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The mechanical properties of reinforced concrete (RC) structures are closely related to the bond stress-slip relationship of rebar-concrete interface. In this paper, a novel bond stress-slip model under monotonic and cyclic loading is proposed. For interfacial behaviour under monotonic loading, an innovative micro-element model, consisting of a spring element, a friction element and a switch element, is proposed to effectively characterize the interfacial micro-damage mechanical behaviours in a physical sense. By adopting a parallel system of micro-elements and setting the fracture threshold of individual spring element as a random variable, the expressions of the bond stress-slip relationship and interfacial damage variable are derived. Subsequently, succinct practical expressions for the bond strength, peak slip under monotonic loading are formulated. The bond stress-slip model for rebar-concrete interface under monotonic loading are further established. For cyclic constitutive model of rebar-concrete interface, a new method of cyclic correction factor for the degradation law of bond strength is developed. The bond strength degradation under cyclic loading affected by the number of cycles, maximum slip value and opposite loading can be reflected with reasonable accuracy. Furthermore, the predictions calculated by the proposed model under monotonic and cyclic loading are in favorable agreement with experimental results. All parameters in the model have clear physical meaning.

A normally off hydrogen-terminated diamond (H-diamond) metal-oxide-semiconductor field effect transistor (MOSFET) is realized by using lanthanum oxide (La2O3) gate dielectric. The threshold voltage is demonstrated to be −0.797 V, indicating that the La2O3-gated H-diamond MOSFET has normally off characteristics. The normally off mode could be greatly ascribed to the low work function of La2O3. Based on the capacitance–voltage (C–V) curves, the dielectric constant of La2O3 is calculated to be as high as 25.6. Moreover, the small hysteresis voltage extracted from the C–V curves exhibits low trapped charge density in the La2O3 layer. The maximum drain–source current, maximum transconductance, subthreshold swing, effective mobility, current on/off ratio, and sheet hole density of La2O3-gated MOSFET with a gate length of 2 μm are calculated to be −13.55 mA/mm, 4.37 mS/mm, 161 mV/dec, 202.2 cm2/V·s, 108, and 6.53 × 1012 cm−2, respectively. This work will significantly promote the development of normally off H-diamond MOSFET devices.

This paper proposes a new kind of U-type reinforcement ferrule (URF) connection for the assembly of precast concrete (PC) components, which has a good fault-tolerance ability and low cost of construction with a simple post-grouting process. The modified connection features welded U ferrules, which can increase the bonding mechanism and internal stress transmission in the spliced region. A series of quasi-static cyclic tests were performed on URF-connected PC columns with different welding patterns. Comprehensive comparisons were performed on the load-bearing capacity, the plastic energy dissipation ability, the internal force transmission, the column ductility and the stiffness. The results indicated that PC columns with welded URF connections can ensure better lateral resistance than the cast-in-place concrete column. The weld type and length influence the connection performance, and the 15d-welded and full-length welded URF connections presented better connection performance and were suggested for the assembly of PC columns. Then, finite element analyses were performed to further reveal the working and failure mechanisms and the affecting mechanism of some working parameters. Parametric simulations indicated that the increase in the axial load ratio and the concrete strength in the postcast region had a slight but limited effect on improving the lateral resistance of the column connection.

Four-wheel alignment parameters are important indexes to promise handling stability when automobile drives in straight line. Aiming at the problems of traditional four-wheel alignment measurement - kingpin inclination angle and kingpin caster angle are calculated indirectly and approximately by geometrical relationship, which cannot be measured directly or have large measurement error, etc. This paper proposes a new technique to obtain the four-wheel alignment parameters by means of installing an intelligent triple-axis acceleration sensor unit on the wheel to make inertia measurement on wheel alignment angle so as to establish a brand new four-wheel alignment attitude parameter calculation model to acquire four-wheel alignment parameters. The new technique proposed here can establish a four-wheel alignment parameters calculation model to acquire the parameters through installing an intelligent triple-axis acceleration sensor unit on the wheel. The experiment results indicate that under experimental conditions, the indication error of wheel alignment parameter (γwi, αwi, γki, θki) is (-0.01°, 0.00°, -0.65°, -0.31°), and the indication variance is (0.45°, 0.63°, 1.00°, 0.49°). This new technique is able to test the four-wheel alignment parameters quickly, dynamically and accurately, achieve a better measurement accuracy on performance index, and realize expected effect.

Aiming at the problem that measurement models on different parameters would produce different theoretical errors, this paper proposes an error analysis method on parameters measurement model of wheel-mounted four-wheel alignment. Through the analysis of theoretical errors on different measurement models, we select the one with less error to improve measurement accuracy. The experiment results indicate that under experimental conditions, measurement model II on wheel camber angle, model I on kingpin inclination angle and model II on kingpin caster angle generate the less error respectively. This error analysis method can help select the optimal four-wheel alignment parameters measurement model and the experimental results are consistent with the theoretical analysis.

In this paper, an optimal output consensus problem is studied for discrete-time linear multiagent systems subject to external disturbances. Each agent is assigned with a local cost function which is known only to itself. Distributed protocols are to be designed to guarantee an output consensus for these high-order agents and meanwhile minimize the aggregate cost as the sum of these local costs. To overcome the difficulties brought by high-order dynamics and external disturbances, we develop an embedded design and constructively present a distributed rule to solve this problem. The proposed control includes three terms: an optimal signal generator under a directed information graph, an observer-based compensator to reject these disturbances, and a reference tracking controller for these linear agents. It is shown to solve the formulated problem with some mild assumptions. A numerical example is also provided to illustrate the effectiveness of our proposed distributed control laws., 15 pages, 3 figures

The potential association between coal-burning arsenic exposure and type 2 diabetes (T2D) was examined through a case control study, conducted in coal-burning arsenic poisoning areas in the Guizhou Province. This study included patients diagnosed with type 2 diabetes. Control subjects without type 2 diabetes were recruited randomly after gender and age 1:1 matching. All subjects completed questionnaire surveys and underwent physical examination and whole blood arsenic level testing. The whole blood arsenic level was associated with a significant increase in the risk of type 2 diabetes (75th versus 25th, adjusted OR = 1.76, 95% CI: 1.03-3.01). However, a nonlinear relationship was observed between the blood arsenic level and type 2 diabetes. The risk of type 2 diabetes increased with blood arsenic levels above 3.69 μg/L (Log As ≥0.57). The subgroup analysis revealed that blood arsenic levels were associated with significantly increased risk of type 2 diabetes in people who ever smoked (P .05), particularly those who smoked ≥15 years (adjusted OR = 3.15, 95% CI: 1.9-7.28). Therefore, prolonged arsenic exposure, even at a low level, is associated with a higher prevalence of type 2 diabetes in a nonlinear pattern. Blood arsenic levels less than 3.69 μg/L may be considered safe with respect to the risk of T2D. However, smoking, particularly smoking ≥15 years, may be associated with the development of diabetes in patients with arsenic exposure.

As a nonlinear multivariate calibration method, extreme learning machine (ELM) has recently received increasing attention for its fast learning speed and excellent generalized performance. However, it is implemented normally under the empirical risk minimization scheme, and is prone to generate a large-scale and over-fitting model. Least absolute shrinkage and selection operator (LASSO) based ELM (LASSO-ELM) is a simple and efficient approach to avoid over-fitting and obtain an appropriate network structure. Unfortunately, when the initial hidden layer output matrix is in a high dimensional feature space, solving the LASSO problem remains a challenge. To improve the efficiency of solving high-dimension LASSO, we propose interval LASSO based ELM (iLASSO-ELM), which is generated by incorporating interval selection of hidden layer output matrix into original LASSO-ELM. The proposed model combines the coarse screening of interval selection and fine screening of LASSO. Thus, it can identify the relevant hidden nodes quickly and prevent over-fitting. A comparison of the proposed iLASSO-ELM with six other models, namely, ELM, partial least square based ELM (PLS-ELM), ridge regression based ELM (RR-ELM), elastic net based ELM (EN-ELM), LASSO-ELM and Least-Squares Support Vector Machines method (LS-SVM), was evaluated on four benchmark-near infrared (NIR) spectroscopic datasets. Additionally, the Wilcoxon signed rank test was used to statistically compare the predictive performance of the two competing calibration models. Experimental results show that iLASSO-ELM has the minimum root mean square errors of predictions and performs, at least statistically, not worse than other models.

This study investigates a novel prefabricated concrete frame beam with an innovative assembly pattern that is cost-effective, reliable, practical, and has a good fault-tolerance. Quasi-static cyclic experiments were carried out on a cast-in-place (CIP) beam and three precast concrete (PC) beams with different lengths or widths of protruding beam stumps. The cracking propagation mode , load-bearing capacity, beam ductility, beam stiffness , and plastic energy dissipation ability were comparatively analysed. It is concluded that the hysteretic behaviour of the PC beams with the innovative assembly pattern suggested in this study was similar to that of the CIP. Moreover, PC beams with a wider protruding beam stump displayed better seismic performance . In particular, development curves of crack width with cyclic load were obtained, which showed that the cracks widened gradually as the number of load cycles increased, and the main crack width increased significantly around the peak load. A finite element model (ABAQUS) was established to further reveal the working mechanisms and conduct a parametric investigation of the PC beams. Parametric simulations indicated that a reasonable and relatively larger thickness of the U-shaped beam shell and higher post-pouring concrete strength are recommended for practical applications of the proposed novel prefabricated concrete frame beams.

The paper presents a passive auto-focusing technology by combining the discrete cosine transform(DCT) algorithm and small univalve segment assimilating nucleus(SUSAN) algorithm with evaluation function. The new method has the advantage of unimodal, local extremum and sensitivity of the focus curve. For the focusing search algorithm, the improved mountain climbing method which combined the large step and small step has been proposed, it makes the micro-vision system could find the focal plane more quickly and accurately.

For concrete, fatigue is an essential mechanical behavior. Concrete structures subjected to fatigue loads usually experience a progressive degradation/damage process and even an abrupt failure. However, in the literature, certain essential damage behaviors are not well considered in the study of the mechanism for fatigue behaviors such as the development of irreversible/residual strains. In this work, a damage model with the concept of mode-II microcracks on the crack face and nearby areas contributing to the development of irreversible strains was proposed. By using the micromechanics method, a micro-cell-based damage model under multi-axial loading was introduced to understand the damage behaviors for concrete. By a thermodynamic interpretation of the damage behaviors, a novel fatigue damage variable (irreversible deformation fatigue damage variable) was defined. This variable is able to describe irreversible strains generated by both mode-II microcracks and irreversible frictional sliding. The proposed model considered both elastic and irreversible deformation fatigue damages. It is found that the prediction by the proposed model of cyclic creep, stiffness degradation and post-fatigue stress-strain relationship of concrete agrees well with experimental results.

In this paper, a distributed output optimization problem is studied for discrete-time linear multi-agent systems. Different from most of existing distributed optimization literature, we assume the decision variables here are determined as outputs of linear agents. The design goal is to find distributed feedbacks for these high-order agents and collectively minimize the sum of individual objective functions. To overcome the difficulties brought by high-order dynamics, we first construct a dynamic estimator to generate the global optimal point, and then embed this estimator to some proper tracking controllers for these agents. The resultant control is shown to solve the formulated problem with some mild assumptions.

This paper presents a novel 3-degree-of-freedom (DOF) micromachined vibratory gyroscope with completely decoupled structural design. A single-DOF drive mode and a complete 2-DOF vibratory structure, including proof mass, proof frame and detection mass, are applied in designed structure. The drive mode and sense mode are linked by proof mass and proof frame, which also isolate driving element (driving frame) and sensing element(detection mass) to achieve structural decouple. Meanwhile, the 2-DOF sense mode could provide a flat region between two resonant frequencies, which improves the robustness of micromachined vibratory gyroscope. The finite element analysis(FEA) illustrated the resonant frequency of drive mode is between two resonant frequencies of sense mode, showing the feasibility of the proposed gyroscope structure. The simulation results demonstrated a 3-dB bandwidth of 400Hz from 4.8 kHz to 5.2 kHz in sense mode, which improves the inherent robustness to structural parameters and environmental variations. In addition, the structure decoupling is verified through the simulation and the driving elements and sensing elements are immune to vibration of sense mode and drive mode respectively.

This paper presents the design principle and analysis of a novel gyroscope employing the motion of charged particle in uniform magnetic field, which is mainly comprised of electronic emitter, magnetic field provider, electronic detector and timer. The operational principle of the proposed gyroscope is that charged particle moving with a circle in steady magnetic field will not be affected by the rotation of the device and then in terms of the moving time difference of charged particle in magnetic field, the rotation angle during its moving could be calculated. Meanwhile, the size of the device and scale factor are discussed.

A novel prefabricated concrete frame beam with an innovative assembling pattern is developed and studied in this paper, and it is characterized by simple operation, economical and good fault-tolerance performance. Quasi-static tests were carried out on one monolithic beam and two PC beams to investigate the influence of the constraint condition of column ends on the hysteretic behavior of the proposed beams. Analyses and comparisons were conducted in terms of the cracking propagation pattern, the failure mode, the load-bearing capacity, the beam ductility, the beam stiffness and the plastic energy dissipation ability. It is concluded that the constraint condition of column ends had a great influence on the failure mode of the proposed beams. Furthermore, the PC frame beam with restricted column ends had relatively higher load-bearing capacity and stiffness, slightly lower ductility and energy dissipation ability compared with that of PC frame beam with free column ends. In general, the seismic performance of the proposed PC beams was similar with that of the monolithic one under the same constraint condition of column ends. Note that the results of the evolution of the crack width versus the cyclic load were obtained and especially a significant increasing of the crack width after the beam yielded. Finite element analysis method was introduced to conduct a further analysis of the PC beams. The parametric analysis results recommended a reasonable and relatively small stirrup spacing for application of the proposed novel PC frame beams.

With its simple theory and strong implementation, extreme learning machine (ELM) becomes a competitive single hidden layer feed forward networks for nonlinear multivariate calibration in chemometrics. To improve the generalization and robustness of ELM further, stacked generalization is introduced into ELM to construct a modified ELM model called stacked ensemble ELM (SE-ELM). The SE-ELM is to create a set of sub-models by applying ELM repeatedly to different sub-regions of the spectra and then combine the predictions of those sub-models according to a weighting strategy. Three different weighting strategies are explored to implement the proposed SE-ELM, such as the Winner-takes-all (WTA) weighting strategy, the constraint non-negative least squares (CNNLS) weighing strategy and the partial least squares (PLS) weighting strategy. Furthermore, PLS is suggested to be selected as the optimal weighting method that can handle the multi-colinearity among the predictions yielded by all the sub-models. The experimental assessment of the three SE-ELM models with different weighting strategies is carried out on six real spectroscopic datasets and compared with ELM, back-propagation neural network (BPNN) and Radial basis function neural network (RBFNN), statistically tested by the Wilcoxon signed rank test. The obtained experimental results suggest that, in general, all the SE-ELM models are more robust and more accurate than traditional ELM. In particular, the proposed PLS-based weighting strategy is at least statistically not worse than, and frequently better than the other two weighting strategies, BPNN, and RBFNN.

This paper presents a novel multi-degree-of-freedom (multi-DOF) micromachined vibratory gyroscope design operated at atmospheric pressure. In this design, the complete 2-DOF vibratory structure is utilized in drive-mode and sense-mode and also, the 2-DOF sense-mode is implemented in both driving frame and proof frame, which form the double 2-DOF sense-modes. The 2-DOF vibratory structure could provide drive-mode and sense-mode with large bandwidth and the double 2-DOF sense-modes could provide high gain of gyroscope system, which improves the inherent robustness and sensitivity simultaneously. The simulation results demonstrate that the summed signal of drive-mode dynamic response is consistent with that of sense-mode and that the gain of proposed multi-DOF micromachined vibratory gyroscope can reach up to −10 dB, increased by above 8 dB compared to the design with single 2-DOF sense-mode. Meanwhile, the 3 dB bandwidth of gyroscope system is larger than 200 Hz.

In this paper, a novel micromachined vibratory gyroscope is proposed, where a two degree-of-freedom (2-DOF) architecture with two masses and three springs is utilized in both drive-mode and sense-mode. The 2-DOF dynamic structure provides the inherently robustness of both modes against structural and environmental variations. The proposed gyroscope demonstrates drive-mode 3-dB bandwidth of 190 Hz and sense-mode 3-dB bandwidth of 300 Hz at the operational frequency 5.0 kHz. The simulation results confirmed that the resonant frequency and bandwidth of the drive-mode are highly matched with those of sense-mode in the region of 4.90-5.09 kHz. So the gyroscope can be operated robustly in the region of 4.90-5.09 kHz. The proposed device can also be started quickly and suppress the pulse noise.

Dynamic contact analysis on the level of surface asperities is a powerful means to study the wear behavior of surface and the physical contact properties of interface. This article established an analytical model of asperity contacts between nominal rough planes during sliding process based on the statistical contact analysis of rough surfaces. The analytical expression describes the mathematical relationship between the total number of asperity contacts and its main influencing factors including the nominal contact area, sliding distance, the spacing between two contact planes, and surface topographical parameters. A simulation model of dynamic contact of asperities on rough planes was established to rapidly calculate the number of asperity contacts during the sliding process. The comparison between the calculation results and simulation results of the number of asperity contacts obtained through multifactor and multilevel analysis verified the rationality of the analytical model.

Naive Bayesian classifier is a simple classification method based on Bayes statistics, which is one of the most popular classifiers and has been successfully applied to many fields. To improve the generalization ability of the naive Bayesian classifier, discriminative learning of the naive Bayesian classifier is researched. In this paper, a parameter learning algorithm AENB of the naive Bayesian classifier is proposed. This algorithm adopts the Adaboost’s classifier ensemble framework, sequentially generates a series of individual classifiers with parameters, and obtains parameter expectations by summing the weighting parameters of each individual classifier. In the final, the naive Bayesian classifier with parameter expectations is constructed. The experimental results show that the AENB algorithm improves classification accuracy of the naive Bayesian classifier in the most cases. Furthermore, compared with the naive Bayesian classifier ensemble, AENB requires less space because there is no need to save parameters of individual classifiers.