Thermophysical Characterization of Local Materials from a Locality in Chad for Use in Eco-Building

With the aim of enhancing the value of local building materials, the subject of this paper is a thermophysical study of natural Chadian clay from the eastern region of Chad, “Abeche”. Samples were taken from a brickwork in Abeche from a depth of 1 m, then using a clay brick-making press, 4 × 5 × 8 cm3 clay test tubes were made with 2%, 4%, 6% and 8% cow dung, and a 100% clay sample was used as a control. These samples underwent thermophysical characterization using the hot-wire method with a hot-plane option, yielding results that could improve thermophysical performance. The thermal conductivity of the test sample is in the order of 0.715 to 0.420 W/m. K, at 8% for cow dung, so the addition of cow dung as a percentage in the clay matrix enabled us to obtain various satisfactory thermal parameters.

Multi-robot mapping based on the adaptive differential evolution

Map building by multi-robot is very important to accomplish autonomous navigation,and one of the basic problems and research hotspots is how to merge the maps into a single one in the field of multi-robot map building.A novel approach is put forward based on adaptive differential evolution to map building for the multi-robot system.The multi-robot mapping-building system adopts the methods of decentralized exploration and concentrated mapping.The adaptive differential evolution algorithm is used to search in the space of possible transformation,and the iterative search is performed with the goal of maximizing overlapping regions.The map is translated and rotated so that the two maps can be overlapped and merged into a single global one successfully.This approach for map building can be realized without any knowledge of their relative positions.Experimental results show that the approach is effective and feasibile.

D2ANet:Difference-aware attention network for multi-level change detection from satellite imagery

Recognizing dynamic variations on the ground,especially changes caused by various natural disasters,is critical for assessing the severity of the damage and directing the disaster response.However,current workflows for disaster assessment usually require human analysts to observe and identify damaged buildings,which is labor-intensive and unsuitable for large-scale disaster areas.In this paper,we propose a difference-aware attention network(D2ANet)for simultaneous building localization and multi-level change detection from the dual-temporal satellite imagery.Considering the differences in different channels in the features of pre-and post-disaster images,we develop a dual-temporal aggregation module using paired features to excite change-sensitive channels of the features and learn the global change pattern.Since the nature of building damage caused by disasters is diverse in complex environments,we design a difference-attention module to exploit local correlations among the multi-level changes,which improves the ability to identify damage on different scales.Extensive experiments on the large-scale building damage assessment dataset xBD demonstrate that our approach provides new state-of-the-art results.Source code is publicly available at https://github.com/mj129/D2ANet.