Artificial neural network based maximum power point tracking controller for photovoltaic standalone system

This article presents a two-stage maximum power point tracking (MPPT) controller using artificial neural network (ANN) for photovoltaic (PV) standalone system, under varying weather conditions of solar irradiation and module temperature. At the first-stage, the ANN algorithm locates the maximum power point (MPP) associated to solar irradiation and module temperature. Then, a simple controller at the second-step, by changing the duty cycle of a DC–DC boost converter, tracks the MPP. In this method, in addition to experimental data collection for training the ANN, a circuit is designed in MATLAB-Simulink to acquire data for whole ranges of weather condition. The whole system is simulated in Simulink. Simulation results show small transient response time, and low power oscillation in steady-state. Furthermore, dynamic response verifies that this method is very fast and precise at tracking the MPP under rapidly changing irradiation, and has very low power oscillation under slowly changing irradiation. Experimental results are provided to verify the simulation results as well.     

Assessment of cytotoxicity and toxicity for phosphonium-based deep eutectic solvents

In this work, the cytotoxicity and toxicity of phosphonium-based deep eutectic solvents (DESs) with three hydrogen bond donors, namely glycerine, ethylene glycol, and triethylene glycol were investigated. The cytotoxicity effect was tested using brine shrimp (Artemia salina). The toxicity was investigated using the two Gram positive bacteria Bacillus subtilis and Staphylococcus aureus, and two Gram negative bacteria Escherichia coli and Pseudomonas aeruginosa. The cytotoxicity of tested DESs was much higher than that of their individual components, indicating their toxicological behavior was different. It was also found that there was toxic effect on the studied bacteria, indicating their potential application as anti-bacterial agents. To the best of our knowledge, this is the first time the cytotoxicity and toxicity of phosphonium-based DESs were studied.     

Nickel oxide nanoparticles exert cytotoxicity via oxidative stress and induce apoptotic response in human liver cells (HepG2)

Increasing use of nickel oxide nanoparticles (NiO NPs) necessitates an improved understanding of their potential impact on human health. Previously, toxic effects of NiO NPs have been investigated, mainly on airway cells. However, information on effect of NiO NPs on human liver cells is largely lacking. In this study, we investigated the reactive oxygen species (ROS) mediated cytotoxicity and induction of apoptotic response in human liver cells (HepG2) due to NiO NPs exposure. Prepared NiO NPs were crystalline and spherical shaped with an average diameter of 44 nm. NiO NPs induced cytotoxicity (cell death) and ROS generation in HepG2 cells in dose-dependent manner. Further, ROS scavenger vitamin C reduced cell death drastically caused by NiO NPs exposure indicating that oxidative stress plays an important role in NiO NPs toxicity. Micronuclei induction, chromatin condensation and DNA damage in HepG2 cells treated with NiO NPs suggest that NiO NPs induced cell death via apoptotic pathway. Quantitative real-time PCR analysis showed that following the exposure of HepG2 cells to NiO NPs, the expression level of mRNA of apoptotic genes (bax and caspase-3) were up-regulated whereas the expression level of anti-apoptotic gene bcl-2 was down-regulated. Moreover, activity of caspase-3 enzyme was also higher in NiO NPs treated cells. To the best of our knowledge this is the first report demonstrating that NiO NPs caused cytotoxicity via ROS and induced apoptosis in HepG2 cells, which is likely to be mediated through bax/bcl-2 pathway. This work warrants careful assessment of Ni NPs before their commercial and industrial applications.     

Discriminant analysis for the prediction of sand mass distribution in an urban stormwater holding pond using simulated depth average flow velocity data

The approach of this paper is to predict the sand mass distribution in an urban stormwater holding pond at the Stormwater Management And Road Tunnel (SMART) Control Centre, Malaysia, using simulated depth average floodwater velocity diverted into the holding during storm events. Discriminant analysis (DA) was applied to derive the classification function to spatially distinguish areas of relatively high and low sand mass compositions based on the simulated water velocity variations at corresponding locations of gravimetrically measured sand mass composition of surface sediment samples. Three inflow parameter values, 16, 40 and 80 m³ s^?1, representing diverted floodwater discharge for three storm event conditions were fixed as input parameters of the hydrodynamic model. The sand (grain size?>?0.063 mm) mass composition of the surface sediment measured at 29 sampling locations ranges from 3.7 to 45.5 %. The sampling locations of the surface sediment were spatially clustered into two groups based on the sand mass composition. The sand mass composition of group 1 is relatively lower (3.69 to 12.20 %) compared to group 2 (16.90 to 45.55 %). Two Fisher’s linear discriminant functions, F ₁ and F ₂, were generated to predict areas; both consist of relatively higher and lower sand mass compositions based on the relationship between the simulated flow velocity and the measured surface sand composition at corresponding sampling locations. F ₁?=??9.405?+?4232.119?×?A???1795.805?×?B?+?281.224?×?C, and F ₂?=??2.842?+?2725.137?×?A???1307.688?×?B?+?231.353?×?C. A, B and C represent the simulated flow velocity generated by inflow parameter values of 16, 40 and 80 m³ s^?1, respectively. The model correctly predicts 88.9 and 100.0 % of sampling locations consisting of relatively high and low sand mass percentages, respectively, with the cross-validated classification showing that, overall, 82.8 % are correctly classified. The model predicts that 31.4 % of the model domain areas consist of high-sand mass composition areas and the remaining 68.6 % comprise low-sand mass composition areas.     

Water quality assessment of Lake Pandu Bodhan, Andhra Pradesh State, India

A systematic investigation of variations in some nutrient levels at Pandu Lake from August 2002 to July 2004 was carried out. The untreated domestic wastes from various parts of Bodhan town are directly discharged into Pandu Lake leading to gross pollution. Therefore present investigation was under taken to assess the magnitude of sewage pollution by monitoring key water quality parameters dissolved oxygen, biological oxygen demand, alkalinity, calcium, nitrates and phosphates etc. Monthly water samples were collected from three different sampling stations. Low Dissolved oxygen and high biological oxygen demand, elevated Nitrates and Phosphates levels were found, which gives the information about conversion of this water body from oligotrophic to eutrophic nature. Phosphates were found to be in the range of 0.9 to 4.0 mg/L. Nitrates were found to be higher in Pandu Lake and their number is more in summer, and suitable explanation was given. Nitrates were found to be in range between 24.8mg/L to 71.2mg/L. Data on various chemical characteristics vary at different sites in different months in Pandu Lake. Some of the characteristics like Dissolved Oxygen, Biological Oxygen Demand, Nitrates, Phosphates and nutrient loading are contributing to eutrophication process in this lake and the lake seems to be eutrophic through out the year.     

Multivariate statistical techniques for the assessment of seasonal variations in surface water quality of pasture ecosystems

This study investigates the applicability of multivariate statistical techniques including cluster analysis (CA), discriminant analysis (DA), and factor analysis (FA) for the assessment of seasonal variations in the surface water quality of tropical pastures. The study was carried out in the TPU catchment, Kuala Lumpur, Malaysia. The dataset consisted of 1-year monitoring of 14 parameters at six sampling sites. The CA yielded two groups of similarity between the sampling sites, i.e., less polluted (LP) and moderately polluted (MP) at temporal scale. Fecal coliform (FC), NO₃, DO, and pH were significantly related to the stream grouping in the dry season, whereas NH₃, BOD, Escherichia coli, and FC were significantly related to the stream grouping in the rainy season. The best predictors for distinguishing clusters in temporal scale were FC, NH₃, and E. coli, respectively. FC, E. coli, and BOD with strong positive loadings were introduced as the first varifactors in the dry season which indicates the biological source of variability. EC with a strong positive loading and DO with a strong negative loading were introduced as the first varifactors in the rainy season, which represents the physiochemical source of variability. Multivariate statistical techniques were effective analytical techniques for classification and processing of large datasets of water quality and the identification of major sources of water pollution in tropical pastures.     

Promising role of plant hormones in translocation of lead in Sesbania drummondii shoots

Lead (Pb) accumulation in Sesbania drummondii shoots was enhanced by 654 and 415% in the presence of 100 microM IAA and 100 microM NAA, respectively, compared to control plants (Pb alone). However, when IAA or NAA was added along with EDTA, Pb accumulation further increased in shoots by 1349% and 1252%, respectively. Scanning electron microscopic observations revealed that Pb particles in both leaf and stem of Pb+EDTA+IAA and Pb+EDTA+NAA treated plants were concentrated in the region of vascular bundles. In root tissue, Pb particles were present between epidermis to stele region. Plant growth in both treatments (Pb+100 microM IAA and Pb+100 microM NAA) was comparable to control plants; however, it was significantly inhibited in the treatments containing Pb+EDTA and EDTA at concentrations of 10 microM of IAA or NAA. Moreover, the photosynthetic efficiency and strength of the treated plants were not affected in the presence of IAA or NAA and EDTA.     

Noble metal catalysts for methane removal

Using a bench-scale rig, the activities of Pt, Pd and Pt+Pd catalysts supported on gamma-Al(2)O(3) and on TiO(2) (anatase) for the complete oxidation of methane (300 ppmv) in air have been measured as a function of temperature; values of T(10), T(50) and T(90) together with the Arrhenius parameters (activation energy and pre-exponential factor) are reported. Pt is less active than Pd when deposited on the surface of the TiO(2), but more active when deposited on gamma-Al(2)O(3), however when combined, the Pt+Pd mixture is more active than either metal individually. The T(10) for Pt+Pd/gamma-Al(2)O(3) was being as low as 228 degrees C. The significance of the Arrhenius parameters, for metal containing catalysts is that they exhibit compensation with increasing activation energy, while securing a more rapid increase in conversion from 0% to 100% when the temperature is increased.