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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]

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]

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]

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]

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]

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]

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]

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]

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]

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]

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]

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]

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]

Aim Parrots are thought to have originated on Gondwana during the Cretaceous. The initial split within crown group parrots separated the New Zealand taxa from the remaining extant species and was considered to coincide with the separation of New Zealand from Gondwana 82-85 Ma, assuming that the diversification of parrots was mainly shaped by vicariance. However, the distribution patterns of several extant parrot groups cannot be explained without invoking transoceanic dispersal, challenging this assumption. Here, we present a temporal and spatial framework for the diversification of parrots using external avian fossils as calibration points in order to evaluate the relative importance of the influences of past climate change, plate tectonics and ecological opportunity. Location Australasian, African, Indo-Malayan and Neotropical regions. Methods Phylogenetic relationships were investigated using partial sequences of the nuclear genes c-mos, RAG-1 and Zenk of 75 parrot and 21 other avian taxa. Divergence dates and confidence intervals were estimated using a Bayesian relaxed molecular clock approach. Biogeographic patterns were evaluated taking temporal connectivity between areas into account. We tested whether diversification remained constant over time and if some parrot groups were more species-rich than expected given their age. Results Crown group diversification of parrots started only about 58 Ma, in the Palaeogene, significantly later than previously thought. The Australasian lories and possibly also the Neotropical Arini were found to be unexpectedly species-rich. Diversification rates probably increased around the Eocene/Oligocene boundary and in the middle Miocene, during two periods of major global climatic aberrations characterized by global cooling. Main conclusions The diversification of parrots was shaped by climatic and geological events as well as by key innovations. Initial vicariance events caused by continental break-up were followed by transoceanic dispersal and local radiations. Habitat shifts caused by climate change and mountain orogenesis may have acted as a catalyst to the diversification by providing new ecological opportunities and challenges as well as by causing isolation as a result of habitat fragmentation. The lories constitute the only highly nectarivorous parrot clade, and their diet shift, associated with morphological innovation, may have acted as an evolutionary key innovation, allowing them to explore underutilized niches and promoting their diversification. [ABSTRACT FROM AUTHOR]

Single Spectrum Bipartite Graph (SSBG) model is developed to forecast thunderstorms over Kolkata (22°32′N,88°20′E) during the premonsoon season (April-May). The statistical distribution of normal probability is observed for temperature, relative humidity, convective available potential energy (CAPE), and convective inhibition energy (CIN) to quantify the threshold values of the parameters for the prevalence of thunderstorms.Method of conditional probability is implemented to ascertain the possibilities of the occurrence of thunderstorms within the ranges of the threshold values. The single spectrum bipartite graph connectivitymodel developed in this study consists of two sets of vertices; one set includes two time vertices (00UTC, 12UTC) and the other includes four meteorological parameters: temperature, relative humidity, CAPE, and CIN. Three distinct ranges of maximal eigen values are obtained for the three categories of thunderstorms. Maximal eigenvalues for severe, ordinary, and no thunderstorm events are observed to be (2.6 ± 0.12), (1.88 ± 0.09), and (1.26 ± .03), respectively. The ranges of the threshold values obtained using ten year data (1997-2006) are considered as the reference range and the result is validated with the IMD (India Meteorological Department) observation, DopplerWeather Radar (DWR) Products, and satellite images of 2007. The result reveals that the model provides 12- to 6-hour forecast (nowcasting) of thunderstorms with 96% to 98% accuracy. [ABSTRACT FROM AUTHOR]

The mass accumulation of population in the larger cities of India has led to accelerated and unprecedented peripheral urban expansion over the last few decades. This rapid peripheral growth is characterized by an uncontrolled, low density, fragmented and haphazard patchwork of development popularly known as urban sprawl. The Kolkata Metropolitan Area (KMA) has been one of the fastest-growing metropolitan areas in India and is experiencing rampant suburbanization and peripheral expansion. Hence, understanding urban growth and its dynamics in these rapidly changing environments is critical for city planners and resource managers. Furthermore, understanding urban expansion and urban growth patterns are essential for achieving inclusive and sustainable urbanization as defined by the United Nations in the Sustainable Development Goals (e.g., SDGs, 11.3). The present research attempts to quantify and model the urban growth dynamics of large and diverse metropolitan areas with a distinct methodology considering the case of KMA. In the study, land use and land cover (LULC) maps of KMA were prepared for three different years (i.e., for 1996, 2006, and 2016) through the classification of Landsat imagery using a support vector machine (SVM) classification approach. Then, change detection analysis, landscape metrics, a concentric zone approach, and Shannon's entropy approach were applied for spatiotemporal assessment and quantification of urban growth in KMA. The achieved classification accuracies were found to be 89.75%, 92.00%, and 92.75%, with corresponding Kappa values of 0.879, 0.904, and 0.912 for 1996, 2006, and 2016, respectively. It is concluded that KMA has been experiencing typical urban sprawl. The peri-urban areas (i.e., KMA-rural) are growing rapidly, and are characterized by leapfrogging and fragmented built-up area development, compared to the central KMA (i.e., KMA-urban), which has become more compact in recent years. [ABSTRACT FROM AUTHOR]

Activities exert control in urban systems by facilitating the direct and indirect flows of other activities. If a matrix of direct flows is raised to successive powers, the power at which each cell first becomes positive represents the minimum number of direct and indirect steps linking activities. From a matrix of minimum steps an average number of steps can be computed for each activity. To find an activity's degree of control its direct flows are removed from the system and this average number of steps is recomputed for activities remaining. The mean absolute difference between these averages in the original and reduced systems reflects the changed connectivity faced by the flows of remaining activities. The control of activities can be ranked using this mean. Applying this technique to India's system of airline flows identified an urban hierarchy with for levels of connectivity: national, regional, subregional and route endings. [ABSTRACT FROM AUTHOR]