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Analyzing sectorial level determinants of inward foreign direct investment (FDI) in ASEAN

Abdul Hadi A. R., Zafar S., Iqbal T., Zafar Z., Iqbal Hussain H.

Polish Journal of Management Studies

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2018

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Vol. 17, No. 2

7--17

EN

The paper studies economic determinants of sectorial level (Extractive sector, manufacturing and assembly sector, infrastructure sector and service sector) foreign direct investment (FDI) to six ASEAN countries (Malaysia, Indonesia, Singapore, Thailand, Vietnam and Philippine). The study covers over a period of sixteen years, from 2001 to 2016, by employing static panel data model. This study includes inflation, gross domestic product growth, government expenditure on education, electric power consumption, exchange rate, trade openness and lending interest rate as economic variables. These factors are based on their relative importance from previous empirical literature. Overall findings reveal that there is a mix result in terms of key determinants of sectorial level inward FDI which proves that FDI is not a single phenomenon and that each sector must be treated on its own terms to attract FDI into the country.

PL

W artykule przedstawiono uwarunkowania gospodarcze na poziomie sektorowym (sektor wydobywczy, sektor produkcji i montażu, sektor infrastruktury i sektor usług) bezpośrednich inwestycji zagranicznych (BIZ) dla sześciu krajów ASEAN (Malezja, Indonezja, Singapur, Tajlandia, Wietnam i Filipiny). Badanie obejmuje okres szesnastu lat, od 2001 do 2016 r., poprzez zastosowanie statycznego modelu danych panelowych. Badanie to obejmuje inflację, wzrost produktu krajowego brutto, wydatki rządowe na edukację, zużycie energii elektrycznej, kurs walutowy, otwartość handlową i oprocentowanie kredytu jako zmienne ekonomiczne. Czynniki te opierają się na ich względnej wadze z poprzedniej literatury empirycznej. Ogólne wyniki wskazują, że istnieje mieszany wynik pod względem kluczowych czynników wpływających na napływ BIZ na poziomie sektorowym, co dowodzi, że bezpośrednie inwestycje zagraniczne nie są pojedynczym zjawiskiem i że każdy sektor musi być traktowany na własnych warunkach w celu przyciągnięcia bezpośrednich inwestycji zagranicznych do tego kraju.

2

Classification of rocks radionuclide data using machine learning techniques

Khan A. R., Mir A. A., Saeed S., Rafique M., Asim K. M., Iqbal T., Jabbar A., Rahman S. U.

Acta Geophysica

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2018

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Vol. 66, no. 5

1073--1079

EN

The aim of this study is to assess the performance of linear discriminate analysis, support vector machines (SVMs) with linear and radial basis, classification and regression trees and random forest (RF) in the classification of radionuclide data obtained from three different types of rocks. Radionuclide data were obtained for metamorphic, sedimentary and igneous rocks using gamma spectroscopic method. A P-type high-purity germanium detector was used for the radiometric study. For analysis purpose, we have determined activity concentrations of 232Th, 226Ra and 40K radionuclides, published elsewhere (Rafique et al. in Russ Geol Geophys 55:1073–1082, 2014), in different rock samples and built the classification model after pre-processing the data using three times tenfold cross-validation. Using this model, we have classified the new samples into known categories of sedimentary, igneous and metamorphic. The statistics depicts that RF and SVM with radial kernel outperform as compared to other classification methods in terms of error rate, area under the curve and with respect to other performance measures.

3

Seismic activity prediction using computational intelligence techniques in northern Pakistan

Asim K. M., Awais M., Martinez-Alvarez F., Iqbal T.

Acta Geophysica

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2017

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Vol. 65, no. 5

919--930

EN

Earthquake prediction study is carried out for the region of northern Pakistan. The prediction methodology includes interdisciplinary interaction of seismology and computational intelligence. Eight seismic parameters are computed based upon the past earthquakes. Predictive ability of these eight seismic parameters is evaluated in terms of information gain, which leads to the selection of six parameters to be used in prediction. Multiple computationally intelligent models have been developed for earthquake prediction using selected seismic parameters. These models include feed-forward neural network, recurrent neural network, random forest, multi layer perceptron, radial basis neural network, and support vector machine. The performance of every prediction model is evaluated and McNemar’s statistical test is applied to observe the statistical significance of computational methodologies. Feed-forward neural network shows statistically significant predictions along with accuracy of 75% and positive predictive value of 78% in context of northern Pakistan.

4

Effect of demineralization on the physiochemical structure and thermal degradation of acid treated indigenous rice husk

Aslam U., Ramzan N., Iqbal T., Kazmi M., Ikhlaq A.

Polish Journal of Chemical Technology

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2016

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Vol. 18, nr 3

117--121

EN

Energy generation from biomass presents some serious problems like slagging, fouling and corrosion of boilers. To address these problems, demineralization of biomass is performed using different leaching agents. This study is focused on determining the influence of leaching agents and leaching time on the physiochemical structure of rice husk during demineralization. Dilute (5% wt) solutions of HCl and H2SO4 were used for the demineralization of rice husk separately with leaching time of 15, 60 and 120 minutes. It is shown that H2SO4 exhibited higher removal of alkali and alkaline earth metals (AAEM) comparatively as depicted by the 34.2% decrease in ash content along with an increase of 7.10% in the heating value. The acid has been seen to induce more notable changes in physiochemical structure as depicted by the FTIR spectra and SEM micrographs. The thermal degradation behavior of the demineralized rice husk has also been reported.