate patterns is essential for infrastructure planning and design, given the persistent challenges posed by these synchronous extremes. Key Points: Despite growing spatial non‐uniformity, synchronous monsoon rainfall extremes confine to nearly the same geographical regionHighly connected locations persist in Central India and synchronize locally, indicating a stable network of extreme rainfall eventsTime‐invariant network properties linked to ocean‐atmosphere processes could serve as key covariates for predicting synchronous extremes [ABSTRACT FROM AUTHOR]

Three workshops with representatives of stakeholders from academia, business, civil society and government in India were organized in Chennai, Kolkata, and Mumbai with the aim to identify and elaborate on key barriers to resource recovery in wastewater treatment (WWT). A structured questionnaire was designed to capture the views of participating stakeholders. Using a new Mathematica function, namely, Around, the responses of the representatives of each stakeholder were aggregated to an approximate number representing that stakeholder's view. Overall, the stakeholder consensus on WWT technologies was rather conservative, with a focus on the functioning of WWT. Concerning the drivers and barriers for resource recovery and policies to support the implementation of recycling technologies in WWT, stakeholders expected government action to drive recycling. A social network analysis identified potential conflicts between the stakeholder groups. [ABSTRACT FROM AUTHOR]

The evolving international economic instability and international trade relationship demand a nation to move towards a self-reliant integrated system at a sub-national scale to address the growing human needs. Given India's role in the global trade network, it is critical to explore the underlying extensive complex trade network at the domestic scale. The potential advantages of complex interaction among the different commodities remain unexplored despite the known importance of trade networks in maintaining food security and industrial sustainability. Here we perform a comprehensive analysis of agricultural flows in contrast with non-agricultural commodities across Indian states. The spatio-temporal evolution of the networks from 2010–2018 was studied by evaluating topological network characteristics of consistent spatially disaggregated trade data. Our results show an increase in average annual trade value by 23.3% and 15.4% for agriculture and non-agriculture commodities, respectively, with no significant increase in connectivity observed in both networks. However, they depict contrasting behavior concerning the spatio-temporal changes, with non-agriculture trade becoming more dependent on production hubs and the agriculture trade progressing toward self-reliance, which signifies the evolution of the diversification in the existing agrarian trade network. Our findings could serve as an important element in deepening the knowledge of practical applications like resilience and recovery by devising design appropriate policy interventions for sustainable development. [ABSTRACT FROM AUTHOR]

This paper empirically explores dynamic risk impact of extreme volatility between stock and foreign exchange markets in emerging markets. Based on CoVaR analysis, we construct networks to investigate the nonlinear and dynamic evolution characteristics of risk contagion. The results show that stock and foreign exchange markets exhibit asymmetric tail dependence. All networks present small world network characteristics. The risk tolerance impact is more noticeable than risk overflow and stock markets are more sensitive to risk contagion. In the minimum spanning tree (MST), Korean stock market and foreign exchange markets of India and China are critical nodes in the minimum path of risk overflow. [ABSTRACT FROM AUTHOR]

According to United Nations forecasts, India is now expected to pass China as the most populous country in the world in 2023. This is due to the fact that in 2022, China saw its first population decline in over 60 years. In order to keep pace with the rapid rise in its population, India will need to significantly raise food production in the future. Specific soil selection can help in achieving expected food production. In this article, we use Laplacian energy and regression coefficient measurements to face decision-making issues based on intuitionistic fuzzy preference relations (IFPRs). We present a novel statistical measure for evaluating the appropriate position weights of authority by computing the fuzzy evidence of IFPRs and the specific similarity grade among one distinct intuitionistic preference connection to the others. This new way of thinking bases decisions on evidence from both external and internal authorities. We evolved a statistical (regression coefficient measure) approach to determine the importance of alternatives and the best of the alternatives after integrating the weights of authority into IFPRs. This statistical analysis can be put to good use to choose the best soil for different crops to provide food for India's rapidly growing population in the future. To show how useful and realistic the suggested statistical measure is, a good example from real life is given. Additionally, we discovered how correlation and regression coefficient measurements are related to one another in intuitionistic fuzzy graphs. [ABSTRACT FROM AUTHOR]

Introduction: The blood--brain barrier (BBB) is a semipermeable border that is responsible for maintaining central nervous system (CNS) homeostasis in the brain. Screening compounds based on their BBB permeability is an important consideration for CNS-acting drug development. Several studies have attempted to link physicochemical properties to specific BBB permeability; however, there is no widely accepted rule that can determine how and to what extent molecular properties affect the BBB permeability of compounds. To understand the complex phenomenon of BBB permeability, we explored the vast molecular space of the compounds to determine whether some features could differentiate the compounds based on their BBB permeability. Materials and Methods: A dataset of 1951 compounds containing 1246 BBB-permeable and 705 BBB-nonpermeable molecules was used in the study. Each compound encoded 499 molecular features. Feature selection was performed using feature selection algorithms, feature-to-feature, and feature-to-permeability correlation analysis. The findings of the feature selection algorithms were merged to select the best set of 53 features. Results: The descriptive analysis of the molecular features suggests that nCXr (number of X on ring C[sp3]) feature values for BBB nonpermeable compounds are zero for all considered compounds except for compounds with PubChem ID 71260, Flurithromycin. In addition, the majority of compounds were found to have nCrq (number of ring quaternary C[sp3]) feature values of zero for BBB nonpermeable compounds. For BBB-permeable compounds, MACCS fingerprints 8 feature values for all 1951 compounds were found to be zero except for the compound with PubChem ID 146291, Dezinamide. Conclusion: The descriptive and nonparametric tests confirm that molecular feature distributions are significantly different for BBB permeable and BBB nonpermeable compounds. [ABSTRACT FROM AUTHOR]

To combat the effects of climate change and meet the need for clean energy, the global power sector, over the past few decades, has been undergoing a major transformation for which all possible renewable energy sources are currently being utilized. To achieve sustainable growth, India, like many other countries, is also in the process of energy transition, aiming to shift to renewable energy-based power generation. In this transition, research in Ocean Renewable Energy (ORE) technologies is rising to rapid prominence. This study examines the state of ORE research in India and compares it with global research activities in this field using a graph-theoretical framework for collaboration co-authorship networks in ORE using bibliometric data on published scholarly articles indexed in two well-known electronic databases covering two 10-year windows: 1999-2008 and 2009-2018, inclusive. A strategic analysis of a number of metrics characterizing the networks’ large-scale structures reveals that the Indian network is highly fragmented, resulting in a singular dearth of large-scale connections for Indian ORE researchers. We recommend effective research policies to improve knowledge generation and dissemination in ORE research collaboration in India (and many other countries in similar situations), based on our findings for Indian networks and pertinent parallels with global ORE. With growing concerns about sustainable energy utilization, our study has policy implications for pressing issues of energy demands in the country. [ABSTRACT FROM AUTHOR]

In this paper, we propose a new approach to analyze financial contagion using a causality-based complex network and value-at-risk (VaR). We innovatively combine the use of VaR and an expected shortfall (ES)-based causality network with impulse response analysis to discover features of financial contagion. We improve the current research methods by building a Granger causality network on VaR and ES and using conclusions drawn from network analysis as a foundational step before impulse response analysis. First of all, we select 30 stock indices that are very well-known globally and collect their trading data. After calculating the risk indicators of VaR and ES, we perform the Granger causality test on them and then build networks based on their respective Granger causality square matrix. Next, we examine the networks' topological features to discover different degrees of risk transmission among all stock indices in the system. Lastly, we identify the most and the least active stock indices in the risk transmission network and conduct impulse response analysis on them. We discover that BSESN (India S&P BSE SENSEX) is the most risk-sensitive stock index as its VaR significantly increases by 0.03–0.04% and its ES jumps even more, by 0.07–0.08%, in response to an impulse from a few key stock indices. We also find that either PSI20 or XU100 is the most risk-proof stock index, depending on whether we choose VaR or ES as a risk indicator. [ABSTRACT FROM AUTHOR]

COVID-19 has emerged as the greatest threat in recent times, causing extensive mortality and morbidity in the entire world. India is among the highly affected countries suffering severe disruptions due this pandemic. To overcome the adverse effects of COVID-19, vaccination has been identified as the most effective preventive measure globally. However, a growing amount of hesitancy has been observed among the general public regarding the efficacy and possible side-effects of vaccination. Such hesitancy may proved to be the greatest hindrance towards combating this deadly pandemic. This paper introduces a multimodal deep learning method for Indian Twitter user classification, leveraging both content-based and network-based features. To explore the fundamental features of different modalities, improvisations of transformer models, BERT and GraphBERT are utilized to encode the textual and network structure information. The proposed approach thus integrates multiple data representations, utilizing the advances in both transformer based deep learning as well as multimodal learning. Experimental results demonstrates the efficacy of proposed approach over state of the art approaches. Aggregated feature representations from multiple modalities embed additional information that improves the classification results. The findings of the proposed model has been further utilized to perform a study on the dynamics of COVID-19 vaccine hesitancy in India. [ABSTRACT FROM AUTHOR]

Helping behaviour is thought to play a major role in the evolution of group-living animals. Yet, it is unclear to what extent human males and human females use the same strategies to secure support. Accordingly, we investigate help-seeking over a 5-year period in relation to gender using data from virtually all adults in two Tamil villages (N = 782). Simulations of network dynamics (i.e. stochastic actor-oriented models) calibrated to these data broadly indicate that women are more inclined than men to create and maintain supportive bonds via multiple mechanisms of cooperation (e.g. reciprocity, kin bias, friend bias, generalized exchange). However, gender-related differences in the simulated dynamics of help-seeking are modest, vary based on structural position (e.g. out-degree), and do not appear to translate to divergence in the observed structure of respondents' egocentric networks. Findings ultimately suggest that men and women in the two villages are similarly social but channel their sociality differently. This article is part of the theme issue 'Cooperation among women: evolutionary and cross-cultural perspectives'. [ABSTRACT FROM AUTHOR]

Scientific analysis of public transport systems at the urban, regional, and national levels is vital in this contemporary, highly connected world. Quantifying the accessibility of nodes (locations) in a transport network is considered a holistic measure of transportation and land use and an important research area. In recent years, complex networks have been employed for modeling and analyzing the topology of transport systems and services networks. However, the design of network hierarchy-based accessibility measures has not been fully explored in transport research. Thus, we propose a set of three novel accessibility metrics based on the k-core decomposition of the transport network. Core-based accessibility metrics leverage the network topology by eliciting the hierarchy while accommodating variations like travel cost, travel time, distance, and frequency of service as edge weights. The proposed metrics quantify the accessibility of nodes at different geographical scales, ranging from local to global. We use these metrics to compute the accessibility of geographical locations connected by air transport services in India. Finally, we show that the measures are responsive to changes in the topology of the transport network by analyzing the changes in accessibility for the domestic air services network for both pre-covid and post-covid times. [ABSTRACT FROM AUTHOR]

Wildlife corridors in a landscape include local vegetation, topography, prey base, water and are associated with isolated wildlife habitat patches. They facilitate maintenance of ecological structure and function as well as provide connectivity to faunal populations supporting genetic transfers, and are elements critical to wildlife management. In this work, habitat patches for tiger, both inside as well as outside of Protected Areas have been identified by developing a Habitat Suitability Index model utilizing Remote Sensing and Geographical Information System datasets for the Terai Arc landscape, India. By using a computational approach based on the framework of theory of complex networks, for exclusively pairwise interactions between the habitat patches, a potential tiger corridor network has been structurally identified and studied in this landscape. The interactions between these habitat patches on a spatial scale has been analyzed as a clique of the corridor network. Further, the Clique Percolation Method has been applied to detect overlapping communities of habitat patches in the landscape. The Cliques required for maintaining contiguity between the habitat patches in order to support tiger movement are validated using field observations of tiger communities within the landscape matrix. The model developed for identification of tiger corridors in this study could potentially be of a vital importance for wildlife stakeholders to better understand and manage tiger populations both within and outside of protected areas. The study also highlights Critical Habitat Patches and their importance in maintaining landscape connectivity for tiger dispersal in the landscape. Using a report published by the Government of India as a benchmark, the model presented in the work is found to have an accuracy of 90.73% in predicting tiger carrying patches and the corridor network in the focal landscape. [ABSTRACT FROM AUTHOR]

Ghaggar river basin of northern India has been experiencing flash floods many times during the last three decades despite locating in semi-arid/arid regions. Identification of the flood risk zone of a basin is essential where the availability of flood-related data is limited. The present study proposed the graph theory to identify the flood risk zone using spatial database of the digital elevation model, land use and land cover, soil cover, population, rainfall, drainage density, distance from roads, rivers, and flood shelters. Further, the results obtained from graph theory were compared with the results obtained by Analytical Hierarchy Process. It is observed that graph theory can also identify the flood risk zone of a basin precisely with acceptable accuracy. Around 94.1% of the study area is observed to have a high flood risk using Analytical Hierarchy Process, whereas 100% area is observed to have a high flood risk using graph theory. [ABSTRACT FROM AUTHOR]

Beejamrit is an ancient organic formulation commonly used as a seed treatment in organic and natural farming in India. This low-cost formulation is primarily a product of dairy excreta (e.g., cow dung and cow urine) and forest soil, often supplemented with limestone. Growing data suggest that dairy excreta are the potential sources of enriched microbial niche, including several plant growth-promoting bacteria capable of synthesizing plant growth regulators. However, the microbiological properties of Beejamrit and their temporal changes after different incubation periods, delineating its application in seed treatment, remain largely unexplored. Here, we aimed to analyze the decomposition rate of Beejamrit over 7-consecutive days of incubation. This study further elucidates the microbial niche and their dynamics in Beejamrit, including the plant beneficial bacteria. We have shown that the population of plant beneficial bacteria, such as the free-living nitrogen fixers (FNFs) and the phosphate solubilizers (PSBs), proliferates progressively up to 4- and 5-days of incubation, respectively (p < 0.0001). This study also reports the total indolic content of Beejamrit, including indole 3-acetic acid (IAA), which further tends to oscillate in concentration based on the incubation periods incurred during the Beejamrit preparation. Our analyses, together, establish that Beejamrit provides a dynamic, microbe-based metabolic network and may, therefore, act as a plant biostimulant to crop plants. A plant-based bioassay finally demonstrates the role of Beejamrit in the seed treatment to improve seed germination, seedling survival rate, and shoot length trait in French beans (p < 0.01). In conclusion, this study highlights, for the first time, the scientific insights of Beejamrit as a potential seed priming agent in agriculture. [ABSTRACT FROM AUTHOR]

Seeding influential social network members is crucial for the success of a viral marketing campaign and product diffusion. In line with the assumption that connections between customers in social networks are binary (either present or absent), previous research has generally recommended seeding network members who are well-connected. However, the importance of connections between customers varies substantially depending on the relationship’s characteristics, such as its type (i.e., friend, colleague, or acquaintance), duration, and interaction intensity. This research introduces a new Bayesian methodology to identify influential network members and takes into account the relative influence of different relationship characteristics on product diffusion. Two applications of the proposed methodology—the launch of a microfinance program across 43 Indian villages and information propagation in a large online social network—demonstrate the importance of weighting connections in social networks. Compared with traditional seeding strategies, the proposed methodology recommends substantially different sets of seeds that increased the reach by up to 10% in the first empirical application and up to 92% in the second. [ABSTRACT FROM AUTHOR]

Early detection of SARS-CoV-2 variants enables timely tracking of clinically important strains in order to inform the public health response. Current subtype-based variant surveillance depending on prior subtype assignment according to lag features and their continuous risk assessment may delay this process. We proposed a weighted network framework to model the frequency trajectories of mutations (FTMs) for SARS-CoV-2 variant tracing, without requiring prior subtype assignment. This framework modularizes the FTMs and conglomerates synchronous FTMs together to represent the variants. It also generates module clusters to unveil the epidemic stages and their contemporaneous variants. Eventually, the module-based variants are assessed by phylogenetic tree through sub-sampling to facilitate communication and control of the epidemic. This process was benchmarked using worldwide GISAID data, which not only demonstrated all the methodology features but also showed the module-based variant identification had highly specific and sensitive mapping with the global phylogenetic tree. When applying this process to regional data like India and South Africa for SARS-CoV-2 variant surveillance, the approach clearly elucidated the national dispersal history of the viral variants and their co-circulation pattern, and provided much earlier warning of Beta (B.1.351), Delta (B.1.617.2), and Omicron (B.1.1.529). In summary, our work showed that the weighted network modeling of FTMs enables us to rapidly and easily track down SARS-CoV-2 variants overcoming prior viral subtyping with lag features, accelerating the understanding and surveillance of COVID-19. [ABSTRACT FROM AUTHOR]

Despite the development of sophisticated statistical and dynamical climate models, a relative long-term and reliable prediction of the Indian summer monsoon rainfall (ISMR) has remained a challenging problem. Toward achieving this goal, here we construct a series of dynamical and physical climate networks based on the global near-surface air temperature field. We show that some characteristics of the directed and weighted climate networks can serve as efficient long-term predictors for ISMR forecasting. The developed prediction method produces a forecasting skill of 0.54 (Pearson correlation) with a 5-month lead time by using the previous calendar year's data. The skill of our ISMR forecast is better than that of operational forecasts models, which have, however, quite a short lead time. We discuss the underlying mechanism of our predictor and associate it with network–ENSO and ENSO–monsoon connections. Moreover, our approach allows predicting the all-India rainfall, as well as the rainfall different homogeneous Indian regions, which is crucial for agriculture in India. We reveal that global warming affects the climate network by enhancing cross-equatorial teleconnections between the southwest Atlantic, the western part of the Indian Ocean, and the North Asia–Pacific region, with significant impacts on the precipitation in India. A stronger connection through the chain of the main atmospheric circulations patterns benefits the prediction of the amount of rainfall. We uncover a hotspot area in the midlatitude South Atlantic, which is the basis for our predictor, the southwest Atlantic subtropical index (SWAS index). Remarkably, the significant warming trend in this area yields an improvement of the prediction skill. [ABSTRACT FROM AUTHOR]

Proper regionalisation (identification of homogeneous regions) is key to reliable regional flood frequency analysis. Several methods have been proposed in the literature for regionalisation, including the method of residuals, L-moment method, and fuzzy c-means clustering algorithm. The present study explores the suitability of the theory of complex networks for regionalisation of watersheds, with an aim to perform regional flood frequency analysis. The west-flowing rivers of Kerala in India are considered for this study. Two complex networks-based methods, namely degree centrality and clustering coefficient, are applied for regionalisation, and daily streamflow data are analysed. To identify possible links between streamflow gauging stations, different correlation threshold values (i.e. linear correlations in streamflow between stations) are used. Two approaches are adopted in the use of correlation threshold values: the first (Method I) with threshold as mean, median, and mode of correlation values; and the second (Method II) with arbitrary threshold values. The regionalisation results suggest that Method II yields better results, both with degree centrality and clustering coefficient. Based on Method II, the use of degree centrality results in seven regions (five homogeneous and two heterogeneous) and clustering coefficient results in eight regions (seven homogeneous and one heterogeneous). Comparison of predicted and observed flood quantiles indicates that the degree centrality-based regionalisation yields R2 values in the range 0.94–0.86 for return periods 2, 5, 20, 50, and 100 years, whereas the clustering coefficient-based regionalisation yields R2 values in the range 0.98–0.91. The results from this present study suggest that complex network theory is a suitable alternative for identifying homogeneous regions for regional flood frequency analysis. [ABSTRACT FROM AUTHOR]

Optimal travel route selection in real time basis minimizes the transportation cost for the tourists and guarantees the ease of travel as well. Accordingly, this paper deals with the problem of selecting the appropriate route selection to visit the identified tourists' spots in Tripura, India using the vehicle routing problem with time windows. The different tourist spots in the study area are identified from the Tourism Department of the Government of Tripura. This study considers only two cities as the gateway to reach to Tripura. After which, based on Bing map portal, geographic information for all the selected tourist spots is mapped; and one to one distance matrix is prepared considering the identified tourist spots and the two-gateway point of Tripura. The result so derived is compared and contrasted with the traditional route followed by the travel operators of Tripura; and found significant differences in terms of cost of transportation and time spent to visit the different tourist spots in Tripura. Finally, this paper concluded that selection of optimal route for transport network of tourism spots in Tripura, India using multi-trip vehicle routing problem can ensure the ease of travel and satisfaction for the tourists visiting in Tripura, India. [ABSTRACT FROM AUTHOR]

Scarce water uses driven by hotspots in production and consumption stages of global supply chains have been well studied. However, hotspots in primary inputs and intermediate transmission stages also leading to large amounts of global scarce water uses are overlooked. This gap can lead to the underestimation of the impacts of certain nation sectors on global scarce water uses. This study identifies critical primary suppliers and transmission centers in global supply chains contributing to scarce water uses, based on environmentally extended multi‐regional input‐output (EE‐MRIO) model and complex network analysis methods. Results show that some critical primary suppliers (e.g., the service auxiliary to financial intermediation sector in the United States and the financial intermediation services sector in India) and transmission centers (e.g., the raw milk sector in the United States and the transmission services of electricity sector in China) are unidentifiable in previous studies. These findings provide hotspots for supply‐side measures (e.g., optimization of primary input and product allocation behaviors) and productivity improvement measures. The critical inter‐sectoral transactions (mainly involving the agricultural and food products sectors in India, China, and the United States) further provide explicit directions for these measures. Moreover, this study conducts a community detection, which identifies communities (i.e., the clusters of nation sectors closely interconnected) leading to global scarce water uses. Most of the communities involve sectors from different nations, providing foundations for international cooperation strategies. [ABSTRACT FROM AUTHOR]

Infected individuals must be aware of disease symptoms to seek care, so outreach and education programs are critical to disease control. However, public health organizations often only have limited resources for outreach and must carefully design campaigns to maximize effectiveness, potentially leveraging word-of-mouth information spread. We show how classic epidemiological models can be reformulated such that identifying an efficient disease control resource allocation policy in the context of information spread becomes a submodular maximization problem. This means that our framework can simultaneously handle multiple, interacting dynamic processes coupled through the likelihood of disease clearance, allowing our framework to provide insight into optimal resource allocation while considering social dynamics in addition to disease dynamics (e.g., knowledge spread and disease spread). We then demonstrate that this problem can be algorithmically solved and can handle stochasticity in input parameters by examining a numerical example of tuberculosis control in India. [ABSTRACT FROM AUTHOR]

In the recent phenomenon of social networks, both online and offline, two nodes may be connected, but they may not follow each other. Thus there are two separate links to be given to capture the notion. Directed links are given if the nodes follow each other, and undirected links represent the regular connections (without following). Thus, this network may have both types of relationships/ links simultaneously. This type of network can be represented by mixed graphs. But, uncertainties in following and connectedness exist in complex systems. To capture the uncertainties, fuzzy mixed graphs are introduced in this article. Some operations, completeness, and regularity and few other properties of fuzzy mixed graphs are explained. Representation of fuzzy mixed graphs as matrix and isomorphism theorems on fuzzy mixed graphs are developed. A network of COVID19 affected areas in India are assumed, and central regions are identified as per the proposed theory. [ABSTRACT FROM AUTHOR]

Resource scarcity is capable of affecting economic activity. Though the dependence of direct users is easily acknowledged, indirect vulnerability imposed on downstream sectors of the economic system is not as easily understood. In the context of growing water scarcity across India, this study maps the dependence of prominent sectors of the Indian economy to the water‐withdrawal model of India (Bogra, Bakshi, & Mathur, 2016). From the suppliers' perspective, the results indicate that embodied water of the largest direct water‐withdrawing food sectors namely, Paddy, Wheat, and Sugarcane (PWS) is indirectly consumed mostly by the consumptive food sectors. However, from the users' perspective, even non‐food sectors exhibit a significant dependence on the embodied water of PWS. Further, blue‐water‐based structural path analysis (SPA) of Paddy and Wheat indicate significant contributions to Land transport, Construction, and Beverages, among others, whereas Land transport is important in terms of green water too. The out‐degree measure indicates a higher dependence of the economy on Electricity (blue water) and Forestry (green water) sectors. Specifically, infrastructural sectors exhibit a significant dependence on Electricity; whereas Forestry products contribute to non‐food sectors. State‐wise water‐scarcity indices (WSIs) indicate higher dependence of Electricity on scarce surface‐water flows of north‐western and central states, whereas forested areas in the north and north‐eastern parts of India exhibit lowest ground WSIs. By integrating regional flows with sectoral dependencies, it is observed that the risk to a reduction of the economy's throughput is higher from water withdrawn by Electricity compared to food sectors, PWS. [ABSTRACT FROM AUTHOR]

Participatory modelling is an emerging approach in the decision-making process through which stakeholders contribute to the representation of the perceived causal linkages of a complex system. The use of fuzzy cognitive maps (FCMs) for participatory modelling helps policy-makers develop dynamic quantitative models for strategising development interventions. The aggregation of knowledge from multiple stakeholders provides consolidated and more reliable results. Average aggregation is the most common aggregation method used in FCMs-based modelling for weighted interconnections between concepts. This paper proposes a new aggregation method using learning OWA (ordered weighted averaging) operators for aggregating FCM weights assigned by various stakeholders. Besides, we report a comparative analysis of 'OWA learning aggregation' with the conventional average aggregation method, while evaluating the theory of change for the world's most extensive poverty alleviation programme in India. The results of the FCMWizard web-based tool show that the proposed method provides an opportunity to policy-makers for evaluating outcomes of proposed policies while addressing social resilience and economic mobility. [ABSTRACT FROM AUTHOR]

This paper reports the development of a public transport trip planner to help the urban traveller in planning and preparing for his commute using public transportation in the city. A Genetic Algorithm (GA) approach that handles realtime Global Positioning Systems (GPS) data from buses of the Metropolitan Transport Corporation (MTC) in Chennai City (India) has been used to develop the planner. The GA has been shown to provide good solutions within the problem's computation time constraints. The developed trip planner has been implemented for static network data first and subsequently extended to use real-time data. The "walk mode" and Chennai Mass Rapid Transit System (MRTS) have also been included in the geospatial database to extend the route-planner's capabilities. The algorithm has subsequently been segmented to speed up the prediction process. In addition, a temporal cache has also been introduced during implementation, to handle multiple queries generated simultaneously. The results showed that there is promise for scalability and citywide implementation for the proposed real-time route-planner. The uncertainty and poor service quality perceived with public transport bus services in India could potentially be mitigated by further developments in the route-planner introduced in this paper. [ABSTRACT FROM AUTHOR]

A slew of participatory and community-demand-driven approaches have emerged in order to address the multi-dimensional nature of poverty in developing nations. The present study identifies critical factors responsible for poverty alleviation in India with the aid of fuzzy cognitive maps (FCMs) deployed for showcasing causal reasoning. It is through FCM-based simulations that the study evaluates the efficacy of existing poverty alleviation approaches, including community organisation based micro-financing, capability and social security, market-based and good governance. Our findings confirm, to some degree, the complementarity of various approaches to poverty alleviation that need to be implemented simultaneously for a comprehensive poverty alleviation drive. FCM-based simulations underscore the need for applying an integrated and multi-dimensional approach incorporating elements of various approaches for eradicating poverty, which happens to be a multi-dimensional phenomenon. Besides, the study offers policy implications for the design, management, and implementation of poverty eradication programmes. On the methodological front, the study enriches FCM literature in the areas of knowledge capture, sample adequacy, and robustness of the dynamic system model. [ABSTRACT FROM AUTHOR]

Can we identify highly central individuals in a network without collecting network data, simply by asking community members? Can seeding information via such nominated individuals lead to significantly wider diffusion than via randomly chosen people, or even respected ones? In two separate large field experiments in India, we answer both questions in the affirmative. In particular, in 521 villages in Haryana, we provided information on monthly immunization camps to either randomly selected individuals (in some villages) or to individuals nominated by villagers as people who would be good at transmitting information (in other villages). We find that the number of children vaccinated every month is 22% higher in villages in which nominees received the information. We show that people's knowledge of who are highly central individuals and good seeds can be explained by a model in which community members simply track how often they hear gossip about others. Indeed, we find in a third data set that nominated seeds are central in a network sense, and are not just those with many friends or in powerful positions. [ABSTRACT FROM AUTHOR]

In this paper, we explore the problem of establishing a network among the stocks of a market at high frequency level and give an application to program trading. Our work uses high frequency data from the National Stock Exchange, India, for the year 2014. To begin, we analyse the spectrum of the correlation matrix to establish the presence of linear relations amongst the stock returns. A comparison of correlations with pairwise mutual information shows the further existence of non-linear relations which are not captured by correlation. We also see that the non-linear relations are more pronounced at the high frequency level in comparison to the daily returns used in earlier work. We provide two applications of this approach. First, we construct minimal spanning trees for the stock network based on mutual information and study their topology. The year 2014 saw the conduct of general elections in India and the data allows us to explore their impact on aspects of the network, such as the scale-free property and sectorial clusters. Second, having established the presence of non-linear relations, we would like to be able to exploit them. Previous authors have suggested that peripheral stocks in the network would make good proxies for the Markowitz portfolio but with a much smaller number of stocks. We show that peripheral stocks selected using mutual information perform significantly better than ones selected using correlation. [ABSTRACT FROM AUTHOR]

Riverine systems are influenced by historical and current land use practices linked to alterations in the riparian zone across multiple scales. This study aims to explore the trend of spatiotemporal land cover changes and configuration along a major headstream of the river Ganges in India. Geo-spatial tools were used to quantify the changes in the landscape across a time span of 22 years. Landscape configuration was quantified by applying class level metrics using the software FRAGSTATS. A loss of 18.3% and 5.5% in the dense forest class was perceived in the upland and the riverine areas respectively aided by conversion of large contiguous forests into smaller isolated patches. Markov analysis showed that the forests in the landscape still possess inherent resilience capacity as indicated by a probability of 47% conversion of open forest class to dense forest class in future based on current land-use practices. Land cover changes and forest fragmentation can have inevitable impacts on ecological functioning and species persistence. Monitoring these changes is fundamental in planning future strategies for riverine landscape management. The present study underpins the utility of remote sensing and GIS in building useful baseline data for inaccessible mountainous landscapes at both local and regional scales. [ABSTRACT FROM AUTHOR]

A better understanding of precipitation dynamics in the Indian subcontinent is required since India's society depends heavily on reliable monsoon forecasts. We introduce a non-linear, multiscale approach, based on wavelets and event synchronization, for unravelling teleconnection influences on precipitation. We consider those climate patterns with the highest relevance for Indian precipitation. Our results suggest significant influences which are not well captured by only the wavelet coherence analysis, the state-of-the-art method in understanding linkages at multiple timescales. We find substantial variation across India and across timescales. In particular, El Niño–Southern Oscillation (ENSO) and the Indian Ocean Dipole (IOD) mainly influence precipitation in the south-east at interannual and decadal scales, respectively, whereas the North Atlantic Oscillation (NAO) has a strong connection to precipitation, particularly in the northern regions. The effect of the Pacific Decadal Oscillation (PDO) stretches across the whole country, whereas the Atlantic Multidecadal Oscillation (AMO) influences precipitation particularly in the central arid and semi-arid regions. The proposed method provides a powerful approach for capturing the dynamics of precipitation and, hence, helps improve precipitation forecasting. [ABSTRACT FROM AUTHOR]

Many existing statistical and machine learning tools for social network analysis focus on a single level of analysis. Methods designed for clustering optimize a global partition of the graph, whereas projection-based approaches (e.g., the latent space model in the statistics literature) represent in rich detail the roles of individuals. Many pertinent questions in sociology and economics, however, span multiple scales of analysis. Further, many questions involve comparisons across disconnected graphs that will, inevitably be of different sizes, either due to missing data or the inherent heterogeneity in real-world networks. We propose a class of network models that represent network structure on multiple scales and facilitate comparison across graphs with different numbers of individuals. These models differentially invest modeling effort within subgraphs of high density, often termed communities, while maintaining a parsimonious structure between said subgraphs. We show that our model class is projective, highlighting an ongoing discussion in the social network modeling literature on the dependence of inference paradigms on the size of the observed graph. We illustrate the utility of our method using data on household relations from Karnataka, India. Supplementary material for this article is available online. [ABSTRACT FROM AUTHOR]

The study analyzes the small-world nature of the board interlock network for National Stock Exchange–listed firms in India. The small world is the notion that each firm can reach any other firm in the network through a small number of intermediate firms. We find that since the introduction of corporate governance regulations in India, the small worldliness of the network has increased, indicating the concentration of directorial positions in the hands of few elites in the country. The small-world nature of the network can be attributed to the presence of few linchpins in the network and the majority of the linchpins are business group firms in the country. The linchpin acts as a bridge between otherwise sparsely connected clusters in the network. The firms that act as linchpins have higher profitability on account of access to a larger pool of resources. [ABSTRACT FROM AUTHOR]

Drought is among the most dreaded threats as far as natural disasters are concerned wielding, as it does, a significant impact on ecosystems, people's livelihoods, and the socio-economic development of a country. A significantly large geographic area of India happens to be drought prone. In order to understand people's response to the impacts of drought and their coping strategies, it is crucial to understand their perceptions. Studies investigating community perceptions relevant to drought impacts and concomitant adaptive behaviours are rare in India. This paper documents communities' perceptions of impacts of drought on their livelihood assets and adaptation practices. It does this with the help of the fuzzy cognitive mapping approach in the Mahabubnagar district of India's Telangana state. In order to develop pathways for drought resilient livelihoods we ran simulations for future drought scenarios with various bundles of adaptation strategies enabling us to evaluate their effectiveness in providing resilience against drought. The study also tested the suitability of various activation rules and transformation functions, used for running simulations. Incorporating stakeholders' perceptions, knowledge and beliefs about impacts of droughts, and engaging them in the process of developing drought resilient livelihoods is expected to fine-tune the drought related policy-making. [ABSTRACT FROM AUTHOR]

In this paper, we model the bus networks of six major Indian cities as graphs in L-space, and evaluate their various statistical properties. While airline and railway networks have been extensively studied, a comprehensive study on the structure and growth of bus networks is lacking. In India, where bus transport plays an important role in day-to-day commutation, it is of significant interest to analyze its topological structure and answer basic questions on its evolution, growth, robustness and resiliency. Although the common feature of small-world property is observed, our analysis reveals a wide spectrum of network topologies arising due to significant variation in the degree-distribution patterns in the networks. We also observe that these networks although, robust and resilient to random attacks are particularly degree-sensitive. Unlike real-world networks, such as Internet, WWW and airline, that are virtual, bus networks are physically constrained. Our findings therefore, throw light on the evolution of such geographically and constrained networks that will help us in designing more efficient bus networks in the future. [ABSTRACT FROM AUTHOR]

Bus transportation is the most convenient and cheapest way of public transportation in Indian cities. Due to cost-effectiveness and wide reachability, buses bring people to their destinations every day. Although the bus transportation has numerous advantages over other ways of public transportation, this mode of transportation also poses a serious threat of spreading contagious diseases throughout the city. It is extremely difficult to predict the extent and spread of such an epidemic. Earlier studies have focused on the contagion processes on scale-free network topologies; whereas, real-world networks such as bus networks exhibit a wide-spectrum of network topology. Therefore, we aim in this study to understand this complex dynamical process of epidemic outbreak and information diffusion on the bus networks for six different Indian cities using SI and SIR models. We identify epidemic thresholds for these networks which help us in controlling outbreaks by developing node-based immunization techniques. © 2016 Wiley Periodicals, Inc. Complexity 21: 451-458, 2016 [ABSTRACT FROM AUTHOR]

Drawing on resource-dependence theory, we hypothesized that in an emerging economy characterized by weak underdeveloped institutions, director interlocks would be a conduit of resources external to the firm. Using data from 651 large Indian firms for the years 2006-2012, we found that directors', especially independent directors', social capital and industry-level expertise enhanced firm performance. Our findings contribute by providing evidence of the beneficial role of directors in providing resources through their interlocks, contrary to the basic tenet of corporate governance that independent directors are chosen for their monitoring role to reduce agency costs. [ABSTRACT FROM AUTHOR]

The article focuses on the air pollution problem of Delhi, India, including air pollution (re)distribution inside and outside the city and policies enacted by the local government to address the issue. Topics discussed include data used to assess coverage of air pollution in the Indian city, the use of conditional autoregressive modelling to examine the impact of Land-Use and Land-Cover (LULC) types and findings that revealed small changes to LULC.

This study describes economic information in Indian newspapers in relation to right to information (RTI) and the Indian RTI Act, by analyzing correlations between information provided through the Act and information in the Times of India during Indian presidential elections in 2012 and the "IMF crisis" of 2013. UCINET 6 was used to visualize connections between keywords. The results show that the economic information provided by Indian newspapers does not meet guarantees for provision of information under RTI, and that economic news articles deliver only imperfect information on foreign exchange and related matters, restricting their utility for people's economic behavior. [ABSTRACT FROM AUTHOR]

The main objective of the study is to design and estimate water distribution network and underground drainage system for a residential layout of Vijayanagara 2nd stage of Mysore city using LOOP and SEWER software. The project area is 7.9 hectares consists of 353 sites. The water supply system is designed for this layout. The reliable source of water supply for this layout is mainly from Vanivilas water supply board. The present study provides estimation of overall cost of the project. The major cost components like pipe cost, manholes, laying of sewer pipe lines, are included in the analysis. Total cost of UGD for the layout is estimated to be (in 1000) ₹ 8817.56. [ABSTRACT FROM AUTHOR]

The popular perception about economic reforms having benefitted only the richer districts of India between 1999/2000 and 2004/2005 is investigated. Using the spatial dynamics of district-level per-capita income it was found that income distribution did not change between the years examined. It is argued that this is because of per-capita income across districts being spatially positively correlated. Physical infrastructure, human capital, and factories are identified as factors responsible for increase in income for both the rich as well as the poor districts. Infrastructure, physical or social, is a key component of growth in India. A policy impact analysis shows development of better drainage and potable water systems has a large impact on income. For the year 2001/02, it was found that for every 1% increase in closed drainage system and potable water, district-level median income increases by 1.39% and 0.21%, respectively. [ABSTRACT FROM AUTHOR]

Digital divide initiatives in developing countries are an important avenue for the socioeconomic advancement of those countries. Yet little research has focused on understanding the success of such initiatives. We develop a model of technology use and economic outcomes of digital divide initiatives in developing countries. We use social networks as the guiding theoretical lens because it is well suited to this context, given the low literacy, high poverty, high collectivism, and an oral tradition of information dissemination in developing countries. We test our model with longitudinal data gathered from 210 families in a rural village in India in the context of a digital divide initiative. As theorized, we found that the social network constructs contributed significantly to the explanation of technology use (R2 D 0039). Also as we predicted, technology use partially mediated the effect of social network constructs on economic outcomes (R2 D0047). We discuss implications for theory and practice. [ABSTRACT FROM AUTHOR]

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