China 
Africa 
Explore chapters and articles related to this topic
Space and place in resource extraction
Published in Juha Kotilainen, Resource Extraction, Space and Resilience, 2020
The above has sought to define the spaces of extraction with the different places which are important for understanding the processes of extraction. It has been emphasised that the relations of the places to one another are important, in line with the ideas of relational space. Additionally, within the spaces of extraction, there are other forms of spatialities that are relevant for the analysis of the politics, political economy and political ecologies of the extraction of minerals. First, it is possible to distinguish the spatial scales through which the extraction of the minerals and the trade in them occurs. These scales run from the micro to the scale of the Earth. While not using the terminology of spatial scales, Klinger (2017) has implicitly expanded the scales to cover the Moon. Overall, there are spatial scales of very different kinds. Spatial scales can refer to the ways in which physical entities are organised at various scales, but they can just as well be about the social organisation that occurs at different scales and across scales so that there are relations between scales that are important. That is, spatial scales can refer to how social action occurs relationally through the different scales, which means that what occurs at one of the scales has repercussions on the occurrences, as well as the actions and behaviour of actors, on at least one of the other scales. Focussing on the extraction of minerals, we would be interested in how the physical entities would be scaled spatially. On the most micro scale, there are the ores located in the specific deposits. In addition, there are the ores existing and available at a global scale, including those about the geopotential of which little is known at the current moment (Scholz & Wellmer, 2013). As to the social organisation, human communities, households and the people inhabiting the landscapes are entities at smaller scales. At wider scales, there are the global markets and global speculations on the profitability of minerals and their extraction in tangible places. The scale of the nation-state is important because national governments usually control the access to sub-soil resources and their exploration, even if the governments often grant the permissions to mining and exploration companies, operating at various scales, to carry out the tasks of exploration and extraction of minerals.
Optimization of process parameter in AI7075 turning using grey relational, desirability function and metaheuristics
Published in Materials and Manufacturing Processes, 2023
Dillip Kumar Mohanta, Bidyadhar Sahoo, Ardhendu Mouli Mohanty
Grey relational analysis has emerged as a useful method for assessing processes with many performance criteria in recent years. Complex multiple response optimization problems can be condensed into the optimization of a single response grey relational grade using grey relational analysis. The overall analyzed table has been stated in Table 3. The normalized value of original sequence for Surface roughness (Ra) and cutting fornce (Fz) has been calculated considering smaller is better in the prospects of low power consideration. Thereafter deviation sequence has been calculated and tabulated which quantify the degree of comparability sequence is the closest to the reference sequence. Next, Grey relational coefficient is being calculated,Eqn.2. At last grey relational grade is calculated, Eqn.3 is the weighted sum of individual grey relational coefficient for both the output parameter. It shows the quantification of grey relational space. So, there the rank has been shown in the ascending order to assign best parameter set for optimum output in the stated experiment. Table 3 shows that the grey relational grade is directly proportional to the multiple performance characteristics. Therefore, cutting speed of 80 mm/min (Level-1), feed of 0.05 mm/rev (Level-1) and depth of cut as1.20 mm (Level-3) is the optimal input setting as concluded using grey relational grade and rank.
New architectural intervention in historically sensitive contexts: humanistic approach in historic Cairo
Published in HBRC Journal, 2021
In attaining the intervention objectives, the proposed design adopted a reliable approach based on principles and guidelines derived from the experiment of the Abu Aldahab community wall. The design provided two types of spaces. Firstly, the indoor spaces (the container space), where the required facility spaces are provided through a comprehensive design process. Secondly, the relational space on the building edge between the building and neighborhood. This space was dealt with at two levels. The first level has focussed on the formalistic aspects of the space, while the second level focuses on the humanistic aspects through creating interactive socio-cultural spaces.
Knowledge reasoning with multiple relational paths
Published in Connection Science, 2023
The multi-step relation path reasoning based on low-dimensional vector space is represented by vectorised knowledge graph. Different from knowledge representation learning, the knowledge reasoning method introduces the information of multi-step relation path in the process of building the model. Based on the TransE model by Guu et al. (2015), regarding the low-dimensional vector space model as the traversal operation of the relationship, we directly model the intermediate steps of the path, and regularise the distribution of the entity vector space. On the basis of TransE, Lin et al. (2015) added the constraints of relational paths, proposed the PTransE model, and modeled path information through the combined operation of relations. Later, Lin et al. (2016) proposed a combined learning model RPE based on relational path representation. Unlike the PTransE model, RPE projects each entity to the corresponding relational space and path space through the relational mapping matrix and path mapping matrix at the same time. Relationship or path vectors are viewed as translations between projected entity vector representations. For the first time, Feng et al. (2016) utilises more information in knowledge graphs to learn vector representations of entities and relationships based on GAKE, a graph-aware knowledge reasoning model. GAKE introduces three types of information, neighbor context, path context and edge context, to reflect knowledge characteristics from different perspectives, and at the same time, an attention mechanism is designed to learn entities or relations with important contributions. Singh and Lakshmanan (2021) presented a popularity, interests, location used hidden Naive Bayesian-based model for link prediction in dynamic social networks by considering behavioral controlling elements like relationship network structure, nodes’ attributes, location-based information of nodes, nodes’ popularity, users’ interests, and learning the evolution pattern of these factors in the networks.