Choose an application

• Reference Manager
• EndNote
• RefWorks (Direct export to RefWorks)

Accurate energy forecasting is important to facilitate the decision-making process in order to achieve higher efficiency and reliability in power system operation and security, economic energy use, contingency scheduling, the planning and maintenance of energy supply systems, and so on. In recent decades, many energy forecasting models have been continuously proposed to improve forecasting accuracy, including traditional statistical models (e.g., ARIMA, SARIMA, ARMAX, multi-variate regression, exponential smoothing models, Kalman filtering, Bayesian estimation models, etc.) and artificial intelligence models (e.g., artificial neural networks (ANNs), knowledge-based expert systems, evolutionary computation models, support vector regression, etc.). Recently, due to the great development of optimization modeling methods (e.g., quadratic programming method, differential empirical mode method, evolutionary algorithms, meta-heuristic algorithms, etc.) and intelligent computing mechanisms (e.g., quantum computing, chaotic mapping, cloud mapping, seasonal mechanism, etc.), many novel hybrid models or models combined with the above-mentioned intelligent-optimization-based models have also been proposed to achieve satisfactory forecasting accuracy levels. It is important to explore the tendency and development of intelligent-optimization-based modeling methodologies and to enrich their practical performances, particularly for marine renewable energy forecasting.

Information technology.

Choose an application

• Reference Manager
• EndNote
• RefWorks (Direct export to RefWorks)

This volume explores the issues of using e-learning in university and of developing and using a “blended learning” teaching method for the course of Computer Science from the first year of the the Medicine and Surgery Degree of the University of Florence. This course was used as a trial for the peer-review teaching method, which can be useful in classes with a high number of students; this publication also includes a selection of the best works produced by the students. The results of the trial prove the advantages of using the peer-review teaching method for other university courses, and the students' works on e-health give current medical practitioners insights on how to use ICT in their practice with skill and care.

Information technology.

Choose an application

• Reference Manager
• EndNote
• RefWorks (Direct export to RefWorks)

Bioinformatics, that is the interdisciplinary field that blends computer science and biostatistics with biological and biomedical sciences, is expected to gain a central role in next feature. Indeed, it has now affected several fields of biology, providing crucial hints for the understanding of biological systems and also allowing a more accurate design of wet lab experiments. In this work, the analysis of sequence data has be used in different fields, such as evolution (e.g. the assembly and evolution of metabolism), infections control (e.g. the horizontal flow of antibiotic resistance), ecology (bacterial bioremediation).

Information technology.

Choose an application

• Reference Manager
• EndNote
• RefWorks (Direct export to RefWorks)

Mathematical modeling is a powerful approach supporting the investigation of open problems in natural sciences, in particular physics, biology and medicine. Applied mathematics allows to translate the available information about real-world phenomena into mathematical objects and concepts. Mathematical models are useful descriptive tools that allow to gather the salient aspects of complex biological systems along with their fundamental governing laws, by elucidating the system behavior in time and space, also evidencing symmetry, or symmetry breaking, in geometry and morphology. Additionally, mathematical models are useful predictive tools able to reliably forecast the future system evolution or its response to specific inputs. More importantly, concerning biomedical systems, such models can even become prescriptive tools, allowing effective, sometimes optimal, intervention strategies for the treatment and control of pathological states to be planned. The application of mathematical physics, nonlinear analysis, systems and control theory to the study of biological and medical systems results in the formulation of new challenging problems for the scientific community. This Special Issue includes innovative contributions of experienced researchers in the field of mathematical modelling applied to biology and medicine.

Information technology.

Choose an application

• Reference Manager
• EndNote
• RefWorks (Direct export to RefWorks)

Machine Learning under Resource Constraints addresses novel machine learning algorithms that are challenged by high-throughput data, by high dimensions, or by complex structures of the data in three volumes. Resource constraints are given by the relation between the demands for processing the data and the capacity of the computing machinery. The resources are runtime, memory, communication, and energy. Hence, modern computer architectures play a significant role. Novel machine learning algorithms are optimized with regard to minimal resource consumption. Moreover, learned predictions are executed on diverse architectures to save resources. It provides a comprehensive overview of the novel approaches to machine learning research that consider resource constraints, as well as the application of the described methods in various domains of science and engineering.Volume 3 describes how the resource-aware machine learning methods and techniques are used to successfully solve real-world problems. The book provides numerous specific application examples. In the areas of health and medicine, it is demonstrated how machine learning can improve risk modelling, diagnosis, and treatment selection for diseases. Machine learning supported quality control during the manufacturing process in a factory allows to reduce material and energy cost and save testing times is shown by the diverse real-time applications in electronics and steel production as well as milling. Additional application examples show, how machine-learning can make traffic, logistics and smart cities more effi cient and sustainable. Finally, mobile communications can benefi t substantially from machine learning, for example by uncovering hidden characteristics of the wireless channel.

Information technology.