Status of fish consumption per capita of Tehran citizens

Status of fish consumption was analyzed by completing the 295 questionnaires in all 22 metropolitan regions of Tehran from different households in 2008. After reviewing the descriptive and statistics analysis along with the non-parametric statistics, the fish consumption per capita was extracted through formulas. The average and mode of purchasing of each household occurs 11 times per year with 5.1 Kg in each time . Considering the higher fish consumption growth rate in Iran, the sequence of interest in all kinds of protein is as follows: poultry, mutton, beef, Trout, wild fishes and Chinese carps. The highest interest of households to buy fish more than other protein resources is due to the nutrient value of it. An average of 33.2% purchasing is dedicated to the farmed fish. 59% of purchasers are interested to buy packed up fish products and pay attention to the label of nutrient values on the product package. Fish consumption per capita is 13.3 Kg, which is divided to 6.4 kg for farmed fishes, 5.8 Kg for wild fishes and 1.1 Kg for canned fish. The higher consumption per capita of Tehran citizens in comparison with other people from other cities, who are living in Tehran, is because of their tendency and freshness of farmed fishes. In contrast, the consumption of canned and wild fishes among people of littoral regions who live in Tehran is higher than others

Identifying the main factors affecting home consumption attitude to farmed fishes among Tehrani households

Using reference books, monitoring buyers behavior and interviewing the experts, the effective parameters on home consumption behavior of farmed fish were identified and compiled as a questionnaire. After the reliability and viability evaluation the questionnaire was filled randomly in the spring of 2008 for 295 households of different localities in Tehran. The allocation of questionnaires was conducted based on the proportion of population in each locality. The degree of importance of effective parameters and their priority were calculated by Friedman test. The results showed that, quality, taste, smell and protein content of grocery basket are the most important purchasing factors among Tehrani households. However, those who use only farmed fish or those who do not use fish at all, after quality, smell has the highest importance, and in the fourth priority, instead of protein content of grocery basket, fish bone has higher effect. We suggest that to improve consumption of farmed fish, apart from quality, the strategies of deodorizing, taste improvement and decreasing fish bones, shall be taken into consideration during harvest, transport and fish processing

A new hybrid method for size and topology optimization of truss structures using modified ALGA and QPGA

Modified Augmented Lagrangian Genetic Algorithm (ALGA) and Quadratic Penalty Function Genetic Algorithm (QPGA) optimization methods are proposed to obtain truss structures with minimum structural weight using both continuous and discrete design variables. To achieve robust solutions, Compressed Sparse Row (CSR) with reordering of Cholesky factorization and Moore Penrose Pseudoinverse are used in case of non-singular and singular stiffness matrix, respectively. The efficiency of the proposed nonlinear optimization methods is demonstrated on several practical examples. The results obtained from the Pratt truss bridge show that the optimum design solution using discrete parameters is 21% lighter than the traditional design with uniform cross sections. Similarly, the results obtained from the 57-bar planar tower truss indicate that the proposed design method using continuous and discrete design parameters can be up to 29% and 9% lighter than traditional design solutions, respectively. Through sensitivity analysis, it is shown that the proposed methodology is robust and leads to significant improvements in convergence rates, which should prove useful in large-scale applications

Saliency Benchmarking Made Easy: Separating Models, Maps and Metrics

Dozens of new models on fixation prediction are published every year and compared on open benchmarks such as MIT300 and LSUN. However, progress in the field can be difficult to judge because models are compared using a variety of inconsistent metrics. Here we show that no single saliency map can perform well under all metrics. Instead, we propose a principled approach to solve the benchmarking problem by separating the notions of saliency models, maps and metrics. Inspired by Bayesian decision theory, we define a saliency model to be a probabilistic model of fixation density prediction and a saliency map to be a metric-specific prediction derived from the model density which maximizes the expected performance on that metric given the model density. We derive these optimal saliency maps for the most commonly used saliency metrics (AUC, sAUC, NSS, CC, SIM, KL-Div) and show that they can be computed analytically or approximated with high precision. We show that this leads to consistent rankings in all metrics and avoids the penalties of using one saliency map for all metrics. Our method allows researchers to have their model compete on many different metrics with state-of-the-art in those metrics: "good" models will perform well in all metrics.Comment: published at ECCV 201

Multiview classification and dimensionality reduction of scalp and intracranial EEG data through tensor factorisation

Electroencephalography (EEG) signals arise as a mixture of various neural processes that occur in different spatial, frequency and temporal locations. In classification paradigms, algorithms are developed that can distinguish between these processes. In this work, we apply tensor factorisation to a set of EEG data from a group of epileptic patients and factorise the data into three modes; space, time and frequency with each mode containing a number of components or signatures. We train separate classifiers on various feature sets corresponding to complementary combinations of those modes and components and test the classification accuracy of each set. The relative influence on the classification accuracy of the respective spatial, temporal or frequency signatures can then be analysed and useful interpretations can be made. Additionaly, we show that through tensor factorisation we can perform dimensionality reduction by evaluating the classification performance with regards to the number mode components and by rejecting components with insignificant contribution to the classification accuracy

A novel selection of optimal statistical features in the DWPT domain for discrimination of ictal and seizure-free electroencephalography signals

Properly determining the discriminative features which characterize the inherent behaviors of electroencephalography (EEG) signals remains a great challenge for epileptic seizure detection. In this present study, a novel feature selection scheme based on the discrete wavelet packet decomposition and cuckoo search algorithm (CSA) was proposed. The normal as well as epileptic EEG recordings were frst decomposed into various frequency bands by means of wavelet packet decomposition, and subsequently, statistical features at all developed nodes in the wavelet packet decomposition tree were derived. Instead of using the complete set of the extracted features to construct a wavelet neural networks-based classifer, an optimal feature subset that maximizes the predictive competence of the classifer was selected by using the CSA. Experimental results on the publicly available benchmarks demonstrated that the proposed feature subset selection scheme achieved promising recognition accuracies of 98.43–100%, and the results were statistically signifcant using z-test with p value <0.0001

Online Optimal Neuro-Fuzzy Flux Controller for DTC Based Induction Motor Drives

In this paper a fast flux search controller based on the Neuro-fuzzy systems is proposed to achieve the best efficiency of a direct torque controlled induction motor at light load. In this method the reference flux value is determined through a minimization algorithm with stator current as objective function. This paper discusses and demonstrates the application of Neurofuzzy filtering to stator current estimation. Simulation and experimental results are presented to show the fast response of proposed controller

A Structural Parametrization of the Brain Using Hidden Markov Models Based Paths in Alzheimer's Disease

The usage of biomedical imaging in the diagnosis of dementia is increasingly widespread. A number of works explore the possibilities of computational techniques and algorithms in what is called Computed Aided Diagnosis. Our work presents an automatic parametrization of the brain structure by means of a path generation algorithm based on Hidden Markov Models. The path is traced using information of intensity and spatial orientation in each node, adapting to the structural changes of the brain. Each path is itself a useful way to extract features from the MRI image, being the intensity levels at each node the most straightforward. However, a further processing consisting of a modification of the Gray Level Co-occurrence Matrix can be used to characterize the textural changes that occur throughout the path, yielding more meaningful values that could be associated to the structural changes in Alzheimer's Disease, as well as providing a significant feature reduction. This methodology achieves high performance, up to 80.3\% of accuracy using a single path in differential diagnosis involving Alzheimer-affected subjects versus controls belonging to the Alzheimer's Disease Neuroimaging Initiative (ADNI).TIC218, MINECO TEC2008-02113 and TEC2012-34306 projects, Consejería de Economía, Innovación, Ciencia y Empleo de la Junta de Andalucía P09-TIC-4530 and P11-TIC-71

Geological Record of Water and Wind Processes on Mars as Observed by the Mars Express High Resolution Stereo Camera

This review paper summarizes the observations and results of the Mars Express Mission and its application in the analysis of geological processes and landforms on Mars during the last 20 years. The Mars Express observations provided an extended data base allowing a comparative evaluation of different geological surface landforms and their time-based delimitation. High-resolution imagery and digital elevations models on a local to regional scale and spectral measurements are the basis for geological analyses of water-related surface processes on Mars. This includes the nature and discharges of valley networks, formation timescale of deltas, volumina of sedimentary deposits as well as estimating the age of geological units by crater size–frequency distribution measurements. Both the quantifying of geological processes and the determination of absolute model ages allows to constraint the evolution of Martian water-related activity in space and time. Comparative age estimation of fluvial, glacial, and lacustrine deposits, as well as their timing and episodicity, has revealed the nature and evolution of the Martian surface hydrological cycle. Fluvial and lacustrine activity phases are spread over a time span from Noachian until Amazonian periods, but detailed studies show that they have been interrupted by multiple and long-lasting phases of cessation and quiescent. In addition, evidence of glacial activity shows discrete phases of enhanced intensity correlating with increased spin-axis obliquity amplitude. The episodicity of geological processes, erosion, deposition, and glaciation on Mars demonstrate a close correlation between individual surface processes and endogenic activity as well as spin-axis/orbital variations and changing climate condition

Intestinal ischemia due to mesenteric vascular thrombosis in a patient with positive SARS-CoV-2 RNA without primary pulmonary symptom: A case report

Coronavirus disease 2019 (COVID-19) is an acute respiratory illness caused by novel coronavirus SARS-CoV-2. The clinical manifestations of this infection have a range and typically include impairment of smell, taste disturbance, cough, fever, and shortness of breath. Gastrointestinal manifestations have been reported in anywhere from 3 to 50 of patients with concomitant SARS-CoV-2 pulmonary infection. Abnormalities in coagulation markers have been reported in patients hospitalized with COVID-19. During this article, we will introduce a patient with COVID 19 but with the most manifestation of abdominal pain due to intestinal ischemia and mesenteric vascular thrombosis. © 2021 The Author(s)