Developing the attenuation relation for damage spectrum in X-braced steel structures with neural network

Evaluating structural damage, caused by earthquakes, is very important in seismic risk management. Zoning maps of structural damage are directly used in evaluating damage of different zones as well as planning to retrofit structures. Attenuation relation is applied in preparing the acceleration zoning of regions. Similarly, damage attenuation relations which are used in analyzing probabilistic hazard and preparing damage zoning are obtained by structural damage spectrum. This spectrum is nonlinear and designed by considering nonlinear parameters of a series of one-degree-of-freedom structures and time history dynamic analysis. After gathering and modifying 778 records of the earthquakes happened in Iran, the damage spectrum was prepared for X-braced steel structures with different specifications (yield force, hysteresis curves, and ductility capacity). Damage attenuation relation was developed for the structures through regression analysis and the obtained results were compared with those of artificial neural network method. Damage of three samples with different specifications was calculated by the developed attenuation relation. The obtained results were compared with those of time history dynamic analysis. The developed relations were used for analyzing the probabilistic damage risk and preparing the damage zoning maps for city of Qazvin, as a seismic region in Iran

Feeding habits of yellowfin seabream (Acanthopagrus latus) in the northern region of the Persian Gulf

Feeding habits of yellowfin seabream (Acanthopagrus latus) was investigated in coastal waters of the Northern Persian Gulf. This investigation was conducted by monthly sampling of thirty fish from September 2011 through August 2012. Fish size ranged from 17.98 ± 2.07 to 32.31 ± 6.52 cm in total length and from 134.01 ± 45.62 to 720.46 ± 292.58 g in weight. The highest value of gastro-somatic index was obtained in September (5.22 ± 0.04) and the lowest in December (1.61 ± 0.03) with annual average of 2.50 ± 0.60. The result of gastro-somatic index revealed that the highest feeding activity of A. latus was during autumn. The highest level of vacuity index was observed in summer (34.95 ± 4.71) and the lowest in autumn (25.88 ± 2.71) indicating that the highest number of empty stomachs was in summer. Annual average of vacuity index was 30.14 ± 5.72 exhibiting that A. latus was comparatively gluttonous in the Northern Persian Gulf. Bivalves and shrimps were the major food items found in the stomach of A. latus showing food preference indices of 45.86% and 30.67%, respectively. Other food items included crabs (12.66%), aquatic plants (4.05%), animal derivatives (4.52%) and gastropods (2.23%). According to the results, animal derivatives, aquatic plants and gastropods were eaten accidentally and were not the food items of A. latus in coastal waters of Hormozgan. The average relative length of gut was 1.41 ± 0.15 showing that A. latus was omnivorous in this region

Favorable results after conservative management of 316 valproate intoxicated patients

Valproic acid (VPA) is an effective antiepileptic drug widely used worldwide. Despite several studies indicating the usefulness of intravenous L-carnitine in the treatment of VPA poisoning, this drug is not readily available in Iran. The aim of this study was to determine whether supportive care without antidote would result in acceptable outcomes in VPA poisoned patients. Materials and Methods: In an observational, retrospective, single-center case series, all patients >12-year-old with VPA overdose who had referred to a tertiary center between 2009 and 2013 were consecutively enrolled. Patients� demographic and presenting features, physical examinations, clinical management, laboratory data, and outcomes were recorded. Results: A total of 316 patients were enrolled with pure VPA toxicity. The most common presenting signs/symptoms were drowsiness, nausea and vomiting, vertigo, and headache. In the course of the disease, 14 patients (4.4) were intubated and three (0.9) required hemodialysis with mean dialysis sessions of two. Fourteen patients were admitted to Intensive Care Unit, and seizures occurred in five. The initial level of consciousness was lower in patients with poor outcome. The median ingested dose of VPA in patients who required dialysis was significantly higher (20 vs. 6 g; P = 0.006). Multivariate analyses revealed that coma on presentation was associated with a worse outcome (P = 0.001; odds ratio = 61.5, 95 CI = 5.8-646.7). Conclusion: Prognosis of VPA poisoned patients appears to be good even with supportive care. According to our study, older age, ingestion of higher amounts of VPA and lower PCO2, HCO3, base excess, and CPK levels prone the patients to more severe toxicities in univariate analysis, but the main poor prognostic factor is coma on presentation in multivariate analysis. © 2015 Journal of Research in Medical Sciences

Performance Analysis of DNN Inference/Training with Convolution and non-Convolution Operations

Today's performance analysis frameworks for deep learning accelerators suffer from two significant limitations. First, although modern convolutional neural network (CNNs) consist of many types of layers other than convolution, especially during training, these frameworks largely focus on convolution layers only. Second, these frameworks are generally targeted towards inference, and lack support for training operations. This work proposes a novel performance analysis framework, SimDIT, for general ASIC-based systolic hardware accelerator platforms. The modeling effort of SimDIT comprehensively covers convolution and non-convolution operations of both CNN inference and training on a highly parameterizable hardware substrate. SimDIT is integrated with a backend silicon implementation flow and provides detailed end-to-end performance statistics (i.e., data access cost, cycle counts, energy, and power) for executing CNN inference and training workloads. SimDIT-enabled performance analysis reveals that on a 64X64 processing array, non-convolution operations constitute 59.5% of total runtime for ResNet-50 training workload. In addition, by optimally distributing available off-chip DRAM bandwidth and on-chip SRAM resources, SimDIT achieves 18X performance improvement over a generic static resource allocation for ResNet-50 inference

Estimation of MSY on six species of commercially important demersal fishes in the Persian Gulf & Oman Sea (Hormuzgan province)

Today, calculation of MSY is one of the necessary fisheries management in control and prevention of the fish population reduction and is obtained with different methods. This study has focused on six species of commercial fish, including Tiger-toothed croaker, Javelin grunter, John`s snapper, Indian spiny turbot, Yellowfin seabream and Silver pomfret. The study was done monthly, from January 2007 to March 2008, in three fish landing regions including: Bandar Lengeh, Bandar Abbas and Qeshm Island (Slakh, Basydu and Chahoshrqy). Total 5163 Silver pomfret (Pompus argenteus), 1766 Javelin grunter (Pomadasys kaakan), 2151 John`s snapper (Lutjanus johnii), 3280 Tiger-toothed croaker (Otolithes ruber), 1628 Indian spiny turbot (Psettodes erumei) and the number of 759 Yellowfin seabream (Acnthopagrus latus) were assessed and length biometry has been done, monthly. In this study, two methods were used to determine the maximum sustainable yield (MSY): 1- virtual population analysis (Cohort analysis) 2- use of statistics and information that was estimated with two method, catch prediction and biomass (Standing stock). The results showed that in 2007, MSY value was estimated through catch prediction for Silver pomfret, Tigertoothed croaker, Javelin grunter, John`s snapper, Indian spiny turbot and Yellowfin seabream 1354, 1116, 1099.6, 1045.5, 914.5 and 529.5 tons, respectively. Moreover, this estimation have been done through standing stock for Silver pomfret, Tiger-toothed croaker, Javelin grunter, John`s snapper, Indian spiny turbot and Yellowfin seabream 1215, 633, 1304, 878, 1095 and 441 tons, respectively; and through VPA for Silver pomfret, Tiger-toothed croaker, Javelin grunter, John`s snapper, Indian spiny turbot and Yellowfin seabream 1100, 850, 920, 732.5, 1002.3 and 403 tons, respectively. Amount of biomass (Standing Stock) was estimated for Silver pomfret, Tiger-toothed croaker, Javelin grunter, John`s snapper, Indian spiny turbot and Yellowfin seabream 2530, 1172, 1738, 1689, 1470 and 1110 tons, respectively. In general, by assessing the obtained results for the studied species, except the fishing prediction results of the Javelin grunter and Tiger-toothed croaker species, which is less valuable due to the low correlation coefficient, it can be stated that except fishing pressure on John`s snapper and Tiger-toothed croaker, in other species studied, fishing conditions are in optimal situation

A Stable Biologically Motivated Learning Mechanism for Visual Feature Extraction to Handle Facial Categorization

The brain mechanism of extracting visual features for recognizing various objects has consistently been a controversial issue in computational models of object recognition. To extract visual features, we introduce a new, biologically motivated model for facial categorization, which is an extension of the Hubel and Wiesel simple-to-complex cell hierarchy. To address the synaptic stability versus plasticity dilemma, we apply the Adaptive Resonance Theory (ART) for extracting informative intermediate level visual features during the learning process, which also makes this model stable against the destruction of previously learned information while learning new information. Such a mechanism has been suggested to be embedded within known laminar microcircuits of the cerebral cortex. To reveal the strength of the proposed visual feature learning mechanism, we show that when we use this mechanism in the training process of a well-known biologically motivated object recognition model (the HMAX model), it performs better than the HMAX model in face/non-face classification tasks. Furthermore, we demonstrate that our proposed mechanism is capable of following similar trends in performance as humans in a psychophysical experiment using a face versus non-face rapid categorization task

How Can Selection of Biologically Inspired Features Improve the Performance of a Robust Object Recognition Model?

Humans can effectively and swiftly recognize objects in complex natural scenes. This outstanding ability has motivated many computational object recognition models. Most of these models try to emulate the behavior of this remarkable system. The human visual system hierarchically recognizes objects in several processing stages. Along these stages a set of features with increasing complexity is extracted by different parts of visual system. Elementary features like bars and edges are processed in earlier levels of visual pathway and as far as one goes upper in this pathway more complex features will be spotted. It is an important interrogation in the field of visual processing to see which features of an object are selected and represented by the visual cortex. To address this issue, we extended a hierarchical model, which is motivated by biology, for different object recognition tasks. In this model, a set of object parts, named patches, extracted in the intermediate stages. These object parts are used for training procedure in the model and have an important role in object recognition. These patches are selected indiscriminately from different positions of an image and this can lead to the extraction of non-discriminating patches which eventually may reduce the performance. In the proposed model we used an evolutionary algorithm approach to select a set of informative patches. Our reported results indicate that these patches are more informative than usual random patches. We demonstrate the strength of the proposed model on a range of object recognition tasks. The proposed model outperforms the original model in diverse object recognition tasks. It can be seen from the experiments that selected features are generally particular parts of target images. Our results suggest that selected features which are parts of target objects provide an efficient set for robust object recognition

Monitoring of Cochlodinium sp for shrimp farms in Hormozgan Province

The Blooming due to the some species of phytoplanktons especially Dynoflagellates has made some problems for water ecosystems and aquaculture. In this study, the density of phytoplanktons specially Cochlodinium sp. and also environmental factors such as temperature, pH, dissolved Oxygen, and transparency were recorded two weekly in 18 stations of Hormozgan province, Iran in order to monitoring of the possibility of phytoplankton blooming. During six months monitoring, the target phytoplankton, Cochlodinium sp was not observed in shrimp farms. But, other phytoplanktons and zooplanktons were observed as follow: 13 genus of phytoplankton and six genuses of zooplanktons has found in ponds, main water channel and sea. The diatoms with 10 genuses had the highest abundance and Dynoflagellates with 3 genuses had the lowest abundance and blue-green phytoplankton with one genus was in lowest group. Totally, diatoms with 77%, Dynoflagellate with 15% and blue-green alga with 8% abundance were the main populations of planktons in the studied area

Intermediate scalings in holographic RG flows and conductivities

We construct numerically finite density domain-wall solutions which interpolate between two AdS 4 fixed points and exhibit an intermediate regime of hyperscaling violation, with or without Lifshitz scaling. Such RG flows can be realized in gravitational models containing a dilatonic scalar and a massive vector field with appropriate choices of the scalar potential and couplings. The infrared AdS 4 fixed point describes a new ground state for strongly coupled quantum systems realizing such scalings, thus avoiding the well-known extensive zero temperature entropy associated with AdS2×R2. We also examine the zero temperature behavior of the optical conductivity in these backgrounds and identify two scaling regimes before the UV CFT scaling is reached. The scaling of the conductivity is controlled by the emergent IR conformal symmetry at very low frequencies, and by the intermediate scaling regime at higher frequencies