• Journal of Navigation and Port Research
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• v.32 no.6
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• pp.453-458
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• 2008

Flow characteristics of the control-rod-attached 2-dimensional circular cylinder was accomplished using by PIV techniques. model tests had been carried out with different diameters of control rods(d/D=0.1 through d/D=0.5). and the Reynolds number Re=15,000 based on the cylinder diameter(D=50mm) to predict the performance of the model and the two-frame grey-level cross-correlation method had been used to obtain the velocity distribution in the flow field. 50mm circular cylinder had been used during the whole experiments and measured results had been compared with each other. The measured results have been compared with each case. therefore this article identifies not only the mean velocity profiles but also the control effects of the control rods.

• Earthquakes and Structures
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• v.18 no.6
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• pp.691-707
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• 2020

One of the most important issues in structural systems is evaluation of the margin of safety in low and mid-rise buildings against the progressive collapse mechanism due to the earthquake loads. In this paper, modeling of collapse propagation in structural elements of RC frame buildings is evaluated by tracing down the collapse points in beam and column structural elements, one after another, under earthquake loads and the influence of column removal is investigated on how the collapse expansion in beam and column structural members. For this reason, progressive collapse phenomenon is studied in 3-story and 5-story intermediate moment resisting frame buildings due to the corner and edge column removal in presence of the earthquake loads. In this way, distribution and propagation of the collapse in progressive collapse mechanism is studied, from the first element of the structure to the collapse of a large part of the building with investigating and comparing the results of nonlinear time history analyses (NLTHA) in presence of two-component accelograms proposed by FEMA_P695. Evaluation of the results, including the statistical survey of the number and sequence of the collapsed points in process of the collapse distribution in structural system, show that the progressive collapse distribution are special and similar in low-rise and mid-rise RC buildings due to the simultaneous effects of the column removal and the earthquake loads and various patterns of the progressive collapse distribution are proposed and presented to predict the collapse propagation in structural elements of similar buildings. So, the results of collapse distribution patterns and comparing the values of collapse can be utilized to provide practical methods in codes and guidelines to enhance the structural resistance against the progressive collapse mechanism and eventually, the value of damage can be controlled and minimized in similar buildings.

• Advances in Computational Design
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• v.4 no.1
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• pp.15-32
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• 2019

One of the efficient and useful tools to achieve the optimal design of structures is employing the sensitivity analysis in the finite element model. In the numerical optimization process, often the semi-analytical method is used for estimation of derivatives of the objective function with respect to design variables. Numerical methods for calculation of sensitivities are susceptible to the step size in design parameters perturbation and this is one of the great disadvantages of these methods. This article uses complex variables method to calculate the sensitivity analysis and combine it with discrete sensitivity analysis. Finally, it provides a new method to obtain the sensitivity analysis for linear structures. The use of complex variables method for sensitivity analysis has several advantages compared to other numerical methods. Implementing the finite element to calculate first derivatives of sensitivity using this method has no complexity and only requires the change in finite element meshing in the imaginary axis. This means that the real value of coordinates does not change. Second, this method has the lower dependency on the step size. In this research, the process of sensitivity analysis calculation using a finite element model based on complex variables is explained for linear problems, and some examples that have known analytical solution are solved. Results obtained by using the presented method in comparison with exact solution and also finite difference method indicate the excellent efficiency of the proposed method, and it can predict the sustainable and accurate results with the several different step sizes, despite low dependence on step size.

• Steel and Composite Structures
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• v.42 no.4
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• pp.539-552
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• 2022

Concentrically braced frames (CBFs) possess high stiffness and strength against lateral loads; however, they suffer from low energy absorption capacity against seismic loads due to the susceptibility of CBF diagonal elements to bucking under compression loading. To address this problem, in this study, an innovative damper was proposed and investigated experimentally and numerically. The proposed damper comprises main plates and includes a flange plate angled at θ and a trapezius-shaped web plate surrounded by the plate at the top and bottom sections. To investigate the damper behaviour, dampers with θ = 0°, 30°, 45°, 60°, and 90° were evaluated with different flange plate thicknesses of 10, 15, 20, 25 and 30 mm. Dampers with θ = 0° and 90° create rectangular-shaped and I-shaped shear links, respectively. The results indicate that the damper with θ = 30° exhibits better performance in terms of ultimate strength, stiffness, overstrength, and distribution stress over the damper as compared to dampers with other angles. The hysteresis curves of the dampers confirm that the proposed damper acts as a ductile fuse. Furthermore, the web and flange plates contribute to the shear resistance, with the flange carrying approximately 80% and 10% of the shear force for dampers with θ = 30° and 90°, respectively. Moreover, dampers that have a larger flange-plate shear strength than the shear strength of the web exhibit behaviours in linear and nonlinear zones. In addition, the over-strength obtained for the damper was greater than 1.5 (proposed by AISC for shear links). Relevant relationships are determined to predict and design the damper and the elements outside it.

• Geomechanics and Engineering
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• v.28 no.4
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• pp.385-396
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• 2022

The accurate determination of the subgrade reaction modulus (K_s) of soil is an important factor for geotechnical engineers. This study estimated the K_s of soft soil improved with floating deep cement mixing (DCM) columns. A novel prediction model was developed that emphasizes the accuracy of identifying the most significant parameters of K_s. Several multi-layer perceptron (MLP) models that were trained using the Levenberg Marquardt (LM) backpropagation method were developed to estimate K_s. The models were trained using a reliable database containing the results of 36 physical modelling tests. The input parameters were the undrained shear strength of the DCM columns, undrained shear strength of soft soil, area improvement ratio and length-to-diameter ratio of the DCM columns. Grey wolf optimization (GWO) was coupled with the MLPs to improve the performance indices of the MLPs. Sensitivity tests were carried out to determine the importance of the input parameters for prediction of K_s. The results showed that both the MLP-LM and MLP-GWO methods showed high ability to predict K_s. However, it was shown that MLP-GWO (R = 0.9917, MSE = 0.28 (MN/m²/m)) performed better than MLP-LM (R =0.9126, MSE =6.1916 (MN/m²/m)). This proves the greater reliability of the proposed hybrid model of MLP-GWO in approximating the subgrade reaction modulus of soft soil improved with floating DCM columns. The results revealed that the undrained shear strength of the soil was the most effective factor for estimation of K_s.

• Geomechanics and Engineering
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• v.32 no.6
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• pp.583-600
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• 2023

The resilient modulus (M_R) of various pavement materials plays a significant role in the pavement design by a mechanistic-empirical method. The M_R determination is done by experimental tests that need time and money, along with special experimental tools. The present paper suggested a novel hybridized extreme gradient boosting (XGB) structure for forecasting the M_R of modified base materials subject to wet-dry cycles. The models were created by various combinations of input variables called deep learning. Input variables consist of the number of W-D cycles (WDC), the ratio of free lime to SAF (CSAFR), the ratio of maximum dry density to the optimum moisture content (DMR), confining pressure (σ₃), and deviatoric stress (σ_d). Two XGB structures were produced for the estimation aims, where determinative variables were optimized by particle swarm optimization (PSO) and black widow optimization algorithm (BWOA). According to the results' description and outputs of Taylor diagram, M1 model with the combination of WDC, CSAFR, DMR, σ₃, and σ_d is recognized as the most suitable model, with R² and RMSE values of BWOA-XGB for model M1 equal to 0.9991 and 55.19 MPa, respectively. Interestingly, the lowest value of RMSE for literature was at 116.94 MPa, while this study could gain the extremely lower RMSE owned by BWOA-XGB model at 55.198 MPa. At last, the explanations indicate the BWO algorithm's capability in determining the optimal value of XGB determinative parameters in M_R prediction procedure.

• Steel and Composite Structures
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• v.52 no.1
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• pp.31-43
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• 2024

A very widely used analytical method (mathematical model), mentioned in Eurocode 3, to examine the connections' bending behavior is the component-based method that has certain weak points shown in the plastic behavior part of the moment-rotation curves. In the component method available in Eurocode 3, for simplicity, the effect of strain hardening is omitted, and the bending behavior of the connection is modeled with the help of a two-line diagram. To make the component method more efficient and reliable, this research proposed its advanced version, wherein the plastic part of the diagram was developed beyond the guidelines of the mentioned Regulation, implemented to connect the end plate, and verified with the moment-rotation curves found from the laboratory model and the finite element method in ABAQUS. The findings indicated that the advanced component method (the method developed in this research) could predict the plastic part of the moment-rotation curve as well as the conventional component-based method in Eurocode 3. The comparison between the laboratory model and the outputs of the conventional and advanced component methods, as well as the outputs of the finite elements approach using ABAQUS, revealed a different percentage in the ultimate moment for bolt-extended end-plate connections. Specifically, the difference percentages were -31.56%, 2.46%, and 9.84%, respectively. Another aim of this research was to determine the optimal dimensions of the end plate joint to reduce costs without letting the mechanical constraints related to the bending moment and the resulting initial stiffness, are not compromised as well as the safety and integrity of the connection. In this research, the thickness and dimensions of the end plate and the location and diameter of the bolts were the design variables, which were optimized using Particle Swarm Optimization (PSO), Snake Optimization (SO), and Teaching Learning-Based Optimization (TLBO) to minimization the connection cost of the end plate connection. According to the results, the TLBO method yielded better solutions than others, reducing the connection costs from 43.97 to 17.45€ (60.3%), which shows the method's proper efficiency.

• International Journal of Heart Failure
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• v.6 no.1
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• pp.11-19
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• 2024

The prevalence of heart failure (HF) is increasing, necessitating accurate diagnosis and tailored treatment. The accumulation of clinical information from patients with HF generates big data, which poses challenges for traditional analytical methods. To address this, big data approaches and artificial intelligence (AI) have been developed that can effectively predict future observations and outcomes, enabling precise diagnoses and personalized treatments of patients with HF. Machine learning (ML) is a subfield of AI that allows computers to analyze data, find patterns, and make predictions without explicit instructions. ML can be supervised, unsupervised, or semi-supervised. Deep learning is a branch of ML that uses artificial neural networks with multiple layers to find complex patterns. These AI technologies have shown significant potential in various aspects of HF research, including diagnosis, outcome prediction, classification of HF phenotypes, and optimization of treatment strategies. In addition, integrating multiple data sources, such as electrocardiography, electronic health records, and imaging data, can enhance the diagnostic accuracy of AI algorithms. Currently, wearable devices and remote monitoring aided by AI enable the earlier detection of HF and improved patient care. This review focuses on the rationale behind utilizing AI in HF and explores its various applications.

• Journal of Cardiovascular Imaging
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• v.30 no.4
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• pp.307-319
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• 2022

BACKGROUND: Current guidelines indicate electrical dyssynchrony as the major criteria for selecting patients for cardiac resynchronization therapy, and 25-35% of patients exhibit unfavorable responses to cardiac resynchronization therapy (CRT). We aimed to evaluate different cardiac mechanical dyssynchrony parameters in heart failure patients using current echo-Doppler modalities and we analyzed their association with electrical dyssynchrony. METHODS: The study included 120 heart failure with reduced ejection fraction (HFrEF) who underwent assessments for left ventricular mechanical dyssynchrony (LVMD) and interventricular mechanical dyssynchrony (IVMD). RESULTS: Patients were classified according to QRS duration: group I with QRS < 120 ms, group II with QRS 120-149 ms, and group III with QRS ≥ 150 ms. Group III had significantly higher IVMD, LVMD indices, TS-SD speckle-tracking echocardiography (STE) 12 segments (standard deviation of time to peak longitudinal strain speckle tracking echocardiography in 12 LV-segments), and LVMD score compared with group I and group II. Group II and group III were classified according to QRS morphology into left bundle branch block (LBBB) and non-LBBB subgroups. LVMD score, TS-SD 12 TDI, and TS-SD 12 STE had good correlations with QRS duration. CONCLUSIONS: HFrEF patients with wide QRS duration (> 150 ms) had more evident LVMD compared with patients with narrow or intermediate QRS. Those patients with intermediate QRS duration (120-150 ms) had substantial LVMD assessed by both TDI and 2D STE, regardless of QRS morphology. Subsequently, we suggest that LVMD indices might be employed as additive criteria to predict CRT response in that patient subgroup. Electrical and mechanical dyssynchrony were strongly correlated in HFrEF patients.

• Asian Pacific Journal of Cancer Prevention
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• v.17 no.2
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• pp.807-813
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• 2016

Triple-negative breast cancers constitute about 15% of all cases, but despite their higher response to neoadjuvant chemotherapy, the tumors are very aggressive and associated with a poor prognosis as well as a higher risk of early recurrence. This study was retrospectively performed on 101 patients with stage II and III invasive breast cancer who received 6-8 cycles of neo-adjuvant chemotherapy. Out of the total, 23 were in the triple negative breast cancer subgroup. Nuclear Ki-67 expression in both the large cohort group (n=101) and triple negative breast cancer subgroup (n=23) and its relation to the pathological response were evaluated. The purpose of the study was to identify the predictive value of nuclear protein Ki-67 expression among patients with invasive breast cancers, involving the triple negative breast cancer subgroup, treated with neoadjuvant chemotherapy in correlation to the rate of pathological complete response. The proliferation marker Ki-67 expression was highest in the triple negative breast cancer subgroup. No appreciable difference in the rate of Ki-67 expression in triple negative breast cancer subgroup using either a cutoff of 14% or 35%. Triple negative breast cancer subgroup showed lower rates of pathological complete response. Achievement of pathological complete response was significantly correlated with smaller tumor size and higher Ki-67 expression. The majority of triple negative breast cancer cases achieved pathological partial response. The study concluded that Ki-67 is a useful tool to predict chemosensitivity in the setting of neoadjuvant chemotherapy for invasive breast cancer but not for the triple negative breast cancer subgroup.