Hybrid Model for Short-Term Water Demand Forecasting Based on Error Correction Using Chaotic Time Series

open access articleShort-term water demand forecasting plays an important role in smart management and real-time simulation of water distribution systems (WDSs). This paper proposes a hybrid model for the short-term forecasting in the horizon of one day with 15 minute time steps, which improves the forecasting accuracy by adding an error correction module to the initial forecasting model. The initial forecasting model is firstly established based on the least square support vector machine (LSSVM), the errors time series obtained by comparing the observed values and the initial forecasted values is next transformed into chaotic time series, and then the error correction model is established by the LSSVM method to forecast errors at the next time step. The hybrid model is tested on three real-world district metering areas (DMAs) in Beijing, China, with different demand patterns. The results show that, with the help of the error correction module, the hybrid model reduced the mean absolute percentage error (MAPE) of forecasted demand from (5.64%, 4.06%, 5.84%) to (4.84%, 3.15%, 3.47%) for the three DMAs, compared with using LSSVM without error correction. Therefore, the proposed hybrid model provides a better solution for short-term water demand forecasting on the tested cases

Convergence and Stability in Collocation Methods of Equation u

This paper is concerned with the convergence, global superconvergence, local superconvergence, and stability of collocation methods for u′(t)=au(t)+bu([t]). The optimal convergence order and superconvergence order are obtained, and the stability regions for the collocation methods are determined. The conditions that the analytic stability region is contained in the numerical stability region are obtained, and some numerical experiments are given

A Wireless Electronic Nose System Using a Fe2O3 Gas Sensing Array and Least Squares Support Vector Regression

This paper describes the design and implementation of a wireless electronic nose (WEN) system which can online detect the combustible gases methane and hydrogen (CH4/H2) and estimate their concentrations, either singly or in mixtures. The system is composed of two wireless sensor nodes—a slave node and a master node. The former comprises a Fe2O3 gas sensing array for the combustible gas detection, a digital signal processor (DSP) system for real-time sampling and processing the sensor array data and a wireless transceiver unit (WTU) by which the detection results can be transmitted to the master node connected with a computer. A type of Fe2O3 gas sensor insensitive to humidity is developed for resistance to environmental influences. A threshold-based least square support vector regression (LS-SVR)estimator is implemented on a DSP for classification and concentration measurements. Experimental results confirm that LS-SVR produces higher accuracy compared with artificial neural networks (ANNs) and a faster convergence rate than the standard support vector regression (SVR). The designed WEN system effectively achieves gas mixture analysis in a real-time process

Microbe-Assisted Synthesis and Luminescence Properties of Monodispersed Tb 3+

Tb3+-doped zinc sulfide (ZnS:Tb3+) nanocrystals were synthesized by spray precipitation with sulfate-reducing bacterial (SRB) culture at room temperature. The morphology of the SRB and ZnS:Tb3+ nanocrystals was examined by scanning electron microscopy, and the ZnS:Tb3+ nanocrystals were characterized by X-ray diffractometry and photoluminescence (PL) spectroscopy. The PL mechanism of ZnS:Tb3+ nanocrystals was further analyzed, and the effects of Tb3+ ion concentration on the luminescence properties of ZnS:Tb3+ nanocrystals were studied. ZnS:Tb3+ nanocrystals showed a sphalerite phase, and the prepared ZnS:Tb3+ nanocrystals had high luminescence intensity under excitation at 369 nm. The main peak position of the absorption spectra positively blueshifted with increasing concentrations of Tb3+ dopant. Based on the strength of the peak of the excitation and emission spectra, we inferred that the optimum concentration of the Tb3+ dopant is 5 mol%. Four main emission peaks were obtained under excitation at 369 nm:489 nm (5D4→7F6), 545 nm (5D4→7F5), 594 nm (5D4→7F4), and 625 nm (5D4→7F3). Our findings suggest that nanocrystals have potential applications in photoelectronic devices and biomarkers

A review and meta-analysis of corneal cross-linking for post-laser vision correction ectasia

Purpose: The aim of this study was to review the safety and stability of cornea cross-linking (CXL) for the treatment of keratectasia after Excimer Laser Refractive Surgery. Methods: Eligible studies were identified by systematically searching PubMed, Embase, Web of Science and reference lists. Meta-analysis was performed using Stata 12.1 software. The primary outcome parameters included the changes of corrected distant visual acuity (CDVA), uncorrected visual acuity (UCVA), the maximum keratometry value (Kmax) and minimum keratometry value (Kmin), the surface regularity index (SRI), the surface asymmetry index (SAI), the keratoconus prediction index (KPI), corneal thickness, and endothelial cell count. Efficacy estimates were evaluated by weighted mean difference (WMD) and 95% confidence interval (CI) for absolute changes of the interested outcomes. Results: Seven studies involving 118 patients treated with CXL for progressive ectasia after laser-assisted in situ keratomileusis (LASIK) or photorefractive keratectomy (PRK) (140 eyes; the follow-up time range from 12 to 62 months) were included in the meta-analysis. The pooled results showed that there were no significant differences in Kmax and Kmin values after CXL (WMD = 0.584; 95% CI: −0.289 to 1.458; P = 0.19; WMD = 0.466; 95% CI: −0.625 to 1.556; P = 0.403, respectively). The CDVA improved significantly after CXL (WMD = 0.045; 95% CI: 0.010 to 0.079; P = 0.011), whereas UCVA did not differ statistically (WMD = 0.011; 95% CI: −0.055 to 0.077; P = 0.746). The changes were not statistically significant in SRI, SAI, and KPI (WMD = 0.116; 95% CI: −0.090 to 0.322; P = 0.269; WMD = 0.240; 95% CI: −0.200 to 0.681; P = 0.285; WMD = 0.045; 95% CI: −0.001 to 0.090; P = 0.056, respectively). Endothelial cell count and corneal thickness did not deteriorate (WMD = 12.634; 95% CI: −29.460 to 54.729; P = 0.556; WMD = 0.657; 95% CI: −9.402 to 10.717; P = 0.898, respectively). Conclusion: The study showed that CXL is a promising treatment to stabilize the keratectasia after Excimer Laser Refractive Surgery. Further long-term follow-up studies are necessary to assess the persistence of the effect of the CXL. Keywords: Cross-linking, Keratectasia, Refractive surgery, Meta-analysi

Golgi phosphoprotein-3 promotes invasiveness of gastric cancer cells through the mTOR signalling pathway

Purpose: Golgi phosphoprotein-3 (GOLPH3) is an oncogene that is overexpressed in multiple cancers and is associated with poor prognosis. The aim of this study was to examine the impact of GOLPH3 on the migration and metastasis of gastric cancer cells. Methods: Following the shRNA-mediated knockdown of GOLPH3, we analyzed cytoskeletal reorganization and cell invasion, migration and adhesion, and determined the impact of components of the mammalian target of the rapamycin (mTOR) signalling pathway. Results: The GOLPH3 mRNA and protein expression were significantly lower in both SGC-7901 and MKN-28 cells as compared with poorly-differentiated BGC-823 cells. The GOLPH3 knockdown also significantly reduced cell invasion in all three cell lines through reduced migration as compared with the non-targeting control sequence group. The GOLPH3 knockdown also reduced F-actin in all three cell lines, and decreased cell adhesion in BGC-823 and SGC-7901 cells. Finally, p-mTOR, p70S6K, p-4EBP1 and RhoA protein levels were significantly downregulated in shGOLPH3-1-treated cells. Conclusions: In conclusion, GOLPH3 increased in poorly-differentiated gastric cancer cells, activating the mTOR-70S6K/4EBP1-RhoA signalling pathway to promote the migration and metastasis of gastric cancer cells

Probabilistic Forecasting Method of Metro Station Environment Based on Autoregressive LSTM Network

With the increasing number of metros, the comfort and safety of crew and passengers in metro stations have been paid great attention. The environment forecasting has become very important for decision-making. The outputs of the traditional point prediction methods are some exact values in the future. However, it might be closer to the real conditions that the predicted variables are given a probability range with a different confidence rather than exact values. This paper proposes a probabilistic forecasting method of metro station environment based on autoregressive Long Short Term Memory (LSTM) network. It has a good performance to quantify the uncertainty of environment trend in a metro station. Seven-day field tests were carried out to obtain the measured data of 7 internal environmental parameters in a metro station and 8 external environment parameters. In order to ensure the prediction performance, the random forest algorithm is used to select the input variables for the proposed probabilistic forecasting method. The selected input variables and the previous predicted values are as the input variables to build the probabilistic forecasting model. The proposed method can realize to predict the probabilistic distribution of internal environmental parameters in a metro station. This work may contribute to prevent emergency events and regulate environment control system reasonably