Optimized processing power and trainability of neural network in numerical modeling of Al Matrix nano composites

In this research, an experimental study of reinforcing alumina nano-particles into the aluminum alloy matrix was implemented to verify the accuracy of modeling results obtained by feed forward neural networks. Artificial neural network combined with numerical technique were used to predict the various parameters of mechanical properties such as hardness, tensile and compressive yield stress, UTS and elongation percentage. Much experimentation were taken to discover a suitable number of hidden neurons, avoid detraction from the trainability and enable feed forward neural networks to solve more complex problems. The predictions were found to be consistent with experimental measurements.     

Photoassisted and photocatalytic degradation of sulfur mustard using TiO2 nanoparticles and polyoxometalates

The decomposition of highly toxic chemical warfare agent, sulfur mustard (bis(2-chloroethyl) sulfide or HD), has been studied by homogeneous photolysis and heterogeneous photocatalytic degradation on titania nanoparticles. Direct photolysis degradation of HD with irradiation system was investigated. The photocatalytic degradation of HD was investigated in the presence of TiO₂ nanoparticles and polyoxometalates embedded in titania nanoparticles in liquid phase at room temperature (33?±?2 °C). Degradation products during the treatment were identified by gas chromatography–mass spectrometry. Whereas apparent first-order kinetics of ultraviolet (UV) photolysis were slow (0.0091 min^?1), the highest degradation rate is obtained in the presence of TiO₂ nanoparticles as nanophotocatalyst. Simultaneous photolysis and photocatalysis under the full UV radiation leads to HD complete destruction in 3 h. No degradation products observed in the presence of nanophotocatalyst without irradiation in 3 h. It was found that up to 90 % of agent was decomposed under of UV irradiation without TiO₂, in 6 h. The decontamination mechanisms are often quite complex and multiple mechanisms can be operable such as hydrolysis, oxidation, and elimination. By simultaneously carrying out photolysis and photocatalysis in hexane, we have succeeded in achieving faster HD decontamination after 90 min with low catalyst loading. TiO₂ nanoparticles proved to be a superior photocatalyst under UV irradiation for HD decontamination.     

Metal uptake capability of <Emphasis Type="Italic">Cyperus articulatus</Emphasis> L. and its role in mitigating heavy metals from contaminated wetlands

Wetland plants are biological filters that play an important role in maintaining aquatic ecosystem and can take up toxic metals from sediments and water. The present study investigated the seasonal variation in the accumulation potential of heavy metals by Cyperus articulatus in contaminated watercourses. Forty quadrats, distributed equally in 8 sites (six contaminated sites along Ismailia canal and two uncontaminated sites along the River Nile), were selected seasonally for sediment, water, and plant investigations. Autumn was the flourishing season of C. articulatus with the highest shoot density, length, and diameter as well as aboveground biomass, while summer showed the least growth performance. The photosynthetic pigments were markedly reduced under contamination stress. C. articulatus plants accumulated concentrations of most heavy metals, except Pb, in their roots higher than the shoots. The plant tissues accumulated the highest concentrations of Fe, Cd, Ni, and Zn during autumn, while Cu and Mn during spring, and Cr and Co during winter. It was found that Cd, Cu, Ni, Zn, Pb, and Co had seasonal bioaccumulation factor (BF) > 1 with the highest BF for Cd, Ni, and Zn during autumn, Co, Cu, and Pb in winter, spring, and summer, respectively. The translocation factor of most heavy metals, except Pb in spring, was <1 indicating potential phytostabilization of these metals. In conclusion, autumn is an ideal season for harvesting C. articulatus in order to monitor pollution in contaminated wetlands.     

Assessment of natural coagulants to remediate Tunisian textile wastewater by combining physicochemical,analytical, and toxicological data

Environmental Science and Pollution Research - Due to the complexity and variability of textile wastewater composition, a constant search for new treatment strategies that are efficient,...     

Chemically degraded soil rehabilitation process using medicinal and aromatic plants: review

In recent decades, the increasing number of degraded lands worldwide makes their rehabilitation essential and crucial. Various techniques have emerged to fulfill these needs but most of them are expensive and difficult to be applied. Revegetation is a cost effective, environmental friendly, and aesthetically pleasing approach suitable for degraded areas. However, the use of edible crops, especially for areas with heavy metals (HM) contamination, is not ecologically suitable because the HM may enter the food chain. Alternatively, non-edible, fast-growing, deep-rooting, and metal-stabilizing plants with high biomass, which can produce high-value products hold a great potential and have been regarded as potential candidates of edible crops. This current review presents the benefits of using aromatic and medicinal plants (AMPs) and their associated microorganisms for revegetation of degraded sites as they are high-value economic crops. We discussed the effect of various stress on productivity of secondary metabolites in AMPs in addition to the potential health risk with human consumption of these plants and their products. A focus was also given to the effect of HM stress on the essential oil (EO) content of certain AMPs. Reported data showed that AMPs growing on HM-contaminated soils are safe products to use as they are not significantly contaminated themselves by HM.

     

Gossypol as an inducer or inhibitor in Spodoptera littoralis larvae

Gossypol occurs naturally in the pigment glands in cotton. It has a role in protecting cotton plants from insect pests such as bollworms, yet it was not toxic to the cotton leafworm larvae Spodoptera littoralis up to 2-5% concentration in artificial diet or at 125 micrograms/ larva by topical application. The compound inhibited protease and lipid peroxidase activities in larvae with in vitro I50 values of 1.5 X 10(-3)M, and 4.4 X 10(-4)M respectively. When gossypol was fed to Spodoptera larvae, it stimulated the microsomal N-demethylase in vitro. This inductive effect was time-dependent similar to that of phenobarbital. Gossypol stimulates ATPase at lower concentrations and inhibited it at higher concentrations. The I50 for mitochondrial ATPase was 1.7 X 10(-4)M, while the corresponding values for DDT and fenvalerate were 1.1 X 10(-4)M and 7.0 X 10(-4)M respectively. Gossypol at 1.5% concentration in the diet reduced the larval weight to 50% of the control within two days, and increased the duration of each larval stage. The number of eggs and their hatchability was seriously decreased in larvae treated for three consecutive generations. Such an effect can be attributed to the ability of gossypol to interfere with protein bio-synthesis.     

Predictors of poor outcomes among patients of acute methanol intoxication with particular reference to Sequential Organ Failure Assessment (SOFA) score

Environmental Science and Pollution Research - Methanol intoxication is a global problem with serious morbidities and mortalities. Apart from the lifelong disabilities experienced by methanol...     

Impact of Length of Dataset on Streamflow Calibration Parameters and Performance of APEX Model

Due to resource constraints, long‐term monitoring data for calibration and validation of hydrologic and water quality models are rare. As a result, most models are calibrated and, if possible, validated using limited measured data. However, little research has been done to determine the impact of length of available calibration data on model parameterization and performance. The main objective of this study was to evaluate the impact of length of calibration data (LCD) on parameterization and performance of the Agricultural Policy Environmental eXtender model for predicting daily, monthly, and annual streamflow. Long‐term (1984‐2015) measured daily streamflow data from Rock Creek watershed, an agricultural watershed in northern Ohio, were used for this study. Data were divided into five Short (5‐year), two Medium (15‐year), and one Long (25‐year) streamflow calibration data scenarios. All LCD scenarios were calibrated and validated at three time steps: daily, monthly, and annual. Results showed LCD affected the ability of the model to accurately capture temporal variability in simulated streamflow. However, overall average streamflow, water budgets, and crop yields were simulated reasonably well for all LCD scenarios.