Fungal Bioleaching of Metals From Mine Tailing

The present study was undertaken to develop an economically feasible and environmental-friendly technique to recover metals (Ni, Cu, Co, Zn, Mg) from mine tailing by employing Penicillium chrysogenum strain KBS3. The potential of Penicillium chrysogenum in generating a variety of organic acids was studied and the effect of different organic wastes as substrates was evaluated. Maximum solubilization of nickel (55%), copper (67%), magnesium (69%), cobalt (60%), and zinc (65%) was achieved in the media containing glucose, tea leaves, molasses, and waste winery grapes as substrates.     

Elastomeric ablative nanocomposites used in hyperthermal environments

Pristine multiwalled carbon nanotubes (MWCNTs) along with the silane coupling agent were incorporated into ethylene propylene diene monomer (EPDM) rubber using dispersion kneader and two roller mixing mill to fabricate ablative nanocomposites used in hyperthermal environment encountered by space vehicle or rocket motor. The 1 wt% addition of MWCNTS in the rubber matrix has remarkably reduced the backface temperature elevation up to 40°C during the ablation testing of the ablatives. The linear and mass ablation resistances have been diminished up to 125% and 74%, respectively, while insulation indexes at 110°C backface temperature of the composite specimens have been elevated up to 51% with increasing the MWCNTS incorporation into the EPDM matrix. Thermal stability and heat absorbance capability of the polymer composites were progressed with increasing the filler to matrix ratio. Thermal conductivity/impedance of the ablatives have been conducted according to the ASTM E1225‐99 and D5470‐03, respectively to execute the effect of MWCNTs concentration on the thermal transport characteristics of the tested specimens. Tensile strength of the composite specimen was augmented up to 42% with increasing nanotubes to polymer ratio. Evenly dispersed MWCNTs in the polymer matrix, polymer pyrolysis, and voids formation in the ablated samples can be scrutinized in the scanning electron microscopy images. POLYM. ENG. SCI., 54:255–263, 2014. © 2013 Society of Plastics Engineers     

Influence of Nonionic Rosin Surfactants on Surface Activity of Silica Particles and Stability of Oil in Water Emulsions

Rosin as a natural product has become a source for production of less toxic bio-surfactants to produce emulsions which are widely used in various agriculture and food products, cosmetic, and pharmaceutical industries. In this respect, a nonionic surfactant was prepared from reaction of rosin acids and rosin maleic anhydride adduct with poly(ethylene glycol) monomethyl ether 750 (MPEG 750) to produce a rosin ester (RMPEG 750). The surface activity parameters of the prepared surfactants, such as surface excess concentration (Γ _max), the area per molecule at interface (A _min), and the effectiveness of surface tension reduction, were measured to determine the micellization and adsorption characteristics of the prepared surfactants at the water/air interface. The adsorption of the prepared surfactants on the surface of either hydrophilic or hydrophobic silica particles was determined using a spectrophotometric method. Interfacial tension between water and toluene were measured to select the best condition to obtain toluene/water emulsion in the presence of modified solid silica particles. The effects of silica particle hydrophilicity and the surfactant concentrations on the surface, interfacial activity, and on the emulsion drop size were also investigated.     

Comparison of release behaviour from microcapsules and microspheres

The plasticizing effect of core oil in the polymer shell of core–shell particles, so-called microcapsules, was here examined. The study involved release measurements of 4-nitroanisole into aqueous solution from two types of microparticles. In the first study, a microcapsule with PMMA shell and hexadecane core was examined. The second type of particle was a homogeneous PMMA microsphere containing no oil. Experimental data were compared to models that describe release by a diffusion mechanism out from core–shell particles as well as homogeneous spherical particles. Results clearly show that the alkane oil has a plasticizing effect on the PMMA shell, thereby generating a faster release of 4-nitroanisole. In both types of microparticles, the diffusion coefficients of 4-nitroanisole could be determined. The findings increase our understanding of important parameters in the microcapsule design with regard to tuning the release behaviour. Using microparticles, the ambition is to prolong the protection of paint and other coatings against fouling.     

Determining nutritional labeling data for fats and oils by 1H NMR

The nutrition labeling compositional data (NLCD) required for fat‐containing food products consists of the percentages of saturated, cis‐monounsaturated, and cis‐polyunsaturated fat as well as trans content. The capability of ¹H NMR spectroscopy to determine the NLCD components in oils that do not contain significant levels of trans isomers has already been established in the literature, but not its capability to differentiate between cis‐ and trans‐unsaturation. In the present study, the determination of all four NLCD components in fats and oils has been demonstrated for the first time. A preliminary analysis of the intensity‐normalized ¹H NMR spectra of defined mixtures of pure triacylglycerols (TAG) by partial least squares (PLS) regression revealed that the (mono)allylic proton resonances of cis and trans bonds were sufficiently well separated to allow for accurate quantitation of trans content by simple peak integration. This chemometric approach also served to facilitate the identification of optimal integration limits for these cis‐ and trans‐allylic resonances. Fixed integration limits were also set for the other resonances employed in the determination of the four NLCD components, and a standardized spectral preprocessing procedure was established. The ¹H NMR NLCD data obtained for the TAG mixtures by this methodology was a good match to the actual values, calculated from the known molar composition of these gravimetrically prepared mixtures. A procedure for the conversion of the NMR mol% NLCD to units of wt%, previously developed for ¹³C NMR, was adapted for ¹H NMR and shown to be effective in compensating for the overestimation of wt% saturates and underestimation of wt% unsaturates by ¹H NMR if this conversion is not made. The ¹H NMR methodology for NLCD determination was validated by analyzing AOCS Laboratory Proficiency Program GC samples as well as samples taken from a hydrogenator over time and analyzed for trans content by GC and IR spectroscopy. Comparison of the ¹H NMR mol% and wt% NLCD obtained for these validation samples with the data obtained from the reference methods indicated that ¹H NMR can deliver high‐quality, accurate NLCD, much like ¹³C NMR, but in a much shorter time frame. Thus, ¹H NMR provides a more rapid and cost‐effective means of obtaining NLCD than ¹³C NMR and can replace GC as a primary reference method for the calibration of simpler and automatable instrumental methods such as Fourier transform infrared (FTIR) spectroscopy.     

Semi-automatic knowledge extraction, representation and context-sensitive intelligent retrieval of video content using collateral context modelling with scalable ontological networks

Automatic indexing and retrieval of digital data poses major challenges. The main problem arises from the ever increasing mass of digital media and the lack of efficient methods for indexing and retrieval of such data based on the semantic content rather than keywords. To enable intelligent web interactions, or even web filtering, we need to be capable of interpreting the information base in an intelligent manner. For a number of years research has been ongoing in the field of ontological engineering with the aim of using ontologies to add such (meta) knowledge to information. In this paper, we describe the architecture of a system (Dynamic REtrieval Analysis and semantic metadata Management (DREAM)) designed to automatically and intelligently index huge repositories of special effects video clips, based on their semantic content, using a network of scalable ontologies to enable intelligent retrieval. The DREAM Demonstrator has been evaluated as deployed in the film post-production phase to support the process of storage, indexing and retrieval of large data sets of special effects video clips as an exemplar application domain. This paper provides its performance and usability results and highlights the scope for future enhancements of the DREAM architecture which has proven successful in its first and possibly most challenging proving ground, namely film production, where it is already in routine use within our test bed Partners’ creative processes.     

Numerical Study of Aeroacoustic Sound on Performance of Bladeless Fan

Aeroacoustic performance of fans is essential due to their widespread application. Therefore, the original aim of this paper is to evaluate the generated noise owing to different geometric parameters. In current study, effect of five geometric parameters was investigated on well performance of a Bladeless fan. Airflow through this fan was analyzed simulating a Bladeless fan within a 2 m×2m×4 m room. Analysis of the flow field inside the fan and evaluating its performance were obtained by solving conservations of mass and momentum equations for aerodynamic investigations and FW-H noise equations for aeroacoustic analysis. In order to design Bladeless fan Eppler 473 airfoil profile was used as the cross section of this fan. Five distinct parameters, namely height of cross section of the fan, outlet angle of the flow relative to the fan axis, thickness of airflow outlet slit, hydraulic diameter and aspect ratio for circular and quadratic cross sections were considered. Validating acoustic code results, we compared numerical solution of FW-H noise equations for NACA0012 with experimental results. FW-H model was selected to predict the noise generated by the Bladeless fan as the numerical results indicated a good agreement with experimental ones for NACA0012. To validate 3-D numerical results, the experimental results of a round jet showed good agreement with those simulation data. In order to indicate the effect of each mentioned parameter on the fan performance, SPL and OASPL diagrams were illustrated.     

Optimal Deep Hybrid Boltzmann Machine Based Arabic Corpus Classification Model

Natural Language Processing (NLP) for the Arabic language has gained much significance in recent years. The most commonly-utilized NLP task is the ‘Text Classification’ process. Its main intention is to apply the Machine Learning (ML) approaches for automatically classifying the textual files into one or more pre-defined categories. In ML approaches, the first and foremost crucial step is identifying an appropriate large dataset to test and train the method. One of the trending ML techniques, i.e., Deep Learning (DL) technique needs huge volumes of different types of datasets for training to yield the best outcomes. The current study designs a new Dice Optimization with a Deep Hybrid Boltzmann Machine-based Arabic Corpus Classification (DODHBM-ACC) model in this background. The presented DODHBM-ACC model primarily relies upon different stages of pre-processing and the word2vec word embedding process. For Arabic text classification, the DHBM technique is utilized. This technique is a hybrid version of the Deep Boltzmann Machine (DBM) and Deep Belief Network (DBN). It has the advantage of learning the decisive intention of the classification process. To adjust the hyperparameters of the DHBM technique, the Dice Optimization Algorithm (DOA) is exploited in this study. The experimental analysis was conducted to establish the superior performance of the proposed DODHBM-ACC model. The outcomes inferred the better performance of the proposed DODHBM-ACC model over other recent approaches.     

Optimal Quad Channel Long Short-Term Memory Based Fake News Classification on English Corpus

The term ‘corpus’ refers to a huge volume of structured datasets containing machine-readable texts. Such texts are generated in a natural communicative setting. The explosion of social media permitted individuals to spread data with minimal examination and filters freely. Due to this, the old problem of fake news has resurfaced. It has become an important concern due to its negative impact on the community. To manage the spread of fake news, automatic recognition approaches have been investigated earlier using Artificial Intelligence (AI) and Machine Learning (ML) techniques. To perform the medicinal text classification tasks, the ML approaches were applied, and they performed quite effectively. Still, a huge effort is required from the human side to generate the labelled training data. The recent progress of the Deep Learning (DL) methods seems to be a promising solution to tackle difficult types of Natural Language Processing (NLP) tasks, especially fake news detection. To unlock social media data, an automatic text classifier is highly helpful in the domain of NLP. The current research article focuses on the design of the Optimal Quad Channel Hybrid Long Short-Term Memory-based Fake News Classification (QCLSTM-FNC) approach. The presented QCLSTM-FNC approach aims to identify and differentiate fake news from actual news. To attain this, the proposed QCLSTM-FNC approach follows two methods such as the pre-processing data method and the Glove-based word embedding process. Besides, the QCLSTM model is utilized for classification. To boost the classification results of the QCLSTM model, a Quasi-Oppositional Sandpiper Optimization (QOSPO) algorithm is utilized to fine-tune the hyperparameters. The proposed QCLSTM-FNC approach was experimentally validated against a benchmark dataset. The QCLSTM-FNC approach successfully outperformed all other existing DL models under different measures.