Scour Propagation Rates around Offshore Pipelines Exposed to Currents by Applying Data-Driven Models

Offshore pipelines are occasionally exposed to scouring processes; detrimental impacts on their safety are inevitable. The process of scouring propagation around offshore pipelines is naturally complex and is mainly due to currents and/or waves. There is a considerable demand for the safe design of offshore pipelines exposed to scouring phenomena. Therefore, scouring propagation patterns must be focused on. In the present research, machine learning (ML) models are applied to achieve equations for the prediction of the scouring propagation rate around pipelines due to currents. The approaching flow Froude number, the ratio of embedment depth to pipeline diameter, the Shields parameter, and the current angle of attack to the pipeline were considered the main dimensionless factors from the reliable literature. ML models were developed based on various setting parameters and optimization strategies coming from evolutionary and classification contents. Moreover, the explicit equations yielded from ML models were used to demonstrate how the proposed approaches are in harmony with experimental observations. The performance of ML models was assessed utilizing statistical benchmarks. The results revealed that the equations given by ML models provided reliable and physically consistent predictions of scouring propagation rates regarding their comparison with scouring tests

PROPERTIES OF A NEW SUBCLASS OF ANALYTIC FUNCTIONS ASSOCIATED TO RAFID - OPERATOR AND q-DERIVATIVE

In this article, we introduce a new subclass of analytic functions, using the exponent operators of Rafid and $q$-derivative. The coefficient estimates, extreme points, convex linear combination, radii of starlikeness, convexity, and finally integral are investigated

NEW SUBCLASS OF MEROMORPHIC FUNCTIONS BY THE GENERALIZATION OF THE q-DERIVATIVE OPERATOR

In this paper, we introduce a new  class of meromorphic functions, using the exponent $q$-derivative operator, and then look at it coefficient estimates, extreme points, convex linear combination, Radii of starlikeness, convexity and finally partial sum property are investigated

Estimation of the Maximum Scour Depth at Bridge Pier under Effects of Debris Accumulations using NF-GMDH Model and Evolutionary Algorithms

Rivers accumulate huge amounts of floating debris including the trunk, branches and leaves during the floods, leading to increase the depth of local scour around bridge piers. A large number of the laboratorial and field studies have been performed to understand the mechanism of scouring phenomenon under floating debris. Over two past decades, different types of the artificial intelligence methods have been used to estimate the maximum scour depth around bridges piers. In this study, the Neuro-Fuzzy model based on group method data handling (NF-GMDH) was used to estimate the scour under effect of debris accumulations. The NF-GMDH network was developed using evolutional algorithms: genetic algorithm (GA), particle swarm optimization (PSO), and gravitational search algorithm (GSA). Parameters effective on the maximum scour depth included average velocity of upstream flow of the bridge pier, critical velocity of river bed sediments, depth of flow in section without debris, thickness of submerged debris, debris diameter, average particle size, pier diameter, and channel width. After training and experiencing each NF-GMDH models, the performances of each one was evaluated through statistical parameters. The results showed that the models proposed had better performance compared with emperical relationships. NF-GMDH-PSO (R=0.8413 and RMSE=0.37) and NF-GMDH-GA (R=0.8407 and RMSE=0.3640) had relatively similar performance. Finally, sensitivity analysis indicated that the ratio of pile diameter (D) to mean diameter of bed sediments (d50) has the most influence on determination of maximum scour depth

Daily Rainfall Estimation using ANFIS Combination Models Trained by Clustering of Fuzzy c-Means and Evolutionary Algorithms

Nowadays, due to the high uncertainty in estimating precipitation in different geographical areas, the use of computational intelligence methods based on optimization algorithms to accurately estimate daily precipitation has been considered by water engineers. In the present study, the combined Adaptive Neuro Fuzzy Inference System and Wavelet transform (W-ANFIS) method was used as a pre-processor for daily rainfall data to estimate precipitation values. The structure of the W-ANFIS hybrid model was developed using the Fuzzy Clustering Means (FCM) method in the training phase. Moreover, constant coefficients of membership functions applied in the ANFIS model were optimized using four optimization algorithms including Genetic Algorithm (GA), Particle Swarm Optimization (PSO), Differential Evolution (DE), and Ant Colony community (ACO). In the present study, rainfall statistics of Izmir basin in the western part of Turkey were used. Through applying five-time delays in daily rainfall statistics as well as decomposing each time delay in the three levels of wavelet transform, each of the W-ANFIS optimal models had twenty input variables. The results of the statistical analysis for both training and testing stages by the Root Mean Square Error (RMSE) and Mean Absolute Error (MAE) showed that the application of the DE algorithm into W-ANFIS structure had the best performance (RMSE = 22.22 and MAE = 17.11mm) than other combined models with PSO (RMSE = 28.11 and MAE = 24.11 mm), ACO (RMSE = 30.41 and MAE = 26.50 mm), and GA (RMSE = 25.70 and MAE = 18.11 mm)

The effect of follicular fluid selenium concentration on oocyte maturation in women with polycystic ovary syndrome undergoing in vitro fertilization/Intracytoplasmic sperm injection: A cross-sectional study

Background: A high level of free radicals and oxidative substances in women with polycystic ovary syndrome (PCOS) can affect the ovaries through oxidative stress. Antioxidants such as selenium, a vital trace element in human health, can improve the prognosis of PCOS by reducing oxidative stress. Objective: This study was performed due to the lack of comprehensive information about selenium concentration in follicular fluid and its effect on the oocyte count and quality in infertile women with PCOS. Materials and Methods: In this cross-sectional study, 78 women with PCOS referred to Umm-al-Banin Infertility Clinic Center, Ganjavian Hospital, Dezful, Iran for in-vitro fertilization from March to November 2019 were enrolled. After ovarian stimulation with the antagonist protocol, the oocytes were retrieved under transvaginal ultrasound in in-vitro fertilization/intracytoplasmic sperm injection cycles, and selenium concentrations were measured in the follicular fluid using an atomic absorption method by spectrophotometer device. Oocyte count and morphology were evaluated using inverted optical microscopy. Results: There were no significant differences between follicular fluid selenium concentrations in terms of the total number of oocytes and immature oocytes in the metaphase I and germinal vesicle stages. However, a significantly reduced number of metaphase II oocytes was observed at selenium levels < 40 μg/dL (p = 0.001). Conclusion: Based on our results, low levels of follicular selenium concentration in infertile women with PCOS can reduce the quality and potency of oocyte maturation. Key words: Polycystic ovary syndrome, Oxidative stress, Selenium, In vitro fertilization, Oocyte quality, Follicular fluid

Evaluation of Regression-Based Soft Computing Techniques for Estimating Energy Loss in Gabion Spillways

Estimation of flow energy loss in gabion spillways can be effective in managing erosion downstream of structures, flood control, and riverbed stabilization. Therefore, in this research, using two soft computing models evolutionary polynomial regression (EPR) and multivariate adaptive regression spline (MARS), the amount of energy loss in these spillways was estimated. About 75% of the 74 laboratory data samples were used for training and the remaining 25% were used for testing the models. The dimensionless parameters of Froude number (Fr), spillway slope (S), gabion number (GN), and porosity (n) were used as input parameters. The results showed that the MARS model predicted the energy loss values by root mean square error (RMSE), mean absolute percentage error (MAPE), and correlation coefficient (CC) of 0.05, 0.017, and 0.99, respectively, which has better performance than the EPR model has. The results of the Taylor diagram also showed that the performance of MARS and EPR are satisfying, and their accuracy is very close to each other. The regression equation by the EPR model was more complex than the regression equation by the MARS model. According to the obtained results, the use of the two soft computing models in estimating energy loss in spillways is recommended

Deep Boosting Multi-Modal Ensemble Face Recognition with Sample-Level Weighting

Deep convolutional neural networks have achieved remarkable success in face recognition (FR), partly due to the abundant data availability. However, the current training benchmarks exhibit an imbalanced quality distribution; most images are of high quality. This poses issues for generalization on hard samples since they are underrepresented during training. In this work, we employ the multi-model boosting technique to deal with this issue. Inspired by the well-known AdaBoost, we propose a sample-level weighting approach to incorporate the importance of different samples into the FR loss. Individual models of the proposed framework are experts at distinct levels of sample hardness. Therefore, the combination of models leads to a robust feature extractor without losing the discriminability on the easy samples. Also, for incorporating the sample hardness into the training criterion, we analytically show the effect of sample mining on the important aspects of current angular margin loss functions, i.e., margin and scale. The proposed method shows superior performance in comparison with the state-of-the-art algorithms in extensive experiments on the CFP-FP, LFW, CPLFW, CALFW, AgeDB, TinyFace, IJB-B, and IJB-C evaluation datasets.Comment: 2023 IEEE International Joint Conference on Biometrics (IJCB

ROS-induced Oxidative Damage in Lymphocytes Ex Vivo/in Vitro From Healthy Individuals and MGUS Patients: Protection by Myricetin Bulk and Nanoforms

YesWe investigated the protective role of myricetin bulk and nanoforms, against reactive oxygen species (ROS)-induced oxidative stress caused by hydrogen peroxide and tertiary-butyl hydro peroxide in lymphocytes in vitro from healthy individuals and those from pre-cancerous patients suffering with monoclonal gammopathy of undetermined significance (MGUS). The change in intracellular reactive oxygen species was measured once cells were treated with myricetin bulk forms and nanoforms with and without either hydrogen peroxide or tertiary-butyl hydro peroxide co-supplementation. The direct and indirect antioxidant activity of myricetin was spectrofluometrically measured using the fluorescent dye 2',7'-dichlorofluorescin diacetate and using the Comet assay, respectively. Hydrogen peroxide (50 µM) and tertiary-butyl hydro peroxide (300 µM) induced a higher level of reactive oxygen species-related DNA damage and strand breaks. Addition of myricetin nanoform (20 µM) and bulk (10 µM) form could, however, significantly prevent hydrogen peroxide- and tertiary-butyl hydro peroxide-induced oxidative imbalances and the nanoform was more effective. Glutathione levels were also quantified using a non-fluorescent dye. Results suggest that myricetin treatment had no significant effect on the cellular antioxidant enzyme, glutathione. The current study also investigates the effect of myricetin on the induction of double-strand breaks by staining the gamma-H2AX foci immunocytochemically. It was observed that myricetin does not induce double-strand breaks at basal levels rather demonstrated a protective effect

AAFACE: Attribute-aware Attentional Network for Face Recognition

In this paper, we present a new multi-branch neural network that simultaneously performs soft biometric (SB) prediction as an auxiliary modality and face recognition (FR) as the main task. Our proposed network named AAFace utilizes SB attributes to enhance the discriminative ability of FR representation. To achieve this goal, we propose an attribute-aware attentional integration (AAI) module to perform weighted integration of FR with SB feature maps. Our proposed AAI module is not only fully context-aware but also capable of learning complex relationships between input features by means of the sequential multi-scale channel and spatial sub-modules. Experimental results verify the superiority of our proposed network compared with the state-of-the-art (SoTA) SB prediction and FR methods.Comment: Accepted to $30^{th}$ IEEE International Conference on Image Processing (ICIP 2023) as an oral presentatio