130 research outputs found
Fishery and Biological Aspects of Yellowfin Tuna Thunnus albacares along Andhra Coast, India
A potentially rich fishing ground for yellowfin tuna Thunnus albacares discovered off
northern Andhra Pradesh along the east coast of India at depths of 200m and more is being
gainfully exploited by the local fishers since 2002. Hooks and lines as well as trolls operated
mostly from non-mechanized crafts (catamaran) are the major exploitation methods used. The
mechanized sector ventured into oceanic tuna fishing during 2006 with the conversion of a few
existing mechanized trawlers into long liners and for large scale commercial exploitation of
yellowfin tuna in this region. The average annual (2004-2006) landing of tuna by the catamaran
at Visakhapatnam was 1,515t. A wide size group represented the fishery with the fork length of
T.albacares measuring from 25 cm to 190 cm with major modes at 90 and 130 cm. Fishes above
80 cm were found to be mature and the size at first maturity was estimated to be between 90-95
cm. Males were dominant with a male: female ratio of 1: 0.58. The length weight relationship is
given by the formula W= 0.008634L 3.12. Food contents consisted of a variety of prey animals.
Fishes (bony pelagic fishes), crustaceans (crabs and shrimps) and molluscs (squids) were the
major prey groups. The fishery is still in its infancy and more research has to be carried out to
understand its biology and formulate proper management measures to sustainably harvest this
stock
Visual quality testing method used in the field for grading yellowfin tuna
The yellowfin tuna (Thunnus albacares) popularly known as the тАШchicken of the seaтАЩ is harvested along the Indian coast mainly with an eye on the export market. Though the fish meat both in fresh and canned form has a demand in domestic markets in some states, the higher value it fetches in the export market prompts the fishermen to mainly aim at exports. However, certain minimal conditions of fish
quality have to be ascertained and certified before it is accepted for export. The south-east Asian countries are the main market for tunas and tuna meat is consumed both in raw as well as processed forms (canned, fish fingers, fish powder, fish sauce etc.
Power loss minimization and voltage profile improvement of radial and mesh distribution network using sine cosine optimization based DG allocation
Due to the increasing population and emerging technologies, energy requirements are increasing progressively. Distributed generation (DG) is an excellent option to handle the increased power demand. A well optimized DG can help to reduce power losses, CO2 emission and improve voltage profiles. This paper uses sine cosine algorithm to find the optimal location and best size ofDG with power loss minimization as its objective function. It presents the comparison of power loss reduction and voltage profile enhancement due to allocation of DGoperating at 0.85, 0.95 power factor lag andthe optimal power factor that has been obtained by sine cosineoptimization. IEEE 15, 69 bus radial and 33, 69 bus weakly meshed systems with five tie lines are used for power loss analysis. Annual energy saving due to allocating DG with different power factors have been analyzed. All the results are simulated in MATLAB 2021a
MFSNet: A Multi Focus Segmentation Network for Skin Lesion Segmentation
Segmentation is essential for medical image analysis to identify and localize
diseases, monitor morphological changes, and extract discriminative features
for further diagnosis. Skin cancer is one of the most common types of cancer
globally, and its early diagnosis is pivotal for the complete elimination of
malignant tumors from the body. This research develops an Artificial
Intelligence (AI) framework for supervised skin lesion segmentation employing
the deep learning approach. The proposed framework, called MFSNet (Multi-Focus
Segmentation Network), uses differently scaled feature maps for computing the
final segmentation mask using raw input RGB images of skin lesions. In doing
so, initially, the images are preprocessed to remove unwanted artifacts and
noises. The MFSNet employs the Res2Net backbone, a recently proposed
convolutional neural network (CNN), for obtaining deep features used in a
Parallel Partial Decoder (PPD) module to get a global map of the segmentation
mask. In different stages of the network, convolution features and multi-scale
maps are used in two boundary attention (BA) modules and two reverse attention
(RA) modules to generate the final segmentation output. MFSNet, when evaluated
on three publicly available datasets: , ISIC 2017, and HAM10000,
outperforms state-of-the-art methods, justifying the reliability of the
framework. The relevant codes for the proposed approach are accessible at
https://github.com/Rohit-Kundu/MFSNe
Opinion Market Model: Stemming Far-Right Opinion Spread using Positive Interventions
Online extremism has severe societal consequences, including normalizing hate
speech, user radicalization, and increased social divisions. Various mitigation
strategies have been explored to address these consequences. One such strategy
uses positive interventions: controlled signals that add attention to the
opinion ecosystem to boost certain opinions. To evaluate the effectiveness of
positive interventions, we introduce the Opinion Market Model (OMM), a two-tier
online opinion ecosystem model that considers both inter-opinion interactions
and the role of positive interventions. The size of the opinion attention
market is modeled in the first tier using the multivariate discrete-time Hawkes
process; in the second tier, opinions cooperate and compete for market share,
given limited attention using the market share attraction model. We demonstrate
the convergence of our proposed estimation scheme on a synthetic dataset. Next,
we test OMM on two learning tasks, applying to two real-world datasets to
predict attention market shares and uncover latent relationships between online
items. The first dataset comprises Facebook and Twitter discussions containing
moderate and far-right opinions about bushfires and climate change. The second
dataset captures popular VEVO artists' YouTube and Twitter attention volumes.
OMM outperforms the state-of-the-art predictive models on both datasets and
captures latent cooperation-competition relations. We uncover (1) self- and
cross-reinforcement between far-right and moderate opinions on the bushfires
and (2) pairwise artist relations that correlate with real-world interactions
such as collaborations and long-lasting feuds. Lastly, we use OMM as a testbed
for positive interventions and show how media coverage modulates the spread of
far-right opinions.Comment: accepted in the 18th AAAI International Conference on Web and Social
Media (ICWSM'24
Feeding strategies and diet composition of yellowfin tuna Thunnus albacares (Bonnaterre, 1788) caught along Andhra Pradesh, east coast of India
The food of yellowfin tuna, Thunnus albacares caught by longlines off the east coast of India was studied in detail. Contents
of 146 non-empty stomachs were analysed for the Index of relative importance (IRI) and prey specific abundance. T. albacares
caught by the longline were found to be non-selective generalist feeders, foraging on micronektonic, pelagic or benthic
organisms available in the epipelagic waters. Teleost fish, crabs, squids and shrimps were the major component of food
items. Priacanthus hamrur was the most preyed upon fish with a high IRI (40.5%) followed by the swimming crab
Charybdis smithii (23.9%), the squid Sthenoteuthis oualaniensis (15.5%) and prawn Solenocera hextii (10.3%). Being a
large pelagic predator, it formed an important link in the food chain of the ocean system and also formed a good collector of
the less exploited micronekton organisms of the deep scattering layer (DSL)
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Age, growth and population structure of the yellowfin tuna Thunnus albacares (Bonnaterre, 1788) exploited along the east coast of India
Lengths measurements of 6,758 yellowfin tuna (Thunnus albacares), landed by hook and line operators off eastern Indian
coast were taken (20тАУ185 cm FL) from 2003 to 2009. Age and growth were estimated using length based methods. The von
Bertalanffy growth parameters estimated were LтИЭ = 197.42 cm, annual K= 0.30 and t0= -0.1157. Mortality estimates were
M= 0.48 and Z= 0.71 and F= 0.23 with the exploitation ratio E= 0.32. Growth was rapid during the initial years when the
annual growth increment was as high as 36.6 cm during the first year then which to as low as 3.3 cm in the tenth year. The fish
attained a fork length of 56.2 cm at the end of one year. Size at maturity (87.5 cm) corresponded to an age of 1.7 years and
the oldest individual in the sample was 9+ years (186 cm). The annual mean lengths varied from 80.6 cm to 115.3 cm with
an average mean length of 101.9 cm. The fishery comprised of mostly adults with 64% comprising of fishes larger than size
at first maturity
A Review on Speech Recognition Methods
Voice recognition is the identification of a speaker on the basis of the characteristics of voices. For this, features of speech patterns that differ between individuals are used to achieve the objective. In this paper speaker recognition system are discussed. Implementation of speaker's voice recognition system with MATLAB makes possible use of voice for real life applications. This paper provides a brief review of different DSP based techniques applied for speech recognition
Detecting Extreme Ideologies in Shifting Landscapes: an Automatic & Context-Agnostic Approach
In democratic countries, the ideology landscape is foundational to individual
and collective political action; conversely, fringe ideology drives
Ideologically Motivated Violent Extremism (IMVE). Therefore, quantifying
ideology is a crucial first step to an ocean of downstream problems, such as;
understanding and countering IMVE, detecting and intervening in disinformation
campaigns, and broader empirical opinion dynamics modeling. However, online
ideology detection faces two significant hindrances. Firstly, the ground truth
that forms the basis for ideology detection is often prohibitively
labor-intensive for practitioners to collect, requires access to domain experts
and is specific to the context of its collection (i.e., time, location, and
platform). Secondly, to circumvent this expense, researchers generate ground
truth via other ideological signals (like hashtags used or politicians
followed). However, the bias this introduces has not been quantified and often
still requires expert intervention. This work presents an end-to-end ideology
detection pipeline applicable to large-scale datasets. We construct
context-agnostic and automatic ideological signals from widely available media
slant data; show the derived pipeline is performant, compared to pipelines of
common ideology signals and state-of-the-art baselines; employ the pipeline for
left-right ideology, and (the more concerning) detection of extreme ideologies;
generate psychosocial profiles of the inferred ideological groups; and,
generate insights into their morality and preoccupations
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