The Effect of Si/Al on Mechanical Properties and Fracture Behavior of Stainless Steel Mesh/Cr p

In this study, a series stainless steel mesh/Crp reinforced geopolymer composites with different Si/Al molar ratio (N) were designed and prepared, where N = 1.75, 2 and 2.25, respectively. The effect of Si/Al molar ratio in the geopolymer matrix on mechanical properties and fracture behavior of the geopolymer composites were investigated. The microstructure of geopolymer became more compact when Si/Al increased from 1.75 to 2, which was beneficial to the improvement of geopolymer’s mechanical properties. And continuing to rise to 2.25 for Si/Al, the completely curing of geopolymer composites required more time compared with lower Si/Al, which can be attributed to the different microstructure and chemical composition caused by the different Si/Al. The optimum Si/Al molar ratio was about 2 at which the composites samples present the best mechanical properties with the flexure strength of 115.3 MPa and elastic modulus of 11.0 GPa, respectively. The results of fracture behavior suggested that geopolymer composites with N is 2.25 displayed the behavior characteristics of metal materials, which can be attributed to a poor integrated condition in interface between reinforcements and geopolymer matrix

Carbon dioxide sequestration of fly ash alkaline based mortars with recycled aggregates and different sodium hydroxide concentrations: Properties, durability, carbon footprint, and cost analysis

This chapter discloses results of an investigation concerning carbon dioxide sequestration on fly ash/waste glass alkaline-based mortars with recycled aggregates and different sodium hydroxide concentrations. Properties, durability, carbon footprint, and cost analysis were studied on it. Mixtures using a sodium hydroxide concentration of 8M and the additive calcium hydroxide show the best performance and the lowest carbon footprint. Simulations using a carbon tax of 0.0347 Euro/kg show no influence on the cost of the mixtures while the use of the carbon tax of 0.206 Euro/kg show an increase in the cost-efficiency of mixtures, even those using a sodium hydroxide concentration of 8M and additive calcium hydroxide.The authors would like to acknowledge the financial support of the Foundation for Science and Technology (FCT) in the frame of project IF/00706/2014-UM.2.15.info:eu-repo/semantics/publishedVersio

Konzeption und prototypische Umsetzung eines Architekturcockpits

EAM ist ein holistischer Ansatz, um komplexe IT- und Unternehmensstrukturen darzustellen. Dabei ist es von zentraler Bedeutung, diese Strukturen möglichst komplett und übersichtlich zu visualisieren. Ein Ansatz, dies zu erreichen, ist eine multiperspektivische Darstellung von mehreren Views in einem Architekturcockpit. Dabei können mehrere Views simultan betrachtet und analysiert werden. Dadurch ist es möglich, die Auswirkungen einer Analyse des Views eines Stakeholders simultan aus den Views anderer Stakeholder betrachten zu können, um eventuelle Wechselwirkungen zu erkennen und einen allgemeinen Überblick über die Unternehmensarchitektur zu behalten. In dieser Arbeit zeigen wir, von der Konzeption über die Umsetzung bis zu einem Anwendungsbeispiel, wie ein solches Architekturcockpit realisiert werden kann

Demand Forecasting of Outbound Logistics Using Neural Networks

Long short-term volume forecasting is essential for companies regarding their logistics service operations. It is crucial for logistic companies to predict the volumes of goods that will be delivered to various centers at any given day, as this will assist in managing the efficiency of their business operations. This research aims to create a forecasting model for outbound logistics volumes by utilizing design science research methodology in building 3 machine-learning models and evaluating the performance of the models . The dataset is provided by Tetra Pak AB, the World's leading food processing and packaging solutions company,. Research methods were mainly quantitative, based on statistical data and numerical calculations. Three algorithms were implemented: which are encoder–decoder networks based on Long Short-Term Memory (LSTM), Convolutional Long Short-Term Memory (ConvLSTM), and Convolutional Neural Network Long ShortTerm Memory (CNN-LSTM). Comparisons are made with the average Root Mean Square Error (RMSE) for six distribution centers (DC) of Tetra Pak. Results obtained from encoder–decoder networks based on LSTM are compared to results obtained by encoder–decoder networks based on ConvLSTM and CNN-LSTM. The three algorithms performed very well, considering the loss of the Train and Test with our multivariate time series dataset. However, based on the average score of the RMSE, there are slight differences between algorithms for all DCs.

Demand Forecasting of Outbound Logistics Using Neural Networks

Long short-term volume forecasting is essential for companies regarding their logistics service operations. It is crucial for logistic companies to predict the volumes of goods that will be delivered to various centers at any given day, as this will assist in managing the efficiency of their business operations. This research aims to create a forecasting model for outbound logistics volumes by utilizing design science research methodology in building 3 machine-learning models and evaluating the performance of the models . The dataset is provided by Tetra Pak AB, the World's leading food processing and packaging solutions company,. Research methods were mainly quantitative, based on statistical data and numerical calculations. Three algorithms were implemented: which are encoder–decoder networks based on Long Short-Term Memory (LSTM), Convolutional Long Short-Term Memory (ConvLSTM), and Convolutional Neural Network Long ShortTerm Memory (CNN-LSTM). Comparisons are made with the average Root Mean Square Error (RMSE) for six distribution centers (DC) of Tetra Pak. Results obtained from encoder–decoder networks based on LSTM are compared to results obtained by encoder–decoder networks based on ConvLSTM and CNN-LSTM. The three algorithms performed very well, considering the loss of the Train and Test with our multivariate time series dataset. However, based on the average score of the RMSE, there are slight differences between algorithms for all DCs.