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Arctic sea ice is in reduction and has been a key significant indicator of climate change. In this paper, we explore Arctic Sea ice extent data to identify teleconnection with weather change in the polar and sub-tropical jet stream intersection in eastern United States (US) and hence the potential influence in ground level ozone pollution. Several statistical methods including Bayesian techniques such as: spatio-temporal modelling and Bayesian network are implemented to identify the teleconnection and also validated based on theories in atmospheric science. We observe that the teleconnection is relatively strong in autumn, winter and spring seasons compared to the summer. Furthermore, the sudden decremental effect of Arctic sea-ice extent in mid-2000s has a shifting influence in ozone pollutions compared to the previous years. A similar downward shift in the Arctic sea-ice extent has been projected in 2030. These findings indicate to initiate further strategic policies for the Arctic influence, ozone concentrations together the seasonal and global changing patterns of climate., Comment: 19 pages, 6 figures

The Sustainable Development Goals (SDGs) offer a critical global framework for addressing challenges like poverty, inequality, climate change, etc. They encourage a holistic approach integrating economic growth, social inclusion, and environmental sustainability to create a better future. We aim to examine India's responsibility in achieving the SDGs by recognizing the contributions of its diverse states in the federal structure of governance. As the nodal agency in India, the NITI Aayog's existing SDG index, using various socioeconomic indicators to determine the performance across different goals, serves as a foundation for assessing each state's progress. Building on the seminal works of Hidalgo and Hausmann (2009) and Tachhella et al. (2012), which introduced the economic complexity/fitness index, Sciarra et al. (2020) proposed the SDGs-Generalized Economic Complexity (GENEPY) framework to quantify "complexity" by computing "ranks for states" and "scores for goals", treating them as part of a complex bipartite network. In this paper, we apply the SDGs-GENEPY, to evaluate the progress and evolution of Indian states and union territories over several years. This enables us to identify each state's capacity (and rank) in achieving the SDGs. We can interpret these complexity scores as "centrality measures" of a complex bipartite network of the states and the goals. This enhances our understanding of the complex relationship between state capabilities and the achievability of SDGs within the Indian context and enables data-driven policy-making., Comment: 10 pages, 5 figures

In this paper, we have explored the charge transfer mechanism in transition metal dichalcogenide (TMDC) quantum dots (QDs) of molybdenum disulfide ($\rm{MoS_2}$) and tungsten disulfide ($\rm{WS_2}$). Rhodamine 6G (R6G), a dye from the rhodamine family, has been employed as the fluorescent molecule, with MoS$_2$ and WS$_2$ QDs acting as electron acceptors in the photo-induced charge transfer process. The TMDC QDs were synthesized using a top-down approach and characterized through transmission electron microscopy (TEM), UV-Vis spectrophotometry, and fluorimetry. TEM images revealed well-dispersed particles measuring 2 nm in size. These QDs exhibit strong fluorescence emission when excited with light at wavelengths below 350 nm. Under light exposure, photons generate charges in the fluorescent dye molecules, and the TMDC QDs facilitate the charge transfer process. The charge transfer phenomenon was investigated using time-correlated single photon counting (TCSPC), a time-resolved fluorescence spectroscopic technique. The time-resolved fluorescence study indicated a change in the fluorescence (FL) lifetime of R6G molecules in the presence of QDs. The FL lifetime of R6G molecules without QDs was found to be 4.0 ns, which decreased to 1.9 ns and 3.8 ns in the presence of MoS$_2$ and WS$_2$ QDs, respectively. This reduction in FL lifetime suggests that the MoS$_2$ and WS$_2$ QDs provide an additional pathway for photo-generated electrons in the excited state of R6G molecules. This research can be extended to optoelectronic devices, where charge transfer is crucial for device efficiency and performance., Comment: 7 pages, 10 figures

In this article, we review the interdisciplinary techniques (borrowed from physics, mathematics, statistics, machine-learning, etc.) and methodological framework that we have used to understand climate systems, which serve as examples of "complex systems". We believe that this would offer valuable insights to comprehend the complexity of climate variability and pave the way for drafting policies for action against climate change, etc. Our basic aim is to analyse time-series data structures across diverse climate parameters, extract Fourier-transformed features to recognize and model the trends/seasonalities in the climate variables using standard methods like detrended residual series analyses, correlation structures among climate parameters, Granger causal models, and other statistical machine-learning techniques. We cite and briefly explain two case studies: (i) the relationship between the Standardised Precipitation Index (SPI) and specific climate variables including Sea Surface Temperature (SST), El Ni\~no Southern Oscillation (ENSO), and Indian Ocean Dipole (IOD), uncovering temporal shifts in correlations between SPI and these variables, and reveal complex patterns that drive drought and wet climate conditions in South-West Australia; (ii) the complex interactions of North Atlantic Oscillation (NAO) index, with SST and sea ice extent (SIE), potentially arising from positive feedback loops., Comment: 21 pages, 12 figures. Accepted in the Indian Journal of Theoretical Physics (Institute of Theoretical Physics, Calcutta, 1953)

The order parameter for a continuous transition shows diverging fluctuation near the critical point. Here we show, through numerical simulations and scaling arguments, that the inequality (or variability) between the values of an order parameter, measured near a critical point, is independent of the system size. Quantification of such variability through Gini index ($g$), therefore, leads to a scaling form $g=G\left[|F-F_c|N^{1/d\nu}\right]$, where $F$ denotes the driving parameter for the transition (e.g., temperature $T$ for ferromagnetic to paramagnetic transition transition, or lattice occupation probability $p$), $N$ is the system size, $d$ is the spatial dimension and $\nu$ is the correlation length exponent. We demonstrate the scaling for the Ising model in two and three dimensions, site percolation on square lattice and the fiber bundle model of fracture., Comment: 6 pages, 5 figures. Accepted for publication in Phys. Rev. E

The Standardized Precipitation Index (SPI) is used to indicate the meteorological drought situation - a negative (or positive) value of SPI would imply a dry (or wet) condition in a region over a period. The climate system is an excellent example of a complex system since there is an interplay and inter-relation of several climate variables. It is not always easy to identify the factors that may influence the SPI, or their inter-relations (including feedback loops). Here, we aim to study the complex dynamics that SPI has with the SST, NINO 3.4 and Indian Ocean Dipole (IOD), using a machine learning approach. Our findings are: (i) IOD was negatively correlated to SPI till 2008; (ii) until 2004, SST was negatively correlated with SPI; (iii) from 2005 to 2014, the SST had swung between negative and positive correlations; (iv) since 2014, we observed that the regression coefficient ($\delta$) corresponding to SST has always been positive; (v) the SST has an upward trend, and the positive upward trend of $\delta$ implied that SPI has been positively correlated with SST in recent years; and finally, (vi) the current value of SPI has a significant positive correlation with a past SPI value with a periodicity of about 7.5 years. Examining the complex dynamics, we used a statistical machine learning approach to construct an inferential network of these climate variables, which revealed that SST and NINO 3.4 directly couples with SPI, whereas IOD indirectly couples with SPI through SST and NINO 3.4. The system also indicated that Nino 3.4 has a significant negative effect on SPI. Interestingly, there seems to be a structural change in the complex dynamics of the four climate variables, some time in 2008. Though a simple 12-month moving average of SPI has a negative trend towards drought, the complex dynamics of SPI with other climate variables indicate a wet season for western Australia., Comment: 17 pages, 12 figures

The concept of states of financial markets based on correlations has gained increasing attention during the last 10 years. We propose to retrace some important steps up to 2018, and then give a more detailed view of recent developments that attempt to make the use of this more practical. Finally, we try to give a glimpse to the future proposing the analysis of trajectories in correlation matrix space directly or in terms of symbolic dynamics as well as attempts to analyze the clusters that make up the states in a random matrix context., Comment: 16 pages, 9 figures, to appear in the Proceedings Volume, Eds. Anirban Chakraborti, Emmanuel Haven, Sudip Patra and Naresh Singh, "Quantum Decision Theory and Complexity Modelling in Economics and Public Policy" (Springer-Cham, New Economic Windows)

Over the last two decades, financial systems have been studied and analysed from the perspective of complex networks, where the nodes and edges in the network represent the various financial components and the strengths of correlations between them. Here, we adopt a similar network-based approach to analyse the daily closing prices of 69 global financial market indices across 65 countries over a period of 2000-2014. We study the correlations among the indices by constructing threshold networks superimposed over minimum spanning trees at different time frames. We investigate the effect of critical events in financial markets (crashes and bubbles) on the interactions among the indices by performing both static and dynamic analyses of the correlations. We compare and contrast the structures of these networks during periods of crashes and bubbles, with respect to the normal periods in the market. In addition, we study the temporal evolution of traditional market indicators, various global network measures and the recently developed edge-based curvature measures. We show that network-centric measures can be extremely useful in monitoring the fragility in the global financial market indices., Comment: 32 pages, 18 figures, including supplementary material

The complexity of financial markets arise from the strategic interactions among agents trading stocks, which manifest in the form of vibrant correlation patterns among stock prices. Over the past few decades, complex financial markets have often been represented as networks whose interacting pairs of nodes are stocks, connected by edges that signify the correlation strengths. However, we often have interactions that occur in groups of three or more nodes, and these cannot be described simply by pairwise interactions but we also need to take the relations between these interactions into account. Only recently, researchers have started devoting attention to the higher-order architecture of complex financial systems, that can significantly enhance our ability to estimate systemic risk as well as measure the robustness of financial systems in terms of market efficiency. Geometry-inspired network measures, such as the Ollivier-Ricci curvature and Forman-Ricci curvature, can be used to capture the network fragility and continuously monitor financial dynamics. Here, we explore the utility of such discrete Ricci curvatures in characterizing the structure of financial systems, and further, evaluate them as generic indicators of the market instability. For this purpose, we examine the daily returns from a set of stocks comprising the USA S&P-500 and the Japanese Nikkei-225 over a 32-year period, and monitor the changes in the edge-centric network curvatures. We find that the different geometric measures capture well the system-level features of the market and hence we can distinguish between the normal or `business-as-usual' periods and all the major market crashes. This can be very useful in strategic designing of financial systems and regulating the markets in order to tackle financial instabilities., Comment: 34 pages, 17 figures, including Supplementary Material

In the present paper, we investigate the effect of plasmonic Au nanoparticles (NPs) decoration on the photocatalytic efficiency of MoS$_2$ nanosheets. The Au NPs are grown on the surface of chemically exfoliated MoS$_2$ nanosheets by chemical reduction method. Au-MoS$_2$ nanostructures (NSs) are characterized by X-ray diffractometer, Raman spectrometer, absorption spectrophotometer, and transmission electron microscopy. Exfoliated MoS$_2$ and Au-MoS$_2$ NSs are used to study the photocatalytic degradation of organic dyes methyl red (MR) and methylene blue (MB). Under UV-Visible light irradiation, pristine MoS$_2$ shows photo degradation efficiencies between 30% to 46.9% for MR and 23.3% to 44% for MB, with varying exposure times from 30 to 120 min, respectively. However, Au-MoS$_2$ NSs with maximum Au NPs concentration show enhanced degradation efficiency from 70.2 to 96.7% for MR, and from 65.2 to 94.3% for MB. The manifold enhancement of degradation efficiency for both the dyes with Au-MoS$_2$ NSs may be attributed to the presence of Au NPs acting as charge trapping sites in the NSs. We believe this study would have potential application in battling the ill-effects of environmental degradation, which poses a major threat to humans as well as biodiversity., Comment: 22 pages, 12 figures

Distress propagation occurs in connected networks, its rate and extent being dependent on network topology. To study this, we choose economic production networks as a paradigm. An economic network can be examined at many levels: linkages among individual agents (microscopic), among firms/sectors (mesoscopic) or among countries (macroscopic). New emergent dynamical properties appear at every level, so the granularity matters. For viral epidemics, even an individual node may act as an epicenter of distress and potentially affect the entire network. Economic networks, however, are known to be immune at the micro-levels and more prone to failure in the meso/macro-levels. We propose a dynamical interaction model to characterize the mechanism of distress propagation, across different modules of a network, initiated at different epicenters. Vulnerable modules often lead to large degrees of destabilization. We demonstrate our methodology using a unique empirical data-set of input-output linkages across 0.14 million firms in one administrative state of India, a developing economy. The network has multiple hub-and-spoke structures that exhibits moderate disassortativity, which varies with the level of coarse-graining. The novelty lies in characterizing the production network at different levels of granularity or modularity, and finding `too-big-to-fail' modules supersede `too-central-to-fail' modules in distress propagation., Comment: 18 pages, 11 figures

The North Atlantic Oscillation (NAO) index, a measure of sea-level atmospheric pressure variability, holds significant influence over weather patterns in North America and Northern Europe. A negative (positive) NAO value signifies increased cold air outbreaks and storm occurrences (reduced occurrences) in these regions. NAO, a product of multiple climate factors, demonstrates intricate dynamics with sea surface temperature (SST) and sea ice extent (SIE). In this study, we adopt a data-driven approach to explore the complex interplay between NAO, SST, and SIE, revealing a critical instability rooted in positive feedback loops among these climate variables. Our statistical machine learning methodology examines the impacts of melting Arctic SIE and rising SST on NAO, thereby understanding the weather patterns across the North Atlantic region. The skewness analysis yields a negative skewness in NAO across various time intervals -- daily, weekly, and monthly. This skewness, coupled with NAO's mean zero stationary nature, accentuates system instability. To capture these dynamics, we formulate a Bayesian Granger-causal dynamic linear model, which effectively updates the predictor-dependent variable relationship over time. The findings underscore an impending critical instability, indicative of more frequent occurrences of intensely cold climates in eastern North America and northern Europe, theory signifies a notable climate shift. By delving into the intricate feedback mechanisms of NAO, SST, and SIE, our study enhances our comprehension of climate variability, fostering a more informed perspective on the imminent climate changes that lie ahead., Comment: 18 pages, 7 figures

The present paper deals with the systematic growth of plasmonic gold nanoparticles (Au NPs) on molybdenum disulphide (MoS$_2$) nanosheets as well as the effect of Au NPs on the optical and magnetic properties. The crystalline nature of the nanocomposites is confirmed by X-ray diffraction and transmission electron microscopic techniques. The optical properties are characterized using absorption and Raman spectroscopic techniques. The electron paramagnetic resonance technique is used to study the magnetic response of the nanocomposites. The paper attempts to gain a fundamental understanding of the two-dimensional nanomaterial-based composites for their applications in the magnetic and optical devices., Comment: 9 pages, 5 figures

Financial markets, being spectacular examples of complex systems, display rich correlation structures among price returns of different assets. The correlation structures change drastically, akin to phase transitions in physical phenomena, as do the influential stocks (leaders) and sectors (communities), during market events like crashes. It is crucial to detect their signatures for timely intervention or prevention. Here we use eigenvalue decomposition and eigen-entropy, computed from eigen-centralities of different stocks in the cross-correlation matrix, to extract information about the disorder in the market. We construct a `phase space', where different market events (bubbles, crashes, etc.) undergo phase separation and display order-disorder transitions. An entropy functional exhibits scaling behavior. We propose a generic indicator that facilitates the continuous monitoring of the internal structure of the market -- important for managing risk and stress-testing the financial system. Our methodology would help in understanding and foreseeing tipping points or fluctuation patterns in complex systems., Comment: 27 pages, 15 figures, 3 Tables

In this paper, the interaction of few tens of atoms containing gold nanoclusters with two dimensional nanosheets of transition metal dichalcogenides nanosheets has been explored. The gold nanoclusters have been synthesized using chemical reduction method in presence of protein molecules as stabilizing agent. The transition metal dichalcogenides nanosheets of molybdenum disulfide (MoS$_2$) has been chemically exfoliated. Different microscopic and optical spectroscopic tools have been used for characterizing the physical properties of the gold nanoclusters and the two dimensional nanosheets of MoS$_2$. The gold nanoclusters exhibit fluorescence emission at 690 nm. However, the interaction with transition metal dichalcogenides diminishes drastically the fluorescence intensity of the nanoclusters. The spectroscopic methods used for understanding the interaction in the system reveals the absence of energy transfer and dynamic nature of the fluorescence quenching., Comment: 10 pages, 8 figures

We present a brief overview of random matrix theory (RMT) with the objectives of highlighting the computational results and applications in financial markets as complex systems. An oft-encountered problem in computational finance is the choice of an appropriate epoch over which the empirical cross-correlation return matrix is computed. A long epoch would smoothen the fluctuations in the return time series and suffers from non-stationarity, whereas a short epoch results in noisy fluctuations in the return time series and the correlation matrices turn out to be highly singular. An effective method to tackle this issue is the use of the power mapping, where a non-linear distortion is applied to a short epoch correlation matrix. The value of distortion parameter controls the noise-suppression. The distortion also removes the degeneracy of zero eigenvalues. Depending on the correlation structures, interesting properties of the eigenvalue spectra are found. We simulate different correlated Wishart matrices to compare the results with empirical return matrices computed using the S&P 500 (USA) market data for the period 1985-2016. We also briefly review two recent applications of RMT in financial stock markets: (i) Identification of "market states" and long-term precursor to a critical state; (ii) Characterization of catastrophic instabilities (market crashes)., Comment: 22 pages, 12 figures. To appear in the Proceedings of Econophys-2017 and APEC-2017, New Delhi

The study of the critical dynamics in complex systems is always interesting yet challenging. Here, we choose financial market as an example of a complex system, and do a comparative analyses of two stock markets - the S&P 500 (USA) and Nikkei 225 (JPN). Our analyses are based on the evolution of crosscorrelation structure patterns of short time-epochs for a 32-year period (1985-2016). We identify "market states" as clusters of similar correlation structures, which occur more frequently than by pure chance (randomness). The dynamical transitions between the correlation structures reflect the evolution of the market states. Power mapping method from the random matrix theory is used to suppress the noise on correlation patterns, and an adaptation of the intra-cluster distance method is used to obtain the "optimum" number of market states. We find that the USA is characterized by four market states and JPN by five. We further analyze the co-occurrence of paired market states; the probability of remaining in the same state is much higher than the transition to a different state. The transitions to other states mainly occur among the immediately adjacent states, with a few rare intermittent transitions to the remote states. The state adjacent to the critical state (market crash) may serve as an indicator or a "precursor" for the critical state and this novel method of identifying the long-term precursors may be very helpful for constructing the early warning system in financial markets, as well as in other complex systems., Comment: 30 pages, 10 figures, including supplementary figures and information

We demonstrate using multi-layered networks, the existence of an empirical linkage between the dynamics of the financial network constructed from the market indices and the macroeconomic networks constructed from macroeconomic variables such as trade, foreign direct investments, etc. for several countries across the globe. The temporal scales of the dynamics of the financial variables and the macroeconomic fundamentals are very different, which make the empirical linkage even more interesting and significant. Also, we find that there exist in the respective networks, core-periphery structures (determined through centrality measures) that are composed of the similar set of countries -- a result that may be related through the `gravity model' of the country-level macroeconomic networks. Thus, from a multi-lateral openness perspective, we elucidate that for individual countries, larger trade connectivity is positively associated with higher financial return correlations. Furthermore, we show that the Economic Complexity Index and the equity markets have a positive relationship among themselves, as is the case for Gross Domestic Product. The data science methodology using network theory, coupled with standard econometric techniques constitute a new approach to studying multi-level economic phenomena in a comprehensive manner., Comment: 43 pages, 7 figures, 31 tables

We present the results from dissipative particle dynamics (DPD) simulations of phase separation dynamics in ternary (ABC) fluids mixture in $d=3$ where components A and B represent the simple fluids and component C represents a polymeric fluid. Here, we study the role of polymeric fluid (C) on domain morphology by varying composition ratio, polymer chain length, and polymer stiffness. We observe that the system under consideration lies in the same dynamical universality class as a simple ternary fluids mixture. However, the scaling functions depend upon the parameters mentioned above as they change the time scale of the evolution morphologies. In all cases, the characteristic domain size follows: $l(t) \sim t^{\phi} $ with dynamic growth exponent $\phi$, showing a crossover from the viscous hydrodynamic regime $(\phi=1)$ to the inertial hydrodynamic regime $(\phi=2/3)$ in the system at late times., Comment: 26 pages, 7 figures and supplementary information

We present a new model for the diffusion of innovation. Here, the population is segmented into distinct groups. Adoption by a particular group of some cultural product may be inhibited both by large numbers of its own members already having adopted but also, in particular, by members of another group having adopted. Intergroup migration is also permitted. We determine the equilibrium points and carry out stability analysis for the model for a two-group population. We also simulate a discrete time version of the model. Lastly, we present data on tablet use in eight countries from 2012-2016 and show that the relationship between use in the "under 25" age group and "55+" age group conforms to the model.

Terrorism instills fear in the minds of people and takes away the freedom of individuals to act as they will. Terrorism has turned out to be an international menace today. Here, we study the terrorist attack incidents which occurred in the last half-century across the globe from the open source, Global Terrorism Database, and develop a view on their spatio-temporal dynamics. We construct a complex network of global terrorism and study its growth dynamics, along with the statistical properties of the anti-social network, which are quite intriguing. Normally, each nation pursues its own vision of international security based upon its mandate and particular notions of politics and its policies to counter the threat of terrorism that could naturally include the use of tactical measures and strategic negotiations, or even physical power. We study the network resilience against targeted attacks and random failures, which could guide the counterterrorist outfits in designing strategies to fight terrorism. We then use a disparity filter method to isolate backbone of the network, and identify the terror hubs and vulnerable motifs of global terrorism. We also examine evolution of the hubs and motifs in a few exemplary cases like Afghanistan, Colombia, India, Israel, Pakistan and the United Kingdom. The dynamics of the terror hubs and the vulnerable motifs that we discover in the network backbone turn out to be very significant, and may provide deep insight on their formations and spreading, and thereby help in contending terrorism or framing public policies that can check their spread., Comment: 33 pages, 11 figures, 5 tables

Catastrophic events, though rare, do occur and when they occur, they have devastating effects. It is, therefore, of utmost importance to understand the complexity of the underlying dynamics and signatures of catastrophic events, such as market crashes. For deeper understanding, we choose the US and Japanese markets from 1985 onward, and study the evolution of the cross-correlation structures of stock return matrices and their eigenspectra over different short time-intervals or "epochs". A slight non-linear distortion is applied to the correlation matrix computed for any epoch, leading to the emerging spectrum of eigenvalues. The statistical properties of the emerging spectrum display: (i) the shape of the emerging spectrum reflects the market instability, (ii) the smallest eigenvalue may be able to statistically distinguish the nature of a market turbulence or crisis -- internal instability or external shock, and (iii) the time-lagged smallest eigenvalue has a statistically significant correlation with the mean market cross-correlation. The smallest eigenvalue seems to indicate that the financial market has become more turbulent in a similar way as the mean does. Yet we show features of the smallest eigenvalue of the emerging spectrum that distinguish different types of market instabilities related to internal or external causes. Based on the paradigmatic character of financial time series for other complex systems, the capacity of the emerging spectrum to understand the nature of instability may be a new feature, which can be broadly applied., Comment: 17 pages, 4 figures

A society or country with income equally distributed among its people is truly a fiction! The phenomena of socioeconomic inequalities have been plaguing mankind from times immemorial. We are interested in gaining an insight about the co-evolution of the countries in the inequality space, from a data science perspective. For this purpose, we use the time series data for Gini indices of different countries, and construct the equal-time cross-correlation matrix. We then use this to construct a similarity matrix and generate a map with the countries as different points generated through a multi-dimensional scaling technique. We also produce a similar map of different countries using the time series data for Gross Domestic Savings (% of GDP). We also pose a different, yet significant, question: Can higher savings moderate the income inequality? In this paper, we have tried to address this question through another data science technique - linear regression, to seek an empirical linkage between the income inequality and savings, mainly for relatively small or closed economies. This question was inspired from an existing theoretical model proposed by Chakraborti-Chakrabarti (2000), based on the principle of kinetic theory of gases. We tested our model empirically using Gini index and Gross Domestic Savings, and observed that the model holds reasonably true for many economies of the world., Comment: 8 pages, 6 figures. IEEE format. Accepted for publication in 5th IEEE DSAA 2018 conference at Torino, Italy

In this article, we present a brief narration of the origin and the overview of the recent developments done on the Kolkata Paise Restaurant (KPR) problem, which can serve as a prototype for a broader class of resource allocation problems in the presence of a large number of competing agents, typically studied using coordination and anti-coordination games. We discuss the KPR and its several extensions, as well as its applications in many economic and social phenomena. We end the article with some discussions on our ongoing experimental analysis of the same problem. We demonstrate that this provides an interesting picture of how people analyze complex situations, and design their strategies or react to them., Comment: 14 pages, Submitted for publication in a special issue in Science and Culture (Kolkata, India)

News reports in media contain records of a wide range of socio-economic and political events in time. Using a publicly available, large digital database of news records, and aggregating them over time, we study the network of ethnic conflicts and human rights violations. Complex network analyses of the events and the involved actors provide important insights on the engaging actors, groups, establishments and sometimes nations, pointing at their long range effect over space and time. We find power law decays in distributions of actor mentions, co-actor mentions and degrees and dominance of influential actors and groups. Most influential actors or groups form a giant connected component which grows in time, and is expected to encompass all actors globally in the long run. We demonstrate how targeted removal of actors may help stop spreading unruly events. We study the cause-effect relation between types of events, and our quantitative analysis confirm that ethnic conflicts lead to human rights violations, while it does not support the converse., Comment: 16 pages; main paper + Supplementary Information file; accepted for publication in Scientific Reports

We present Monte Carlo (MC) simulation studies of phase separation in binary (AB) mixtures with bond-disorder that is introduced in two different ways: (i) at randomly selected lattice sites and (ii) at regularly selected sites. The Ising model with spin exchange (Kawasaki) dynamics represents the segregation kinetics in conserved binary mixtures. We find that the dynamical scaling changes significantly by varying the number of disordered sites in the case where bond-disorder is introduced at the randomly selected sites. On the other hand, when we introduce the bond-disorder in a regular fashion, the system follows the dynamical scaling for the modest number of disordered sites. For higher number of disordered sites, the evolution morphology illustrates a lamellar pattern formation. Our MC results are consistent with the Lifshitz-Slyozov (LS) power-law growth in all the cases., Comment: 21 pages including 8 figures

We demonstrate the existence of an empirical linkage between the nominal financial networks and the underlying economic fundamentals across countries. We construct the nominal return correlation networks from daily data to encapsulate sector-level dynamics and figure the relative importance of the sectors in the nominal network through a measure of centrality and clustering algorithms. The eigenvector centrality robustly identifies the backbone of the minimum spanning tree defined on the return networks as well as the primary cluster in the multidimensional scaling map. We show that the sectors that are relatively large in size, defined with the metrics market capitalization, revenue and number of employees, constitute the core of the return networks, whereas the periphery is mostly populated by relatively smaller sectors. Therefore, sector-level nominal return dynamics is anchored to the real size effect, which ultimately shapes the optimal portfolios for risk management. Our results are reasonably robust across 27 countries of varying degrees of prosperity and across periods of market turbulence (2008-09) as well as relative calmness (2015-16)., Comment: 30 pages, 6 figures, including supplementary material

In the framework of complexity theory, which provides a unified framework for natural and social sciences, we study the complex and interesting problem of the internal structure, similarities, and differences between the Mazatec dialects, an endangered Otomanguean language spoken in south-east Mexico. The analysis is based on some databases, which are used to compute linguistic distances between the dialects. The results are interpreted in the light of linguistics as well as statistical considerations and used to infer the history of the development of the observed pattern of diversity., Comment: 23 pages, 9 figures. A similar version was submitted to the Proceedings of Econophys-2015 to be published in "Econophysics and Sociophysics: Recent Progress and Future Directions" Editors: Abergel, F., Aoyama, H., Chakrabarti, B.K., Chakraborti, A., Deo, N., Raina, D., Vodenska, I

The resonance energy transfer (RET) from tryptophan present in bovine serum albumin (BSA) to two dimensional (2D) nanomaterials has been reported. In these bio-nano systems, BSA molecules act as efficient donors while 2D van der Waals nanosheets of graphene, molybdenum disulfide (Mo$S_2$) and tungsten disulfide (W$S_2$) act as acceptors. The fluorescence emission from tryptophan arises at 350 nm, in presence of 2D nanosheets, the fluorescence intensity of BSA decreases. The time resolved fluorescence study shows that there is decrease in the fluorescence lifetime of BSA molecules in presence of 2D nanosheets. The decrease in fluorescence lifetime of BSA confirms the energy transfer phenomenon in bio-nano interactions. This study is important for finding the bio-compatibility of 2D nanomaterials in biological applications., Comment: 5 pages, 6 figures

In this paper we discuss some examples of systems composed of $N$ units, which exchange a conserved quantity $x$ according to some given stochastic rule, from some standard kinetic model of condensed matter physics to the kinetic exchange models used for studying the wealth dynamics of social systems. The focus is on the similarity of the equilibrium state of the various examples considered, which all relax toward a canonical Gibbs-Boltzmann equilibrium distribution for the quantity $x$, given by a $\Gamma$-distribution with shape parameter $\alpha = D/2$, which implicitly defines an effective dimension $D$ of the system. We study various systems exploring (continuous) values of $D$ in the interval $[1,\infty)$., Comment: 7 pages, 4 figures

In this article we review several techniques to extract information from stock market data. We discuss recurrence analysis of time series, decomposition of aggregate correlation matrices to study co-movements in financial data, stock level partial correlations with market indices, multidimensional scaling and minimum spanning tree. We apply these techniques to daily return time series from the Indian stock market. The analysis allows us to construct networks based on correlation matrices of individual stocks in one hand and on the other, we discuss dynamics of market indices. Thus both micro level and macro level dynamics can be analyzed using such tools. We use the multi-dimensional scaling methods to visualize the sectoral structure of the stock market, and analyze the comovements among the sectoral stocks. Finally, we construct a mesoscopic network based on sectoral indices. Minimum spanning tree technique is seen to be extremely useful in order to separate technologically related sectors and the mapping corresponds to actual production relationship to a reasonable extent., Comment: 28 pages, 14 figures. To be submitted for the proceedings of the JAFEE 20th International Conference on "SOCIO-ECONOMIC SYSTEMS WITH ICT AND NETWORKS" held on 26-27 March, 2016 at the University of Tokyo, Japan

A brief overview of the models and data analyses of income, wealth, consumption distributions by the physicists, are presented here. It has been found empirically that the distributions of income and wealth possess fairly robust features, like the bulk of both the income and wealth distributions seem to reasonably fit both the log-normal and Gamma distributions, while the tail of the distribution fits well to a power law (as first observed by sociologist Pareto). We also present our recent studies of the unit-level expenditure on consumption across multiple countries and multiple years, where it was found that there exist invariant features of consumption distribution: the bulk is log-normally distributed, followed by a power law tail at the limit. The mechanisms leading to such inequalities and invariant features for the distributions of socio-economic variables are not well-understood. We also present some simple models from physics and demonstrate how they can be used to explain some of these findings and their consequences., Comment: 19 pages, 6 figures. Springer-Verlag format. Lecture note based on the seminars delivered at International Christian University-Mitaka, Japan, Indian Institute of Advanced Study (IIAS)-Shimla, India and Asian Development Research Institute-Patna, India

In the light of contemporary discussions of inter and transdisciplinarity, this paper approaches econophysics and sociophysics to seek a response to the question -- whether these interdisciplinary fields could contribute to physics and economics. Drawing upon the literature on history and philosophy of science, the paper argues that the two way traffic between physics and economics has a long history and this is likely to continue in the future., Comment: 9 pages, EPJ format. To appear in the European Physical Journal Special Topics, entitled "Can economics be a physical science?" Eds. A.S. Chakrabarti et al

Gold nanoflowers are known for their use as efficient host for surface enhanced Raman scattering of organic dye molecules. In this article, gold nanoflowers have been synthesised and rhodamine 6 G (R6G) molecules have been used as probe molecules. It is found that the gold nanoflowers can detect the presence of R6G molecules upto 10^{-10} M. The petals of these nanoflowers play an important role for the enhancement of Raman signals, and the intensity of Raman signals is enhanced many folds in presence of gold nanoflowers - the enhancement factor is of the order of 10^6. This is explained in terms of electromagnetic mechanism., Comment: 8 pages, 4 figures

The presence of electron energy loss (EELS) peak is proposed theoretically in molybdenum disulfide (MoS2) nanosheets. Using density functional theory simulations and calculations, one EELS peak is identified in the visible energy range, for MoS2 nanosheets with molybdenum vacancy. Experimentally, four different laser sources are used for the Raman scattering study of MoS2 nanosheets, which show two distinct Raman peaks, one at 385 cm-1 (E12g) and the other at 408 cm-1 (A1g). In the cases of three laser sources with wavelengths 405 nm (3.06 eV), 632 nm (1.96 eV) and 785 nm (1.58 eV), respectively, the intensity of E12g Raman peak is more than the A1g Raman peak, while in the case of excitation source of 532 nm (2.33 eV), the intensity profile is reversed and A1g peak is the most intense. Thus a resonance Raman scattering phenomenon is observed for 532 nm laser source., Comment: 10 pages, 6 figures, uses IOP style

We study unit-level expenditure on consumption across multiple countries and multiple years, in order to extract invariant features of consumption distribution. We show that the bulk of it is lognormally distributed, followed by a power law tail at the limit. The distributions coincide with each other under normalization by mean expenditure and log scaling even though the data is sampled across multiple dimension including, e.g., time, social structure and locations. This phenomenon indicates that the dispersions in consumption expenditure across various social and economic groups are significantly similar subject to suitable scaling and normalization. Further, the results provide a measurement of the core distributional features. Other descriptive factors including those of sociological, demographic and political nature, add further layers of variation on the this core distribution. We present a stochastic multiplicative model to quantitatively characterize the invariance and the distributional features.

We study the distributional features and inequality of consumption expenditure across India, for different states, castes, religion and urban-rural divide. We find that even though the aggregate measures of inequality are fairly diversified across states, the consumption distributions show near identical statistics, once properly normalized. This feature is seen to be robust with respect to variations in sociological and economic factors. We also show that state-wise inequality seems to be positively correlated with growth which is in accord with the traditional idea of Kuznets' curve. We present a brief model to account for the invariance found empirically and show that better but riskier technology draws can create a positive correlation between inequality and growth., Comment: 14 pages, 5 figs, 4 tables; Accepted in Physica A

Many existing studies on pattern formation in the reaction-diffusion systems rely on deterministic models. However, environmental noise is often a major factor which leads to significant changes in the spatiotemporal dynamics. In this paper, we focus on the spatiotemporal patterns produced by the predator-prey model with ratio-dependent functional response and density dependent death rate of predator. We get the reaction-diffusion equations incorporating the self-diffusion terms, corresponding to random movement of the individuals within two dimensional habitats, into the growth equations for the prey and predator population. In order to have to have the noise added model, small amplitude heterogeneous perturbations to the linear intrinsic growth rates are introduced using uncorrelated Gaussian white noise terms. For the noise added system, we then observe spatial patterns for the parameter values lying outside the Turing instability region. With thorough numerical simulations we characterize the patterns corresponding to Turing and Turing-Hopf domain and study their dependence on different system parameters like noise-intensity, etc., Comment: 11 pages, 6 figures. Uses style files of Journal of Physics: Conference Series (IOPSCIENCE). Submitted for the Proceedings of the Statphys-Kolkata VIII held during December 1-5, 2014 at Kolkata, India