Behaviour of laterised normal and self compacting concrete subjected to elevated temperatures

Concrete is a universal material in the construction industry. With natural resources like sand and aggregate, fast depleting, it is time to look for alternate materials to substitute these in the process of making concrete. There are instances like exposure to solar radiation, fire, furnaces, and nuclear reactor vessels, special applications like missile launching pads etc., where concrete is exposed to temperature variations In this research work, an attempt has been made to understand the behaviour of concrete when weathered laterite aggregate is used in both conventional and self compacting normal strength concrete. The study has been extended to understand the thermal behaviour of both types of laterised concretes and to check suitability as a fire protection material. A systematic study of laterised concrete considering parameters like source of laterite aggregate, grades of Ordinary Portland Cement (OPC) and types of supplementary cementitious materials (fly ash and GGBFS) has been carried out to arrive at a feasible combination of various ingredients in laterised concrete. A mix design methodology has been proposed for making normal strength laterised self compacting concrete based on trial mixes and the same has also been validated. The physical and mechanical properties of laterised concretes have been studied with respect to different variables like exposure temperature (200°C, 400°C and 600°C) and cooling environment (air cooled and water cooled). The behaviour of ferrocement elements with laterised self compacting concrete has also been studied by varying the cover to mesh reinforcement (10mm to 50mm at an interval of 10mm), exposure temperature and cooling environment.

Acute salinity stress alters the haemolymph metabolic profile of Penaeus monodon and reduces immunocompetence to white spot syndrome virus infection

Influence of acute salinity stress on the immunological and physiological response of Penaeus monodon to white spot syndrome virus (WSSV) infection was analysed. P. monodon maintained at 15‰ were subjected to acute salinity changes to 0‰ and 35‰ in 7 h and then challenged orally with WSSV. Immune variables viz., total haemocyte count, phenol oxidase activity (PO), nitroblue tetrazolium salt (NBT) reduction, alkaline phosphatase activity (ALP), acid phosphatase activity (ACP) and metabolic variables viz., total protein, total carbohydrates, total free amino acids (TFAA), total lipids, glucose and cholesterol were determined soon after salinity change and on post challenge days 2 (PCD2) and 5 (PCD5). Acute salinity change induced an increase in metabolic variables in shrimps at 35‰ except TFAA. Immune variables reduced significantly (Pb0.05) in shrimps subjected to salinity stress with the exception of ALP and PO at 35‰ and the reduction was found to be more at 0‰. Better performance of metabolic and immune variables in general could be observed in shrimps maintained at 15‰ that showed significantly higher post challenge survival following infection compared to those under salinity stress. Stress was found to be higher in shrimps subjected to salinity change to lower level (0‰) than to higher level (35‰) as being evidenced by the better immune response and survival at 35‰. THC (Pb0.001), ALP (Pb0.01) and PO (Pb0.05) that together explained a greater percentage of variability in survival rate, could be proposed as the most potential health indicators in shrimp haemolymph. It can be concluded from the study that acute salinity stress induces alterations in the haemolymph metabolic and immune variables of P. monodon affecting the immunocompetence and increasing susceptibility to WSSV, particularly at low salinity stress conditions

Study on period search methods of variable stars: Application to ASAS and CRTS databases

Study on variable stars is an important topic of modern astrophysics. After the invention of powerful telescopes and high resolving powered CCD’s, the variable star data is accumulating in the order of peta-bytes. The huge amount of data need lot of automated methods as well as human experts. This thesis is devoted to the data analysis on variable star’s astronomical time series data and hence belong to the inter-disciplinary topic, Astrostatistics. For an observer on earth, stars that have a change in apparent brightness over time are called variable stars. The variation in brightness may be regular (periodic), quasi periodic (semi-periodic) or irregular manner (aperiodic) and are caused by various reasons. In some cases, the variation is due to some internal thermo-nuclear processes, which are generally known as intrinsic vari- ables and in some other cases, it is due to some external processes, like eclipse or rotation, which are known as extrinsic variables. Intrinsic variables can be further grouped into pulsating variables, eruptive variables and flare stars. Extrinsic variables are grouped into eclipsing binary stars and chromospheri- cal stars. Pulsating variables can again classified into Cepheid, RR Lyrae, RV Tauri, Delta Scuti, Mira etc. The eruptive or cataclysmic variables are novae, supernovae, etc., which rarely occurs and are not periodic phenomena. Most of the other variations are periodic in nature. Variable stars can be observed through many ways such as photometry, spectrophotometry and spectroscopy. The sequence of photometric observa- xiv tions on variable stars produces time series data, which contains time, magni- tude and error. The plot between variable star’s apparent magnitude and time are known as light curve. If the time series data is folded on a period, the plot between apparent magnitude and phase is known as phased light curve. The unique shape of phased light curve is a characteristic of each type of variable star. One way to identify the type of variable star and to classify them is by visually looking at the phased light curve by an expert. For last several years, automated algorithms are used to classify a group of variable stars, with the help of computers. Research on variable stars can be divided into different stages like observa- tion, data reduction, data analysis, modeling and classification. The modeling on variable stars helps to determine the short-term and long-term behaviour and to construct theoretical models (for eg:- Wilson-Devinney model for eclips- ing binaries) and to derive stellar properties like mass, radius, luminosity, tem- perature, internal and external structure, chemical composition and evolution. The classification requires the determination of the basic parameters like pe- riod, amplitude and phase and also some other derived parameters. Out of these, period is the most important parameter since the wrong periods can lead to sparse light curves and misleading information. Time series analysis is a method of applying mathematical and statistical tests to data, to quantify the variation, understand the nature of time-varying phenomena, to gain physical understanding of the system and to predict future behavior of the system. Astronomical time series usually suffer from unevenly spaced time instants, varying error conditions and possibility of big gaps. This is due to daily varying daylight and the weather conditions for ground based observations and observations from space may suffer from the impact of cosmic ray particles. Many large scale astronomical surveys such as MACHO, OGLE, EROS, xv ROTSE, PLANET, Hipparcos, MISAO, NSVS, ASAS, Pan-STARRS, Ke- pler,ESA, Gaia, LSST, CRTS provide variable star’s time series data, even though their primary intention is not variable star observation. Center for Astrostatistics, Pennsylvania State University is established to help the astro- nomical community with the aid of statistical tools for harvesting and analysing archival data. Most of these surveys releases the data to the public for further analysis. There exist many period search algorithms through astronomical time se- ries analysis, which can be classified into parametric (assume some underlying distribution for data) and non-parametric (do not assume any statistical model like Gaussian etc.,) methods. Many of the parametric methods are based on variations of discrete Fourier transforms like Generalised Lomb-Scargle peri- odogram (GLSP) by Zechmeister(2009), Significant Spectrum (SigSpec) by Reegen(2007) etc. Non-parametric methods include Phase Dispersion Minimi- sation (PDM) by Stellingwerf(1978) and Cubic spline method by Akerlof(1994) etc. Even though most of the methods can be brought under automation, any of the method stated above could not fully recover the true periods. The wrong detection of period can be due to several reasons such as power leakage to other frequencies which is due to finite total interval, finite sampling interval and finite amount of data. Another problem is aliasing, which is due to the influence of regular sampling. Also spurious periods appear due to long gaps and power flow to harmonic frequencies is an inherent problem of Fourier methods. Hence obtaining the exact period of variable star from it’s time series data is still a difficult problem, in case of huge databases, when subjected to automation. As Matthew Templeton, AAVSO, states “Variable star data analysis is not always straightforward; large-scale, automated analysis design is non-trivial”. Derekas et al. 2007, Deb et.al. 2010 states “The processing of xvi huge amount of data in these databases is quite challenging, even when looking at seemingly small issues such as period determination and classification”. It will be beneficial for the variable star astronomical community, if basic parameters, such as period, amplitude and phase are obtained more accurately, when huge time series databases are subjected to automation. In the present thesis work, the theories of four popular period search methods are studied, the strength and weakness of these methods are evaluated by applying it on two survey databases and finally a modified form of cubic spline method is intro- duced to confirm the exact period of variable star. For the classification of new variable stars discovered and entering them in the “General Catalogue of Vari- able Stars” or other databases like “Variable Star Index“, the characteristics of the variability has to be quantified in term of variable star parameters.