Adsorptive separation of arsenate and arsenite anions from aqueous medium by using orange waste

Cellulose and orange waste were chemically modified by means of phosphorylation. The chemically modified gels were further loaded with iron(III) in order to create a suitable chelating environment for arsenate and arsenite removal. The loading capacity for iron(III) on the gel prepared from orange waste (POW) was 1.21 mmol g⁻¹ compared with 0.96 mmol g⁻¹ for the gel prepared from cellulose (PC). Removal tests of arsenic with the iron(III)-loaded gel were carried out batchwise and by using a column. Arsenite removal was favored under alkaline condition for both PC and POW gels, however, the POW gel showed some removal capability even at neutral pH. On contrary, arsenate removal took place under acidic conditions at pH=2–3 and 2–6 for the PC and POW gels, respectively. Since iron(III) loading is higher on the POW gel than on the PC gel greater arsenic removal has been achieved by the POW gel compared with the PC gel. It can be concluded that the POW gel can be used for the removal and recovery of both arsenite and arsenate from arsenic contaminated wastewater.     

Variable-gain controllers for nonlinear systems using the T-S fuzzy model.

This correspondence proposes two novel control schemes with variable state-feedback gain to stabilize a Takagi-Sugeno (T-S) fuzzy system. The T-S fuzzy model is expressed as a linear plant with nonlinear disturbance terms in both schemes. In controller I, the T-S fuzzy model is expressed as a linear plant around a nominal plant arbitrarily selected from the set of linear subsystems that the T-S fuzzy model consists of. The variable gain then becomes a function of a gain parameter that is computed to neutralize the effect of disturbance term, which is, in essence, the deviation of the actual system dynamics from the nominal plant as the system traverses a specific trajectory. This controller is shown to stabilize the T-S fuzzy model. In controller II, individual linear subsystems are locally stabilized. Fuzzy blending of individual control actions is shown to make the T-S fuzzy system Lyapunov stable. Although applicability of both control schemes depends on the norm bound of unmatched state disturbance, this constraint is relaxed further in controller II. The efficacy of controllers I and II has been tested on two nonlinear systems.     

A secure multi-stage one-round bit-plane permutation operation based chaotic image encryption

Currently, data security is a challenging task in any open source data transmission network. Basically, in most of the networks, images are used, hence security of images is a major challenging task. This paper proposes a combined hyper-chaos and chaos based encryption technique to secure images. In the method, one-round of diffusion and multi-stage bit-plane permutation operations are performed to obtain the better encryption results. The advantages of this scheme are that in one-round encryption operation, the proposed scheme can be realized easily and also confused largely. Apart from that the algorithm is simple as it uses simpler mathematical computations while attaining higher security such as higher key space, higher number of pixel changing rate, higher unified average changing intensity, and better correlation coefficient results. Moreover, hash based keys are used to resist the algorithm against chosen-plaintext and known-plaintext attacks. The security analysis and computer simulations show the good encryption results of the proposed scheme and strong resistivity to the widely used common attacks.

     

Content-based image classification with wavelet relevance vector machines

This paper introduces the use of relevance vector machines (RVMs) for content-based image classification and compares it with the conventional support vector machine (SVM) approach. Different wavelet kernels are included in the formulation of the RVM. We also propose a new wavelet-based feature extraction method that extracts lesser number of features as compared to other wavelet-based feature extraction methods. Experimental results confirm the superiority of RVM over SVM in terms of the trade-off between slightly reduced accuracy but substantially enhanced sparseness of the solution, and also the ease of free parameters tuning.     

Online maintenance of very large random samples on flash storage

Recent advances in flash storage have made it an attractive alternative for data storage in a wide spectrum of computing devices, such as embedded sensors, mobile phones, PDA’s, laptops, and even servers. However, flash storage has many unique characteristics that make existing data management/analytics algorithms designed for magnetic disks perform poorly with flash storage. For example, while random reads can be nearly as fast as sequential reads, random writes and in-place data updates are orders of magnitude slower than sequential writes. In this paper, we consider an important fundamental problem that would seem to be particularly challenging for flash storage: efficiently maintaining a very large random sample of a data stream (e.g., of sensor readings). First, we show that previous algorithms such as reservoir sampling and geometric file are not readily adapted to flash. Second, we propose B-File, an energy-efficient abstraction for flash storage to store self-expiring items, and show how a B-File can be used to efficiently maintain a large sample in flash. Our solution is simple, has a small (RAM) memory footprint, and is designed to cope with flash constraints in order to reduce latency and energy consumption. Third, we provide techniques to maintain biased samples with a B-File and to query the large sample stored in a B-File for a subsample of an arbitrary size. Finally, we present an evaluation with flash storage that shows our techniques are several orders of magnitude faster and more energy-efficient than (flash-friendly versions of) reservoir sampling and geometric file. A key finding of our study, of potential use to many flash algorithms beyond sampling, is that “semi-random” writes (as defined in the paper) on flash cards are over two orders of magnitude faster and more energy-efficient than random writes.     

Environmental Quality of the Boula-Nuasahi Chromite Mine Area in India

Chromite mining can create hostile conditions for organisms in the surrounding environment. Overburden soil and mine water samples were collected and analyzed for their microbial diversity, nutrient content, and water quality at the Boula-Nuasahi Mine. Most of the water parameters that were measured exceed national/international standards. The microbial population was low (soil: 45 ± 0.06 × 10⁵ bacteria, 20 ± 0.03 × 10⁵ fungus) and the overburden soils have a low nutrient (N, P, K) content. The metal ions were found to have leached to nearby agriculture lands, making them less fertile for crop production. Overburden dumps and seepage water were found to be the main sources of chromium pollution.