Contamination assessment of heavy metals in the soils of an abandoned copper mine in Lasail,Northern Oman

ABSTRACT

Abandoned mine areas exhibit heavy metal contamination. This severely reduces the soil quality. This paper concerns the assessment of soils near an abandoned copper mine in Lasail, northern Oman. Seventy-two soil samples were collected and analysed for heavy metals using inductively coupled plasma optical emission spectrometry (ICP-OES). The extent of heavy metal pollution was evaluated based on the geo-accumulation index (Igeo), enrichment factor (EF), and pollution index (PI). According to these indices, Copper (Cu), Mercury (Hg), Zinc (Zn), Arsenic (As) and Iron (Fe) are impacting the soils. Of these, Cu and Hg concentrations are 50 and 300 times more than the permissible limits set by the World Health Organization (WHO). There is an urgent need for the remediation and restoration of the soils in this area and a proposal is presented here.     

Automatic classification of Tamil documents using vector space model and artificial neural network

Automatic text classification based on vector space model (VSM), artificial neural networks (ANN), K-nearest neighbor (KNN), Naives Bayes (NB) and support vector machine (SVM) have been applied on English language documents, and gained popularity among text mining and information retrieval (IR) researchers. This paper proposes the application of VSM and ANN for the classification of Tamil language documents. Tamil is morphologically rich Dravidian classical language. The development of internet led to an exponential increase in the amount of electronic documents not only in English but also other regional languages. The automatic classification of Tamil documents has not been explored in detail so far. In this paper, corpus is used to construct and test the VSM and ANN models. Methods of document representation, assigning weights that reflect the importance of each term are discussed. In a traditional word-matching based categorization system, the most popular document representation is VSM. This method needs a high dimensional space to represent the documents. The ANN classifier requires smaller number of features. The experimental results show that ANN model achieves 93.33% which is better than the performance of VSM which yields 90.33% on Tamil document classification.     

The physicochemical properties of yoghurt supplemented with microencapsulated peanut sprout extract,a possible functional ingredient

This study was designed to determine the physicochemical and sensory properties of yoghurt supplemented with powdered peanut sprout extract microcapsules (PPSEM) (3–10 μm) during storage at 4 °C for 16 days. The releasing rates of the polyphenol, resveratrol, from yoghurts were minimised at lower concentrations of PPSEM (0.25 and 0.5%, w/v). The viscosity decreased gradually with higher concentrations of PPSEM added. In the sensory test, there were significant increases in yellowness, peanut and cooked scores (P < 0.05). Based on the results, it is concluded that the low concentrations (0.25 and 0.50%, w/v) could be used to produce PPSEM‐supplemented yoghurt without significant adverse effects on the physicochemical and functional properties.     

Influence of Different Cations on Chemical Composition and Microstructure of Pidan White and Yolk During Pickling and Aging

Effects of different cations on chemical composition and microstructure of pidan white and yolk were investigated. During 3 weeks of pickling and further 3 weeks of ageing, ammonia and ash contents were increased but varied with types of cations used. Lower protein degradation of pidan white was observed in pidan treated with 0.2% PbO₂, compared with other treatments. Scanning electron microscopic (SEM) studies indicated that the greater aggregation of egg proteins took place in pidan white treated with PbO₂. Yolk of pidan treated with 0.2% PbO₂ had more release of free lipid as visualised by confocal laser scanning microscope (CLSM).     

Multimodel decision support system for psychiatry problem

Psychological distress and disabilities are increasingly identified among general population. Psychiatrist availability in rural areas is poor and often general practitioners have to identify and treat psychiatric problems like depression and anxiety. This work proposes a method to identify the psychiatric problems among patients using multimodel decision support system. Backpropagation neural networks (BPNN), radial basis function neural network (RBFNN) and support vector machine (SVM) models are used to design the decision support system. Forty-four factors are considered for feature extraction. The features are collected from 400 patients and divided into four sets of equal size. Three sets of patient features are used to train the decision support system and one set of patient feature are used to evaluate performance of the system. Experimental results show that the proposed method achieves an accuracy of 98.75% for identifying the psychiatric problems.     

Performance of speaker localization using microphone array

Speaker localization is a technique to locate and track an active speaker from multiple acoustic sources using microphone array. Microphone array is used to improve the speech quality of recorded speech signal in meeting room and other places. In this work, the time delay estimation between source and each microphone is calculated using a localization method called time differences of arrival (TDOA). TDOA localization consists of two steps namely (a) a time delay estimator and (b) a localization estimator. For time delay estimation, the generalized cross-correlation using phase transform, the generalized cross correlation using maximum likelihood, linear prediction (LP) residual and the Hilbert envelope of the LP residual are chosen for estimating the location of a person. A new speaker localization algorithm known as group search optimization (GSO) algorithm is proposed. The performance of this algorithm is analyzed and compared with Gauss–Newton nonlinear least square method and genetic algorithm. Experimental results show that the proposed GSO method outperforms the other methods in terms of mean square error, root mean square error, mean absolute error, mean absolute percentage error, euclidean distance and mean absolute relative error.     

Species classification of aquatic plants using PSVM and ANFIS

This work presents a method for plant species identification using the images of flowers. It focuses on the stable feature extraction of flowers such as color, texture and shape features in addition to fractal dimension. Color based segmentation using K-means clustering and active contour model is used to extract the color features. Texture segmentation using texture filter is used to segment the image and obtain texture features. Sobel, Prewitt and Robert operators are used to extract the boundary of image and to obtain the shape features. Classification of the plants is done using Proximal Support Vector Machine (PSVM) and Adaptive Neuro Fuzzy Inference System (ANFIS) classifiers.     

Prediction and optimization of process parameter of friction stir welded AA5083-H111 aluminum alloy using response surface methodology

A systematic approach was presented to develop the empirical model for predicting the ultimate tensile strength of AA5083-H111 aluminum alloy which is widely used in ship building industry by incorporating friction stir welding (FSW) process parameters such as tool rotational speed, welding speed, and axial force. FSW was carried out considering three-factor five-level central composite rotatable design with full replications technique. Response surface methodology (RSM) was applied to developing linear regression model for establishing the relationship between the FSW process parameters and ultimate tensile strength. Analysis of variance (ANOVA) technique was used to check the adequacy of the developed model. The FSW process parameters were also optimized using response surface methodology (RSM) to maximize the ultimate tensile strength. The joint welded at a tool rotational speed of 1 000 r/min, a welding speed of 69 mm/min and an axial force of 1.33 t exhibits higher tensile strength compared with other joints.     

Optimization of process parameters to maximize ultimate tensile strength of friction stir welded dissimilar aluminum alloys using response surface methodology

Aluminium alloys generally present low weldability by traditional fusion welding process. Development of the friction stir welding （FSW） has provided an alternative improved way of producing aluminium joints in a faster and reliable manner. The quality of a weld joint is stalwartly influenced by process parameter used during welding. An approach to develop a mathematical model was studied for predicting and optimizing the process parameters of dissimilar aluminum alloy （AA6351 T6-AA5083 Hlll）joints by incorporating the FSW process parameters such as tool pin profile, tool rotational speed welding speed and axial force. The effects of the FSW process parameters on the ultimate tensile strength （UTS） of friction welded dissimilar joints were discussed. Optimization was carried out to maximize the UTS using response surface methodology （RSM） and the identified optimum FSW welding parameters were reported.     

Enforcing security in semantics driven policy based networks

Security is an important requirement in scenarios such as mobile computing that allow users to make meaningful ad hoc collaborations. Traditional security solutions are not feasible for these scenarios due to the varying nature of the collaborations. We propose an extensible framework that takes the semantics of the collaboration into account and uses semantics driven policies for enforcing security. Our policies are rooted in semantic web languages which make them amenable to interoperability and high level reasoning. We describe our policy based network that exploits packet content semantics to secure enterprise networks and the BGP routing process.