Topology optimization of the flat steel shear wall based on the volume constraint and strain energy assumptions under the seismic loading conditions.

In structural engineering systems, shear walls are two-dimensional vertical elements designed to endure lateral forces acting in-plane, most frequently seismic and wind loads. Shear walls come in a variety of materials and are typically found in high-rise structures. Because steel shear walls are lighter, more ductile, and stronger than other concrete shear walls, they are advised for usage in steel constructions. It is important to remember that the steel shear wall has an infill plate, which can be produced in a variety of forms. The critical zones in flat steel shear walls are the joints and corners where the infill plate and frame meet. The flat infill plate can be modified to improve the strength and weight performance of the steel shear walls. One of these procedures is Topology Optimization (TO) and this method can reduce the weight and also, increase the strength against the cyclic loading sequences. In the current research paper, the TO of the infill steel plate was considered based on the two methods of volume constraint and maximization of strain energy. Four different volumes (i.e., 60%, 70%, 80%, and 90%) were assumed for the mentioned element in the steel shear wall. The obtained results revealed that the topology configuration of CCSSW with 90% volume constraint presented the highest seismic loading performance. The cumulated energy for this type of SSW was around 700 kJ while it was around 600 kJ for other topology optimization configurations.

Developing a hybrid technique for energy demand forecasting based on optimized improved SVM by the boosted multi-verse optimizer: Investigation on affecting factors.

Electricity demand prediction accuracy is crucial for operational energy resource management and strategy. In this study, we provide a multi-form model for electricity demand prediction in China that based on incorporating of an upgraded Support Vector Machine (SVM) and a Boosted Multi-Verse Optimizer (BMVO). The suggested model is proposed to address the shortcomings of existing prediction approaches, which frequently fail to internment the complicated nonlinear interactions between demand for electricity and the variables that influence it. The improved SVM algorithm incorporates a modified genetic algorithm based on kernel function for enhancing the stability of the model. The BMVO technique is employed to optimize the combined model's weights and increase its generalization effectiveness. The suggested approach is tested by real-world Chinese energy demand data. The findings show that it outperforms existing prediction approaches in terms of reliability and precision. Further, the number of samples chosen affects how well the proposed BMVO linked with the Incremental SVM (ISVM) predicts outcomes. Particularly, when 1735 samples are chosen, the lowest level of Mean Absolute Percent Error (MAPE) was noted. The Root Mean Square Error (RMSE) and MAPE values under the proposed BMVO/ISVM model are reduced by 53.72% and 55.22%, respectively, compared to the Artificial Neural Network (ANN) approach reported in literature. Finally, the suggested model is capable of accurately predicting the electricity demand in China and has the potential to be applied to other energy-demand prediction applications.

Developed multiple-layer perceptron neural network based on developed search and rescue optimizer to predict iron ore price volatility: A case study.

In economic investment, the role of forecasting is very important because in an economic project, the investor must carefully examine the dimensions of the work such that one of the most important and perhaps the main factor of a future investor and an economic enterprise is the work done by Costs and revenues are determined. Due to the fact that the volatility of iron ore price is affected by various factors, so it is not possible to determine a simple and general function to predict its price. There are several methods for predicting price, but the most appropriate of these is a method that examines variables in a nonlinear and dynamic manner that is closer to reality. Therefore, in this research, an improved and optimized neural network is proposed to facilitate this task. The idea is to employ a developed version of Search and Rescue optimization algorithm to enhance the training ability of the neural network to present an efficient forecasting tool for iron ore price volatilities. Different variables are used for the method verification and its results are compared with basic neural network, particle swarm optimization-based, Intelligent Integrated Optimizer, Genetic Neural Network to show its superiority. Simulation results demonstrate that by the proposed method has a satisfying and better fitting with the data compared with the other methods.

Inter-observer reproducibility before and after web-based education in the Gleason grading of the prostate adenocarcinoma among the Iranian pathologists.

This study was aimed at determining intra and inter-observer concordance rates in the Gleason scoring of prostatic adenocarcinoma, before and after a web-based educational course. In this self-controlled study, 150 tissue samples of prostatic adenocarcinoma are re-examined to be scored according to the Gleason scoring system. Then all pathologists attend a free web-based course. Afterwards, the same 150 samples [with different codes compared to the previous ones] are distributed differently among the pathologists to be assigned Gleason scores. After gathering the data, the concordance rate in the first and second reports of pathologists is determined. In the pre web-education, the mean kappa value of Interobserver agreement was 0.25 [fair agreement]. Post web-education significantly improved with the mean kappa value of 0.52 [moderate agreement]. Using weighted kappa values, significant improvement was observed in inter-observer agreement in higher scores of Gleason grade; Score 10 was achieved for the mean kappa value in post web-education was 0.68 [substantial agreement] compared to 0.25 (fair agreement) in pre web-education. Web-based training courses are attractive to pathologists as they do not need to spend much time and money. Therefore, such training courses are strongly recommended for significant pathological issues including the grading of the prostate adenocarcinoma. Through web-based education, pathologists can exchange views and contribute to the rise in the level of reproducibility. Such programs need to be included in post-graduation programs.