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 ‘chicken of the sea’ 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: $PH^2$, 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)

आँध्रप्रदेश की वेलापवर्ती मात्स्यिकी संपदाएं

आँध्रप्रदेश की वेलापवर्ती मात्स्यिकी संपदाए

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 (20–185 cm FL) from 2003 to 2009. Age and growth were estimated using length based methods. The von Bertalanffy growth parameters estimated were L∝ = 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