Currently, the context management solutions for the Internet of Things are the subject of numerous studies which are achieved through the expansion of the context manager for ambient environments. In literature, there are different approaches enabling the context management for internet of things (IoT). This paper studies these approaches, which are well known according to our defined criteria such as heterogeneity, mobility, the influence of the physical world, scalability, security, privacy, quality of context, autonomous deployment of entities, characterization multi scales, and interoperability.

Currently, the context management solutions for the Internet of Things are the subject of numerous studies which are achieved through the expansion of the context manager for ambient environments. In literature, there are different approaches enabling the context management for internet of things (IoT). This paper studies these approaches, which are well known according to our defined criteria such as heterogeneity, mobility, the influence of the physical world, scalability, security, privacy, quality of context, autonomous deployment of entities, characterization multi scales, and interoperability.

Currently, the context management solutions for the Internet of Things are the subject of numerous studies which are achieved through the expansion of the context manager for ambient environments. In literature, there are different approaches enabling the context management for internet of things (IoT). This paper studies these approaches, which are well known according to our defined criteria such as heterogeneity, mobility, the influence of the physical world, scalability, security, privacy, quality of context, autonomous deployment of entities, characterization multi scales, and interoperability.

Currently, the context management solutions for the Internet of Things are the subject of numerous studies which are achieved through the expansion of the context manager for ambient environments. In literature, there are different approaches enabling the context management for internet of things (IoT). This paper studies these approaches, which are well known according to our defined criteria such as heterogeneity, mobility, the influence of the physical world, scalability, security, privacy, quality of context, autonomous deployment of entities, characterization multi scales, and interoperability.

In this paper, we propose diagnostic modules for complex and dynamic systems. These modules are based on three ant colony algorithms, which are AntTreeStoch, Lumer & Faieta and Binay ant colony. We chose these algorithms for their simplicity and their wide application range. However, we cannot use these algorithms in their basement forms as they have several limitations. To use these algorithms in a diagnostic system, we have proposed three variants. We have tested these algorithms on datasets issued from two industrial systems which are clinkering system and pasteurization system.

In this paper, we propose diagnostic modules for complex and dynamic systems. These modules are based on three ant colony algorithms, which are AntTreeStoch, Lumer & Faieta and Binay ant colony. We chose these algorithms for their simplicity and their wide application range. However, we cannot use these algorithms in their basement forms as they have several limitations. To use these algorithms in a diagnostic system, we have proposed three variants. We have tested these algorithms on datasets issued from two industrial systems which are clinkering system and pasteurization system.

Phase diagrams are representations of the sate of a material as a function of temperature of thee constituent components. Multi-components phase diagrams are useful for alloy development processing and understanding. They are considered as roadmaps in design of new materials used in technology. To establish phase diagrams we usually use a thermodynamic approach called CALPHAD (CALculation PHase Diagrams) based on the knowledge and minimization of the Gibbs energy of all phases taking part in the system. We can obtain the Gibbs energy of each phase by using experimental methods (calorimetry). We can also use theoretical method (ab-initio) or a semiempirical model of Miedema. We reviewed and used three calorimetric methods: direct reaction, progressive precipitation and indirect mixing calorimetry. We obtained at high temperatures the enthalpies of formations of eight compounds: Ga3Mo, Ga5Mo, GaNb3, Ga5Nb4, Ga3Nb,  -GaCr3,  -GaCr3 and Ga4Cr. Their values were compared to those estimated theoretically, reviewed and commented. In addition to their exploitation to establish phase diagrams, these data permits to improve thermodynamic models and understand the behaviours of gallium towards the three transition metals molybdenum, niobium and chromium. This procedure can be applied do ternary compound if needed.