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  • Author: Xiaolin Meng x
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The vertical structural dynamics is a crucial factor for structural health monitoring (SHM) of civil structures such as high-rise buildings, suspension bridges and towers. This paper presents an optimal GPS/accelerometer integration algorithm for an automated multi-sensor monitoring system. The closed loop feedback algorithm for integrating the vertical GPS and accelerometer measurements is proposed based on a 5 state extended KALMAN filter (EKF) and then the narrow moving window Fast Fourier Transform (FFT) analysis is applied to extract structural dynamics. A civil structural vibration is simulated and the analysed result shows the proposed algorithm can effectively integrate the online vertical measurements produced by GPS and accelerometer. Furthermore, the accelerometer bias and scale factor can also be estimated which is impossible with traditional integration algorithms. Further analysis shows the vibration frequencies detected in GPS or accelerometer are all included in the integrated vertical defection time series and the accelerometer can effectively compensate the short-term GPS outages with high quality. Finally, the data set collected with a time synchronised and integrated GPS/accelerometer monitoring system installed on the Nottingham Wilford Bridge when excited by 15 people jumping together at its mid-span are utilised to verify the effectiveness of this proposed algorithm. Its implementations are satisfactory and the detected vibration frequencies are 1.720 Hz, 1.870 Hz, 2.104 Hz, 2.905 Hz and also 10.050 Hz, which is not found in GPS or accelerometer only measurements.



To evaluate the effectiveness of integrated nursing interventions for fatigue in patients with advanced cancer.


Medline, Pubmed, Embase, CINAHL, Web of Science, and the Cochrane Library were searched systematically till June 2017. A systematic review was conducted to collect randomized controlled trials (RCTs) reporting on the effect of nurse-driven interventions to improve fatigue in patients with advanced cancer. Quality assessment was conducted using the Cochrane Collaboration’s risk of bias tool.


Six RCTs involving 736 adult participants were included. The fatigue intensity was improved significantly by nursing interventions. The analyzed results revealed significant improvements in the intervention group: less than 3 months (standard mean difference [SMD] = −0.33, 95% confidence interval [CI] [−0.48, −0.19], P < 0.01) and more than or equal to 3 months (SMD = −0.40, 95% CI [−0.57, −0.24], P < 0.01). Four studies with a moderate risk of bias were judged, and the remaining studies were at high risk of bias.


The results indicate that integrated nursing interventions may relieve fatigue in patients with advanced cancer. However, due to the high risk of bias in most of the included studies and the diversity of interventions, the results and implementation process should be carefully monitored.


Based on deeply analysis for optimization process of basic fruit fly optimization algorithm (FOA), a new improved FOA (IFOA) method is proposed, which modifies random search direction, increases the adjustment coefficient of search radius, and establishes a multi-sub-population solution mechanism. The proposed method can process the nonlinear objective function that has non-zero and non-negative extreme points. In the paper, IFOA method is applied to ill-conditioned problem solution in the field of surveying data processing. Application of the proposed method on two practical examples show that solution accuracy of IFOA is superior to that of three well-known intelligent optimization algorithms and two existing improved FOA methods, and it is also better than truncated singular value decomposition method and ridge estimation method. In addition, compared with intelligent search method represented by particle swarm optimization algorithm, The IFOA method has the advantages of less parameter settings, simple optimization process and easy program implementation. So, IFOA method is feasible, effective and practical in solving ill-conditioned problems.


Network-based Real Time Kinematic (NRTK) GPS positioning is considered to be a superior solution compared to the conventional single reference station based Real Time Kinematic (RTK) GPS positioning technique whose accuracy is highly affected by the distance dependent errors such as satellite orbital and atmospheric biases. NRTK GPS positioning uses raw measurements gathered from a network of Continuously Operating Reference Stations (CORS) in order to generate more reliable error models that can mitigate the distance dependent errors within the area covered by the CORS. This technique has been developed and tested considerably during recent years and the overall performance in terms of achievable accuracies, reliability and mobility is as good as or even better than can be achieved using the conventional RTK GPS positioning technique.

Currently, there are several commercial NRTK services around the world. In the United Kingdom (UK), for instance, Leica Geosystems in partnership with Ordnance Survey has been offering a NRTK GPS service since 2006. This service is called SmartNet and it can provide continuous centimetric level of accuracy to its subscribers.

However, NRTK GPS positioning is particularly constrained by wireless data link coverage, correction transmission delay and completeness, GPS signal availability, etc., which could downgrade the positioning quality of the NRTK results.

The paper presents some preliminary testing results of an investigation of the SmartNet service from the end users' point of view. A snapshot of the service's performance was carried out as part of a recent PhD studentship jointly awarded by the UK's Engineering and Physical Sciences Research Council (EPSRC) and Leica Geosystems (UK) to conduct comprehensive research into NRTK GPS quality control measures at the Institute of Engineering Surveying and Space Geodesy (IESSG), the University of Nottingham. In order to evaluate the service's quality several static and kinematic tests were performed using the same type of equipment and in the same way that the SmartNet subscribers would have used it.

Centimetric accuracy was generally attained during both static and kinematic tests. This high accuracy was only affected by some level of unavailability mainly caused by GPS signal blockage. Additionally, the influence of the number of satellites in view, dilution of precision (DOP) and age of corrections (AoC) over the accuracy and stability of the NRTK GPS solution was also investigated during this research and presented in the paper.


C13H16N2O2, orthorhombic, P212121 (no. 19), a = 7.488(2) Å, b = 9.633(2) Å, c = 16.933(3) Å, V = 1221.4 Å3, Z = 4, Rgt(F) = 0.0671, wRref(F2) = 0.1661, T = 293 K.


Bridges are critical to economic and social development of a country. In order to ensure the safe operation of bridges, it is of great significance to accurately predict displacement of monitoring points from bridge Structural Health System (SHM). In the paper, a CEEMDAN-KELM model is proposed to improve the accuracy of displacement prediction of bridge. Firstly, the original displacement monitoring time series of bridge is accurately and effectively decomposed into multiple components called intrinsic mode functions (IMFs) and one residual component using a method named complete ensemble empirical mode decomposition with adaptive noise (CEEMDAN). Then, these components are forecasted by establishing appropriate kernel extreme learning machine (KELM) prediction models, respectively. At last, the prediction results of all components including residual component are summed as the final prediction results. A case study using global navigation satellite system (GNSS) monitoring data is used to illustrate the feasibility and validity of the proposed model. Practical results show that prediction accuracy using CEEMDAN-KELM model is superior to BP neural network model, EMD-ELM model and EMD-KELM model in terms of the same monitoring data.


The first Bridge Monitoring surveying was carried out in 1996 by the authors, through attaching Ashtech ZXII GPS receivers onto the Humber Bridge’ parapet, and gathering and further analysing the resulting 1 Hz RTK GPS data. Various surveys have subsequently been conducted on the Humber Bridge, the Millennium Bridge, the Forth Road Bridge, the Severn Suspension Bridge and the Avonmouth Viaduct. These were all carried out using survey grade carrier phase/pseudorange GPS and later GNSS receivers. These receivers were primarily dual frequency receivers, but the work has also investigated the use of single frequency receivers, gathering data at 1 Hz, 10 Hz, 20 Hz and even 100 Hz. Various aspects of the research conducted are reported here, as well as the historical approach. Conclusions are shown in the paper, as well as lessons learnt during the development of this work. The results are compared to various models that exist of the bridges’ movements, and compare well. The results also illustrate that calculating the frequencies of the movements, as well as looking at the magnitudes of the movements, is an important aspect of this work. It is also shown that in instances where the magnitudes of the movements of the bridge under investigation are small, it is still possible to derive very accurate frequencies of the movements, in comparison to the existing models.


Samples of activated bentonite and activated bentonite modified with CuCl and CuCl2, separately, were tested as dimethyl sulfide (DMS) adsorbents. The adsorption and desorption behaviours of DMS on the adsorbents were studied systematically. The adsorbents were characterised by nitrogen adsorption, XRD, and DMS-TPD tests. The addition of CuCl and CuCl2 to the activated carbon significantly enhanced the adsorption capacity of DMS, despite a notable decrease in the specific surface area and total pore volume of the activated bentonite. It is presumed that copper cation species may act as an adsorption site for DMS. The adsorption capacity of Cu(II)-bentonite was better than that of Cu(I)-bentonite. The DMS-TPD patterns indicate that the stronger electrophilicity of Cu(II) compared to that of Cu(I) caused it to interact with the DMS molecules more strongly, thus contributing to a better adsorptive performance. The Cu(II)-bentonite calcined at 150°C had the best DMS removal performance with a high sulphur capacity of 70.56 mg S g−1 adsorbent. The DMS removal performance became much lower with the increase in the calcination temperature, which appeared to be due to the decrease in the CuCl2·2H2O phase and the formation of the monoclinic Cu(OH)Cl phase.