Fisheries Stakeholders and Their Livelihoods in Tamil Nadu and Puducherry

Fisheries Management for Sustainable Livelihoods (FIMSUL), is a project implemented by the Food and Agriculture Organization of the United Nations (FAO) with the Government of Tamil Nadu and Puducherry in India under the World Bank Trust Fund. The project aims at establishing frameworks, processes and building capacities of various stakeholders especially the Government, to facilitate the planning, design and implementation of appropriate fisheries development and management policies. The project includes a series of stakeholder consultations and consensus building apart from detailed review and analysis in the areas of stakeholders, livelihoods, policy, legal and institutional frame work and fisheries management. Based on this, the project comes up with various options. Stakeholder and livelihoods analysis is an essential part of the project. Hence, the team developed a detailed methodology for stakeholder consultations which includes district level stake holder consultation, focus group discussions, household interviews and validation meetings. The stakeholder and livelihoods analysis following the above steps were done through six NGO partners working along the coast of Tamil Nadu and Puducherry who were initially trained on the methodology. The NGO partners : PLANT, GUIDE, FERAL, SIFFS, DHAN Foundation and TMSSS, especially a team of dedicated staff engaged by them had done an excellent work in completing comprehensive field exercises and bringing out 12 district/regional reports. These are published separately. This report is a compilation, and complete analysis of the stakeholders and livelihoods based on all the field level consultations.This report is expected to be an important reference to primary stakeholders' perspective of the important stakeholders in the sector, the livelihoods and livelihoods changes, the adaptive and coping mechanism, the relationships between the stakeholders and their hopes and aspirations. For any development intervention for any sector or stakeholder group, region-wise in marine fisheries in Tamil Nadu and Puducherry, the information from this report could be an important starting point

Eight Forestation form in Kabilar songs

The oldest grammar book Tolkappiyam is the reason for the emergence of many literary forms. Like Tolkappiyam, Sangam period literary works were written focusing on the life of the ancient Tamil people and the songs of those era is about both the internal and external life.  Sangam literary books are collections of songs sung by many poets of different eras. Kabilar is notable among those Sangam literary poets. Kapila's songs are written in praise of the mountainous land of Kurinji. Kabilar's songs are beautiful and interesting. His songs are classified into eight forestation form. Tolkappiyam says that description about forests add beauty to poetry. This article is intended to reveal the eight forestation form in poet kabilar’s songs

Studies on clustering of chilli (Capsicum annum L.) genotypes based on genetic distance

Mahalanobis' D2 statistics was used to analyse forty-five chilli (Capsicum annum L.) genotypes based on eighteen characters in order to pick out supreme potential parents for hybridization. Based on D2 values, the genotypes were divided into eleven groups with extreme divergence. Cluster I had the majority of genotypes (sixteen), whereas the fewest genotypes were identified in clusters VII, VIII, X and XI (one). Cluster XI had the greatest distance within the cluster. Clusters V and XI had the maximum generalized distance between them, followed by clusters VII and XI, clusters IV and VII, clusters IV and V and clusters II and XI. This suggests that the genotypes in these groups had more genetic variation. Following cluster VII and VIII, cluster V showed the highest cluster mean for green, dry fruit yield (846g and 95.50g) and several yield-related features. At clusters I, II and VI, no observation for high cluster means but had fair trait performance. It may be suggested to directly advance the genotypes from clusters V, VII, VII in hybridization to obtain unique recombinants

Finite Length Analysis of Caching-Aided Coded Multicasting

In this work, we study a noiseless broadcast link serving $K$ users whose requests arise from a library of $N$ files. Every user is equipped with a cache of size $M$ files each. It has been shown that by splitting all the files into packets and placing individual packets in a random independent manner across all the caches, it requires at most $N/M$ file transmissions for any set of demands from the library. The achievable delivery scheme involves linearly combining packets of different files following a greedy clique cover solution to the underlying index coding problem. This remarkable multiplicative gain of random placement and coded delivery has been established in the asymptotic regime when the number of packets per file $F$ scales to infinity. In this work, we initiate the finite-length analysis of random caching schemes when the number of packets $F$ is a function of the system parameters $M,N,K$. Specifically, we show that existing random placement and clique cover delivery schemes that achieve optimality in the asymptotic regime can have at most a multiplicative gain of $2$ if the number of packets is sub-exponential. Further, for any clique cover based coded delivery and a large class of random caching schemes, that includes the existing ones, we show that the number of packets required to get a multiplicative gain of $\frac{4}{3}g$ is at least $O((N/M)^g)$. We exhibit a random placement and an efficient clique cover based coded delivery scheme that approximately achieves this lower bound. We also provide tight concentration results that show that the average (over the random caching involved) number of transmissions concentrates very well requiring only polynomial number of packets in the rest of the parameters.Comment: A shorter version appeared in the 52nd Annual Allerton Conference on Communication, Control, and Computing (Allerton), 201

An Improved Fire Fly Algorithm to Solve Economic Load Dispatch Problem including Practical Constraints

The main objective of Economic Load Dispatch Problem of power generation is to schedule the committed generating units optimally so as to meet the required load demand while satisfying all the units with equal and inequality constraints. In this Paper an improved firefly algorithm has been implemented for the solution of economic load dispatch problem with non-smooth fuel cost curves considering the transmission loss coefficients and emission cost coefficientsbased on new version of firefly algorithm.Itis one of the evolutionary algorithm which is inspired by the idealized behavior of flashing characteristics of firefly to identify the nearest one. This approach considers the direct fireflies movement to global best if there is only one best solution in and around them. The effectiveness of the proposed method has been implemented to IEEE standard test system that demonstrates the capability of this approach in generating non dominated solutions of multi objective Economic Load Dispatch Problem

Role of multi detector computed tomography (MDCT) in evaluation of renal masses

Background: Due to rapid pace in development of imaging techniques and increasing number of investigations being done, more number of renal masses are discovered incidentally during evaluation of unrelated or unspecific symptoms. Hence it is vital to differentiate neoplastic and non-neoplastic masses. Among the neoplastic masses, there is a need to differentiate benign and malignant masses so that appropriate treatment strategies like nephron sparing surgery, radio frequency ablation etc. can be planned at an early stage and avoiding unnecessary radical treatments for improved patients survival.Methods: A Cross-sectional Observational study was done in 35 patients. Patients of either sex in any age group who had presented with suspected renal mass by clinical signs and symptoms (palpable renal angle mass, renal angle pain, hematuria) confirmed on USG examination or an incidental Renal mass diagnosed on USG/CT examination were included in our study.Results: Ultrasound is the initial imaging modality of choice since it is inexpensive, easy to perform and no radiation exposure. On USG, the renal lesions are classified as solid or cystic. Anechoic, thin walled cyst without any septations or solid components is usually Bosniak I cyst (simple cyst) and does not need any further evaluation. Rest of the cystic and solid lesions cannot be characterized by ultrasound and hence need further evaluation.Conclusions: Multidetector Computed Tomography is the imaging modality of choice for further evaluation and characterization. CT is done in four phases viz., unenhanced, corticomedullary, nephrographic and excretory phase especially in cases of malignancy while in benign conditions like angiomyolipoma and abscess, evaluation with unenhanced and single phase post contrast in portovenous phase is sufficient

Automated object detection of mechanical fasteners using faster region based convolutional neural networks

Mechanical fasteners are widely used in manufacturing of hardware and mechanical components such as automobiles, turbine & power generation and industries. Object detection method play a vital role to make a smart system for the society. Internet of things (IoT) leads to automation based on sensors and actuators not enough to build the systems due to limitations of sensors. Computer vision is the one which makes IoT too much smarter using deep learning techniques. Object detection is used to detect, recognize and localize the object in an image or a real time video. In industry revolution, robot arm is used to fit the fasteners to the automobile components. This system will helps the robot to detect the object of fasteners such as screw and nails accordingly to fit to the vehicle moved in the assembly line. Faster R-CNN deep learning algorithm is used to train the custom dataset and object detection is used to detect the fasteners. Region based convolutional neural networks (Faster R-CNN) uses a region proposed network (RPN) network to train the model efficiently and also with the help of Region of Interest able to localize the screw and nails objects with a mean average precision of 0.72 percent leads to accuracy of 95 percent object detectio