Finite size scaling and first order phase transition in a modified XY-model

Monte Carlo simulation has been performed in a two-dimensional modified XY-model first proposed by Domany et. al [E. Domany, M. Schick and R. H. Swendsen, Phys. Rev. Lett. 52, 1535 (1984)]. The cluster algorithm of Wolff has been used and multiple histogram reweighting is performed. The first order scaling behavior of the quantities like specific heat, order parameter susceptibility and free energy barrier are found to be obeyed accurately. While the lowest order correlation function was found to decay to zero at long distance just above the transition, the next higher order correlation function shows a non-zero plateau.Comment: 18 pages, 10 figures, Accepted for publication in Phys. Rev.

Role of topological defects in the phase transition of modified XY model : A Monte Carlo study

Monte Carlo simulation has been performed on a classical two dimensional XY- model with a modified form of interaction potential to investigate the role of topological defects on the phase transition exhibited by the model. In simulations in a restricted ensemble without defects, the system appears to remain ordered at all temperatures. Suppression of topological defects on the square plaquettes in the modified XY- model leads to complete elimination of the phase transition observed in this model.Comment: 19 pages, 12 figures, Accepted for publication in Phys. Rev.

The effect of peculiar velocities on the epoch of reionization (EoR) 21-cm signal

We have used semi-numerical simulations of reionization to study the behaviour of the power spectrum of the EoR 21-cm signal in redshift space. We have considered two models of reionization, one which has homogeneous recombination (HR) and the other incorporating inhomogeneous recombination (IR). We have estimated the observable quantities --- quadrupole and monopole moments of HI power spectrum at redshift space from our simulated data. We find that the magnitude and nature of the ratio between the quadrupole and monopole moments of the power spectrum ($P^s_2 /P^s_0$) can be a possible probe for the epoch of reionization. We observe that this ratio becomes negative at large scales for $x_{HI} \leq 0.7$ irrespective of the reionization model, which is a direct signature of an inside-out reionization at large scales. It is possible to qualitatively interpret the results of the simulations in terms of the fluctuations in the matter distribution and the fluctuations in the neutral fraction which have power spectra and cross-correlation $P_{\Delta \Delta}(k)$, $P_{xx}(k)$ and $P_{\Delta x}(k)$ respectively. We find that at large scales the fluctuations in matter density and neutral fraction is exactly anti-correlated through all stages of reionization. This provides a simple picture where we are able to qualitatively interpret the behaviour of the redshift space power spectra at large scales with varying $x_{HI}$ entirely in terms of a just two quantities, namely $x_{HI}$ and the ratio $P_{xx}/P_{\Delta \Delta}$. The nature of $P_{\Delta x}$ becomes different for HR and IR scenarios at intermediate and small scales. We further find that it is possible to distinguish between an inside-out and an outside-in reionization scenario from the nature of the ratio $P^s_2 /P^s_0$ at intermediate length scales.Comment: 11 pages, 6 figures. Accepted for publication in MNRAS. Replaced to match the accepted version. Added one appendix to quantify the possible uncertainties in the estimation of the multipole moments of redshift space power spectru

Utilizing cloud speed data to predict the time of occurrence of irradiance drops

The ability to predict short-term fluctuations in irradiance is a necessary component of ensuring the reliability of a solar PV system at a given location. Local cloud phenomena that are responsible for these changes are dynamic and difficult to model; on-site measurements of irradiance provide an easy way to capture these effects. In this study, cloud movement is tracked in time through its effect on irradiance signals at different locations and compared with the estimates of the travel time from cloud speed data. The results showed a good correlation between the two different time lags for a short separation distance of 228m, but there was almost no relation for higher distances. Using different similarity measures like Correlation, Euclidean distance or the location of Local Minima for the signal analysis didn’t affect the results widely for a separation distance

On cross-domain social semantic learning

Approximately 2.4 billion people are now connected to the Internet, generating massive amounts of data through laptops, mobile phones, sensors and other electronic devices or gadgets. Not surprisingly then, ninety percent of the world's digital data was created in the last two years. This massive explosion of data provides tremendous opportunity to study, model and improve conceptual and physical systems from which the data is produced. It also permits scientists to test pre-existing hypotheses in various fields with large scale experimental evidence. Thus, developing computational algorithms that automatically explores this data is the holy grail of the current generation of computer scientists. Making sense of this data algorithmically can be a complex process, specifically due to two reasons. Firstly, the data is generated by different devices, capturing different aspects of information and resides in different web resources/ platforms on the Internet. Therefore, even if two pieces of data bear singular conceptual similarity, their generation, format and domain of existence on the web can make them seem considerably dissimilar. Secondly, since humans are social creatures, the data often possesses inherent but murky correlations, primarily caused by the causal nature of direct or indirect social interactions. This drastically alters what algorithms must now achieve, necessitating intelligent comprehension of the underlying social nature and semantic contexts within the disparate domain data and a quantifiable way of transferring knowledge gained from one domain to another. Finally, the data is often encountered as a stream and not as static pages on the Internet. Therefore, we must learn, and re-learn as the stream propagates. The main objective of this dissertation is to develop learning algorithms that can identify specific patterns in one domain of data which can consequently augment predictive performance in another domain. The research explores existence of specific data domains which can function in synergy with another and more importantly, proposes models to quantify the synergetic information transfer among such domains. We include large-scale data from various domains in our study: social media data from Twitter, multimedia video data from YouTube, video search query data from Bing Videos, Natural Language search queries from the web, Internet resources in form of web logs (blogs) and spatio-temporal social trends from Twitter. Our work presents a series of solutions to address the key challenges in cross-domain learning, particularly in the field of social and semantic data. We propose the concept of bridging media from disparate sources by building a common latent topic space, which represents one of the first attempts toward answering sociological problems using cross-domain (social) media. This allows information transfer between social and non-social domains, fostering real-time socially relevant applications. We also engineer a concept network from the semantic web, called semNet, that can assist in identifying concept relations and modeling information granularity for robust natural language search. Further, by studying spatio-temporal patterns in this data, we can discover categorical concepts that stimulate collective attention within user groups.Includes bibliographical references (pages 210-214)

SIMILE BETWEEN THE MODUS OPERANDI OF ANALGESIA OF TRAMADOL AND POISON OAK (RHUS TOXICODENDRON) ON FIBROMYALGIA

Fibromyalgia is a disorder characterized by widespread musculoskeletal pain accompanied by fatigue, sleep, memory and mood issues. Symptoms sometimes begin after a physical trauma, surgery, infection or significant psychological stress. In other cases, symptoms gradually accumulate over time with no single triggering event.Women are much more likely to develop fibromyalgia than are men. Many people who have fibromyalgia also have tension headaches, temporo-mandibular joint (TMJ) disorders, irritable bowel syndrome, anxiety and depression.Fibromyalgia is one of the most common chronic pain conditions. The disorder affects an estimated 10 million people in the U.S. and an estimated 3-6% of the world population. While it is most prevalent in women - 75-90 percent of the people who have fibromyalgia are women - it also occurs in men and children of all ethnic groups. The disorder is often seen in families, among siblings or mothers and their children. The diagnosis is usually made between the ages of 20 to 50 years, but the incidence rises with age so that by age 80, approximately 8% of adults meet the American College of Rheumatology classification of fibromyalgia.While there is no cure for fibromyalgia, a variety of medications can help control symptoms. In general, treatments for fibromyalgia include both medication and self-care. The emphasis is on minimizing symptoms and improving general health. Medications can help reduce the pain of fibromyalgia and improve sleep. Common choices include: Analgesics (Tramadol). In this article we like to discuss on the similarity of action between the analgesic medicine (Tramadol) and the homoeopathic medicine Poison oak (Rhus toxicodendron) on fibromyalgia from the aetiopathogenetic point of view. Poison oak, a wild growing plant of the anacardiacea family is widely distributed and easily accessible and also a very common Homoeopathic remedy as Rhus toxicodendron

A Decidable Timeout based Extension of Propositional Linear Temporal Logic

We develop a timeout based extension of propositional linear temporal logic (which we call TLTL) to specify timing properties of timeout based models of real time systems. TLTL formulas explicitly refer to a running global clock together with static timing variables as well as a dynamic variable abstracting the timeout behavior. We extend LTL with the capability to express timeout constraints. From the expressiveness view point, TLTL is not comparable with important known clock based real-time logics including TPTL, XCTL, and MTL, i.e., TLTL can specify certain properties, which cannot be specified in these logics (also vice-versa). We define a corresponding timeout tableau for satisfiability checking of the TLTL formulas. Also a model checking algorithm over timeout Kripke structure is presented. Further we prove that the validity checking for such an extended logic remains PSPACE-complete even in the presence of timeout constraints and infinite state models. Under discrete time semantics, with bounded timeout increments, the model-checking problem that if a TLTL-formula holds in a timeout Kripke structure is also PSPACE complete. We further prove that when TLTL is interpreted over discrete time, it can be embedded in the monadic second order logic with time, and when TLTL is interpreted over dense time without the condition of non-zenoness, the resulting logic becomes $\Sigma_1^1$-complete