The automatic layout model in the sequencing of construction activities of multi-objective diaphragm wall unit by using self-learning neural network

Foundation construction process has been an important key point in a successful construction engineering. The frequency of using diaphragm wall construction method among many deep excavation construction methods in Taiwan is the highest in the world. The traditional view of managing diaphragm wall unit in the sequencing of construction activities is to establish each phase of the sequencing of construction activities by heuristics. However, it conflicts final phase of engineering construction with unit construction and effects planning construction time. In order to avoid this kind of situation, we use management of science in the study of diaphragm wall unit construction to formulate multi-objective combinational optimization problem. Because the characteristic (belong to NP-Complete problem) of problem mathematic model is multi-objective and combining explosive, it is advised that using the 2-type Self-Learning Neural Network (SLNN) to solve the N=12, 24, 36 of diaphragm wall unit in the sequencing of construction activities program problem. In order to compare the liability of the results, this study will use random researching method in comparison with the SLNN. It is found that the testing result of SLNN is superior to random researching method in whether solution-quality or Solving-efficiency.

A three-regime model of speculative behaviour: modelling the evolution of the S&P 500 composite index

We examine whether a three-regime model that allows for dormant, explosive and collapsing speculative behaviour can explain the dynamics of the S&P 500. We extend existing models of speculative behaviour by including a third regime that allows a bubble to grow at a steady rate, and propose abnormal volume as an indicator of the probable time of bubble collapse. We also examine the financial usefulness of the three-regime model by studying a trading rule formed using inferences from it, whose use leads to higher Sharpe ratios and end of period wealth than from employing existing models or a buy-and-hold strategy.

Climate model calculations of the impact of aerosols from road transport and shipping

Road transport and shipping are copious sources of aerosols, which exert a 9 significant radiative forcing, compared to, for example, the CO2 emitted by these sectors. An 10 advanced atmospheric general circulation model, coupled to a mixed-layer ocean, is used to 11 calculate the climate response to the direct radiative forcing from such aerosols. The cases 12 considered include imposed distributions of black carbon and sulphate aerosols from road 13 transport, and sulphate aerosols from shipping; these are compared to the climate response 14 due to CO2 increases. The difficulties in calculating the climate response due to small 15 forcings are discussed, as the actual forcings have to be scaled by large amounts to enable a 16 climate response to be easily detected. Despite the much greater geographical inhomogeneity 17 in the sulphate forcing, the patterns of zonal and annual-mean surface temperature response 18 (although opposite in sign) closely resembles that resulting from homogeneous changes in 19 CO2. The surface temperature response to black carbon aerosols from road transport is shown 20 to be notably non-linear in scaling applied, probably due to the semi-direct response of clouds 21 to these aerosols. For the aerosol forcings considered here, the most widespread method of 22 calculating radiative forcing significantly overestimates their effect, relative to CO2, 23 compared to surface temperature changes calculated using the climate model.

Northern Hemisphere extratropical cyclones in a warming climate in the HiGEM high-resolution climate model

Changes to the Northern Hemisphere winter (December, January and February) extratropical storm tracks and cyclones in a warming climate are investigated. Two idealised climate change experiments with HiGEM1.1, a doubled CO2 and a quadrupled CO2 experiment, are compared against a present day control run. An objective feature tracking method is used and a focus given to regional changes. The climatology of extratropical storm tracks from the control run is shown to be in good agreement with ERA-40, while the frequency distribution of cyclone intensity also compares well. In both simulations the mean climate changes are generally consistent with the simulations of the IPCC AR4 models, with a strongly enhanced surface warming at the winter pole and the reduced lower tropospheric warming over the North Atlantic Ocean associated with the slowdown of the Meridional Overturning Circulation. The circulation changes in the North Atlantic are different between the two idealised simulations with different CO2 forcings. In the North Atlantic the storm tracks are influenced by the slowdown of the MOC, the enhanced surface polar warming, and the enhanced upper tropical troposphere warming, giving a north eastward shift of the storm tracks in the 2XCO2 experiment, but no shift in the 4XCO2 experiment. Over the Pacific, in the 2XCO2 experiment, changes in the mean climate are associated with local temperature changes, while in the 4XCO2 experiment the changes in the Pacific are impacted by the weakened tropical circulation. The storm track changes are consistent with the shifts in the zonal wind. Total cyclone numbers are found to decrease over the Northern Hemisphere with increasing CO2 forcing. Changes in cyclone intensity are found using 850hPa vorticity, mean sea level pressure, and 850hPa winds. The intensity of the Northern Hemisphere cyclones is found to decrease relative to the control.

Comparing a discrete and continuum model of the intestinal crypt

The integration of processes at different scales is a key problem in the modelling of cell populations. Owing to increased computational resources and the accumulation of data at the cellular and subcellular scales, the use of discrete, cell-level models, which are typically solved using numerical simulations, has become prominent. One of the merits of this approach is that important biological factors, such as cell heterogeneity and noise, can be easily incorporated. However, it can be difficult to efficiently draw generalizations from the simulation results, as, often, many simulation runs are required to investigate model behaviour in typically large parameter spaces. In some cases, discrete cell-level models can be coarse-grained, yielding continuum models whose analysis can lead to the development of insight into the underlying simulations. In this paper we apply such an approach to the case of a discrete model of cell dynamics in the intestinal crypt. An analysis of the resulting continuum model demonstrates that there is a limited region of parameter space within which steady-state (and hence biologically realistic) solutions exist. Continuum model predictions show good agreement with corresponding results from the underlying simulations and experimental data taken from murine intestinal crypts.

Limitations of the MRL mouse as a model for cardiac regeneration

Objective Myocardial repair following injury in mammals is restricted such that damaged areas are replaced by scar tissue, impairing cardiac function. MRL mice exhibit exceptional regenerative healing in an ear punch wound model. Some myocardial repair with restoration of heart function has also been reported following cryoinjury. Increased cardiomyocyte proliferation and a foetal liver stem cell population were implicated. We investigated molecular mechanisms facilitating myocardial repair in MRL mice to identify potential therapeutic targets in non-regenerative species. Methods Expressions of specific cell-cycle regulators that might account for regeneration (CDKs 1, 2, 4 and 6; cyclins A, E, D1 and B1; p21, p27 and E2F5) were compared by immunoblotting in MRL and control C57BL/6 ventricles during development. Flow cytometry was used to investigate stem cell populations in livers from foetal mice, and infarct sizes were compared in coronary artery-ligated and sham-treated MRL and C57BL/6 adult mice. Key findings No differences in the expressions of cell cycle regulators were observed between the two strains. Expressions of CD34+Sca1+ckit-, CD34+Sca1+ckit+ and CD34+Sca1-ckit+ increased in livers from C57BL/6 vs MRL mice. No differences were observed in infarct sizes, levels of fibrosis, Ki67 staining or cardiac function between MRL and C57BL/6 mice. Conclusions No intrinsic differences were observed in cell cycle control molecules or stem cell populations between MRL and control C57BL mouse hearts. Pathophysiologically relevant ischaemic injury is not repaired more efficiently in MRL myocardium, questioning the use of the MRL mouse as a reliable model for cardiac regeneration in response to pathophysiologically relevant forms of injury.

Inverting recurrent neural networks for internal model control of nonlinear systems

In this paper, we show how a set of recently derived theoretical results for recurrent neural networks can be applied to the production of an internal model control system for a nonlinear plant. The results include determination of the relative order of a recurrent neural network and invertibility of such a network. A closed loop controller is produced without the need to retrain the neural network plant model. Stability of the closed-loop controller is also demonstrated.

Internal model control of nonlinear systems through the inversion of recurrent neural networks

Recurrent neural networks can be used for both the identification and control of nonlinear systems. This paper takes a previously derived set of theoretical results about recurrent neural networks and applies them to the task of providing internal model control for a nonlinear plant. Using the theoretical results, we show how an inverse controller can be produced from a neural network model of the plant, without the need to train an additional network to perform the inverse control.

Evaluation of satellite-based and model re-analysis rainfall estimates for Uganda

The dependence of much of Africa on rain fed agriculture leads to a high vulnerability to fluctuations in rainfall amount. Hence, accurate monitoring of near-real time rainfall is particularly useful, for example in forewarning possible crop shortfalls in drought-prone areas. Unfortunately, ground based observations are often inadequate. Rainfall estimates from satellite-based algorithms and numerical model outputs can fill this data gap, however rigorous assessment of such estimates is required. In this case, three satellite based products (NOAA-RFE 2.0, GPCP-1DD and TAMSAT) and two numerical model outputs (ERA-40 and ERA-Interim) have been evaluated for Uganda in East Africa using a network of 27 rain gauges. The study focuses on the years 2001 to 2005 and considers the main rainy season (February to June). All data sets were converted to the same temporal and spatial scales. Kriging was used for the spatial interpolation of the gauge data. All three satellite products showed similar characteristics and had a high level of skill that exceeded both model outputs. ERA-Interim had a tendency to overestimate whilst ERA-40 consistently underestimated the Ugandan rainfall.

Biomarker discovery and redundancy reduction towards classification using a multi-factorial MALDI-TOF MS T2DM mouse model dataset

Diabetes like many diseases and biological processes is not mono-causal. On the one hand multifactorial studies with complex experimental design are required for its comprehensive analysis. On the other hand, the data from these studies often include a substantial amount of redundancy such as proteins that are typically represented by a multitude of peptides. Coping simultaneously with both complexities (experimental and technological) makes data analysis a challenge for Bioinformatics.