Water crisis through the analytic of urban transformation: an analysis of Bangalore’s hydrosocial regimes

ABSTRACT

This paper explores intensified water crisis in Bangalore (or Bengaluru) in India by using the analytic of three hydrosocial regimes: the catchment-based regime, the hydraulic regime and the speculative urban regime. It uses a wide range of qualitative interviews, scientific reports and secondary sources to analyze shifting urban trajectories, agrarian relations and their interlinkages with water. Historical ruptures (in the realm of governance, urban growth and changing urban–rural dynamics) allow one to highlight the complex role of speculative logics that shape urban expansion and water scarcity.     

Dictionary-based classifiers for exploiting feature sequence information and their application to hyperspectral remotely sensed data

The problem of classification is shared across various disciplines. Designing even less computationally demanding and more effective classifiers has been a key challenge for researchers for many years. No single classifier can be highly effective for all types of datasets and thus, depending on the data distribution, various classifiers have been proposed in the literature. To our knowledge, feature values have been vastly exploited as the base for discriminating classes, while feature sequence information has been somehow under-exploited so far. In the proposed approach normalised features are sorted and ranked, creating a sequence of finite numbers. The associated rank of the created sequence is used as an additional feature, which in a way defines the sample-specific intra-feature relationship. Three novel dictionary-based approaches such as Sequence Classifier (SC), Sequence-dictionary-based k-Nearest Neighbours Classifier (SDk-NN) and Combined-dictionary-based k-Nearest Neighbours Classifier (CDk-NN) are proposed in this paper.

In the case of remotely sensed data, and specifically in Hyper-Spectral Images (HSI), the spectral features (Spectral signatures) represent a strong, object-specific spectral relationship, which is a key point in our proposed approach. In this case, indeed, the proposed classifiers were tested over various (five) HS datasets and found to be effective. Based on the classifiers features, two derived distance measures are proposed and validated for the HS dataset, namely: the Normalised Sequence Distance (NSD) measure and Combined Distance (CD) measure. These measures appear to overperform the conventional Normalised Euclidean Distance (NED) in this context. Also, validation for both binary and multi-class datasets are experimented and their performances are evaluated in terms of accuracy and other standard measures. Experimental results over 21 datasets revealed that the proposed approaches perform comparably, and in some cases even better than other classifiers. Stack-operated, class-specific sparse dictionaries are also introduced in order to reduce the computational complexity, which can be used as an active learning-based approach for optimal training sample selection. Additional tests were performed with variable levels of dictionary sparsity for assessing its impact on accuracy.     

Natural Voltage-Gated Sodium Channel Ligands: Biosynthesis and Biology

Natural product biosynthetic pathways are composed of enzymes that use powerful chemistry to assemble complex molecules. Small molecule neurotoxins are examples of natural products with intricate scaffolds which often have high affinities for their biological targets. The focus of this Minireview is small molecule neurotoxins targeting voltage-gated sodium channels (VGSCs) and the state of knowledge on their associated biosynthetic pathways. There are three small molecule neurotoxin receptor sites on VGSCs associated with three different classes of molecules: guanidinium toxins, alkaloid toxins, and ladder polyethers. Each of these types of toxins have unique structural features which are assembled by biosynthetic enzymes and the extent of information known about these enzymes varies among each class. The biosynthetic enzymes involved in the formation of these toxins have the potential to become useful tools in the efficient synthesis of VGSC probes.     

Carborane-Containing Matrix Metalloprotease (MMP) Ligands as Candidates for Boron Neutron-Capture Therapy (BNCT)

Based on the previously reported potent and selective sulfone hydroxamate inhibitors SC-76276, SC-78080 (SD-2590), and SC-77964, potent MMP inhibitors have been designed and synthesized to append a boron-rich carborane cluster by employing click chemistry to target tumor cells that are known to upregulate gelatinases. Docking against MMP-2 suggests binding involving the hydroxamate zinc-binding group, key H-bonds by the sulfone moiety with the peptide backbone residues Leu82 and Leu83, and a hydrophobic interaction with the deep P1’ pocket. The more potent of the two triazole regioisomers exhibits an IC₅₀ of 3.7 nM versus MMP-2 and IC₅₀ of 46 nM versus MMP-9.     

Poly(N-vinylpyrrolidone)-stabilized colloidal graphene-reinforced poly(ethylene-co-methyl acrylate) to mitigate electromagnetic radiation pollution

Polymer Bulletin - Electrically conducting flexible polymeric nanocomposite has been fabricated through wet mixing method where conducting inclusion was acoustically exfoliated pristine graphene...     

Experimental Investigation for Performance Study of Wire Electrochemical Spark Cutting of Silica Epoxy Nanocomposites

Purpose

Polymer Nanocomposites are advanced engineering composites with enhanced properties. These materials play a central role in various industrial sectors. The growing awareness of the key parameters (which influence the physical properties) with different combination of matrix-reinforcement, are making them more attractive in various applications. Machining of these materials is a challenging task for engineers with their properties (hardness and brittleness) due to various combinations of matrix-reinforcement. Therefore, the aim of present work is to investigate the machining behaviour of Silicon Dioxide (silica) Epoxy Nanocomposite due to straight cutting by using Wire Electrochemical Spark Cutting (WECSC) process.

Method

A specific number of experiments were conducted based on one parameter at-a-time approach to study the effect of influencing input parameters.

Result

The effect of various process parameters namely voltage supply, electrolyte concentration, wire velocity, pulse-on time and silica particle concentration (Cp) such as 3%, 4% and 5% (weight percent) on performance measures such as material removal rate (MRR) and surface roughness were demonstrated experimentally.

Conclusion

WECSC has been found effective technique for cutting of Silicon Dioxide Epoxy Nanocomposite. It is reported that MRR increases with decrease in silica particle concentration in Silicon Dioxide Epoxy Nanocomposite.

     

Radar Absorbing Structures Using Frequency Selective Surfaces: Trends and Perspectives

Journal of Electronic Materials - Microwave radar absorbers are widely used in the strategic sector and wireless communication systems to reduce the radar cross-section of a target and...