Removal of organics in water using hydrogen peroxide in presence of ultraviolet light

The optimum conditions for the removal of dissolved organic impurities from water using hydrogen peroxide (50%) followed by ultraviolet irradiation were investigated. The photochemically initiated hydroxyl radical (OH) oxidation reduced the total organic carbon (TOC) content of distilled water samples by about 88% and of tap water by 98%. Extraction with hexane of equal volumes of water samples before and after H₂O₂/u.v. treatment followed by gas chromatographic analysis of the concentrated extracts indicated that about 12% of the electron-capturing, residual organics remained after this treatment. These results support the conclusion drawn from total organic carbon analysis that this simple method yields water nearly free of organic impurities.     

Purification and kinetic characterization of polyphenol oxidase from Barbados cherry (Malpighia glabra L.)

Polyphenol oxidase (PPO) of Barbados cherry was extracted and purified through ammonium sulfate precipitation, gel filtration, and affinity chromatography. The purification factor for PPO was 60% with 8.3% yield. The enzyme was characterized for thermal stability, pH and kinetic parameters. The molecular mass of PPO was approximately the sum of 52 and 38 kDa estimated by SDS–PAGE. The purity was checked by native PAGE, showing a single prominent band. The optimum pH was 7.2. The enzyme had a temperature optimum at 40 °C and was relatively stable at 60 °C, with 55% loss of activity. Sodium diethyl dithiocarbamate (SDDC), l-cysteine and ascorbate significantly inhibited PPO activity. 4-Methyl catechol and catechol were found to be efficient diphenolic substrates for cherry PPO, considering the _Vmax/_Km$V_{\text{max}}/K_{\mathrm{m}}$ ratio. The data obtained in this study may help to understand cherry fruit browning.     

Water Pipeline Leak Measurement Using Wavelet Packet-based Adaptive ICA

Water Resources Management - The localization of the leaks is estimated using the time difference method in the past decade. Generalized and Basic Cross-correlation techniques are frequently...     

Dark fermentative biohydrogen production by Acinetobacter junii-AH4 utilizing various industry wastewaters

Hydrogen (H₂) is one of the most promising renewable energy sources, anaerobic bacterial H₂ fermentation is considered as one of the most environmentally sustainable alternatives to meet the potential fossil fuel demand. Bio-H₂ is the cleanest and most effective source of energy provided by the dark fermentation utilizing organic substrates and different wastewaters. In this study, the bio-H₂ production was achieved by using the bacteria Acinetobacter junii-AH4. Further, optimization was carried out at different pH (5.0–8.0) in the presence of wastewaters as substrates (Rice mill wastewater (RMWW), Food wastewater (FWW) and Sugar wastewater (SWW). In this way, the optimized experiments excelled with the maximum cumulative H₂ production of 566.44 ± 3.5 mL/L (100% FWW at pH 7.5) in the presence of Acinetobacter junii-AH4. To achieve this, a bioreactor (3 L) was employed for the effective production of H₂ and Acinetobacter junii-AH4 has shown the highest cumulative H₂ of 613.2 ± 3.0 mL/L, HPR of 8.5 ± 0.4 mL/L/h, HY of 1.8 ± 0.09 mol H₂/mol glucose. Altogether, the present study showed a COD removal efficiency of 79.9 ± 3.5% by utilizing 100% food wastewater at pH 7.5. The modeled data established a batch fermentation system for sustainable H₂ production. This study has aided to achieve an ecofriendly approach using specific wastewaters for the production of bio-H₂.     

A conflict-based priority dispatching rule and operation-based approaches to job shops

Job shop scheduling (JSS) problems consist of a set of machines and a collection of jobs to be scheduled. Each job consists of several operations with a specified processing order. In this paper, a job shop model problem is scheduled with the help of the Giffler and Thompson algorithm using a priority dispatching rule (PDR). A conflict based PDR is used to schedule the job shop model by using Genetic Algorithms (GAs). An iterative method is applied to the job model to find the optimal conflict-based PDR order and the operation sequence. The same job shop model is also scheduled based on an operation using simulated annealing (SA) and hybrid simulated annealing (HSA). A makespan of the job model is used as an objective. These four methods are considered as different solutions for each problem. A two-way analysis of variance (ANOVA) is applied to test its significance.     

Cluster head selection for energy efficient and delay-less routing in wireless sensor network

Wireless sensor network (WSN) is comprised of tiny, cheap and power-efficient sensor nodes which effectively transmit data to the base station. The main challenge of WSN is the distance, energy and time delay. The power resource of the sensor node is a non-rechargeable battery. Here the greater the distance between the nodes, higher the energy consumption. For having the effective transmission of data with less energy, the cluster-head approach is used. It is well known that the time delay is directly proportional to the distance between the nodes and the base station. The cluster head is selected in such a way that it is spatially closer enough to the base station as well as the sensor nodes. So, the time delay can be substantially reduced. This, in turn, the transmission speed of the data packets can be increased. Firefly algorithm is developed for maximizing the energy efficiency of network and lifetime of nodes by selecting the cluster head optimally. In this paper firefly with cyclic randomization is proposed for selecting the best cluster head. The network performance is increased in this method when compared to the other conventional algorithms.

     

An Efficient Image Analysis Framework for the Classification of Glioma Brain Images Using CNN Approach

The identification of brain tumors is multifarious work for the separation of the similar intensity pixels from their surrounding neighbours. The detection of tumors is performed with the help of automatic computing technique as presented in the proposed work. The non-active cells in brain region are known to be benign and they will never cause the death of the patient. These non-active cells follow a uniform pattern in brain and have lower density than the surrounding pixels. The Magnetic Resonance (MR) image contrast is improved by the cost map construction technique. The deep learning algorithm for differentiating the normal brain MRI images from glioma cases is implemented in the proposed method. This technique permits to extract the linear features from the brain MR image and glioma tumors are detected based on these extracted features. Using k-mean clustering algorithm the tumor regions in glioma are classified. The proposed algorithm provides high sensitivity, specificity and tumor segmentation accuracy.