Stellar structure of quark stars in a modified Starobinsky gravity

We propose a form of gravity-matter interaction given by $\omega RT$ in the framework of $f(R,T)$ gravity and examine the effect of such interaction in spherically symmetric compact stars. Treating the gravity-matter coupling as a perturbative term on the background of Starobinsky gravity, we develop a perturbation theory for equilibrium configurations. For illustration, we take the case of quark stars and explore their various stellar properties. We find that the gravity-matter coupling causes an increase in the stable maximal mass which is relevant for recent observations on binary pulsars

Satellite ocean colour sensors

The 70% of the earth’s surface is covered by the ocean and the life inhabiting the oceans play an important role in shaping the earth’s climate. Phytoplankton, also known as microalgae, are the single celled, autotrophic components of the plankton community and a key part of oceans, seas and freshwater basin ecosystems. They are significant factor in the ocean carbon cycle and, hence, important in all pathways of carbon in the ocean. Phytoplankton contain chlorophyll pigments for photosynthesis, similar to terrestrial plants and require sunlight in order to live and grow. Most of them are buoyant and float in the upper part of the ocean, where plenty of sunlight is available. They also require inorganic nutrients such as nitrates, phosphates, and sulphur which they convert into proteins, fats, and carbohydrates. In a balanced ecosystem, phytoplankton are the base of the food web and provide food for a wide range of sea creatures (NOAA). The measurement of phytoplankton can be indexed as chlorophyll concentration and is important as they are fundamental to understanding how the marine ecosystem responds to climate variability and climate change

Regional and seasonal variations in phytoplankton

One of the main goals of remote-sensing observations is the study of seasonal cycles of phytoplankton biomass in different regions of the World Ocean. In many regions these cycles repeat every year including minor details. This pattern is a result of seasonal oscillations of physical environment. In high latitudes these oscillations are more pronounced, and the response of phytoplankton is more evident

Identifying mesoscale eddies- Relevance to mud banks and fishery

The most popular fishing area during mud bank formation in Kerala is off Punnapra coast in Alapuzha district. This place is equipped with unique crafts such as one-man operated expanded polystyrene thermocol made gill netters, and several other traditional crafts. The fishermen community along this coast is vigilant against any mechanized fishing during mud bank period which falls in the southwest monsoon months when there is a ban on mechanized crafts. There are comparable datasets, from mud bank vis-à-vis non mud bank in this region, which indicate that, the catch per unit effort (CPUE) do not vary significantly. Fishing in Thrissur and Malappuram districts are not restricted by the formation of mud banks. In these districts modified outboard crafts such as pair trawlers operating double net and the high horse power of the out board engines are generally on a look out for nonmud bank resources also. In Malappuram district, the occurrence of the mud bank fishery is for limited days and generally less reported. Therefore, the analysis of data sets indicated better production and CPUE from non-mud banks in Malappuram. In general we can say that there is no significant increase in abundance of fishes reported from the mud bank regions. But the calm waters generated at certain pockets of the otherwise disturbed coastal waters act as areas for seasonal landings of fish

Fundamentals of ocean colour remote sensing

Remote sensing refers to collection of information about an object without being in direct contact with the object. Remote sensing aids in measuring remote areas which are inaccessible by any other means and offer less expense than in-situ measurements. Remote sensing facilitates creation of long time series and extended measurement. This has the advantage that several parameters can be measured at same time and satellite-based remote sensing measurements allow global observations. Remote sensing has its own advantages and disadvantages. The limitation includes indirect measurements of large areas which are not of interest to the user. The automated instrument degradation creates retrieval errors and are affected by several factors/processes, and not only by the object of interest. Additional assumptions and models are needed for the interpretation of the measurements and before using these models in oceanographic studies, it is extremely important to validate the performance of the various ocean colour algorithms with in-situ observations (Swirgon et al., 2015)

Comparison of Seasonal Cycles of Phytoplankton Chlorophyll, Aerosols, Winds and Sea-Surface Temperature off Somalia

In climate research, an important task is to characterize the relationships between Essential Climate Variables (ECVs). Here, satellite-derived data sets have been used to examine the seasonal cycle of phytoplankton (chlorophyll concentration) in the waters off Somalia, and its relationship to aerosols, winds and Sea Surface Temperature (SST). Chlorophyll-a (Chl-a) concentration, Aerosol Optical Thickness (AOT), Ångström Exponent (AE), Dust Optical Thickness (DOT), SST and sea-surface wind data for a 16-year period were assembled from various sources. The data were used to explore whether there is evidence to show that dust aerosols enhance Chl-a concentration in the study area. The Cross Correlation Function (CCF) showed highest positive correlation (r2 = 0.3) in the western Arabian Sea when AOT led Chl-a by 1–2 time steps (here, 1 time step is 8 days). A 2 × 2◦ box off Somalia was selected for further investigations. The correlations of alongshore wind speed, Ekman Mass Transport (EMT) and SST with Chl-a were higher than that of AOT, for a lag of 8 days. When all four variables were considered together in a multiple linear regression, the increase in r2 associated with the AOT is only about 0.02, a consequence of covariance among AOT, SST, EMT and alongshore wind speed. The AOT data show presence of dust aerosols most frequently during the summer monsoon season (June–September). When the analyses were repeated for the dust aerosol events, the correlations were generally lower, but still significant. Again, the inclusion of DOT in the multiple linear regression increased the correlation coefficient by only 2%, indicating minor enhancement in Chl-a concentration. Interestingly, during summer monsoon season, there is a higher probability of finding more instances of positive changes in Chl-a after one time step, regardless of whether there is dust aerosol or not. On the other hand, during the winter monsoon season (November–December) and rest of the year, the probability of Chl-a enhancement is higher when dust aerosol is present than when it is absent. The phase relationship in the 8-day climatologies of Chl-a and AOT (derived from NASA’s SeaWiFS and MODIS-A ocean colour processing chain) showed that AOTled Chl-a for most of the summer monsoon season, except when Chl-a was very high, during which time, Chl-a led AOT. The phase shift in the Chl-a and AOT climatological relationship at the Chl-a peak was not observed when AOT from Aerosol Climate Change Initiative (Aerosol-CCI) was used