Statistical analysis and isotherm study of uranium biosorption by <Emphasis Type="Italic">Padina</Emphasis> sp. algae biomass

Introduction  

The application of response surface methodology is presented for optimizing the removal of U ions from aqueous solutions using Padina sp., a brown marine algal biomass.     

AI-based runoff simulation based on remote sensing observations: A case study of two river basins in the United States and Canada

Data-driven techniques are used extensively for hydrologic time-series prediction. We created various data-driven models (DDMs) based on machine learning: long short-term memory (LSTM), support vector regression (SVR), extreme learning machines, and an artificial neural network with backpropagation, to define the optimal approach to predicting streamflow time series in the Carson River (California, USA) and Montmorency (Canada) catchments. The moderate resolution imaging spectroradiometer (MODIS) snow-coverage dataset was applied to improve the streamflow estimate. In addition to the DDMs, the conceptual snowmelt runoff model was applied to simulate and forecast daily streamflow. The four main predictor variables, namely snow-coverage (S-C), precipitation (P), maximum temperature (T_max), and minimum temperature (T_min), and their corresponding values for each river basin, were obtained from National Climatic Data Center and National Snow and Ice Data Center to develop the model. The most relevant predictor variable was chosen using the support vector machine-recursive feature elimination feature selection approach. The results show that incorporating the MODIS snow-coverage dataset improves the models' prediction accuracies in the snowmelt-dominated basin. SVR and LSTM exhibited the best performances (root mean square error = 8.63 and 9.80) using monthly and daily snowmelt time series, respectively. In summary, machine learning is a reliable method to forecast runoff as it can be employed in global climate forecasts that require high-volume data processing.     

Laboratory waste minimization by recovery of silver as nano-silver colloidal dispersion from waste silver chloride

The use of cyclic experiments, where the product of one reaction becomes the starting material for the next experiment, was proposed as an effective protocol for waste minimization in an educational lab. A simple, cheap and pollution-free method was developed for recovering silver as nano-silver colloidal dispersion from waste silver chloride in the laboratories of the Faculty of Health, Safety and Environment. Silver nanoparticles of the size 5–18 nm were recovered in the presence of sodium borohydride as a reducing agent and polyvinylpyrrolidone as a stabilizer agent. The nano-silver particles were studied for their formation, structure, stability and size using UV–Vis spectroscopy, transmission electron microscopy and dynamic light scattering techniques. The antibacterial assays of nanoparticles showed satisfactory results for Escherichia coli ATCC25922, Staphylococcus aureus ATCC 29213, and Acinetobacter baumanii (Clinical isolate). A laboratory experiment was designed in which students synthesize yellow colloidal silver solution from chemical waste silver chloride and estimate particle size using visible spectroscopy.     

Musculoskeletal symptoms as related to ergonomic factors in Iranian hand-woven carpet industry and general guidelines for workstation design.

Carpet weaving is a high risk occupation for developing musculoskeletal disorders (MSDs). The objectives of the present study, which was carried out in the Iranian hand-woven carpet industry, were determination of the prevalence of MSD symptoms, identification of major factors associated with MSD symptoms and development of guidelines for workstation design. 1,439 randomly selected weavers participated in this study. A questionnaire was used to collect data on MSD symptoms. The results revealed that the prevalence rates for symptoms in different body regions were high as compared to the general Iranian population (for neck, back and large joints, p <.0001). The results of multivariate analyses showed that major ergonomic factors associated with musculoskeletal symptoms were loom type, working posture, daily working time and seat type. Based on the results, some general guidelines for designing weaving workstations were developed. A prototype test showed that the new workstation was acceptable for subject tests and that it improved working posture.     

Study of MoO3-γAl2O3 catalysts behavior in selective catalytic reduction of SO2 toxic gas to sulfur with CH4

Environmental Science and Pollution Research - In the present study, a detailed investigation was carried out on MoO3 alumina-supported catalysts behavior in selective catalytic reduction of SO2 to...