A hybrid proper orthogonal decomposition and next generation reservoir computing approach for high-dimensional chaotic prediction: Application to flow-induced vibration of tube bundles.

Chaotic time series prediction is a central science problem in diverse areas, ranging from engineering, economy to nature. Classical chaotic prediction techniques are limited to short-term prediction of low- or moderate-dimensional systems. Chaotic prediction of high-dimensional engineering problems is notoriously challenging. Here, we report a hybrid approach by combining proper orthogonal decomposition (POD) with the recently developed next generation reservoir computing (NGRC) for the chaotic forecasting of high-dimensional systems. The hybrid approach integrates the synergistic features of the POD for model reduction and the high efficiency of NGRC for temporal data analysis, resulting in a new paradigm on data-driven chaotic prediction. We perform the first chaotic prediction of the nonlinear flow-induced vibration (FIV) of loosely supported tube bundles in crossflow. Reducing the FIV of a continuous beam into a 3-degree-of-freedom system using POD modes and training the three time coefficients via a NGRC network with three layers, the hybrid approach can predict time series of a weakly chaotic system with root mean square prediction error less than 1% to 19.3 Lyapunov time, while a three Lyapunov time prediction is still achieved for a highly chaotic system. A comparative study demonstrates that the POD-NGRC outperforms the other existing methods in terms of either predictability or efficiency. The efforts open a new avenue for the chaotic prediction of high-dimensional nonlinear dynamic systems.

Antiurolithiatic effect of triptonide in ethylene glycol-induced urolithiasis in rats.

Urolithiasis is one of the most prevalent benign urological disorders globally with a high incidence rate. Male Sprague-Dawley rats were chemically induced to have urolithiasis and treated with triptonide and the standard antiurolithic drug cystone. Kidney weight was measured to detect calculi formation, and urinary parameters such as pH, 24-h urine volume, and protein content were measured to analyze the urolithiasis induction in rats. The inorganic ions, organic solutes, antioxidant levels, and inflammatory cytokines were measured in the experimental rats. Triptonide treatment significantly modulated the urinary pH, decreased the protein concentration, and increased the urinary outflow in urolithiasis induced rats. It also significantly decreased the urinary excretion of calcium and phosphorous and increased the excretion of magnesium, potassium, sodium, creatinine, and uric acid. SOD, CAT, and GPx levels were increased in triptonide-treated rats, and it significantly reduced the MDA levels. Triptonide treatment also decreased the levels of inflammatory cytokines and prevented the renal tissue from inflammation. To conclude, our results prove that triptonide significantly prevents calculi formation and protects renal tissue from urolithiasis-induced damage in rats. Further studies may prove triptonide a potent alternative to currently available antiurolithic drugs.

The protective role of corilagin on renal calcium oxalate crystal-induced oxidative stress, inflammatory response, and apoptosis via PPAR-γ and PI3K/Akt pathway in rats.

Kidney stones, also known as calcium oxalate (CaOx) nephrolithiasis, are often asymptomatic, leading to kidney injury and renal failure complications. Corilagin is a gallotannin found in various plants and is known to elicit various biological activities. The present study aimed to elucidate the renoprotective effect of corilagin against the rats' renal stones deposition. The rats were induced for nephrolithiasis (CaOx deposition) using 0.75% ethylene glycol in their drinking water. Then, they were treated with corilagin at 50 and 100 mg/kg/day for 4 weeks. At the end of the experimental period, the rats were killed; blood and renal tissues were collected for various histological, biochemical, and gene expression analyses. The results demonstrated that the rats had renal calculi displaying a significant increase in serum creatinine (59.39 µmol/L) and blood urea nitrogen (19.03 mmol/L) levels compared with controls. Moreover, the malondialdehyde (13.29 nmol/mg) level was found to increase with a profound reduction in antioxidants' activities with upregulated inflammatory cytokines. In contrast, the RT-PCR and immunohistochemistry analysis demonstrated a substantial reduction in cell survival markers PPAR-γ and PI3K/Akt with an apparent increase in apoptosis markers genes expressions in rats suffering from renal stones. Thus, the present study results suggest that corilagin could suppress renal CaOx crystal-induced oxidative stress, inflammatory response, and apoptosis via PPAR-γ and PI3K/Akt-mediated pathway.

A Data-Driven Damage Identification Framework Based on Transmissibility Function Datasets and One-Dimensional Convolutional Neural Networks: Verification on a Structural Health Monitoring Benchmark Structure.

Vibration-based data-driven structural damage identification methods have gained large popularity because of their independence of high-fidelity models of target systems. However, the effectiveness of existing methods is constrained by critical shortcomings. For example, the measured vibration responses may contain insufficient damage-sensitive features and suffer from high instability under the interference of random excitations. Moreover, the capability of conventional intelligent algorithms in damage feature extraction and noise influence suppression is limited. To address the above issues, a novel damage identification framework was established in this study by integrating massive datasets constructed by structural transmissibility functions (TFs) and a deep learning strategy based on one-dimensional convolutional neural networks (1D CNNs). The effectiveness and efficiency of the TF-1D CNN framework were verified using an American Society of Civil Engineers (ASCE) structural health monitoring benchmark structure, from which dynamic responses were captured, subject to white noise random excitations and a number of different damage scenarios. The damage identification accuracy of the framework was examined and compared with others by using different dataset types and intelligent algorithms. Specifically, compared with time series (TS) and fast Fourier transform (FFT)-based frequency-domain signals, the TF signals exhibited more significant damage-sensitive features and stronger stability under excitation interference. The utilization of 1D CNN, on the other hand, exhibited some unique advantages over other machine learning algorithms (e.g., traditional artificial neural networks (ANNs)), particularly in aspects of computation efficiency, generalization ability, and noise immunity when treating massive, high-dimensional datasets. The developed TF-1D CNN damage identification framework was demonstrated to have practical value in future applications.

Enhanced Intelligent Identification of Concrete Cracks Using Multi-Layered Image Preprocessing-Aided Convolutional Neural Networks.

Crack identification plays an essential role in the health diagnosis of various concrete structures. Among different intelligent algorithms, the convolutional neural networks (CNNs) has been demonstrated as a promising tool capable of efficiently identifying the existence and evolution of concrete cracks by adaptively recognizing crack features from a large amount of concrete surface images. However, the accuracy as well as the versatility of conventional CNNs in crack identification is largely limited, due to the influence of noise contained in the background of the concrete surface images. The noise originates from highly diverse sources, such as light spots, blurs, surface roughness/wear/stains. With the aim of enhancing the accuracy, noise immunity, and versatility of CNN-based crack identification methods, a framework of enhanced intelligent identification of concrete cracks is established in this study, based on a hybrid utilization of conventional CNNs with a multi-layered image preprocessing strategy (MLP), of which the key components are homomorphic filtering and the Otsu thresholding method. Relying on the comparison and fine-tuning of classic CNN structures, networks for detection of crack position and identification of crack type are built, trained, and tested, based on a dataset composed of a large number of concrete crack images. The effectiveness and efficiency of the proposed framework involving the MLP and the CNN in crack identification are examined by comparative studies, with and without the implementation of the MLP strategy. Crack identification accuracy subject to different sources and levels of noise influence is investigated.

Study on Bond Performance between Corroded Deformed Steel Bar and DS-ECC.

In order to study the bond performance between desert sands engineered cementitious composites (DS-ECC) and corrosion steel bars, seven groups of specimens were designed and manufactured. Through the center pull-out test, the effects of different types of desert sands, the rate of corrosion (0, 5, 10 and 15%), and the anchorage length of steel bars (5d and 8d) on the bonding properties of DS-ECC and corrosion steel bars were studied. Moreover, a de-rusting agent was used to remove the corrosion, and three groups of specimens were pulled out from the center of the de-rusted steel bars. The results showed that both Tengger DS-ECC and Mu Us DS-ECC have good bond properties with corrosion steel bars. The bond stress slip curves between DS-ECC and corrosion steel bars can be divided into four stages: the micro-slip, slip stage, failure stage and residual stage. The bond stress slip curves between DS-ECC and de-rusted steel bars can be divided into the micro-slip stage, failure stage and residual stage, and splitting and pulling-out failure occurs in DS-ECC specimens. The ultimate bond strength is the highest when the corrosion rate is 5%. The bond toughness index is positively correlated with the anchorage length of steel bars, and negatively correlated with the corrosion rate of steel bars. According to the test results, the bond-slip mathematical relationship is established.

MicroRNA-218 inhibits the cell proliferation and migration in clear cell renal cell carcinoma through targeting cancerous inhibitor of protein phosphatase 2A.

MicroRNAs (miRs) have emerged as critical modulators of tumor initiation and progression in numerous types of human cancer, including clear cell renal cell carcinoma (ccRCC), which is the most common subtype of renal cell carcinoma. Cancerous inhibitor of protein phosphatase 2A (CIP2A) is a newly characterized oncoprotein and its overexpression has been reported to promote cellular epithelial-mesenchymal transition and the tumor progression of ccRCC. The present study examined the effects of miR-218 on CIP2A expression in ccRCC cells. The results demonstrated that the expression level of miR-218 was lower in ccRCC tissues compared with that in adjacent non-tumor renal tissues. In addition, it was identified that miR-128 could directly bind to the 3'-untranslated region of CIP2A. Furthermore, a negative correlation between the expression levels of miR-218 and CIP2A was detected in ccRCC. Additionally, the downregulation of CIP2A or overexpression of miR-218 in ccRCC cells was revealed to inhibit cell proliferation and migration. In summary, these data suggest that miR-218 serves a role in the regulation of CIP2A and elucidate its consequences on tumor progression, tumor cell proliferation and migration. These results indicate that miR-218 may exhibit potential as a molecular target for the treatment of ccRCC.

Demethylzeylasteral ameliorates inflammation in a rat model of unilateral ureteral obstruction through inhibiting activation of the NF­κB pathway.

The present study investigated the pharmacodynamic role and therapeutic mechanism of demethylzeylasteral in the suppression of inflammation in a rat model of unilateral ureteral obstruction and reduction in nuclear factor (NF)­κB pathway activity. The rats in the unilateral ureteral obstruction model were treated with 30­120 mg/kg demethylzeylasteral for 8 weeks. The activities of tumor necrosis factor (TNF)­α, interleukin (IL)­6 and caspase­3/9, and the protein expression levels of cyclooxygenase (COX)­2 and intercellular adhesion molecule­1 (ICAM­1) and NF­κB p65 were analyzed using ELISA kits and western blot analyses, respectively. Compared with the rats in the unilateral ureteral obstruction model group, demethylzeylasteral treatment markedly inhibited the increased concentrations of serum creatinine and blood urea nitrogen, urinary protein/creatinine ratio, and concentrations of high­density lipoprotein and low­density lipoprotein cholesterol, and prevented kidney damage. In addition, demethylzeylasteral inhibited the levels of TNF­α andIL­6 and suppressed the protein expression levels of COX­2 and ICAM­1 in the kidneys of the rats in the unilateral ureteral obstruction model. Demethylzeylasteral also significantly suppressed the protein expression of NF­κB p65. The results of the present study suggested that demethylzeylasteral unilateral ureteral obstruction and inhibited inflammation via inhibiting the activation of COX­2, ICAM­1 and NF­κB p65, and suppressing the activities of caspase­3/9 in rats with unilateral ureteral obstruction.