Iron pyrite films prepared by sulfur vapor transport

Iron films deposited via thermal evaporation, with a thickness between 100 and 250 nm, were converted into FeS₂ by open sulfur transport using nitrogen as a gas vector. The films thus obtained constituted a single pyrite phase and were optically highly absorbing. The sulfurization process was optimized. As a result, sample temperature and conversion time were found to be the major determining parameters. The films were characterized using several methods. The crystallinity and phase identification were determined by X-ray diffractometry. Scanning electron microscopy showed a homogeneous surface of both iron and pyrite layers. Optical transmission measurements confirmed the highly absorbing character of FeS₂ and allowed the determination of direct (1.35 eV) and indirect (0.82 eV) transitions.     

Solar cookers for outdoors and indoors

The solar cooking process has been investigated to develop safe, simple, portable, and reliable solar cookers. The concept of insulated and vapor-tight pots has been introduced and applied to oven, point-focus and heat-pipe cookers. A new, flat-plate cooker with heat pipes has been developed. It requires no tracking and allows cooking to be done in the shade or indoors. Also, a novel-portable cooker, the Mina Oven, featuring a vapor-tight pot and an integral collector with reflector flaps, has been constructed and tested. A second portable cooker that has been developed is the Arafa Cooker, which comprises a parabolic dish focused at a glazed and insulated receiver. Experiments indicated that all cookers yielded satisfactory performance, with cooking times of 25–45 min per kg of food per m² of solar collection area while operating from 10 a.m. to 4 p.m.     

Speech enhancement with an adaptive Wiener filter

This paper proposes an adaptive Wiener filtering method for speech enhancement. This method depends on the adaptation of the filter transfer function from sample to sample based on the speech signal statistics; the local mean and the local variance. It is implemented in the time domain rather than in the frequency domain to accommodate for the time-varying nature of the speech signals. The proposed method is compared to the traditional frequency-domain Wiener filtering, spectral subtraction and wavelet denoising methods using different speech quality metrics. The simulation results reveal the superiority of the proposed Wiener filtering method in the case of Additive White Gaussian Noise (AWGN) as well as colored noise.     

Synthesis and Polymerization of Cationic bis(cyclopentadienyliron)arene Complexes Containing Arylazo Dyes

The synthesis of the title complexes was achieved via the reaction of -p-dichlorobenzene- -cyclopentadienyliron cations with 4,4′-bis(4-hydroxyphenyl)valeric acid to produce the diiron complexes which were then reacted with a number of arylazo dyes to give cationic bis(cyclopentadienyliron)arene complexes containing the arylazo dyes. These iron-containing monomers were subsequently polymerized via nucleophilic aromatic substitution using 1,8-octanedithiol, 4,4′-thiobisbenzenethiol, or bisphenol A to produce the desired coloured cationic organoiron polymers. The weight – average molecular weights were estimated to range from 11,800 to 31,600. UV–vis studies conducted in dimethylformamide (DMF) showed that the metallated polymers exhibited of 412–491 nm. Addition of HCl to the polymer solution caused a bathochromic shift into the range of 515–530 nm. Thermogravimetric analysis (TGA) revealed that the iron moieties were cleaved between 205 and 248 °C while the polyether/thioether backbone degraded between 380 and 613 °C. Differential scanning calorimetry (DSC) showed that the polymers exhibited glass transition temperatures (Tg) ranging from 106 to 184°C.This paper is dedicated to Professor Richard J. Puddephatt in recognition of his outstanding contribution to the field of metal-containing polymers.     

Fuzzy-based hunger games search algorithm for global optimization and feature selection using medical data

Neural Computing and Applications - Feature selection (FS) is one of the basic data preprocessing steps in data mining and machine learning. It is used to reduce feature size and increase model...     

Big Data Analytics: Deep Content-Based Prediction with Sampling Perspective

The world of information technology is more than ever being flooded with huge amounts of data, nearly 2.5 quintillion bytes every day. This large stream of data is called big data, and the amount is increasing each day. This research uses a technique called sampling, which selects a representative subset of the data points, manipulates and analyzes this subset to identify patterns and trends in the larger dataset being examined, and finally, creates models. Sampling uses a small proportion of the original data for analysis and model training, so that it is relatively faster while maintaining data integrity and achieving accurate results. Two deep neural networks, AlexNet and DenseNet, were used in this research to test two sampling techniques, namely sampling with replacement and reservoir sampling. The dataset used for this research was divided into three classes: acceptable, flagged as easy, and flagged as hard. The base models were trained with the whole dataset, whereas the other models were trained on 50% of the original dataset. There were four combinations of model and sampling technique. The F-measure for the AlexNet model was 0.807 while that for the DenseNet model was 0.808. Combination 1 was the AlexNet model and sampling with replacement, achieving an average F-measure of 0.8852. Combination 3 was the AlexNet model and reservoir sampling. It had an average F-measure of 0.8545. Combination 2 was the DenseNet model and sampling with replacement, achieving an average F-measure of 0.8017. Finally, combination 4 was the DenseNet model and reservoir sampling. It had an average F-measure of 0.8111. Overall, we conclude that both models trained on a sampled dataset gave equal or better results compared to the base models, which used the whole dataset.     

Investigations on mode-division multiplexed free-space optical transmission for inter-satellite communication link

Wireless Networks - Inter-satellite data transmission links are very crucial for providing global inter-connectivity. We report designing and investigations on high date rate inter-satellite...     

Behaviour of sand–clay mixtures for road pavement subgrade

Materials forming sand grains and colluvial soil deposits have a distinct structure, consisting of a composite matrix of coarse and fine soil grains. The influence of sand grains content on the behaviour of sand–clay mixtures was investigated by a series of intensive laboratory experiments. The California bearing ratio (CBR), unconfined compression strength (UCS) and compaction tests were carried out on various contents of sand and clay mixtures. The sand–clay mixtures were prepared with sand contents of 0, 10, 20, 30, 40, and 50% by weight. The laboratory tests on these mixtures have indicated that their behaviour will depend on the relative concentration of the sand and clay samples. The results of the tests showed a decrease in the UCS, and an increase the CBR values with an increase in the amount of sand. An increase in dry unit weight and a decrease in respective moisture content by an increase in the amount of sand were observed in the compaction tests.     

Evaluation of rutting potential for asphalt concrete mixes containing copper slag

Copper slag (CS) is a by-product of the copper extraction process, which can be used as coarse and/or fine aggregate in hot mix asphalt (HMA) pavements. This study used CS as a replacement of the fine aggregate with a percentage of up to 40% by total aggregate weight. The objective of this study was to evaluate the effect of CS on the rutting potential of the asphalt concrete mix using two methods. One method is based on the Dynamic modulus |E*| testing result. Actual pavement temperature data from a test section were used with the developed |E*| master curves. EverStressFE finite element program was used to perform a linear elastic load-deformation analysis for a pavement section and to determine the vertical resilient strain in a 40-mm HMA surface layer. The M-E PDG permanent deformation model was used with and Excel Visual Basic for Applications code to predict the accumulated rutting for different CS mixes for 10 million ESALs. The other method used the data from the flow number (FN) test. Based on the |E*| approach, the results indicated that adding 5% CS in the mix increased the predicted rutting from 0.59 to 0.98 mm at 10 million ESALs (increase by 68%). When 40% CS was used, rutting increased by more than 700% compared with the control mix. After analysing the FN results with the Francken model, the results indicated a decrease in FN as CS content is increased, indicating higher rutting potential. The decrease in FN ranged from 9% for 5% CS to 95% for 40% CS. The mixes containing up to 10% CS satisfied the minimum FN criteria for rutting. A calibration process for the M-E PDG distress prediction models that allows the use of waste and by-product materials such as CS should be considered in the future.