ALBERT

Description: Sustainability, Vol. 10, Pages 2655: Substructure Hybrid Simulation Boundary Technique Based on Beam/Column Inflection Points Sustainability doi: 10.3390/su10082655 Authors: Zaixian Chen Xueyuan Yan Hao Wang Xingji Zhu Billie F. Spencer Compatibility among substructures is an issue for hybrid simulation. Traditionally, the structure model is regarded as the idealized shear model. The equilibrium and compatibility of the axial and rotational direction at the substructure boundary are neglected. To improve the traditional boundary technique, this paper presents a novel substructure hybrid simulation boundary technique based on beam/column inflection points, which can effectively avoid the complex operation for realizing the bending moment at the boundary by using the features of the inflection point where the bending moment need not be simulated in the physical substructure. An axial displacement prediction technique and the equivalent force control method are used to realize the proposed method. The numerical simulation test scheme for the different boundary techniques was designed to consider three factors: (i) the different structural layers; (ii) the line stiffness ratio of the beam to column; and (iii) the peak acceleration. The simulation results for a variety of numerical tests show that the proposed technique shows better performance than the traditional technique, demonstrating its potential in improving HS test accuracy. Finally, the accuracy and feasibility of the proposed boundary technique is verified experimentally through the substructure hybrid simulation tests of a six-story steel frame model.

Description: Atmosphere, Vol. 9, Pages 326: Innovative Trend Analysis of Annual and Seasonal Rainfall Variability in Amhara Regional State, Ethiopia Atmosphere doi: 10.3390/atmos9090326 Authors: Mohammed Gedefaw Denghua Yan Hao Wang Tianling Qin Abel Girma Asaminew Abiyu Dorjsuren Batsuren This study investigated the annual and seasonal rainfall variability at five selected stations of Amhara Regional State, by using the innovative trend analysis method (ITAM), Mann-Kendall (MK) and Sen’s slope estimator test. The result showed that the trend of annual rainfall was increasing in Gondar (Z = 1.69), Motta (Z = 0.93), and Bahir Dar (Z = 0.07) stations. However, the trends in Dangla (Z = −0.37) and Adet (Z = −0.32) stations showed a decreasing trend. As far as monthly and seasonal variability of rainfall are concerned, all the stations exhibited sensitivity of change. The trend of rainfall in May, June, July, August, and September was increasing. However, the trend on the rest of other months showed a decreasing trend. The increase in rainfall during Kiremt season, along with the decrease in number of rainy days, leads to an increase of extreme rainfall events over the region during 1980–2016. The consistency in rainfall trends over the study region confirms the robustness of the change in trends. Innovative trend analysis method is very crucial method for detecting the trends in rainfall time series data due to its potential to present the results in graphical format as well. The findings of this paper could help researchers to understand the annual and seasonal variability of rainfall over the study region and become a foundation for further studies.

Description: A lensless blood cell counting system integrating microfluidic channel and a complementary metal oxide semiconductor (CMOS) image sensor is a promising technique to miniaturize the conventional optical lens based imaging system for point-of-care testing (POCT). However, such a system has limited resolution, making it imperative to improve resolution from the system-level using super-resolution (SR) processing. Yet, how to improve resolution towards better cell detection and recognition with low cost of processing resources and without degrading system throughput is still a challenge. In this article, two machine learning based single-frame SR processing types are proposed and compared for lensless blood cell counting, namely the Extreme Learning Machine based SR (ELMSR) and Convolutional Neural Network based SR (CNNSR). Moreover, lensless blood cell counting prototypes using commercial CMOS image sensors and custom designed backside-illuminated CMOS image sensors are demonstrated with ELMSR and CNNSR. When one captured low-resolution lensless cell image is input, an improved high-resolution cell image will be output. The experimental results show that the cell resolution is improved by 4×, and CNNSR has 9.5% improvement over the ELMSR on resolution enhancing performance. The cell counting results also match well with a commercial flow cytometer. Such ELMSR and CNNSR therefore have the potential for efficient resolution improvement in lensless blood cell counting systems towards POCT applications.

Description: Energies, Vol. 11, Pages 712: A Novel Nonlinear Combined Forecasting System for Short-Term Load Forecasting Energies doi: 10.3390/en11040712 Authors: Chengshi Tian Yan Hao Short-term load forecasting plays an indispensable role in electric power systems, which is not only an extremely challenging task but also a concerning issue for all society due to complex nonlinearity characteristics. However, most previous combined forecasting models were based on optimizing weight coefficients to develop a linear combined forecasting model, while ignoring that the linear combined model only considers the contribution of the linear terms to improving the model’s performance, which will lead to poor forecasting results because of the significance of the neglected and potential nonlinear terms. In this paper, a novel nonlinear combined forecasting system, which consists of three modules (improved data pre-processing module, forecasting module and the evaluation module) is developed for short-term load forecasting. Different from the simple data pre-processing of most previous studies, the improved data pre-processing module based on longitudinal data selection is successfully developed in this system, which further improves the effectiveness of data pre-processing and then enhances the final forecasting performance. Furthermore, the modified support vector machine is developed to integrate all the individual predictors and obtain the final prediction, which successfully overcomes the upper drawbacks of the linear combined model. Moreover, the evaluation module is incorporated to perform a scientific evaluation for the developed system. The half-hourly electrical load data from New South Wales are employed to verify the effectiveness of the developed forecasting system, and the results reveal that the developed nonlinear forecasting system can be employed in the dispatching and planning for smart grids.