Search

The right to work means that people have a human right to work or engage in productive employment and may not be prevented from doing so. The right to work is enshrined in the Universal Declaration of Human Rights (UDHR) and recognized in international human rights law through its inclusion in the International Covenant on Economic, Social and Cultural Rights, where the right to work emphasizes economic, social and cultural development. The preamble to the constitution secures social, economic and political justice to all citizens of India. The same resolve is more elaborately repeated in DPSP which, among others, specifically require the state to minimize the inequalities in income and to eliminate inequalities in status, facilities and opportunities as well as to direct its policy towards ensuring that "the ownership and control of the material resources of the community are so distributed as best to sub-serve the common good" and that the operation of the economic system does not result in the concentration of wealth and means of production to the common detriment. At one time, FRs were having primacy over DPSP. But in recent times, some DPSP have transformed into and become an integral part of the named FRs under part III. Therefore, the sanction of the state or the judiciary is needed for the enforcement of DPSP. [ABSTRACT FROM AUTHOR]

Conservation of available natural resources through demarcation of potential zones at micro level are primary necessitate for sustainable development, particularly in the fragile semi-arid tropics. Delineation of potential zones for implementation of conservation measures above the entire watershed at similar occurrence is inaccessible as well as uneconomical; consequently it is a prerequisite to apply viable technique for prioritization of sub-watersheds (SWDs). Keeping this in view, the present research attempted to study various morphological characteristics and to implement Geographical Information System (GIS) and Multi Criteria Decision Making (MCDM) through Fuzzy Analytical Hierarchy Process (FAHP) techniques for identification of critical sub-watersheds situated in transaction zone between mountainous and water scarcity region of Western Part of India. The morphometric characterization was obtained through the measurement of three distinct linear, areal and relief aspects over the eight sub-watersheds. The morphometric characterization showed imperative role in distinguishing the topographical and hydrological behavior of the watershed. Each hydrological unit was ranked with respect to the value and weightages obtained by deriving the relationships between the morphometric parameters obtained through classification of the SWDs by associating the robustness of fuzzy logic and the Analytical Hierarchy Processes (AHP). Based on FAHP approach, sub-watersheds were evaluated as vulnerability assessment zones and alienated into five prioritization levels: very less, less, medium, high and very high classes. The evaluated results illustrated that 60.85% of sub-watersheds (five sub-watersheds) were in the medium to high susceptible zones, which depicted potential areas for necessity of establishment of conservation interventions for the sustainable watershed management planning. The FAHP based technique is a viable approach in illustrating the dilemma particularly over data hungry and complex conventional soil and water risk assessment methods and will be useful to various stakeholders (rural extension community, agriculturists and water resources managers) for better decision making with an obliging rule based system for implementing various assessment measures. [ABSTRACT FROM AUTHOR]

The Indian education system has played a key role in developing the skilled workforce required to capitalize on the wave of offshore outsourcing. India has a long history of organized education that goes back thousands of years in history. Prior to the British reign, India had a well-developed regional system. Much of this was uprooted and replaced with western structure and content. While much was lost in the transition, India emerged with a world-class set of universities including the famous Indian Institutes of Technology (IITs). Though producing a limited number of graduates, IIT is considered one of the top technical universities in the world and, by some metrics, the most difficult to get into. [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.