Basis Of Experimental Data Research Articles

Coverage Effects in Quartz Crystal Microbalance Measurements with Suspended and Adsorbed Nanoparticles.

Quartz crystal microbalance (QCM) biosensors often deal with nanoparticles suspended in the solvent at tens of nanometers above the resonator while being linked to some molecular receptor (DNA, antibody, etc.). This work presents a numerical analysis based on the immersed boundary method for the flow and QCM impedance created by an ensemble of spherical particles of radius R at varying surface coverage Θ and particle-surface gap distance Δ. The trends for the frequency Δf and dissipation ΔD shifts against Θ and Δ are shown to be determined by modifications in the structure of the perturbative flow created by the analytes. Simulations are in good agreement with a relatively large experimental database collected from the literature. Qualitative differences between the adsorbed (Δ ≈ 0) and suspended states (Δ > 0) are highlighted. In the case of adsorbed particles, deviations from the linear scaling Δf ∝ Θ are observed above Θ > 0.05 and largely depend on the specific analyte-substrate combination. Moreover, in general, ΔD(Θ) is not monotonous and usually presents a maximum around Θ ∼ 0.2. In the case of suspended analytes, the agreement with the numerical results is quantitative, indicating that the predicted scalings are universal and determined by hydrodynamics. Up to high coverage, the suspended particles present Δf ∼ Θ and ΔD ∼ Θβ, where β ≈ 0.85 is not largely dependent on R. The present findings should help forecast molecular configurations from QCM signals and have implications on QCM analyses, e.g., in the case of suspended ligands (Δf ∝ Θ), it is safe to use Δf to build Langmuir isotherms and estimate equilibrium constants. Open questions on the transition from the suspended-to-adsorbed state are discussed.

Numerical investigation of blockage effect for natural supercavitation

The aim of supercavitation technology is to minimize the wetted surfaces and reduce the viscous drag forces on the body. In this paper, the phenomenon of natural supercavitation is introduced and the characteristic parameters of this phenomenon are determined. Specific parameters of the supercavitation phenomenon are obtained in terms of drag coefficient, dimensionless cavity length, and cavity diameter from the experimental database. The purpose of gathering the experimental databases is to develop a numerical analyses method that solve supercavitating flows. After validation study is evaluated, a verification study is conducted for two different cavitation numbers. Moreover, the relationship between blockage effect and supercavitation parameters are investigated. The compression effect onto pressure distribution and velocity vectors are evaluated for 16 different blockage ratios at 3 different cavitation numbers via numerical analyses. The percentage differences of the cavity length, diameter and drag coefficient values are revealed based on the blockage ratio.

Schizosaccharomyces pombe as a fundamental model for research on mitochondrial gene expression: Progress, achievements and outlooks.

Schizosaccharomyces pombe (fission yeast) is an attractive model for mitochondrial research. The organism resembles human cells in terms of mitochondrial inheritance, mitochondrial transport, sugar metabolism, mitogenome structure and dependence of viability on the mitogenome (the petite-negative phenotype). Transcriptions of these genomes produce only a few polycistronic transcripts, which then undergo processing as per the tRNA punctuation model. In general, the machinery for mitochondrial gene expression is structurally and functionally conserved between fission yeast and humans. Furthermore, molecular research on S. pombe is supported by a considerable number of experimental techniques and database resources. Owing to these advantages, fission yeast has significantly contributed to biomedical and fundamental research. Here, we review the current state of knowledge regarding S. pombe mitochondrial gene expression, and emphasise the pertinence of fission yeast as both a model and tool, especially for studies on mitochondrial translation.

TEST RESULTS OF REINFORCED CONCRETE CROSS-RIBBED MODEL OF SPAN STRUCTURE OF THE BRIDGE AND THEIR ANALYSIS

Presentation of samples of basic experimental data from research of spatial work of large-scale (scale 1: 4) reinforced concrete model of cross-ribbed span structure of the bridge and separate beams similar in structure to model beams, as well as methodological features and results of their analysis. During the tests, the model and separate (reference) beams were loaded with concentrated force, alternately at the intersections of the longitudinal and transverse ribs of the model. Deflections in the same nodes and support reactions of longitudinal beams were measured. A new methodological feature of this research was the determination of experimental bending moments in the model beams by direct comparison of the deflections of the model beams with similar deflections of separate (reference) beams. Depending on the location of the external load and the stiffness ratio, the load on the beams of the model is different. Taking into account an elastic-plastic stage of their work, the method of calculation of the maximum bending moments in the most loaded beams is offered in this paper.

Extreme Gradient Boosting Combined with Conformal Predictors for Informative Solubility Estimation.

We used the extreme gradient boosting (XGB) algorithm to predict the experimental solubility of chemical compounds in water and organic solvents and to select significant molecular descriptors. The accuracy of prediction of our forward stepwise top-importance XGB (FSTI-XGB) on curated solubility data sets in terms of RMSE was found to be 0.59-0.76 Log(S) for two water data sets, while for organic solvent data sets it was 0.69-0.79 Log(S) for the Methanol data set, 0.65-0.79 for the Ethanol data set, and 0.62-0.70 Log(S) for the Acetone data set. That was the first step. In the second step, we used uncurated and curated AquaSolDB data sets for applicability domain (AD) tests of Drugbank, PubChem, and COCONUT databases and determined that more than 95% of studied ca. 500,000 compounds were within the AD. In the third step, we applied conformal prediction to obtain narrow prediction intervals and we successfully validated them using test sets' true solubility values. With prediction intervals obtained in the last fourth step, we were able to estimate individual error margins and the accuracy class of the solubility prediction for molecules within the AD of three public databases. All that was possible without the knowledge of experimental database solubilities. We find these four steps novel because usually, solubility-related works only study the first step or the first two steps.

Influence of the ANN Hyperparameters on the Forecast Accuracy of RAC's Compressive Strength.

The artificial neural networks (ANNs)-based model has been used to predict the compressive strength of concrete, assisting in creating recycled aggregate concrete mixtures and reducing the environmental impact of the construction industry. Thus, the present study examines the effects of the training algorithm, topology, and activation function on the predictive accuracy of ANN when determining the compressive strength of recycled aggregate concrete. An experimental database of compressive strength with 721 samples was defined considering the literature. The database was used to train, validate, and test the ANN-based models. Altogether, 240 ANNs were trained, defined by combining three training algorithms, two activation functions, and topologies with a hidden layer containing 1-40 neurons. The ANN with a single hidden layer including 28 neurons, trained with the Levenberg-Marquardt algorithm and the hyperbolic tangent function, achieved the best level of accuracy, with a coefficient of determination equal to 0.909 and a mean absolute percentage error equal to 6.81%. Furthermore, the results show that it is crucial to avoid the use of overly complex models. Excessive neurons can lead to exceptional performance during training but poor predictive ability during testing.

Hybrid machine learning models for classifying failure modes of unstiffened steel plate girders subjected to patch loading

This paper, for the first time, develops novel hybrid machine learning models that combine Support Vector Machine, Naïve Bayes, K-Nearest Neighbors, Decision Tree, Random Forest, Adaptive Boosting, Gradient Boosting, and Extreme Gradient Boosting, with Moth-Flame Optimization algorithm, called MFO-SVM, MFO-NB, MFO-KNN, MFO-DT, MFO-RF, MFO-AB, MFO-GB, and MFO-XGB, respectively, to classify the failure modes of unstiffened steel plate girders subjected to patch loading. The experimental database is collected from the tests reported in the literature to develop the hybrid machine learning models. The Synthetic Minority Over-sampling Technique is adopted to address the imbalanced class problems of the database. Hyperparameters of machine learning models are optimized using the Moth-Flame Optimization algorithm. The performance of hybrid machine learning models is evaluated and compared through the confusion matrix using several metrics. The results show that the MFO-RF model proves more effective than other models in identifying the failure models of unstiffened steel plate girders. The Shapely Additive Explanations method is used to analyze the contribution of each variable to the failure modes of unstiffened steel plate girders both globally and locally. Web thickness is the most influencing feature for identifying the failure modes. The MFO-RF model is finally utilized to develop a web application for practical use at low cost and effort.

Mathematical modelling of drilling mud plastic viscosity at downhole conditions using multivariate adaptive regression splines

Viscosity is an essential mud property which enables it to transport cuttings out of a well. Determining the real time value of mud viscosity at downhole conditions is complex and till date lacks a precise model. Literature is filled with models that are only applicable at surface conditions with most of the models being artificial neural network (ANN) based. Neural networks have been criticized for their long training time because they rely on a trial-and-error approach to determine their optimal architecture. The few models related to mud viscosity at downhole conditions are based on non-linear regression which are not flexible in nature. However, Multivariate Adaptive Regression Splines (MARS) has at no time been used notwithstanding some of its known benefits over ANN and other regression methods. The focus of this study is to model mud plastic viscosity at downhole conditions with the MARS method using parameters such as temperature, pressure and initial mud viscosity. MARS is a flexible non-parametric splines-based technique that extends linear regression models by including nonlinearities and interactions between predictors. An experimental database comprising 88 and 149 data points culled from literature for oil-based mud (OBM) and water-based mud (WBM) respectively were utilized for developing the models. For each mud type, the most suitable model was determined by choosing the model with the maximum coefficient of determination (R2) value and the least values of mean square error (MSE), root mean square error (RMSE), and generalized cross-validation criterion (GCV). Using these criteria, the R2, MSE and RMSE of the MARS models according to the mud type were found to be 0.64, 104.35, 10.21 and 0.71, 12.8, 3.57 for OBM and WBM respectively while the GCV values were 129.3 and 14.38 for OBM and WBM respectively. Due to lack of mud rheology data at downhole conditions, it was difficult to compare the performance of the developed models with existing models. Parametric importance analysis indicate that temperature has the highest effect on mud viscosity estimation at downhole conditions. The developed models are valid for 20 °C ≤ Temperature ≤315 °C and 0 psi ≤ Pressure ≤40,000 psi. This pioneer work on using MARS in drilling fluid property prediction points out the implementation protocol, the dataset utilized, the relative importance of input variables and the explicit model for estimating mud viscosity at downhole conditions.

Application of Ion Mobility Spectrometry and the Derived Collision Cross Section in the Analysis of Environmental Organic Micropollutants.

Ion mobility spectrometry (IMS) is a rapid gas-phase separation technique, which can distinguish ions on the basis of their size, shape, and charge. The IMS-derived collision cross section (CCS) can serve as additional identification evidence for the screening of environmental organic micropollutants (OMPs). In this work, we summarize the published experimental CCS values of environmental OMPs, introduce the current CCS prediction tools, summarize the use of IMS and CCS in the analysis of environmental OMPs, and finally discussed the benefits of IMS and CCS in environmental analysis. An up-to-date CCS compendium for environmental contaminants was produced by combining CCS databases and data sets of particular types of environmental OMPs, including pesticides, drugs, mycotoxins, steroids, plastic additives, per- and polyfluoroalkyl substances (PFAS), polycyclic aromatic hydrocarbons (PAHs), polychlorinated biphenyls (PCBs), and polybrominated diphenyl ethers (PBDEs), as well as their well-known transformation products. A total of 9407 experimental CCS values from 4170 OMPs were retrieved from 23 publications, which contain both drift tube CCS in nitrogen (DTCCSN2) and traveling wave CCS in nitrogen (TWCCSN2). A selection of publicly accessible and in-house CCS prediction tools were also investigated; the chemical space covered by the training set and the quality of CCS measurements seem to be vital factors affecting the CCS prediction accuracy. Then, the applications of IMS and the derived CCS in the screening of various OMPs were summarized, and the benefits of IMS and CCS, including increased peak capacity, the elimination of interfering ions, the separation of isomers, and the reduction of false positives and false negatives, were discussed in detail. With the improvement of the resolving power of IMS and enhancements of experimental CCS databases, the practicability of IMS in the analysis of environmental OMPs will continue to improve.

Probabilistic code-based shear capacity model for I-shaped steel reinforced concrete (SRC) beams

A code-based probabilistic prediction model is developed for structural design and reliability analysis of the shear capacity of shear-critical steel reinforced concrete (SRC) beams. First, an experimental database of 83 shear tests was compiled from the literature, including steel-reinforced normal-strength concrete, steel-reinforced high-strength concrete, and steel reinforced lightweight aggregate concrete. Then, three deterministic formulations from the most commonly-used specifications JGJ, AIJ, and ACI-AISC were presented and compared against the database. Based on the different shear mechanism and formula considerations in the code-based deterministic models, this study developed 12 probabilistic models that account for both epistemic and aleatory uncertainty to examine the impact of concrete strength, shear span ratio, number of parameters, and their interactions. The Bayesian theory and Markov Chain Monte Carlo (MCMC) simulation method were utilized to calibrate the models by generating posterior distributions of the model parameters and the standard deviation (SD) of the model error. Optimal model selection hinges upon balancing precision and intricacy. An analysis was then conducted to evaluate the accuracy of the selected model, and the probabilistic model was applied to estimate the shear reliability of a SRC beam. Results demonstrate that the model objectively represents the probabilistic properties of the shear capacity of SRC beams and has the capability to conduct reliability analysis.

Ultra-high strength concrete filled steel tube members: Classification, experimental database, and design

Current design codes do not endorse the use of ultra-high strength steel and concrete materials (Fy ≥ 690 MPa and f’c ≥ 120 MPa) for concrete filled steel tube (CFST) members. This paper makes efforts to address this gap using a four-step method. In the first step, the classification of CFST members based on material strengths and tube slenderness ratio is introduced. In the second step, an experimental database comprised of 805 ultra-high strength concrete filled steel tube (UCFST) specimens under various loading conditions is compiled, and the research gaps in the database are identified. In the third step, the possibility of extending Eurocode 4 and AISC 360–22 to UCFST members are evaluated using the experimental database. Finally, a research program including experimental tests, finite element (FE) parametric studies and new design equations for UCFST rectangular members is introduced.

Airfoil self-noise prediction using deep neural networks

This study explores the prediction of airfoil self-noise through Deep Learning whilst focusing more specifically on the so-called turbulent boundary layer trailing edge (TBL-TE) noise. To this end, a predictive model relying on a Deep Neural Network (DNN) is developed, being then trained using an experimental database of TBL-TE noise signatures previously acquired by NASA. The DNN is favorably benchmarked against the test results, demonstrating its superiority over a popular semi-empirical prediction tool, i.e., the BPM model from NASA. Special attention is paid to the sensitivity of the DNN towards its architecture and/or its training extent. All in all, the DNN proves robust and accurate, reproducing faithfully the TBL-TE noise signatures with an average error of about 1.5 ∼ 2.5 dB in terms of Sound Pressure Level. Special attention is also paid to sensitivity of the DNN towards the composition of its training dataset, whose consistency is enforced by clustering all datapoints belonging to an identical test configuration. This allows evaluating how far the model constitutes a true prediction tool, i.e., can extrapolate the experimental database instead of merely interpolating it through overfitting. Finally, the sensitivity of the DNN towards its training data is further explored to tentatively discriminate which quantities constituting the experimental database may contribute more significantly to the correct prediction of the noise signatures and − by extension – to their underlying physical mechanisms.

Seismic damage assessment for flexural double skin composite walls based on the modified Park-Ang model

The damage indices are widely used to evaluate the performance level and damage state of structural elements in buildings subjected to seismic actions. Despite the widespread promotion of double-skin composite walls (DSCWs) in global construction practices, the research related to the seismic damage assessment of DSCW members is still limited. In order to establish a seismic damage assessment procedure for DSCW members, an experimental database that includes 37 test results of DSCW specimens subjected to cyclic loads was built in this paper. Based on an existing modified Park-Ang model, an empirical expression of the combination coefficient is introduced. The damage state levels of DSCWs are determined as follows: Basically intact (DS0), Slight damage (DS1), Mild damage (DS2), Moderate damage (DS3), Relatively severe damage (DS4), Severe damage (DS5), and Critical collapse (DS6). The typical damage characteristics and repair methods are introduced and discussed. Subsequently, a comprehensive statistical analysis is conducted to study the probabilistic correlation between these damage states and the corresponding index specific to DSCW members. Fragility functions for each damage state associated with the damage index have been developed. The threshold values of the damage index corresponding to the damage states are also provided. This paper intends to provide a theoretical foundation for performance-based seismic design and post-earthquake damage assessment of DSCW structures.

Comprehensive experimental database and analysis of circular concrete-filled double-skin tube stub columns: A review

Comprehensive experimental database and analysis of circular concrete-filled double-skin tube stub columns: A review

Predicting the punching shear capacity of corroded reinforced concrete flat slab

Reinforced concrete (RC) flat slab not only provides benefits in terms of floor-to-floor height, aesthetics but also facilitates the installation of formwork, reducing construction time for the project. However, this type of floor structure is prone to punching failure at the slab-column connection, which can lead to the collapse of the structure. Corrosion of the steel reinforcement at the connection of the slab-column makes the behavior more complex. Predicting the load-bearing capacity in this zone needs to account for the factors caused by corrosion. This paper presents the results of evaluating the punching shear strength of corroded RC flat slabs through 123 experimental databases from previous publication based on three building codes: ACI 318-19, Eurocode 2, and TCVN 5574:2018. The statistical metrics indicate that TCVN 5574:2018 provides the safest prediction results. Additionally, a formula derived from nonlinear regression analysis is proposed to enhance the accuracy in predicting the punching shear strength of the corroded RC flat slabs.

Confidence intervals of inversely identified material model parameters: A novel two-stage error propagation model based on stereo DIC system uncertainty

Digital image correlation (DIC) is a powerful tool for characterising materials and determining material model parameters. To assess the reliability of the full-field measurement-based inverse identification procedures, it is crucial to investigate the impact of the measurement errors on the identified material model parameters. Literature indicates that conventional error propagation models, which rely on Gaussian noise-contaminated data, significantly overestimate the confidence for inversely identified material model parameters, resulting in misleadingly narrow confidence intervals. A more precise assessment of systematic errors originating from the experimental setup leads to an improved prediction of the confidence intervals, but this requires specific information about the DIC equipment, post-processing details, and a skilled experimentalist. In this work, we propose an alternative two-stage error propagation model that yields more realistic confidence interval predictions based solely on a database of past mechanical experiments conducted with the specific stereo DIC system set up in a particular way. We have validated the proposed procedure numerically using the open-hole test and an orthotropic elastic material model. Our predictions reveal a non-Gaussian probability distribution of the inversely identified material model parameters, with confidence intervals considerably wider than those obtained by considering random Gaussian noise. Furthermore, these predictions were experimentally validated in an extensive experimental campaign investigating a pultruded carbon fibre epoxy composite.

Random Forests Machine Learning Applied to PEER Structural Performance Experimental Columns Database

Columns play a very important role in structural performance and, therefore, this paper contributes to the critical need for failure mode prediction of reinforced concrete (RC) columns by exploring the capabilities of random forest machine learning (ML) based on a well-known experimental column database. Known as the PEER structural performance database, it assembles the results of over 400 cyclic, lateral-load tests of reinforced concrete columns. The database describes tests of spiral or circular hoop-confined columns, rectangular tied columns and columns with or without lap splices of longitudinal reinforcement at the critical sections. The efficiency towards the aforementioned goal of supervised ML methods such as random forests using a randomly assigned test set from the Pacific Earthquake Engineering Research Center (PEER) database is examined here. The overall accuracy score for rectangular RC columns is 94% and for circular RC columns is 86%. The latter performances are influenced by the size of the testing and training sets of data and are independent of the number of decision trees in the forest employed in the random forest algorithm. The performances of random forests in postdicting the failure mode of RC columns prove that ML has great promise in revolutionizing the profession of earthquake engineering.

EXFOR-based simultaneous evaluation for neutron-induced fission cross section of plutonium-242

ABSTRACT The 242Pu neutron-induced fission cross section was evaluated from 100 keV to 200 MeV. The experimental 242Pu and 235U fission cross sections and their ratios in the EXFOR library were reviewed and analysed by the least-squares method. Additional simultaneous evaluation was performed by including the experimental database of the 233,238U and 239,240,241Pu fission cross sections and their ratios developed for JENDL-5 evaluation. The 242Pu fission cross sections from our evaluation and JENDL-5 evaluation are close to each other below 1 MeV while they systematically differ from each other above 10 MeV. The cross section from our evaluation is systematically lower than the JENDL-4.0 cross section in the prompt fission neutron spectrum peak region (∼5% lower around 1 MeV). The newly evaluated 242Pu fission cross section was verified against the cross section measured in the 252Cf spontaneous fission neutron field and criticalities of small-sized LANL fast systems and demonstrated better performance than the JENDL-4.0 cross section on the same level with the JENDL-5 cross section.

In Vitro Evidence of Synergistically Enhanced Efficacy of Axitinib When Combined with Asciminib in T315I Mutated Chronic Myeloid Leukemia

Background: Development of ABL1 kinase domain (KD) mutation is one of resistance mechanism to ATP-binding pocket inhibitor (ABPI) therapy. T315I mutation in ABL KD confers broad-spectrum resistance to all 1st- and 2nd-generation ABPIs including Imatinib (IMA), Dasatinib (DAS), Nilotinib (NIL) and Bosutinib (BOS). Ponatinib (PON), a 3 rd generation inhibitor, can overcome T315I-mediated resistance in chronic myeloid leukemia (CML) treatment. However, its increased risk of cardiovascular toxicity limits the broader use of PON in the clinic. Asciminib (ASC) is a Specifically Targeting ABL1 Myristoyl Pocket (STAMP) inhibitor and has been approved for CML treatment in the patients who failed at least two previous lines of TKI therapy. ASC, which blocks the myristoylate pocket in ABL1 KD protein, can potentially overcome ABL1 KD mutation (KDM)-mediated tyrosine kinase inhibitor (TKI) resistance by combining with ABPIs which blocks ATP binding pockets in ABL1 KD protein. However, in vitro evidence to support double blockade approach is still to be further investigated including combination with ABPIs as well with other drug compounds. Axitinib (AXI), which is approved for the treatment of advanced renal cell carcinoma (RCC), is an ABPI that can specifically bind to the ATP-binding pocket in ABL1 KD protein structurally harboring the T315I mutation. In the Cancer Therapeutics Response Portal (CTRP) database, which integrates drug compound and cell line experiment databases, AXI showed very low IC 50 in CML cell lines compared to other cancer cell lines, including RCC. The present study attempted to provide in vitro evidence of the synergistic enhancement of therapeutic efficacy of AXI combined with ASC to overcome T315I mutated CML. Methods and Materials: We analyzed IC 50 values for 649 cell lines tested with AXI using the CTRPv2 database. The drug response of AXI in K562/Wild-Type (WT), K562/T315I mut, BaF3/WT, and BaF3/T315I mut cell lines was evaluated using the WST-8 assay. From the measured IC 50 value in those cell lines as the baseline concentration (conc), AXI and ASC conc were serially diluted. The dose-response matrix and ZIP synergy score were analyzed using SynergyFinder. Results We compared the sensitivity of 649 different cell lines to AXI in the CTRPv2 database and confirmed that the IC 50 of AXI in CML (median IC 50 = 0.31 uM) is 96 times lower than that of various cell lines derived from RCC (median IC 50 = 29.85 uM) (Fig. 1A). This result implies that AXI is more effectively inhibit CML cells at a lower conc than RCC cell lines or other cell lines. When measuring the inhibitory activityof AXI (Fig. 1B), AXI treatment showed 2.22 times lower IC 50 in the K562/T315I mut (IC 50 = 111.6nM) than in the K562/WT (IC 50 = 248nM). Also AXI treatment showed 4.73 times lower IC 50 in the BaF3/T315I mut (IC 50 = 84nM) than in the BaF3/WT (IC 50 = 397.4nM). Overall, the T315I carrying CML cell lines were much more sensitive to lower dose AXI than wild type CML cell lines. In the dose-response matrix analysis (Fig. 1C), AXI treatment alone at 100 nM inhibited the growth of the K562/T315 mut cell line by 38.42%, and ASC treatment alone at 100 nM, by 36.52%. Of note, the combination of AXI at 25nM and ASC at 12.5nM inhibited cell growth by 38.1%, and the combination of AXI at 12.5nM and ASC at 25nM showed 38.42% inhibition, which is similar to the inhibition achieved by 100nM of each drug alone. The result suggests that the combination of reduced dose of each drug, by 25% or 12.5% from the baseline IC 50 dose, showed equivalent inhibitor activity to the baseline IC 50 concentration of each drug monotherapy in the K562/T315 mut cell line. Similarly, when AXI and ASC were combined at a reduced concentration by a half or a quarter and tested in the BaF3/T315I mut cell line, a higher inhibitory activity was observed compared to the baseline IC 50 concentration of each drug monotherapy. The ZIP score was calculated as 31.7 and 20.64 in K562/T315 mut and BaF3/T315I mut cell line. Given that ZIP score was above 10 each, synergistic enhancement of inhibitory activity was demonstrated against T315I mutant CML cells between AXI and ASC. Conclusion: This result suggests synergistically enhanced inhibitory activity of dual blockade using AXI combined with ASC specifically for T315I mutant CML. This approach will be promising against CML cells carrying compound mutation with T315I mutation, which is highly resistant even to Ponatinib or Asciminib.

DATABASE EVALUATION AND RELIABILITY CALIBRATION FOR FLEXURAL STRENGTH OF HYBRID FRP/STEEL-RC BEAMS

Concrete beams reinforced with hybrid fiber-reinforced polymer (FRP) and steel bars combine the advantages of both FRP and steel. The corrosion resistance and service-life are improved by placing the FRP bars, while steel bars will provide better serviceability through the higher elastic modulus and better ductility through yielding. However, there are no design provisions for hybrid FRP/steel-RC members, and the design models were evaluated over limited experimental data. This study presents a statistical and reliability evaluation for designing hybrid FRP/steel-RC beams under flexure over a worldwide experimental database. The database comprises 136 hybrid FRP/steel-RC beams tested under flexure. Theoretical balanced failure states have shown the ability to correctly predict the failure mode of the specimens. Two specimens were classified inaccurately in terms of failure mode, which is attributed to the variability in the materials’ properties. Statistical evaluation for moment predictions for specimens with failure mode 2 (steel yield-concrete crushing) showed a mean, standard deviation, and coefficient of variation of 1.143, 0.169, and 14.7%, respectively. Additionally, a reliability analysis is conducted to calibrate and recommend the strength reduction factor for the ACI 440 provisions. Targeting a reliability index of 3.5, a strength reduction factor of ϕ=0.80 is selected as the appropriate.