scholarly journals Landings profiles and potential métiers in Greek set longliners

2009 ◽  
Vol 67 (4) ◽  
pp. 646-656 ◽  
Author(s):  
Stelios Katsanevakis ◽  
Christos D. Maravelias ◽  
Laurie T. Kell
Keyword(s):  
Aegean Sea ◽  
Sea Bream ◽  
Ionian Sea ◽  
Agglomerative Clustering ◽  
Small Vessels ◽  
Step Procedure ◽  
Diplodus Sargus ◽  
Hierarchical Agglomerative Clustering ◽  
Pagrus Pagrus

Abstract Katsanevakis, S., Maravelias, C. D., and Kell, L. T. 2010. Landings profiles and potential métiers in Greek set longliners. – ICES Journal of Marine Science, 67: 646–656. A very large number (>14 000) of generally small vessels operate as longliners in Greek seas. The aim of this study was to identify potential set longline métiers, based on a large sample of landings records from all over Greece. Landings data from set longliners between 2002 and 2006, collected from several ports in the Aegean and East Ionian Sea, were used. The landings profiles were grouped using a two-step procedure, the first involving factorial analysis of the log-transformed landing profiles, and the second a classification of the factorial coordinates (hierarchical agglomerative clustering). In all, 13 métiers were identified in the Aegean Sea and 7 in the Ionian Sea. The most important métiers identified were those targeting white sea bream (Diplodus sargus), hake (Merluccius merluccius), common sea bream (Pagrus pagrus), and common pandora (Pagellus erythrinus), and mixed métiers. Varying spatial (within the Aegean and Ionian Seas) and seasonal patterns were evident for the métiers identified, indicating that fisher motivation to engage in a specific métier varies both spatially and temporally.

scholarly journals Otter trawls in Greece: Landing profiles and potential mιtiers

2010 ◽  
Vol 11 (1) ◽  
pp. 43 ◽  
Author(s):  
S. KATSANEVAKIS ◽  
C.D. MARAVELIAS ◽  
V. VASSILOPOULOU
Keyword(s):  
Expert Knowledge ◽  
Aegean Sea ◽  
Otter Trawl ◽  
Ionian Sea ◽  
Merluccius Merluccius ◽  
Step Procedure ◽  
Total Fish ◽  
Boops Boops ◽  
European Hake

A fleet of 326 bottom trawlers operate in Greek Seas and their landings represent approximately 30% of the total fish production in Greece. In this study, otter trawl landings data were analyzed in order to identify potential métiers. Landings data between 2002 and 2006 were used, collected from 42 ports in the Aegean and East Ionian Sea. A three-step procedure was applied to identify potential métiers: the first step involved a factorial analysis of the log-transformed landings profiles, the second step a classification of the factorial coordinates, and the third step a further aggregation of clusters based on expert knowledge. In all, six potential métiers were identified in the Aegean Sea, and five in the Ionian Sea. The most important target species were European hake (Merluccius merluccius), deepwater pink shrimp (Parapenaeus longirostris), red mullet (Mullus barbatus), caramote prawn (Melicertus kerathurus), picarel (Spicara smaris), cephalopods, bogue (Boops boops), anglers (Lophiusspp.), and Norway lobster (Nephrops norvegicus). Otter trawls in Greece use more or less the same gear with minor modification, and métier selection is basically reflected as a choice of geographical sub-area and hauling depth. The limitations of using landings profiles to identify métiers and the need for further verification are discussed.

scholarly journals Fishing With (Proto)Net—A Principled Approach to Protein Target Selection

2003 ◽  
Vol 4 (5) ◽  
pp. 542-548
Author(s):  
Michal Linial
Keyword(s):  
Structural Genomics ◽  
Target Selection ◽  
Structural Diversity ◽  
Structural Data ◽  
Agglomerative Clustering ◽  
Homologous Proteins ◽  
Hierarchical Agglomerative Clustering ◽  
Global Classification ◽  
Protein Space

Structural genomics strives to represent the entire protein space. The first step towards achieving this goal is by rationally selecting proteins whose structures have not been determined, but that represent an as yet unknown structural superfamily or fold. Once such a structure is solved, it can be used as a template for modelling homologous proteins. This will aid in unveiling the structural diversity of the protein space. Currently, no reliable method for accurate 3D structural prediction is available when a sequence or a structure homologue is not available. Here we present a systematic methodology for selecting target proteins whose structure is likely to adopt a new, as yet unknown superfamily or fold. Our method takes advantage of a global classification of the sequence space as presented by ProtoNet-3D, which is a hierarchical agglomerative clustering of the proteins of interest (the proteins in Swiss-Prot) along with all solved structures (taken from the PDB). By navigating in the scaffold of ProtoNet-3D, we yield a prioritized list of proteins that are not yet structurally solved, along with the probability of each of the proteins belonging to a new superfamily or fold. The sorted list has been self-validated against real structural data that was not available when the predictions were made. The practical application of using our computational–statistical method to determine novel superfamilies for structural genomics projects is also discussed.

scholarly journals Application of Hierarchical Agglomerative Clustering (HAC) for Systemic Classification of Pop-Up Housing (PUH) Environments

2021 ◽  
Vol 11 (23) ◽  
pp. 11122
Author(s):  
Thomas Märzinger ◽  
Jan Kotík ◽  
Christoph Pfeifer
Keyword(s):  
Urban Planning ◽  
Hierarchical Clustering ◽  
Clustering Algorithms ◽  
Field Studies ◽  
Assessment Tools ◽  
Agglomerative Clustering ◽  
Research Project ◽  
Minkowski Metric ◽  
Hierarchical Agglomerative Clustering

This paper is the result of the first-phase, inter-disciplinary work of a multi-disciplinary research project (“Urban pop-up housing environments and their potential as local innovation systems”) consisting of energy engineers and waste managers, landscape architects and spatial planners, innovation researchers and technology assessors. The project is aiming at globally analyzing and describing existing pop-up housings (PUH), developing modeling and assessment tools for sustainable, energy-efficient and socially innovative temporary housing solutions (THS), especially for sustainable and resilient urban structures. The present paper presents an effective application of hierarchical agglomerative clustering (HAC) for analyses of large datasets typically derived from field studies. As can be shown, the method, although well-known and successfully established in (soft) computing science, can also be used very constructively as a potential urban planning tool. The main aim of the underlying multi-disciplinary research project was to deeply analyze and structure THS and PUE. Multiple aspects are to be considered when it comes to the characterization and classification of such environments. A thorough (global) web survey of PUH and analysis of scientific literature concerning descriptive work of PUH and THS has been performed. Moreover, out of several tested different approaches and methods for classifying PUH, hierarchical clustering algorithms functioned well when properly selected metrics and cut-off criteria were applied. To be specific, the ‘Minkowski’-metric and the ‘Calinski-Harabasz’-criteria, as clustering indices, have shown the best overall results in clustering the inhomogeneous data concerning PUH. Several additional algorithms/functions derived from the field of hierarchical clustering have also been tested to exploit their potential in interpreting and graphically analyzing particular structures and dependencies in the resulting clusters. Hereby, (math.) the significance ‘S’ and (math.) proportion ‘P’ have been concluded to yield the best interpretable and comprehensible results when it comes to analyzing the given set (objects n = 85) of researched PUH-objects together with their properties (n > 190). The resulting easily readable graphs clearly demonstrate the applicability and usability of hierarchical clustering- and their derivative algorithms for scientifically profound building classification tasks in Urban Planning by effectively managing huge inhomogeneous building datasets.

scholarly journals Evaluation of the fillet quality of wild-caught white sea bream (Diplodus sargus L.) and brown meagre (Sciaena umbra L.) captured from the Aegean Sea

2019 ◽  
Vol 20 (2) ◽  
Author(s):  
EMMANOUIL KOUROUPAKIS ◽  
KRITON GRIGORAKIS ◽  
SOFIA VARDALI ◽  
VASSILIKI ILIA ◽  
IOANNIS BATJAKAS ◽  
...  
Keyword(s):  
Amino Acid ◽  
White Sea ◽  
Aegean Sea ◽  
Essential Amino Acids ◽  
Sea Bream ◽  
Human Consumption ◽  
Total N ◽  
High Quality Protein ◽  
Diplodus Sargus ◽  
The Individual

White sea bream (Diplodus sargus) and brown meagre (Sciaena umbra) were caught from the wild in Aegean Sea (Greece), and studied for their proximate, fatty acid and amino acid composition to evaluate their nutritional value for human consumption and their potential as candidate fish species for Mediterranean aquaculture diversification. Both species exhibited very low muscle fat, ranging at 1%. White sea bream was found to have higher muscle eicosapentaenoic (EPA) and docohexaenoic (DHA) contents and total n-3 fatty acids than brown meagre. A superiority of white sea bream in aspects of thrombogenicity was indicated, showing higher atherogenic (0.679) and thrombogenic indices (0.377) comparing to the respective values for brown meagre (0.610 and 0.579). The two-studied species exhibited high-quality protein as food source, with white sea bream showing a higher essential to non-essential amino acid (EAA/NEAA) rate, 0.764 vs 0.704, respectively. The individual to total essential amino acids rates of lysine and leucine were the highest ones for both studied species, while no significant differences were observed among them.

Comparison of iodine and glutaraldehyde as surface disinfectants for red porgy (Pagrus pagrus) and white sea bream (Diplodus sargus sargus) eggs

2007 ◽  
Vol 38 (5) ◽  
pp. 527-536 ◽  
Author(s):  
Pantelis Katharios ◽  
Aggelos Agathaggelou ◽  
Stavros Paraskevopoulos ◽  
Constantinos C Mylonas
Keyword(s):  
White Sea ◽  
Sea Bream ◽  
Red Porgy ◽  
Diplodus Sargus ◽  
Pagrus Pagrus

scholarly journals Clustering Techniques for Secondary Substations Siting

Energies ◽  
2021 ◽  
Vol 14 (4) ◽  
pp. 1028
Author(s):  
Silvia Corigliano ◽  
Federico Rosato ◽  
Carla Ortiz Dominguez ◽  
Marco Merlo
Keyword(s):  
Rural Areas ◽  
Urban Areas ◽  
Universal Access ◽  
Distribution Networks ◽  
Industrialized Countries ◽  
Agglomerative Clustering ◽  
Clustering Techniques ◽  
Hierarchical Agglomerative Clustering ◽  
Efficient Planning ◽  
Target Set

The scientific community is active in developing new models and methods to help reach the ambitious target set by UN SDGs7: universal access to electricity by 2030. Efficient planning of distribution networks is a complex and multivariate task, which is usually split into multiple subproblems to reduce the number of variables. The present work addresses the problem of optimal secondary substation siting, by means of different clustering techniques. In contrast with the majority of approaches found in the literature, which are devoted to the planning of MV grids in already electrified urban areas, this work focuses on greenfield planning in rural areas. K-means algorithm, hierarchical agglomerative clustering, and a method based on optimal weighted tree partitioning are adapted to the problem and run on two real case studies, with different population densities. The algorithms are compared in terms of different indicators useful to assess the feasibility of the solutions found. The algorithms have proven to be effective in addressing some of the crucial aspects of substations siting and to constitute relevant improvements to the classic K-means approach found in the literature. However, it is found that it is very challenging to conjugate an acceptable geographical span of the area served by a single substation with a substation power high enough to justify the installation when the load density is very low. In other words, well known standards adopted in industrialized countries do not fit with developing countries’ requirements.

scholarly journals Identifying organ dysfunction trajectory-based subphenotypes in critically ill patients with COVID-19

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Chang Su ◽  
Zhenxing Xu ◽  
Katherine Hoffman ◽  
Parag Goyal ◽  
Monika M. Safford ◽  
...  
Keyword(s):  
New York ◽  
Respiratory Failure ◽  
Sofa Score ◽  
Severity Of Illness ◽  
Agglomerative Clustering ◽  
Baseline Severity ◽  
Organ Systems ◽  
Hierarchical Agglomerative Clustering ◽  
Dynamic Time ◽  
Post Intubation

AbstractCOVID-19-associated respiratory failure offers the unprecedented opportunity to evaluate the differential host response to a uniform pathogenic insult. Understanding whether there are distinct subphenotypes of severe COVID-19 may offer insight into its pathophysiology. Sequential Organ Failure Assessment (SOFA) score is an objective and comprehensive measurement that measures dysfunction severity of six organ systems, i.e., cardiovascular, central nervous system, coagulation, liver, renal, and respiration. Our aim was to identify and characterize distinct subphenotypes of COVID-19 critical illness defined by the post-intubation trajectory of SOFA score. Intubated COVID-19 patients at two hospitals in New York city were leveraged as development and validation cohorts. Patients were grouped into mild, intermediate, and severe strata by their baseline post-intubation SOFA. Hierarchical agglomerative clustering was performed within each stratum to detect subphenotypes based on similarities amongst SOFA score trajectories evaluated by Dynamic Time Warping. Distinct worsening and recovering subphenotypes were identified within each stratum, which had distinct 7-day post-intubation SOFA progression trends. Patients in the worsening suphenotypes had a higher mortality than those in the recovering subphenotypes within each stratum (mild stratum, 29.7% vs. 10.3%, p = 0.033; intermediate stratum, 29.3% vs. 8.0%, p = 0.002; severe stratum, 53.7% vs. 22.2%, p < 0.001). Pathophysiologic biomarkers associated with progression were distinct at each stratum, including findings suggestive of inflammation in low baseline severity of illness versus hemophagocytic lymphohistiocytosis in higher baseline severity of illness. The findings suggest that there are clear worsening and recovering subphenotypes of COVID-19 respiratory failure after intubation, which are more predictive of outcomes than baseline severity of illness. Distinct progression biomarkers at differential baseline severity of illness suggests a heterogeneous pathobiology in the progression of COVID-19 respiratory failure.

CLASSIFICATION OF HIGH-DIMENSIONAL MICROARRAY DATA WITH A TWO-STEP PROCEDURE VIA A WILCOXON CRITERION AND MULTILAYER PERCEPTRON

2011 ◽  
Vol 10 (01) ◽  
pp. 1-14
Author(s):  
VLADIMIR NIKULIN ◽  
TIAN-HSIANG HUANG ◽  
GEOFFREY J. MCLACHLAN
Keyword(s):  
Data Mining ◽  
Feature Selection ◽  
High Dimensional ◽  
Second Step ◽  
Support Vector ◽  
Step Procedure ◽  
Leave One Out ◽  
Natural Combination ◽  
Feature Selection Techniques

The method presented in this paper is novel as a natural combination of two mutually dependent steps. Feature selection is a key element (first step) in our classification system, which was employed during the 2010 International RSCTC data mining (bioinformatics) Challenge. The second step may be implemented using any suitable classifier such as linear regression, support vector machine or neural networks. We conducted leave-one-out (LOO) experiments with several feature selection techniques and classifiers. Based on the LOO evaluations, we decided to use feature selection with the separation type Wilcoxon-based criterion for all final submissions. The method presented in this paper was tested successfully during the RSCTC data mining Challenge, where we achieved the top score in the Basic track.

scholarly journals Age and Growth of Red Porgy, Pagrus pagrus from the Island of Gökçeada, North Aegean Sea

2020 ◽  
Vol 35 (2) ◽  
pp. 57-63
Author(s):  
Hakan Ayyıldız ◽  
Aytaç Altın ◽  
Bayram Kızılkaya
Keyword(s):  
Aegean Sea ◽  
Age And Growth ◽  
Red Porgy ◽  
North Aegean ◽  
North Aegean Sea ◽  
Pagrus Pagrus
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