Characterization of Coal Spoil Recovered from Coal Mine Drainage Water

Barapukuria Coal Mine situated in the district Dinajpur. Bangladesh is playing an important role in the economy of this country by the mining of top quality coal. With coal mining, mine waste is also generated called coal spoil. Coal spoil can impose environmental threat if not treated carefully. In contrast, it can also be converted to value added product. In the present work, coal spoils collected from Barapukuria coal mine drainage water were investigated to determine the quality of the samples by physico-chemical analysis (proximate and ultimate analysis) as well as by heating value determination. 50% of carbon was detected in the samples after elemental analysis, with sulfur content less than 0.4%. Calorific value around 9300 btu/lb was obtained for the coal spoil. Moreover, moisture content, ash, volatile matter content and fixed carbon also provided fruitful information regarding the quality and economic prospect of the samples in comparison to the quality of Barapukuria coal.

移动与无线通讯网络

“第5届移动无线通讯网络会议”（MWCN2003）于2003年10月27～29日在新加坡举行。这次会议是由新加坡通讯研究所和IFIP—TC6WG6．8合作安排组织的。在这次国际会议上科学家、工业界与开发商聚集在一起，讨论与介绍移动与无线通讯研究及在工业中的新发展。会议收到了140篇论文。经过仔细地审查与选择之后，有60篇论文在会议上宣读。另外，会上还有14篇告示。

An optimized Parkinson’s disorder identification through evolutionary fast learning network

Purpose-Parkinson’s disease(PD)is a well-known complex neurodegenerative disease.Typically,its identification is based on motor disorders,while the computer estimation of its main symptoms with computational machine learning(ML)has a high exposure which is supported by researches conducted.Nevertheless,ML approaches required first to refine their parameters and then to work with the best model generated.This process often requires an expert user to oversee the performance of the algorithm.Therefore,an attention is required towards new approaches for better forecasting accuracy.Design/methodology/approach-To provide an available identification model for Parkinson disease as an auxiliary function for clinicians,the authors suggest a new evolutionary classification model.The core of the prediction model is a fast learning network(FLN)optimized by a genetic algorithm(GA).To get a better subset of features and parameters,a new coding architecture is introduced to improve GA for obtaining an optimal FLN model.Findings-The proposed model is intensively evaluated through a series of experiments based on Speech and HandPD benchmark datasets.The very popular wrappers induction models such as support vector machine(SVM),K-nearest neighbors(KNN)have been tested in the same condition.The results support that the proposed model can achieve the best performances in terms of accuracy and g-mean.Originality/value-A novel efficient PD detectionmodel is proposed,which is called A-W-FLN.The A-W-FLN utilizes FLN as the base classifier;in order to take its higher generalization ability,and identification capability is alsoembedded to discover themost suitable featuremodel in the detection process.Moreover,the proposedmethod automatically optimizes the FLN’s architecture to a smaller number of hidden nodes and solid connecting weights.This helps the network to train on complex PD datasets with non-linear features and yields superior result.

Efficient method for wind velocity estimation and power-extraction maximization using a fourth-order Luenberger observer in a wind-energy-conversion system

This paper proposes an advanced method for estimating numerous parameters in a wind-energy-conversion system with high precision,especially in a transient state,including the rotation speed and mechanical torque of the turbine as well as wind velocity.The suggested approach is designed into two parts.First,a fourth-order Luenberger observer is proposed to take into account the significant fluctuations of the mechanical torque that can be caused by wind gusts.This observer provides an accurate estimate of speed and mechanical torque in all weather conditions and especially when the wind is gusting.At the same time,the wind velocity is calculated using the Luenberger observer outputs and a model of the mechanical power generated by the turbine.Second,these estimated parameters are exploited as input in a maximum-power-point tracking(MPPT)algorithm using the tip-speed ratio(TSR)to improve the sensorless strategy control.Simulation results were performed using MATLAB®/Simulink®for both wind gust and real wind profiles.We have verified that for wind gusts with jumps ranging from 3 to 7 m/s,the new observer manages to better follow the rotation speed and the torque of the turbine compared to a usual observer.In addition,we demonstrated that by applying the proposed estimator in the improved TSR-MPPT strategy,it is possible to extract 3.3%more energy compared to traditional approaches.

Constructing and Cleaning Identity Graphs in the LOD Cloud

In the absence of a central naming authority on the Semantic Web,it is common for different data sets to refer to the same thing by different names.Whenever multiple names are used to denote the same thing,owl:sameAs statements are needed in order to link the data and foster reuse.Studies that date back as far as 2009,observed that the owl:sameAs property is sometimes used incorrectly.In our previous work,we presented an identity graph containing over 500 million explicit and 35 billion implied owl:sameAs statements,and presented a scalable approach for automatically calculating an error degree for each identity statement.In this paper,we generate subgraphs of the overall identity graph that correspond to certain error degrees.We show that even though the Semantic Web contains many erroneous owl:sameAs statements,it is still possible to use Semantic Web data while at the same time minimising the adverse effects of misusing owl:sameAs.

Hamiltonicity of 4-connected Graphs

Let σk（G） denote the minimum degree sum of k independent vertices in G and α（G） denote the number of the vertices of a maximum independent set of G. In this paper we prove that if G is a 4-connected graph of order n and σ5（G） 〉 n ＋ 3σ（G） ＋ 11, then G is Hamiltonian.