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The pavement of a road that is subject to deterioration due to vehicle loads needs quantitative and frequent evaluation. The road profile, which determines the ride quality, is an important property, though its estimation is usually costly and thus, infrequent. In this study, a road profile estimation method using an ordinary-vehicle's responses measured by only a smartphone is developed. The algorithm consists of two steps. At first, an ordinary vehicle is modelled as a half car (HC) and a genetic algorithm (GA) identifies its parameters by using the responses of the vehicle passing over a known-size hump. With the estimated vehicle model, an augmented Kalman filter, in which the road profile is included in the state vector, estimates the road profile; Rauch-Tung-Streiber (RTS) smoothing is employed to improve the accuracy. The observation variables and locations are determined based on an observability analysis. A numerical simulation is conducted to investigate the profile estimation performance in terms of drive speeds, model error, and measurement noise. The experiment is carried out on a 13 km road. The profiles estimated by three types of ordinary vehicles are compared with a reference profile obtained by a laser profiler to validate the proposed method. Results from both the simulation and the experiment show that the combination of the vehicle parameter estimation and the profile estimation methods accurately estimates the road profile with a high degree of accuracy and robustness.

A bridge's natural frequencies are important dynamic properties reflecting the structural condition of the bridge. Numerous studies have been conducted in the field to extract a bridge's natural frequencies from responses of passing vehicles. The bridge frequency peaks are, however, not easily observed, because pavement roughness often influences the spectra of vehicle responses. In this research, a method that extracts the fundamental frequency of a bridge from the responses of an ordinary vehicle with its parameters calibrated in advance is proposed. The method is based on the idea that the vehicle passing across a bridge is excited by two sources, i.e., pavement roughness and bridge vibration. The excitation inputs to the vehicle, i.e., displacement inputs at the front and rear tire locations, are estimated from vehicle responses using a particle filter method. The estimated displacement inputs at the front and rear tires are then subtracted from each other after shifting by a wheel–base distance to eliminate the roughness influence, which commonly appears in both signals. The signal after the subtraction contains only the bridge vibration influence and is used to extract the fundamental frequency of the bridge. This indirect method of bridge frequency extraction is investigated through numerical simulations. A field measurement was also conducted, and it showed that the bridge's fundamental frequency was successfully extracted with a good accuracy for several driving-speed cases.

Passing vehicles cause bridge deformation and vibration. Overloaded vehicles can result in fatigue damage to, or even failure of, the bridge. The bridge response is related to the properties of the passing vehicles, particularly the vehicle weight. Therefore, a bridge weigh-in-motion system for estimating vehicle parameters is important for evaluating the bridge condition under repeated load. However, traditional weigh-in-motion methods, which involve the installation of strain gauges on bridge members and calibration with known weight truck, are often costly and time-consuming. In this paper, a method for the identification of moving vehicle parameters using bridge acceleration responses is investigated. A time-domain method based on the Bayesian theory application of a particle filter is adopted. The bridge pavement roughness is estimated in advance using vehicle responses from a sensor-equipped car with consideration of vehicle-bridge interaction, and it is utilized in the parameter estimation. The method does not require the calibration. Numerical simulations demonstrate that the vehicle parameters, including the vehicle weight, are estimated with high accuracy and robustness against observation noise and modeling error. Finally, this method is validated through field measurement. The resulting estimate of vehicle mass agrees with the measured value, demonstrating the practicality of the proposed method.

Smartphone based Dynamic Response Intelligent Monitoring System (iDRIMS) was developed to evaluate International Roughness Index (IRI) based on dynamic responses of ordinary vehicles [1] . However, the robustness and accuracy were limited. In this paper, iDRIMS is improved mainly by employing frequency domain analysis. The algorithm consists of two steps. The first step is to identify the vehicle model and the second step is to estimate the IRI by utilizing the identified vehicle model. In the first step, a half car (HC) model is selected as the vehicle model and its parameters are identified. The vehicle parameters are identified through a drive tests over a portable hump with a known size. As opposed to previous approach in the time domain using Unscented Kalman filter, the parameters are optimized to minimize the difference between simulation and measured hump responses in the frequency domain using genetic algorithm (GA). IRI is then estimated by measuring acceleration responses of ordinary vehicles. Measured acceleration is converted to the acceleration RMS of the sprung mass of standard quarter car by multiplying a transfer function. The transfer function, estimated through the simulation of the identified HC model as opposed to a QC model in previous approaches, reflects the vehicle pitching motions and sensor installation location. The RMS is further converted to IRI. Numerical simulation is conducted to investigate the IRI estimation performance in terms of various drive speeds and sensor locations. Experiment is carried out at a 13 km road. Inacurate IRI estimation at speed change section is invesigated and compensated. Results from both simulation and experiment indicate that the proposed method accurately estimate IRI with high robustness and efficiency.

Seven Landsat images were used to assess urban expansion and the corresponding thermal characteristics in Fuzhou City, China between 1985 and 2015. The urban surface thermal patterns were investigated by retrieving land surface temperature using a single-window algorithm based on atmospheric calibration. The built-up area doubled from 1985 (10.6 %) to 2015 (23.4 %), mostly from cropland conversions in the urban fringe, and was closely associated with population increase (R2 = 0.93), gross domestic production GDP (R2 = 0.97), and fixed asset investments (R2 = 0.9139), illustrating the contributions of socioeconomic factors to rapid urban expansion. The daytime heating effect (heat source) in urban areas was strongly related to urban expansion, with a high percentage of an impervious surface usually associated with a high surface temperature. The average range over which wetlands affected built-up area, farmland, and road was 381, 321, and 347 m, respectively. The area of influence on construction land and road was 151 and 11 km2, respectively, whereas the average value for farmland was 147 km2. The area of wetland influence decreased during the study period, and therefore one explanation for the increase in the urban heat island (UHI) in Fuzhou City was the weakened cooling effect of wetland.

This paper proposes a robust road profile estimation method and vehicle parameter identification method through an optimization with an objective function and constraint conditions on estimated profiles. The methods require only vehicle response measurements, enabling easy and inexpensive, yet effective, road condition monitoring through the automated processing of smartphone data. A half-car (HC) model, representing both bouncing and pitching motions, is employed for the profile estimation. The road profiles at the front and rear tire locations are included as state variables in the augmented state vector and are estimated by combining the augmented Kalman filter (AKF), Robbins–Monro (RM) algorithm, and Rauch–Tung–Striebel (RTS) smoothing. The two independent state variables, however, correspond to a single physical profile, while their distance coordinates differ by the wheelbase. Therefore, the vehicle parameters are optimized through the minimization of the difference between the identified road profiles at the front and rear tire locations using a genetic algorithm. Three objective functions and three constraint conditions are proposed to automatically select the best vehicle parameters. With this HC model, the road profile is subsequently estimated by combining the AKF, RM, and RTS methods. Through numerical simulations, the accuracy of the profile estimation and validity of the parameter identification are clarified. The influences of different drive speeds and difference between the left and the right profiles are numerically investigated. Drive tests with three different vehicles and a reference laser profiler show that the algorithm can automatically compensate for differences among vehicles with different drive speeds and estimate profiles accurately.

The structural damage incurred in a seismic event is always time-variant. In this paper, a new time-variant structural system identification method is proposed based on a two-stage strategy and incomplete structural acceleration responses. In the first stage, an external excitation identification method is developed for a time-variant structural system. The unknown structural response could be re-constructed with the average acceleration discrete algorithm in this stage. In the second stage, structural parameter is identified and updated with a reduced extended Kalman filter which can improve the computational effort. The re-constructed structural response and identified external excitation are used in the second stage for the damage identification and model updating. The proposed method is validated numerically with the simulation of a fifteen-storey shear frame structure subject to earthquake excitation. A model of a fourteen-storey concrete shear wall building was also studied experimentally with shaking table tests to further validate the proposed method. This shear wall building has a two-storey steel frame on top with base isolation. Both the stiffness of the model and the interface force in the isolator at the bottom of the steel frame during the seismic excitation were estimated with the proposed method. Results from both numerical simulations and laboratory tests indicate that the proposed method can be used to identify structural parameters and external excitations effectively based on a few number of polluted structural acceleration measurements.

Carisbamate (RWJ-333369; (S)-2-O-carbamoyl-1-o-chlorophenyl-ethanol) is a novel investigational antiepileptic drug that exhibits a broad-spectrum of activity in a number of animal models of seizure and drug refractory epilepsy. In an effort to understand the molecular mechanism by which carisbamate produces its antiepileptic actions, we studied its effects on the function of voltage-gated, rat brain sodium and potassium channels and on the repetitive firing of action potentials in cultured rat hippocampal neurons. In whole-cell patch clamp recording, carisbamate resulted in a concentration-, voltage- and use-dependent inhibition of rat Nav1.2, with an IC(50) value of 68 microM at -67 mV. In rat hippocampal neurons, carisbamate similarly blocked voltage-gated sodium channels, with an IC(50) value of 89 microM at -67 mV, and inhibited repetitive firing of action potentials in a concentration-dependent manner (by 46% at 30 microM and 87% at 100 microM, respectively). Carisbamate had no effect on the steady-state membrane potential or voltage-gated potassium channels (K(v)) in these neurons. These inhibitory effects of carisbamate occurred at therapeutically relevant concentrations in vivo, raising the possibility that block of voltage-gated sodium channels by carisbamate contributes to its antiepileptic activity.

Objective To explore physician recommendations regarding radioiodine remnant ablation (RRA) as adjuvant treatment in early-stage well-differentiated thyroid carcinoma (WDTC), their rationale for administration of RRA, and their willingness to involve patients’ opinions in decision making about the use of RRA. Methods We surveyed a representative sample of specialty physicians in Canada and the United States and asked survey participants whether they would recommend adjuvant RRA after thyroidectomy for a 1.6-cm papillary thyroid carcinoma (Likert scale of agreement responses from 1 to 7; strong agreement ≥ 6). Factor analysis was performed to explore the rationale for recommendations. We asked whether physicians accepted the role of patients’ preferences in decision making about administration of RRA, and backward conditional logistic regression analysis was used to identify predictors of strong acceptance. Results The effective response rate for the survey was 56.3% (486 of 864), with 62.8% (295 of 470 respondents) strongly recommending RRA. Strong RRA recommendations were founded in opinions that RRA (1) decreases WDTC-related mortality and recurrence and (2) facilitates WDTC follow-up at low risk of adverse effects. Approximately a third of the survey respondents (152 of 474) strongly agreed with incorporation of patients’ preferences in decision making regarding the use of RRA. Physicians without firm convictions about the efficacy of RRA in decreasing disease-related outcomes and those practicing in the United States were most likely to indicate strong support for incorporating patients’ preferences in decision making about RRA. Conclusion The recommendations of physicians regarding use of adjuvant RRA are founded in beliefs in intervention efficacy and follow-up practices. Physicians in medical practice in the United States and those without strong convictions about RRA efficacy are most likely to incorporate patients’ views in individualizing decisions about RRA therapy. (Endocr Pract. 2008;14:175-184)

The amyloid precursor protein (APP) is a type I integral membrane protein and is processed to generate several intra-cellular and secreted fragments. The physiological role of APP and its processed fragments is unclear. Several mutations have been discovered in APP, which are causative of early-onset, familial, neurological disease, including Alzheimer's disease (FAD). These mutations alter the processing of APP and lead to excess production and extra-cellular deposition of A-beta peptide (Abeta). We have examined the role of APP in a cell culture model of endothelial cell function. The endothelial cell line, Hep-1, was stably transfected with wild-type (wt) and FAD mutant forms of APP (mAPP). Secretion of sAPPalpha was reduced in cell lines over-expressing mAPP when these cells were grown on several different substrates. Levels of secreted Abeta were increased as measured by ELISA in the mutant cell lines. Cell adhesion to laminin-, fibronectin-, collagen I-, and collagen IV-coated culture flasks was reduced in all mAPP-expressing cell lines, while in lines over-expressing wt-APP, adhesiveness was slightly increased. Cell lines over-expressing mAPP differentiated more slowly into capillary network-like structures on Matrigel than those expressing wt-APP. No differences were detected among all cell lines in a migration/invasion assay. The results suggest that APP may have a role in cell adhesiveness and maturation of endothelial cells into capillary-like networks. The reduction in adhesion and differentiation in mutant cell lines may be due to reduced amounts of sAPPalpha released into the culture media or toxic effects of increased extracellular Abeta.