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The contribution of four variables, namely Chlorophyll-a (Chl-a), Sea Surface Temperature (SST), diffuse attenuation coefficient (Kd_490 or Kd), and Photosynthetically Active Radiation (PAR), in predicting the catches of major pelagic fish species (Indian mackerel, horse mackerel, Bombay duck, oil sardine, and other sardines) was evaluated using Canonical Correlation Analysis (CCA). The outcome of the analysis was compared with those obtained by using the following models and methods: the Generalized Linear Model (GLM), the Generalized Additive Model (GAM), connection weight methods, and the explanatory methods of Artificial Neural Networks (ANNs). Both the sets of results were in agreement. Neither the GAM nor the ANNs method showed any clear advantage over each other, although the GAM performed better than the GLM. [ABSTRACT FROM AUTHOR]

Quarterly landings or catches of total fishes and the major pelagic fish species, were forecasted using the methods and models viz. autoregressive integrated moving average (ARIMA), non-linear autoregressive (NAR) artificial neural network (ANN), autoregressive integrated moving average with exogenous inputs (ARIMAX), non-linear autoregressive with external (exogenous) inputs (NARX) artificial neural network. The models were also developed by considering only two important variables (differ for total fish and selected fish species) obtained from the ANN model. These simplified models proved nearly as good in their predictions. Simulated sea surface temperature (SST) for the A2 climate change scenario was used as an input for the NARX model to estimate the catches of Bombay duck over a short term (2020 - 2025) and a long term (2030 - 2050) with the last two years' (2012 - 2013) average catch of training data as a benchmark. The catches increased on average by 41 % in the short term but decreased by 17.72 % in the long term. [ABSTRACT FROM AUTHOR]

Women constitute half of world population and makeup a significant percentage of the labor work force. In this regard, the United Nations (UN) has chartered gender-specific laws to protect the rights and interests of working women. In the last few decades Indian society has witnessed transition of women from their perceived conventional role in full-time household work to a larger participation in workforce. Globally, the Human Rights framework promotes the basic rights for both men and women. In 1948, the Universal Declaration of Human Rights proclaimed equal entitlements to all human beings on issues of education, housing and property, etc. However, women continue to face multiple challenges at workplace like sexual harassment, fear of job loss due to pregnancy, late night shift and so on which can be intimidating and have negative impact on their mental health. There is a bidirectional relationship between human rights and mental health. Violation of human rights has a negative effect on the individual's mental health; inversely following human rights can positively influence the mental health. The laws are made to protect rights of working women, but it cannot be denied that the discrimination is structurally embedded in our society. At the level of society, structural changes, change in attitude towards women is required. There is a high demand to raise the awareness on women's rights at workplace and women must advocate their rights. The first part of this paper discloses the mental health of Indian women at workplace. The second part consists of human rights of Indian working women, few relevant case laws wherein Indian women were given justice and the general association of these with the mental health of working women worldwide. At the end, suggestions are made to increase the awareness and strict implementation of the laws for the mental well-being of working women. [ABSTRACT FROM AUTHOR]

In-situ data of chlorophyll-a concentrations (Chl-a) and sea surface temperature (SST) of the Gujarat region for the period, 2002-2009 were obtained from Indian National Centre for Ocean Information Services (INCOIS), Hyderabad. Out of nearly 100 sampling points, 22 and 67 points qualified for comparison with the satellite measurements of Chl-a and SST, respectively. Chl-a concentrations were estimated from the MODIS satellite data (4 km resolution) with the existing global ocean color algorithms, namely, OC2V4, OC4V4, and OC3M. The SST was calculated with the help of bands 31 and 32 using MODIS-Aqua sensor long wave SST algorithm and European Centre for Medium-Range Weather Forecasts (ECMWF) assimilation SST retrieval model (split window method). The satellite images were processed using global Sea WiFS Data Analysis System (SeaDAS) software v.7.3.1. Chl-a retrieved from OC3M algorithm had high coefficient of determination (R2=0.74) and less root mean square error (RMSE=1.24) as compared to OC2V4 and OC4V4 (R2=0.541 & 0.542 and RMSE=1.94 and 1.84, respectively) with in-situ data. The SST retrieved from MODIS-Aqua sensor long wave SST algorithm had a high coefficient of correlation as compared to ECMWF assimilation model (0.798 & 0.32 respectively) with in-situ data and RMSE were 0.80 and 2.65, respectively. SST and Chl-a showed an inverse correlation, with a coefficient of correlation (R) =0.530. Daily retrieval of Chl-a and SST value had very high degree of correlation with remote sensed eight days composite and monthly composite value (0.958 & 0.876, respectively). Retrieval of the value of diffuse attenuation coefficient at 490 nm wavelength (Kd or Kd_490), photosynthetically active radiation (PAR) and vertical attenuation coefficient of PAR (Kd(PAR)) were done and found that Kd and Kd(PAR) had very high degree of positive correlation (R=0.994). In addition, it was found that PAR had a positive correlation with SST(R=0.512) and negative correlation with Chl-a (R=-0.446). The range of this parameter values supports the case-I water and fish assemblage area. [ABSTRACT FROM AUTHOR]

Drought is a detrimental factor to gaining higher yields in rice ( Oryza sativa L.), especially amid the rising occurrence of drought across the globe. To combat this situation, it is essential to develop novel drought-resilient varieties. Therefore, screening of drought-adaptive genotypes is required with high precision and high throughput. In contemporary emerging science, high throughput plant phenotyping (HTPP) is a crucial technology that attempts to break the bottleneck of traditional phenotyping. In traditional phenotyping, screening significant genotypes is a tedious task and prone to human error while measuring various plant traits. In contrast, owing to the potential advantage of HTPP over traditional phenotyping, image-based traits, also known as i-traits, were used in our study to discriminate 110 genotypes grown for genome-wide association study experiments under controlled (well-watered), and drought-stress (limited water) conditions, under a phenomics experiment in a controlled environment with RGB images. Our proposed framework non-destructively estimated drought-adaptive plant traits from the images, such as the number of leaves, convex hull, plant-aspect ratio (plant spread), and similarly associated geometrical and morphological traits for analyzing and discriminating genotypes. The results showed that a single trait, the number of leaves, can also be used for discriminating genotypes. This critical drought-adaptive trait was associated with plant size, architecture, and biomass. In this work, the number of leaves and other characteristics were estimated non-destructively from top view images of the rice plant for each genotype. The estimation of the number of leaves for each rice plant was conducted with the deep learning model, YOLO (You Only Look Once). The leaves were counted by detecting corresponding visible leaf tips in the rice plant. The detection accuracy was 86-92% for dense to moderate spread large plants, and 98% for sparse spread small plants. With this framework, the susceptible genotypes (MTU1010, PUSA-1121 and similar genotypes) and drought-resistant genotypes (Heera, Anjali, Dular and similar genotypes) were grouped in the core set with a respective group of drought-susceptible and drought-tolerant genotypes based on the number of leaves, and the leaves' emergence during the peak drought-stress period. Moreover, it was found that the number of leaves was significantly associated with other pertinent morphological, physiological and geometrical traits. Other geometrical traits were measured from the RGB images with the help of computer vision.