Development of deep neural network model to predict the compressive strength of FRCM confined columns

The present study describes a reliability analysis of the strength model for predicting concrete columns confinement influence with Fabric-Reinforced Cementitious Matrix (FRCM). through both physical models and Deep Neural Network model (artificial neural network (ANN) with double and triple hidden layers). The database of 330 samples collected for the training model contains many important parameters, i.e., section type (circle or square), corner radius r_c, unconfined concrete strength f_co, thickness n_t, the elastic modulus of fiber E_f , the elastic modulus of mortar E_m. The results revealed that the proposed ANN models well predicted the compressive strength of FRCM with high prediction accuracy. The ANN model with double hidden layers (APDL-1) was shown to be the best to predict the compressive strength of FRCM confined columns compared with the ACI design code and five physical models. Furthermore, the results also reveal that the unconfined compressive strength of concrete, type of fiber mesh for FRCM, type of section, and the corner radius ratio, are the most significant input variables in the efficiency of FRCM confinement prediction. The performance of the proposed ANN models (including double and triple hidden layers) had high precision with R higher than 0.93 and RMSE smaller than 0.13, as compared with other models from the literature available.     

Application of machine learning technique for predicting and evaluating chloride ingress in concrete

The degradation of concrete structure in the marine environment is often related to chloride-induced corrosion of reinforcement steel. Therefore, the chloride concentration in concrete is a vital parameter for estimating the corrosion level of reinforcement steel. This research aims at predicting the chloride content in concrete using three hybrid models of gradient boosting (GB), artificial neural network (ANN), and random forest (RF) in combination with particle swarm optimization (PSO). The input variables for modeling include exposure condition, water/binder ratio (W/B), cement content, silica fume, time exposure, and depth of measurement. The results indicate that three models performed well with high accuracy of prediction (R²≥ 0.90). Among three hybrid models, the model using GB_PSO achieved the highest prediction accuracy (R² = 0.9551, RMSE = 0.0327, and MAE = 0.0181). Based on the results of sensitivity analysis using SHapley Additive exPlanation (SHAP) and partial dependence plots 1D (PDP-1D), it was found that the exposure condition and depth of measurement were the two most vital variables affecting the prediction of chloride content. When the number of different exposure conditions is larger than two, the exposure significantly impacted the chloride content of concrete because the chloride ion ingress is affected by both chemical and physical processes. This study provides an insight into the evaluation and prediction of the chloride content of concrete in the marine environment.     

Comparison of Sin+ and Gen+ (n=2-15) Cationic Structures

We designed and optimized a large number of the isomers of Si12+ at the level of density functional theory (DFT)-B3LYP/6-311++G(d) using the Gaussian 03 software package. An unambiguous structure of the Si12+ cluster is presented, whose IR spectrum agrees well with the experiment result. The most stable geometric structures of Gen+(n=2-15) clusters were determined by the all-electron PBE/DND method in DMol3 of the Material Studio Package, and compared with those of the corresponding Sin+ geometries. Most structures of Gen+ and Sin+ are similar except the ones of those for n=9, 12, 13 and 14, and the pentagonal bipyramid motif and the tri-capped trigonal prism(TTP) motif often appear in the Sin+ and Gen+(n=7-15) structures(except for Si14+).     

Comparison of Two Methods of Obtaining Approximate Confidence Intervals for System Reliability

Some specific comparisons are made in this note between the use of the asymptotic Chi-square distribution of the likelihood ratio and the asymptotic normality of the maximum likelihood estimates to obtain confidence interval for reliabilities of arbitrary systems when only failure data on the components is known. In all the comparisons made, using moderate samples and systems of average complexity, the asymptotic Chi-square appears to give much more accurate confidence intervals. Although the asymptotic Chi-square method requires more computation for most systems than does the method based on asymptotic normality, these examples indicate the Chi-square method would yield superior results in most practical instances.     

EVENT-BASED INTELLIGENT CONTROL USING ENDOMORPHIC NEURAL NETWORK MODEL

In event-based control, a controller checks the responses of sensors about commands with time constraints. To do this, the event-based controller should have some information about the dynamics of the plant at discrete levels, its desired state transitions, and inputs to move the state transitions. In an existing modelling method, the information is represented by a tabular form, which is not adaptable to the variation of set positions. An artificial neural network was taken as a new modelling method to solve this problem. Experiments show that this neural network model works well in the dynamic variation of set positions. This endomorphic neural network modelling helps us to construct a more autonomous event-based controller.     

企业并购后信息整合策略之个案研究

企业并购是企业强化竞争优势的工具，信息整合则是并购后整合成功的关键因素。诸多学者认为并购后的信息整合策略应校准于并购策略，本研究则认为信息整合策略应考虑：（1）信息部门的协同合作；（2）各部门在整合策略上的相互配合，将以实务观点探讨企业并购后的信息整合策略。 本研究基于企业工程（Enterprise Engineering）以及校准观点发展一信息整合策略分析架构，藉由个案研究探讨并购后：（1）并购双方规划信息整合策略时的关键因素；（2）信息整合策略规划与执行的成功因素；（3）信息系统与营运流程整合的标准化。研究结果显示，信息整合策略的规划应参考其它部门的整合策略，而各部门关键人员参与，以及信息流程与商业流程标准化皆有助信息整合策略的执行。     

SEQUENTIAL LOOP CLOSING IDENTIFICATION OF MULTIVARIABLE PROCESSES USING THE BIASED RELAY FEEDBACK METHOD

Each controller in multiloop control systems for multivariable processes can be tuned sequentially with the ultimate information for the paired input and output while former loops have been closed, and hence, single-input single-output autotuning methods can be applied. In this sequential autotuning for multiloop control systems, several iterations are usually required for better control performances. Especially when pairings are undesirable, the autotuning sequences should be repeated with correct pairings, which result in long field experiments. Here, to avoid this drawback, a simple method to identify process models while loops are being sequentially tuned is proposed. The identified models can be used to correct pairings of multiloop control systems and to improve tuning performances without several field iterations. In addition, they can be used to obtain model-based control systems such as decoupling control systems.     

HLA class II associations in juvenile Graves' disease: indication of a strong protective role of the DRB1*0701,DQA1*0201 haplotype

Previous studies have shown that HLA-DRB1*0301 and DQA1*0501 are associated with susceptibility to Graves' disease. Ninety Danish patients with early onset of Graves' disease and 102-192 controls were analyzed for HLA-DR and -DQ to investigate if the same associations exist in the juvenile form of Graves' disease. Both DRB1*0301 and DQA1*0501 were highly significantly increased in the patients with relative risks of 8.0 and 4.6, which are higher than those seen in adults. Stratification showed that DRB1*0301 is more strongly associated than DQA1*0501. Surprisingly, the DRB1*0701,DQA1*0201 haplotype was completely absent from this group of patients, indicating a strong protective role of this haplotype in juvenile Graves' disease.     

Inactivation of Pseudomonas fluorescens by High Voltage Electric Pulses

A 30 kV pulsed power treatment system was designed and developed to process fluid materials. Our objective was to investigate the effects of high voltage electric pulses on microbial inactivation in an aqueous solution under different operating conditions and fluid properties. Electric field strength at 10 kV/cm for 10 pulses (2 set pulse period and 2 Fsec pulse width) with a spike of reverse polarity resulted in significant microbial control. P. fluorescens in various aqueous solutions were reduced in population by more than six log cycles. However, the critical electric field strength was affected by the nature of the pulse waveform across the treatment chamber which, in turn, was a function of electrode distance and fluid properties. Sudden charge reversal immediately at the end of a pulse provided maximum microbial decay.