• Computers and Concrete
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• v.30 no.5
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• pp.301-310
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• 2022

Cement-based sensors have been widely used as structural health monitoring systems, however, their long-term sensing performance have not actively investigated. In this study, a deep learning-based methodology is adopted to predict the long-term piezoresistive properties of cement-based sensors. Samples with different multi-walled carbon nanotube contents (0.1, 0.3, and 0.5 wt.%) are fabricated, and piezoresistive tests are conducted over 10,000 loading cycles to obtain the training data. Time-dependent degradation is predicted using a modified long short-term memory (LSTM) model. The effects of different model variables including the amount of training data, number of epochs, and dropout ratio on the accuracy of predictions are analyzed. Finally, the effectiveness of the proposed approach is evaluated by comparing the predictions for long-term piezoresistive sensing performance with untrained experimental data. A sensitivity of 6% is experimentally examined in the sample containing 0.1 wt.% of MWCNTs, and predictions with accuracy up to 98% are found using the proposed LSTM model. Based on the experimental results, the proposed model is expected to be applied in the structural health monitoring systems to predict their long-term piezoresistice sensing performances during their service life.

• Computers and Concrete
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• v.30 no.5
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• pp.339-355
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• 2022

The high cost of ultra-high-performance engineered cementitious composite (UHP-ECC) is currently a crucial issue, especially in terms of the polyethylene (PE) fibers use. In this paper, cheap calcium carbonate whiskers (CW) were evaluated on the feasibility of hybrid with PE fibers. Diverse combinations of PE fibers and CW were employed to investigate the multi-scale enhancement on the UHP-ECC performance. A probabilistic-based UHP-ECC tensile strain reliability analysis approach was utilized, which was in general agreement with the experimental results. Furthermore, a multi-dimensional integrated representation was conducted for the comprehensive assessment of UHP-ECC. Results illustrated that CW improved the compressive strength and energy dissipation capacity of UHP-ECC owing to the microscopic strengthening mechanism. CW and PE fiber further promoted the saturated cracking of composite by multi-scale crack arresting effect. In particular, PE1.75-CW0.5 specimen possessed the best overall performance. The ultimate cracking width of PE1.75-CW0.5 group had 98 ㎛, which was 46.18% lower compared to PE2-CW0 group, the 28d compressive strength were slightly improved, the tensile strain capacity was comparable to that of PE2-CW0 group. The results above demonstrated that combinations of PE fiber and CW could significantly enhance the comprehensive performance of UHP-ECC, which was beneficial for large-scale engineering applications.

• Journal of Digestive Cancer Research
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• v.4 no.1
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• pp.1-9
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• 2016

The abundance of multi-drug resistance ATPase binding cassette and deranged self-renewal pathways shown in cancer stem cells (CSCs) played a crucial role in tumorigenesis, tumor resistance, tumor recurrence, and tumor metastasis. Therefore, elucidation of CSCs biology can improve diagnosis, enable targeted treatment, and guide the follow up of GI cancer patients. In order to achieve chemoquiescence, seizing cancer through complete ablation of CSCs, CSCs are rational targets for the design of interventions that will enhance responsiveness to traditional therapeutic strategies and contribute in the prevention of local recurrence as well as metastasis. However, current cancer treatment strategies fail to either detect or differentiate the CSCs from their non-tumorigenic progenies mostly due to the absence of specific biomarkers and potent agents to kill CSCs. Recent advances in knowledge of CSCs enable to produce several candidates to ablate CSCs in gastrointestinal (GI) cancers, especially cancers originated from inflammation-driven mutagenesis such as Barrett's esophagus (BE), Helicobacter pylori-associated gastric cancer, and colitis-associated cancer (CAC). Our research teams elucidated through revisiting old drugs that proton pump inhibitor (PPI) and potassium competitive acid blocker (p-CAB) beyond authentic acid suppression, chloroquine for autophage inhibition, sonic hedgehog (SHH) inhibitors, and Wnt/β-catenin/NOTCH inhibitor can ablate CSCs specifically and efficiently. Furthermore, nanoformulations of these molecules could provide an additional advantage for more selective targeting of the pathways existing in CSCs just like current molecular targeted therapeutics and sustained action, while normal stem cells intact. In this review article, the novel approach specifically to ablate CSCs existing in GI cancers will be introduced with the introduction of explored mode of action.

• Computers and Concrete
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• v.33 no.1
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• pp.55-75
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• 2024

The present study introduces an optimum performance soft computing model for predicting the compressive strength of high-performance concrete (HPC) by comparing models based on conventional (kernel-based, covariance function-based, and tree-based), advanced machine (least square support vector machine-LSSVM and minimax probability machine regressor-MPMR), and deep (artificial neural network-ANN) learning approaches using a common database for the first time. A compressive strength database, having results of 1030 concrete samples, has been compiled from the literature and preprocessed. For the purpose of training, testing, and validation of soft computing models, 803, 101, and 101 data points have been selected arbitrarily from preprocessed data points, i.e., 1005. Thirteen performance metrics, including three new metrics, i.e., a20-index, index of agreement, and index of scatter, have been implemented for each model. The performance comparison reveals that the SVM (kernel-based), ET (tree-based), MPMR (advanced), and ANN (deep) models have achieved higher performance in predicting the compressive strength of HPC. From the overall analysis of performance, accuracy, Taylor plot, accuracy metric, regression error characteristics curve, Anderson-Darling, Wilcoxon, Uncertainty, and reliability, it has been observed that model CS4 based on the ensemble tree has been recognized as an optimum performance model with higher performance, i.e., a correlation coefficient of 0.9352, root mean square error of 5.76 MPa, and mean absolute error of 4.1069 MPa. The present study also reveals that multicollinearity affects the prediction accuracy of Gaussian process regression, decision tree, multilinear regression, and adaptive boosting regressor models, novel research in compressive strength prediction of HPC. The cosine sensitivity analysis reveals that the prediction of compressive strength of HPC is highly affected by cement content, fine aggregate, coarse aggregate, and water content.

• Computers and Concrete
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• v.33 no.1
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• pp.27-39
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• 2024

In this study, the authors investigate the free vibration behavior of three-phases functionally graded sandwich plates using a novel nth-order shear deformation theory. These plates are composed of a homogeneous core and two face-sheet layers made of different functionally graded materials. This is the novel type of the sandwich structures that can be applied in many fields of mechanical engineering and industrial. The proposed theory only requires four unknown displacement functions, and the transverse displacement does not need to be separated into bending and shear parts, simplifying the theory. One noteworthy feature of the proposed theory is its ability to capture the parabolic distribution of transverse shear strains and stresses throughout the plate's thickness while ensuring zero values on the two free surfaces. By eliminating the need for shear correction factors, the theory further enhances computational efficiency. Equations of motion are established using Hamilton's principle and solved via Navier's solution. The accuracy and efficiency of the proposed theory are verified by comparing results with available solutions. The authors then use the proposed theory to investigate the free vibration characteristics of three-phases functionally graded sandwich plates, considering the effects of parameters such as aspect ratio, side-to-thickness ratio, skin-core-skin thicknesses, and power-law indexes. Through careful analysis of the free vibration behavior of three-phases functionally graded sandwich plates, the work highlighted the significant roles played by individual material ingredients in influencing their frequencies.

• Computers and Concrete
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• v.33 no.1
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• pp.77-90
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• 2024

Waste foundry sand (WFS) is the waste product that cause environmental hazards. WFS can be used as a partial replacement of cement or fine aggregates in concrete. A database comprising 234 compressive strength tests of concrete fabricated with WFS is used. To construct the machine learning-based prediction models, the water-to-cement ratio, WFS replacement percentage, WFS-to-cement content ratio, and fineness modulus of WFS were considered as the model's inputs, and the compressive strength of concrete is set as the model's output. A base extreme gradient boosting (XGBoost) model together with two hybrid XGBoost models mixed with the tunicate swarm algorithm (TSA) and the salp swarm algorithm (SSA) were applied. The role of TSA and SSA is to identify the optimum values of XGBoost hyperparameters to obtain the higher performance. The results of these hybrid techniques were compared with the results of the base XGBoost model in order to investigate and justify the implementation of optimisation algorithms. The results showed that the hybrid XGBoost models are faster and more accurate compared to the base XGBoost technique. The XGBoost-SSA model shows superior performance compared to previously published works in the literature, offering a reduced system error rate. Although the WFS-to-cement ratio is significant, the WFS replacement percentage has a smaller influence on the compressive strength of concrete. To improve the compressive strength of concrete fabricated with WFS, the simultaneous consideration of the water-to-cement ratio and fineness modulus of WFS is recommended.

• Computers and Concrete
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• v.33 no.1
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• pp.103-119
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• 2024

In this paper, the non-linear behavior of masonry-infill and bare steel frames using different beam-column connections under monotonic static loading was investigated through a parametric study. Numerical models were carried out using one- and two-dimensional modelling to validate the experimental results. After validating the experimental results by using these models, a parametric study was carried out to model the behavior of these frames using flushed, extended, and welded connections. The results showed that using the welded or extended connection is more efficient than using the flushed type in masonry-infilled steel frames, since the lateral capacities, initial stiffness, and toughness have been increased by 155%, 601%, and 165%, respectively in the case of using welded connections compared with those used in bare frames. The FE investigation was broadened to study the influence of the variation of the uniaxial column loads on the lateral capacities of the bare/infill steel frames. As the results showed when increasing the amount of uniaxial loading on the columns, whether in tension or compression, causes the lateral load capacity of the columns to decrease by 26% for welded infilled steel frames. Finally, the influence of using different types of beam-to-column connections on the vertical capacities of the bare/infill steel frames under settlement effect was also studied. As a result, it was found that, the vertical load capacity of all types of frames and with using any type of connections is severely reduced, and this decrease may reach 62% for welded infilled frames. Furthermore, the flushed masonry-infilled steel frame has a higher resistance to the vertical loads than the flushed bare steel frame by 133%.

• Food Science of Animal Resources
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• v.44 no.4
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• pp.873-884
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• 2024

Flunixin is a veterinary nonsteroidal anti-inflammatory agent whose residues have been investigated in their original form within tissues such as muscle and liver. However, flunixin remains in milk as a metabolite, and 5-hydroxy flunixin has been used as the primary marker for its surveillance. This study aimed to develop a quantitative method for detecting flunixin and 5-hydroxy flunixin in milk and to strengthen the monitoring system by applying to other livestock and fishery products. Two different methods were compared, and the target compounds were extracted from milk using an organic solvent, purified with C₁₈, concentrated, and reconstituted using a methanol-based solvent. Following filtering, the final sample was analyzed using liquid chromatography-tandem mass spectrometry. Method 1 is environmentally friendly due to the low use of reagents and is based on a multi-residue, multi-class analysis method approved by the Ministry of Food and Drug Safety. The accuracy and precision of both methods were 84.6%-115% and 0.7%-9.3%, respectively. Owing to the low matrix effect in milk and its convenience, Method 1 was evaluated for other matrices (beef, chicken, egg, flatfish, and shrimp) and its recovery and coefficient of variation are sufficient according to the Codex criteria (CAC/GL 71-2009). The limits of detection and quantification were 2-8 and 5-27 ㎍/kg for flunixin and 2-10 and 6-33 ㎍/kg for 5-hydroxy flunixin, respectively. This study can be used as a monitoring method for a positive list system that regulates veterinary drug residues for all livestock and fisheries products.

• Computers and Concrete
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• v.34 no.1
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• pp.93-122
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• 2024

One well-known reason for using Fiber Reinforced Polymer (FRP) composites is to improve concrete strength and strain capacity via external confinement. Hence, various studies have been undertaken to offer a good illustration of the response of FRP-wrapped concrete for practical design intents. However, in such studies, the strength and strain of the confined concrete were predicted using regression analysis based on a limited number of test data. This study presents an approach based on artificial neural networks (ANNs) to develop models to predict the strength and strain at maximum stress enhancement of circular concrete cross-sections confined with different FRP types (Carbone, Glass, Aramid). To achieve this goal, a large test database comprising 493 axial compression experiments on FRP-confined concrete samples was compiled based on an extensive review of the published literature and used to validate the predicted artificial intelligence techniques. The ANN approach is currently thought to be the preferred learning technique because of its strong prediction effectiveness, interpretability, adaptability, and generalization. The accuracy of the developed ANN model for predicting the behavior of FRP-confined concrete is commensurate with the experimental database compiled from published literature. Statistical measures values, which indicate a better fit, were observed in all of the ANN models. Therefore, compared to existing models, it should be highlighted that the newly developed models based on FRP type are remarkably accurate.

• Computers and Concrete
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• v.34 no.1
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• pp.33-48
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• 2024

This paper presents the detailed experimental and analytical investigation on the evolution of static (E_s) and dynamic modulus of elasticity (E_d) of concrete having 0%, 35%, and 50% FA used as partial cement replacement. Destructive and non-destructive tests were conducted on cylindrical specimens to evaluate the compressive strength and MoE of concrete in compression at the age of 28, 56, 90, and 150 days for all mixes. Experimental results show that the concrete having 35% FA achieved compressive strength and MoE similar to plain concrete at the age of 90 days, while 50% FA concrete attained satisfactory compressive strength and MoE at the age of 150 days. The comprehensive statistical analysis has been carried out in two ways on the basis of the experimental results. Firstly, the 28-day crushing strength of plain concrete in compression was used to design the models for the prediction of E_s and E_d of fly ash concrete at any age and percentage replacement of FA. Secondly, using the values of UPV and RHN, models have been developed to predict the age or time-dependent E_s and E_d of fly ash concrete. These models will be helpful in assessing the E_s and E_d of fly ash concrete without knowing the 28-day crushing strength of plain concrete in compression in the laboratory. Hence, the suggested models in the present study will be beneficial in conducting the health assessment of fly ash based concrete structures.