Water, women, waste, wisdom and wealth: harvesting the confluences and opportunities

This special volume offers a collection of papers that examine challenges and solutions where water meets complex, intersections with women, waste, wisdom or wealth. This unique array of articles offer readers of the Journal of Cleaner Production multidisciplinary views of water issues involving physical and structural perspectives, as well as political, social, cultural and increasingly serious environmental challenges. By building upon extensive literature reviews along with data collected through empirical study and real world observations, the authors effectively present valuable insights into the depth and nature of many of the problems but also present a well-developed array of recommendations, based upon successful projects and programs, world-wide. Among the recommendations are proposals for policies, approaches and regulations that provide system enhancements to prevent pollution and contamination and ideas to monitor and regulate water consumption. This international collection includes studies from 15 countries, documented and written by an equal number of female and male authors.

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.