Seasonal dynamics of physico-chemical characteristics and biological responses of Lake Chilwa,Southern Africa

Lake Chilwa is shared by Malawi and Mozambique, it supports an important fishery and its watershed is undergoing rapid population growth and increasing utilization for agricultural production. It is a shallow, closed basin lake with extensive surrounding wetlands; and it has suffered several desiccation events in the last century. To better understand the current condition of the lake, we monitored a suite of physical, chemical and biological parameters at approximately monthly intervals over an annual cycle in 2004–2005. The limnology of the lake was extremely sensitive to seasonal changes in the lake's seasonal hydrological cycle. The physico-chemical parameters, temperature, electrical conductivity, and total suspended solids exhibited clear seasonal patterns driven by the highly seasonal rainfall and resultant lake levels. In response, phytoplankton and zooplankton abundance, as well as biologically dependent oxygen concentrations and pH, exhibited several maxima levels over the year. The peaks of phytoplankton and zooplankton were out of phase suggesting a lag in the zooplankton grazing in response to pulses in primary productivity. Chlorophyll concentrations can exceed 1 mg/L in surface waters indicative of hypereutrophic conditions, but they fell dramatically during zooplankton peaks. This hydrologically driven, shallow and mesohaline lake is a productive and critical resource to the region. Its management poses challenges arising from the dependence of its limnology and fishery on the lakes hydrology, catchment land use and climate variability.     

Artificial neural networks for machining processes surface roughness modeling

In recent years, several papers on machining processes have focused on the use of artificial neural networks for modeling surface roughness. Even in such a specific niche of engineering literature, the papers differ considerably in terms of how they define network architectures and validate results, as well as in their training algorithms, error measures, and the like. Furthermore, a perusal of the individual papers leaves a researcher without a clear, sweeping view of what the field’s cutting edge is. Hence, this work reviews a number of these papers, providing a summary and analysis of the findings. Based on recommendations made by scholars of neurocomputing and statistics, the review includes a set of comparison criteria as well as assesses how the research findings were validated. This work also identifies trends in the literature and highlights their main differences. Ultimately, this work points to underexplored issues for future research and shows ways to improve how the results are validated.     

Thermal and Optical Properties of Yb3+- and Nd3+-Doped Phosphate Glasses Determined by Thermal Lens Technique

In this work, we study the thermal and optical properties of ion-doped phosphates glasses using the thermal lens (TL) technique. Three samples were characterized: Nd³⁺-doped Q-98; Nd³⁺-doped Q-100; and Yb³⁺-doped QX. We report multiwavelength TL measurements for a more accuracy determination of the fluorescence quantum efficiency and temperature coefficient of the optical path length change (ds/dT). In Nd-doped glasses, it was carried out using four discrete excitation wavelengths (between 514 and 872 nm) chosen to match with the ion absorption lines. In Yb-doped glass, the spectrum of heat generated along the Yb³⁺ transition (² F _5/2rarr² F _7/2) was obtained. In addition, parameters as thermal diffusivity and conductivity, thermal loading, etc were achieved. The advantages to obtain fluorescence quantum efficiency using the TL technique, mainly in Yb³⁺ doped materials, which are normally overestimated due to radiation trapping effect, are presented. The accuracy knowledge of these parameters is very important for design of high-power solid-state lasers, since these properties are directly related to the heat generation.     

Glucose and Fructose Fermentation by Wine Yeasts in Media Containing Structurally Complex Nitrogen Sources

Glucose and fructose fermentations by industrial yeasts strains are strongly affected by both the structural complexity of the nitrogen source and the availability of oxygen. In this study two Saccharomyces cerevisiae industrial wine strains were grown, under shaken and static conditions, in a media containing either a) 20% (w/v) glucose, or b) 10% (w/v) fructose and 10% (w/v) glucose or c) 20% (w/v) fructose, all supplemented with nitrogen sources varying from a single ammonium salt (ammonium sulfate) to free amino acids (casamino acids) and peptides (peptone). Data suggest that a complex structured nitrogen source is not submitted to the same control mechanisms as those involved in the utilization of simpler structured nitrogen sources, and mutual interaction between carbon and nitrogen sources, including the mechanisms involved in the regulation of aerobic/anaerobic metabolism, may play an important role in defining yeast fermentation performance and the differing response to the structural complexity of the nitrogen source, with a strong impact on fermentation performance.     

Polyethylenes produced with zirconocene immobilized on MAO-modified silicas

The effect of Al content on MAO-modified silicas was evaluated on catalyst activity, on polymer properties and on residual metal content in the resulting polyethylenes. MAO-modified silicas were prepared by impregnating MAO toluene solutions in concentration range between 0.5 and 20.0 wt% Al/SiO₂. Commercial MAO-modified silica (Witco) containing 24.4 wt% Al/SiO₂ was used for comparative reasons. The resulting modified-silicas were employed as supports for grafting (nBuCp)₂ZrCl₂. Using external MAO as cocatalyst (Al/Zr=2000) no difference in catalyst activity was observed. Nevertheless, for Al/Zr=500, catalyst activities were shown to be higher for supported zirconocene systems containing 0.0-2.0 wt% Al/SiO₂ range. According to DSC analysis, one T_m peak was detected for polymer obtained with catalyst prepared with 0.5 wt% Al/SiO₂ (135 °C), but two T_m peaks were observed for polymers obtained with catalysts prepared with 10.0 wt% Al/SiO₂ (136 and 141 °C) and 20.0 wt% Al/SiO₂ (133 and 141 °C).     

Discoloration process modeling by neural network

The photo-oxidation of acid orange 52 dye was performed in the presence of H₂O₂, utilizing UV light, aiming the discoloration process modeling and the process variable influence characterization. The discoloration process was modeled by the use of feedforward neural network. Each sample was characterized by five independent variables (dye concentration, pH, hydrogen peroxide volume, temperature and time of operation) and a dependent variable (absorbance). The neural model has also provided, through Garson Partition coefficients and the Pertubation method, the independent variable influence order determination. The results indicated that the time of operation was the predominant variable and reaction mean temperature was the lesser influent variable. The neural model obtained presented coefficients of correlation on the order 0.98, for sets of trainability, validation and testing, indicating the power of prediction of the model and its character of generalization.     

Efficient very‐high‐gravity fermentation of sugarcane molasses by industrial yeast strains

In this study industrial strains were inoculated, in successive cycles, at high cell density into a sugarcane‐based juice containing normal (22%, w/v) and very high sucrose (30%, w/v) levels and supplemented with peptone as a nitrogen source. At 30 °C, in shaken cultures, in the normal gravity situation, efficient sucrose utilization occurred in both the supplemented and unsupplemented medium. At higher sugar concentrations, supplementation with peptone induced a more efficient fermentation compared with the unsupplemented medium, with higher biomass accumulation and maintenance of cell viability, and ethanol levels as high as 16% (v/v). Trehalose was also high during the cycles, probably as a necessary response of the yeast to the high stress fermentation conditions. This suggests that it is possible to increase ethanol production by using very‐high‐fermentation technology and that nutritional supplementation would have a positive effect on the fermentation, allowing for efficient sugar consumption and cell viability maintenance. Copyright © 2016 The Institute of Brewing & Distilling     

Infective endocarditis in adolescents. Analysis of risk factors for hospital mortality

PURPOSE: To study the epidemiological, clinical, therapeutic and evolutive aspects of endocarditis in a group of patients aging 12 to 20 years-old (mean 15.5). METHODS: Thirty-three consecutive patients (14 males, 19 females) admitted with infective endocarditis were retrospectively studied. RESULTS: Infective endocarditis mortality was 42%. Rheumatic heart disease was the predominant underlying condition in 63% of patients. Congenital heart disease (24%) and cardiac prosthesis (12%) were the other affections involved. The majority of patients (78%) were in functional class III and IV, with more deaths than the 22% who were in functional class I and II (p = 0.01). Staphylococcus aureus was the most frequently isolated agent (42% of the positive blood cultures, followed by Staphylococcus viridans, 21%). Multivariate analysis identified total leukocyte count above 10,000/mm3 and functional class, both at admission (p = 0.01 and p = 0.004, respectively), and the occurrence of embolic complications (p = 0.03) as independent predictors of in-hospital mortality. CONCLUSION: Rheumatic heart disease remains, as in adults, the main predisposing factor for infective endocarditis in adolescents, and S. aureus is, like in children, the leading agent. Mortality is high and functional class at hospital admission, embolic complications and leukocytosis are independent predictors of in-hospital mortality.     

Comparison of operational performance and bead characteristics when welding with different tubular wires

The aim of this work was to make a comparative study of the characteristics of the weld bead produced by nationally manufactured tubular wires; all rutilic (ASME SFA-5.20: E71T-1/E71T-9/E71T-9M), basic (ASME SFA-5.20: E71T-5/E71T-5M) and ‘metal cored’ (ASME SFA-5.18: E70C-3M), 1.2 mm in diameter, intended for the welding of structural steels with low and medium levels of carbon. Welding tests were carried out, in the flat position, on thick plates (with a thickness of 12 mm) of common low-carbon steel using a source operating in ‘constant voltage’ mode, with monitoring of the current and voltage signals of the arc and feed speed (fusion) of the wire. The following were varied in welding with each type of tubular wire: the composition of the shielding gas (75%Ar–25%CO₂ and 100%CO₂) and the feed speed of the wire (7 and 9 m/min). The other parameters were kept fixed, including the polarity of the electrode (DC+) and the energized lengths of the electrode (16 mm) and of the arcs (3.5 mm). For the different tubular wires, there was a comparative analysis of the principal weld bead characteristics, including its geometry (penetration, reinforcement, width, fused area, deposited area and dilution), presence of weld discontinuities, microstructure and hardness. Operational conditions that yielded weld bead characteristics that favoured the welding of thick plates of structural steels were determined.     

Optimization of Radial Basis Function neural network employed for prediction of surface roughness in hard turning process using Taguchi’s orthogonal arrays

This work presents a study on the applicability of radial base function (RBF) neural networks for prediction of Roughness Average (R_a) in the turning process of SAE 52100 hardened steel, with the use of Taguchi’s orthogonal arrays as a tool to design parameters of the network. Experiments were conducted with training sets of different sizes to make possible to compare the performance of the best network obtained from each experiment. The following design factors were considered: (i) number of radial units, (ii) algorithm for selection of radial centers and (iii) algorithm for selection of the spread factor of the radial function. Artificial neural networks (ANN) models obtained proved capable to predict surface roughness in accurate, precise and affordable way. Results pointed significant factors for network design have significant influence on network performance for the task proposed. The work concludes that the design of experiments (DOE) methodology constitutes a better approach to the design of RBF networks for roughness prediction than the most common trial and error approach.