Field Tests to Investigate the Penetration Rate of Piles Driven by Vibratory Installation

Factors directly affecting the penetration rate of piles installed by vibratory driving technique are summarized and classified into seven aspects which are driving force, resistance, vibratory amplitude, energy consumption, speeding up at the beginning, pile plumbness keeping, and slowing down at the end, from the mechanism and engineering practice of the vibratory pile driving. In order to find out how these factors affect the penetration rate of the pile in three major actors of vibratory pile driving: (i) the pile to be driven, (ii) the selected driving system, and (iii) the imposed soil conditions, field tests on steel sheet piles driven by vibratory driving technique in different soil conditions are conducted. The penetration rates of three different sheet pile types having up to four different lengths installed using two different vibratory driving systems are documented. Piles with different lengths and types driven with or without clutch have different penetration rates. The working parameters of vibratory hammer, such as driving force and vibratory amplitude, have great influences on the penetration rate of the pile, especially at the later stages of the sinking process. Penetration rate of piles driven in different soil conditions is uniform because of the different penetration resistance including shaft friction and toe resistance.

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Laboratory Performance Evaluation of High Modulus Asphalt Concrete Modified with Different Additives

The objective of this study is to evaluate comprehensive performance of high modulus asphalt concrete (HMAC) and propose common values for establishing evaluation system. Three gradations with different modifiers were conducted to study the high and low temperature performance, shearing behavior, and water stability. The laboratory tests for HMAC included static and dynamic modulus tests, rutting test, uniaxial penetration test, bending test, and immersion Marshall test. Dynamic modulus test results showed that modifier can improve the static modulus and the improvements were remarkable at higher temperature. Moreover, modulus of HMAC-20 was better than those of HMAC-16 and HMAC-25. The results of performance test indicated that HMAC has good performance to resist high temperature rutting, and the resistances of the HMAC-20 and HMAC-25 against rutting were better than that of HMAC-16. Then, the common values of dynamic stability were recommended. Furthermore, common values of HMAC performance were established based on pavement performance tests.

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The Impact of Environmental Factors in Influencing Epigenetics Related to Oxidative States in the Cardiovascular System

Oxidative states exert a significant influence on a wide range of biological and molecular processes and functions. When their balance is shifted towards enhanced amounts of free radicals, pathological phenomena can occur, as the generation of reactive oxygen species (ROS) in tissue microenvironment or in the systemic circulation can be detrimental. Epidemic chronic diseases of western societies, such as cardiovascular disease, obesity, and diabetes correlate with the imbalance of redox homeostasis. Current advances in our understanding of epigenetics have revealed a parallel scenario showing the influence of oxidative stress as a major regulator of epigenetic gene regulation via modification of DNA methylation, histones, and microRNAs. This has provided both the biological link and a potential molecular explanation between oxidative stress and cardiovascular/metabolic phenomena. Accordingly, in this review, we will provide current insights on the physiological and pathological impact of changes in oxidative states on cardiovascular disorders, by specifically focusing on the influence of epigenetic regulation. A special emphasis will highlight the effect on epigenetic regulation of human’s current life habits, external and environmental factors, including food intake, tobacco, air pollution, and antioxidant-based approaches. Additionally, the strategy to quantify oxidative states in humans in order to determine which biological marker could best match a subject’s profile will be discussed.

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Study of Testicular Structure in Fetuses with Prune Belly Syndrome

Purpose. To compare the structure of the testis in fetuses with prune belly syndrome (PBS) to normal controls. Materials and Methods. We studied 6 testes obtained from 3 fetuses with PBS and 14 testes from 7 male fetuses. The testicular specimens were cut into 5-μm thick sections and stained with hematoxylin and eosin (HE), to observe the seminiferous tubules; Weigert’s solution to observe elastic fibers; and picrosirius red to observe collagen. The images were captured with an Olympus BX51 microscope and Olympus DP70 camera. The stereological analysis was done with the Image Pro and Image J programs. Means were statistically compared using the Mann–Whitney test (). Results. Quantitative analysis documented no differences () in number of seminiferous tubules (ST) in PBS testes (mean = 8.87%, ), when compared to the control (mean = 11.4%, ) and no differences () in diameter of ST in PBS testes (mean = 52.85 μm, ) when compared to the control group (mean = 53.17 μm, ), but we did observe a lower number () of Leydig cells in the PBS testes (mean = 67.03% and ) when compared to the control group (mean = 90.1% and ). Conclusions. Our study showed a lower concentration of Leydig cells in the triad syndrome fetuses.

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Inference of Biochemical S-Systems via Mixed-Variable Multiobjective Evolutionary Optimization

Inference of the biochemical systems (BSs) via experimental data is important for understanding how biochemical components in vivo interact with each other. However, it is not a trivial task because BSs usually function with complex and nonlinear dynamics. As a popular ordinary equation (ODE) model, the S-System describes the dynamical properties of BSs by incorporating the power rule of biochemical reactions but behaves as a challenge because it has a lot of parameters to be confirmed. This work is dedicated to proposing a general method for inference of S-Systems by experimental data, using a biobjective optimization (BOO) model and a specially mixed-variable multiobjective evolutionary algorithm (mv-MOEA). Regarding that BSs are sparse in common sense, we introduce binary variables indicating network connections to eliminate the difficulty of threshold presetting and take data fitting error and the -norm as two objectives to be minimized in the BOO model. Then, a selection procedure that automatically runs tradeoff between two objectives is employed to choose final inference results from the obtained nondominated solutions of the mv-MOEA. Inference results of the investigated networks demonstrate that our method can identify their dynamical properties well, although the automatic selection procedure sometimes ignores some weak connections in BSs.

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DCA-Based Real-Time Residual Useful Life Prediction for Critical Faulty Component

Residual useful life (RUL) prediction is significant for condition-based maintenance. Traditional data-driven RUL prediction method can only predict fault trend of the system rather than RUL of a specific system component. Thus it cannot tell the operator which component should be maintained. The innovation of this paper is as follows: () Wavelet filtering based method is developed for early detection of slowly varying fault. () Designated component analysis is introduced as a feature extraction tool to define the fault precursor of a specific component. () Exponential life prediction model is established by nonlinear fitting of the historical RUL and the fault size characterized by the statistics used. Once online detection statistics is obtained, real-time RUL of the critical component can be predicted online. Simulation shows the effectiveness of this algorithm.

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Detection of Emerging Faults on Industrial Gas Turbines Using Extended Gaussian Mixture Models

This paper extends traditional Gaussian mixture model (GMM) techniques to provide recognition of operational states and detection of emerging faults for industrial systems. A variational Bayesian method allows a GMM to cluster with its mixture components to facilitate the extraction of steady-state operational behaviour; this is recognised as being a primary factor in reducing the susceptibility of alternative prognostic/diagnostic techniques, which would initiate false-alarms resulting from control set-point and load changes. Furthermore, a GMM with an outlier component is discussed and applied for direct novelty/fault detection. An advantage of the variational Bayesian method over traditional predefined thresholds is the extraction of steady-state data during both full- and part-load cases, and a primary advantage of the GMM with an outlier component is its applicability for novelty detection when there is a lack of prior knowledge of fault patterns. Results obtained from the real-time measurements on the operational industrial gas turbines have shown that the proposed technique provides integrated preprocessing, benchmarking, and novelty/fault detection methodology.

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