Understanding the resurgence of traditional authorities in post-apartheid South Africa

Drawing their power not from the ballot box but from a supposedly ancient wellspring of power, hereditary traditional authorities in postcolonial Africa have frequently posed challenges for incoming ‘democratic’ governments. The situation in post-apartheid South Africa is no different. However contentious their role under the colonial and apartheid systems of government was, the Constitution of the new South Africa (1996) recognised traditional authorities and afforded them opportunities for a political resurgence. This paper reviews the changing status of traditional authorities in the Eastern Cape Province over the twenty years since 1994. It explores the resurgence of the chiefs in relation to the consolidation of both democratic processes and of emergent, neo-patrimonial modes of government. It briefly considers the role of traditional authorities in three key and closely related spheres, namely the institution of the Eastern Cape House of Traditional Leaders, the question of how gender is handled by and within traditional institutions, and the continuing challenges of land administration and development in rural areas. In all these spheres, and in the face of real opposition, the voice and influence traditional authorities have emerged stronger than ever. We conclude by suggesting that as they are drawn deeper into governance and have to play a formal role in addressing the myriad institutional challenges, new questions will and should be asked about the status and influence of traditional authorities, and their substantive contribution to democracy in South Africa.

Bio-inspired ant colony optimization based clustering algorithm with mobile sinks for applications in consumer home automation networks

With the fast development of wireless communications, ZigBee and semiconductor devices, home automation networks have recently become very popular. Since typical consumer products deployed in home automation networks are often powered by tiny and limited batteries, one of the most challenging research issues is concerning energy reduction and the balancing of energy consumption across the network in order to prolong the home network lifetime for consumer devices. The introduction of clustering and sink mobility techniques into home automation networks have been shown to be an efficient way to improve the network performance and have received significant research attention. Taking inspiration from nature, this paper proposes an Ant Colony Optimization (ACO) based clustering algorithm specifically with mobile sink support for home automation networks. In this work, the network is divided into several clusters and cluster heads are selected within each cluster. Then, a mobile sink communicates with each cluster head to collect data directly through short range communications. The ACO algorithm has been utilized in this work in order to find the optimal mobility trajectory for the mobile sink. Extensive simulation results from this research show that the proposed algorithm significantly improves home network performance when using mobile sinks in terms of energy consumption and network lifetime as compared to other routing algorithms currently deployed for home automation networks.

Different strategies for improving summer thermal comfort in heavyweight traditional buildings

In order to exploit the passive energy potential of the building envelope, it is important to provide a right combination of insulation thickness, heat capacity and night-time ventilation. In this paper, this issue will be tackled with reference to an historic building in Catania (Southern Italy). The building was built at the end of the XIX century, and its opaque envelope is entirely made with lava stones, which is typical of traditional architecture in this area. Starting from the current configuration of the building, many hypotheses for refurbishment are considered, combined with different strategies for passive cooling, such as night-time ventilation, use of shading devices and adoption of highly-reflective coatings. The effectiveness of each solution in terms of summer thermal comfort is evaluated through dynamic thermal simulations carried out with EnergyPlus. The results show the synergic effect of these strategies, as well as their individual impact, and allow to draw some general conclusions about the behaviour of heavyweight buildings under moderately hot weather conditions.

Assessing how retrofit of traditional buildings impacts indoor environment and occupant’s thermal comfort

Buildings consume a large amount of energy, in both their use and production. Retrofitting aims to achieve a reduction in this energy consumption. However, there are concerns that retrofitting can cause negative impacts on the internal environment including poor thermal comfort and health issues. This research investigates the impact of retrofitting the façade of existing traditional buildings and the resulting impact on the indoor environment and occupant thermal comfort. A Case building located at the University of Reading has been monitored experimentally and modelled using IES software with monitored values as input conditions for the model. The proposed façade related retrofit options have been simulated and provide information on their effect on the indoor environment. The findings show a positive impact on the internal environment. The data shows a 16.2% improvement in thermal comfort after retrofit is simulated. This also achieved a 21.6% reduction in energy consumption from the existing building.

Tensor LRR and sparse coding-based subspace clustering

Subspace clustering groups a set of samples from a union of several linear subspaces into clusters, so that the samples in the same cluster are drawn from the same linear subspace. In the majority of the existing work on subspace clustering, clusters are built based on feature information, while sample correlations in their original spatial structure are simply ignored. Besides, original high-dimensional feature vector contains noisy/redundant information, and the time complexity grows exponentially with the number of dimensions. To address these issues, we propose a tensor low-rank representation (TLRR) and sparse coding-based (TLRRSC) subspace clustering method by simultaneously considering feature information and spatial structures. TLRR seeks the lowest rank representation over original spatial structures along all spatial directions. Sparse coding learns a dictionary along feature spaces, so that each sample can be represented by a few atoms of the learned dictionary. The affinity matrix used for spectral clustering is built from the joint similarities in both spatial and feature spaces. TLRRSC can well capture the global structure and inherent feature information of data, and provide a robust subspace segmentation from corrupted data. Experimental results on both synthetic and real-world data sets show that TLRRSC outperforms several established state-of-the-art methods.

Heterogeneous tensor decomposition for clustering via manifold optimization

Tensor clustering is an important tool that exploits intrinsically rich structures in real-world multiarray or Tensor datasets. Often in dealing with those datasets, standard practice is to use subspace clustering that is based on vectorizing multiarray data. However, vectorization of tensorial data does not exploit complete structure information. In this paper, we propose a subspace clustering algorithm without adopting any vectorization process. Our approach is based on a novel heterogeneous Tucker decomposition model taking into account cluster membership information. We propose a new clustering algorithm that alternates between different modes of the proposed heterogeneous tensor model. All but the last mode have closed-form updates. Updating the last mode reduces to optimizing over the multinomial manifold for which we investigate second order Riemannian geometry and propose a trust-region algorithm. Numerical experiments show that our proposed algorithm compete effectively with state-of-the-art clustering algorithms that are based on tensor factorization.