Biblio

The work we present is a comparative study based on an experimental approach to the mechanical and thermal properties of different local clay-based building materials with the incorporation of agricultural waste in Chad. These local building materials have been used since ancient times by the low-income population. They were the subject of a detailed characterization of their mechanical and thermal parameters. The objective is to obtain lightweight materials with good thermomechanical performance and which can contribute to improving thermal comfort, energy-saving, and security in social housing in Chad while reducing the cost of investment. Several clay-based samples with increasing incorporation of 0 to 8% of agricultural waste (cow dung or millet pod) were made. We used appropriate experimental methods for porous materials (the hydraulic press for mechanical tests and the box method for thermal tests). In this article, we have highlighted the values and variations of the mechanical compressive resistances, thermal conductivities, and thermal resistances of test pieces made with these materials. Knowing the mechanical and thermal characteristics, we also carried out a thermomechanical study. The thermal data made it possible to make Dynamic Thermal Simulations (STD) of the buildings thanks to the Pléiades + COMFIE software. The results obtained show that the use of these materials in a building presents good mechanical and thermal performance with low consumption of electrical energy for better thermal comfort of the occupants. Thus agricultural waste can be recovered thanks to its integration into building materials based on clay.

This paper describes a high-performance and space-efficient memory-resident datastore for text analytics systems based on a hash table for fast access, a dynamic trie for staging and a list of Level-Order Unary Degree Sequence (LOUDS) tries for compactness. We achieve efficient memory allocation and data placement by placing freqently access keys in the hash table, and infrequently accessed keys in the LOUDS tries without using conventional cache algorithms. Our algorithm also dynamically changes memory allocation sizes for these data structures according to the remaining available memory size. This technique yields 38.6% to 52.9% better throughput than a double array trie - a conventional fast and compact datastore.