The hydration of slag,part 1: reaction models for alkali-activated slag

Reaction models are proposed to quantify the hydration products and to determine the composition of C–S–H from alkali-activated slags (AAS). Products of the slag hydration are first summarized from observations in literature. The main hydration products include C–S–H, hydrotalcite, hydrogarnet, AFm phases (C₄AH₁₃ and C₂ASH₈) and ettringite. Then, three stoichiometric reaction models are established correlating the mineral composition of slag (the glass part) with the hydration products. Using the proposed models, quantities of hydration products and composition of C–S–H are determined. The models are validated with a number of experimental investigations reported in literature, yielding good agreement, i.e., these models can successfully predict the hydration reaction of AAS. The models are furthermore applied to calculate the retained water in the hydration products of AAS in different hydration states and a general hydration equation of AAS is derived. As an illustration to one of the model applications, chemical shrinkage of the AAS cement paste in different hydration states are predicted. The chemical shrinkage of AAS is shown to be remarkably higher than OPC. Furthermore, phase distribution in the hardened AAS paste and the porosity are calculated.     

A new deep intuitionistic fuzzy time series forecasting method based on long short-term memory

The Journal of Supercomputing - In recent years, deep artificial neural networks can have better forecasting performance than many other artificial neural networks. The long short-term memory...     

A modified genetic algorithm for forecasting fuzzy time series

Fuzzy time series approaches are used when observations of time series contain uncertainty. Moreover, these approaches do not require the assumptions needed for traditional time series approaches. Generally, fuzzy time series methods consist of three stages, namely, fuzzification, determination of fuzzy relations, and defuzzification. Artificial intelligence algorithms are frequently used in these stages with genetic algorithms being the most popular of these algorithms owing to their rich operators and good performance. However, the mutation operator of a GA may cause some negative results in the solution set. Thus, we propose a modified genetic algorithm to find optimal interval lengths and control the effects of the mutation operator. The results of applying our new approach to real datasets show superior forecasting performance when compared with those obtained by other techniques.     

Mangrove forests as a nature-based solution for coastal flood protection: Biophysical and ecological considerations

Nature-based coastal protection is increasingly recognised as a potentially sustainable and cost-effective solution to reduce coastal flood risk. It uses coastal ecosystems such as mangrove forests to create resilient designs for coastal flood protection. However, to use mangroves effectively as a nature-based measure for flood risk reduction, we must understand the biophysical processes that govern risk reduction capacity through mangrove ecosystem size and structure. In this perspective, we evaluate the current state of knowledge on local physical drivers and ecological processes that determine mangrove functioning as part of a nature-based flood defence. We show that the forest properties that comprise coastal flood protection are well-known, but models cannot yet pinpoint how spatial heterogeneity of the forest structure affects the capacity for wave or surge attenuation. Overall, there is relatively good understanding of the ecological processes that drive forest structure and size, but there is a lack of knowledge on how daily bed-level dynamics link to long-term biogeomorphic forest dynamics, and on the role of combined stressors influencing forest retreat. Integrating simulation models of forest structure under changing physical (e.g. due to sea-level change) and ecological drivers with hydrodynamic attenuation models will allow for better projections of long-term natural coastal protection.     

The application of inductively coupled plasma mass spectrometry in clinical pharmacological oncology research

Metal-based anticancer agents are frequently used in the treatment of a wide variety of cancer types. The monitoring of these anticancer agents in biological samples is important to understand their pharmacokinetics, pharmacodynamics, and metabolism. In addition, determination of metals originating from anticancer agents is relevant to assess occupational exposure of health care personnel working with these drugs. The high sensitivity of inductively coupled plasma mass spectrometry (ICP-MS) has resulted in an increased popularity of this technique for the analysis of metal-based anticancer drugs. In addition to the quantitative analysis of the metal of interest in a sample, ICP-MS can be used as an ultrasensitive metal selective detector in combination with speciation techniques such as liquid chromatography. In the current review we provide a systematic survey of publications describing the analysis of platinum- and ruthenium-containing anticancer agents using ICP-MS, focused on the determination of total metal concentrations and on the speciation of metal compounds in biological fluids, DNA- and protein-adducts, and environmental samples. We conclude that ICP-MS is a powerful tool for the quantitative analysis of metal-based anticancer agents from multiple sample sources.     

The Training of Pi-Sigma Artificial Neural Networks with Differential Evolution Algorithm for Forecasting

Computational Economics - Looking at the artificial neural networks’ literature, most of the studies started with feedforward artificial neural networks and the training of many feedforward...     

The relation between EA effectiveness and stakeholder satisfaction

Enterprise Architecture (EA) is increasingly being used by large organizations to get a grip on the complexity of their business processes, information systems and technical infrastructure. Although seen as an important instrument to help solve major organizational problems, effectively applying EA seems no easy task. Active participation of EA stakeholders is one of the main critical success factors for EA. This participation depends on the degree in which EA helps stakeholders achieve their individual goals. A highly related topic is effectiveness of EA, the degree in which EA helps to achieve the collective goals of the organization. In this article we present our work regarding EA stakeholder satisfaction and EA effectiveness, and compare these two topics. We found that, regarding EA, the individual goals of stakeholders map quite well onto the collective goals of the organization. In a case study we conducted, we found that the organization is primarily concerned with the final results of EA, while individual stakeholders also worry about the way the architects operate.