Stock-Watson dynamic OLS (DOLS) and error-correction modelling approaches to estimating long- and short-run elasticities in a demand function: new evidence and methodological implications from an application to the demand for coal in mainland China

In this paper robust elasticity estimates of coal demand for China are derived using annual data 1953–1992. In so doing, we illustrate the use of a powerful yet practically convenient and recently developed modelling procedure devised by Stock and Watson (known as Dynamic OLS (DOLS)), who provide evidence, based on Monte Carlo simulations, of this estimator being superior in small samples compared to a number of alternative estimators, as well as being able not only to accommodate higher orders of integration but also to account for possible simultaneity within regressors of a potential demand system. Furthermore, cointegration and error-correction methods are employed to derive short-run price and income elasticities. Estimated results are quite robust not only in terms of statistical prowess but also in terms of economic intuition and indicate that, over the long run, both price and income elasticities are close to unity. While short-run price and income elasticities are less (in absolute value) than their long-run counterparts, there seems to be some divergence in short-run parameters from a subsample analysis. Overall, results seem to imply that for China, coal consumption should remain relatively constant as future modernization strategies for economic development are pursued. In addition, the study has clear methodological implications for estimating the long- and short-run elasticities in a demand function in general, and in a wide variety of fields in future applied research.     

The Determination of Phendimetrazine and Phenmetrazine in Biological Fluids and in Dosage Forms

A highly sensitive gas chromatographic assay was developed for the simultaneous determination of phendimetrazine and its N-demethylated metabolite, phenmetrazine. The analytical procedure proved adaptable for the determination of these drugs in dosage formulations as well as biological fluids. The procedure involves the addition of Isoxsuprine as the internal standard and extraction into diethyl ether. The two drugs along with the internal standard were well resolved on an SP-2250 column using helium as the carrier gas     

Machine learning-based regression models for predicting signal quality of dense wavelength division multiplexing (DWDM) optical communication network

Over the years, optical communication systems have been a significant source of fast and secure communication. However, factors like noise and mitigation error can degrade the bit error rate (BER) and quality factor (Q factor) of optical communication systems. Predicting the optimal threshold, Q factor, and BER is usually a difficult task. Therefore, in this paper, machine learning-based linear regression, least absolute shrinkage and selection operator (LASSO) regression, and Ridge regression have been used for a dense wavelength division multiplexing (DWDM)-based optical communication network to predict the signal quality. These techniques have been used to predict the desired BER, Q factor, threshold, and eye height of the system. To demonstrate this research concept, a DWDM-based optical communication network of 50 km length is designed and simulated using Optisystem-14.0. After data preparation, regression models have been developed and validated through diagnostic plots. Results show that mean square error (MSE) has a significant decline with an increase in the number of epochs for all four models. LASSO and Ridge regression have effectively resolved the issue of overfitting, which occurred in the linear regression case. Furthermore, the mean MSE plot proved the significant reduction of mean MSE in the case of LASSO regression. Results show that min BER for LASSO regression came out to be −173,627.14, providing a robust and cost-efficient process.     

Dilemmas of integrated water resources management implementation in the Awash River Basin,Ethiopia: irrigation development versus environmental flows

Environmental flows allocation is an intrinsic part of Integrated Water Resources Management (IWRM). This paper analyses socio‐political issues and effects of environmental flows integration on water availability under the context of increased agricultural intensification in an effort to tackle food insecurity. Lack of appropriate framework comprising the procedural requirements and strategic directions as well as prevalence of politically motivated ad hoc development programmes are among major challenges identified. Introducing environmental flows to a perceived satisfactory level would result in a significant increase of unmet irrigation water demand, yet, “productivity first” norm overtakes. This is presumed to be due to skewed focus on irrigation expansion and low awareness on the possible consequences. The particular challenges highlighted generally unveil the inherent contradictions in the IWRM concept putting its claim that the set of principles and entire course stand universally accepted as a means to balance socio‐economic and environmental outcomes under question.     

Synthesis of hexagonal cobalt hydroxide and cobalt oxide nanorings as promising materials for oxygen evolution and supercapacitive processes

Preparing the low-cost nanomaterials for electrocatalytic processes is still a big challenge. Mesoporous cobalt hydroxide and cobalt oxide nanoparticles were prepared through simple soft chemistry as high-performance materials for durable electrocatalyst for OER and supercapacitive applications. The synthesis method is used to prepare nanoring particles in neither emulsion nor template-directed method. The final nanoparticles display mesoporous hexagonal nanoring morphology. The physio-chemical properties of the as-synthesized nanoparticles were characterized by X-ray diffraction (XRD), transmission electron microscopy (TEM), and nitrogen adsorption-desorption techniques. The TEM characterizations prove that NPs retain the topotactical relationship in their structure during the conversion process. The BET measurements prove the mesoporous nature of the nanorings, having good specific surface area and pore volume. Finally, the electrochemical performance toward water splitting and supercapacitor applications were investigated by electrochemical impedance spectroscopy (EIS), linear sweep voltammetry (LSV), and cyclic voltammetry (CV) techniques. The Co₃O₄ NPs exhibits better catalytic properties than Co(OH)₂ NPs when applied as electrocatalyst in an alkaline medium for water splitting and supercapacitor measurements. The enhanced electrocatalytic performance attributed to the mesoporous structure along with high pore volume, which provides more active boundary sites for the electrochemical process, resulted in the enhanced exchange of the intermediates and more efficient electron transfer. This synthetic methodology, with the advantages of inexpensive/non-complicated experimental setup and high electrochemical performance, could shed light on the development of non-expensive electrocatalysts for clean energy production and storage.     

Hydrogen from steam methane reforming by catalytic nonthermal plasma using a dielectric barrier discharge reactor

A scaled-up dielectric barrier discharge (DBD) reactor has been developed and demonstrated for the production of hydrogen from steam methane reforming (SMR) by catalytic nonthermal plasma (CNTP) technology. Compared to SMR, CNTP offers conversion at ambient pressure (101.325 kPa), low temperature with better efficiency, making it suitable for distributed hydrogen production with small footprint. There have been several lab-scale DBD reactors reported in the literature. Dimension of the scaled-up DBD reactor is about six times the lab-scale version and can produce 0.9 kg H₂/day. The scale-up is, however, nonlinear; several technical innovations were required including spray nozzle for homogeneous introduction of steam, perforated tube central electrodes for generation of homogeneous plasma. Conversion efficiency of the scaled-up DBD reactor is 70–80% at 550°C and 500 W. A continuous run of 8 hr was demonstrated with typical product gas composition of 69% H₂, 6% CO₂, 15% CO, 10% CH₄.     

Oil price volatility and stock price fluctuations in an emerging market: Evidence from South Korea

How important are oil price fluctuations and oil price volatility on equity market performance? What are the policy implications if volatility turns out to be significant? We assess this issue in an economics/finance nexus for Korea using a VEC model including interest rates, economic activity, real stock returns, real oil prices and oil price volatility. Our main aim is to capture the effects of crude oil prices on the Korean economy thoroughly covering the period of the Asian Financial Crisis of 1997, which heavily affected the country, and the oil price hikes in the early 1990s after the Gulf War. South Korea was the country most hit by the financial crisis together with Indonesia and Thailand. Results indicate the dominance of oil price volatility on real stock returns and emphasize how this has increased over time. Oil price volatility can have profound effect on the time horizon of investment and firms need adjust their risk management procedures accordingly. This increase in dependency has been found in other net oil importing emerging equity markets. We test the relationship between oil price movements and economic activity by using modern time series techniques in a cointegrating framework. We expand the standard error correction model by examining the dynamics of out of sample causality through the generalized variance decomposition and impulse response function techniques. The evidence from persistence profiles also gives important guidelines based on how fast the entire system adjusts back to equilibrium. In addition, we find the cointegrating relationship to be stable and find that the linear error correction model to be more favorable than an asymmetric 2 period Markov switching model.     

A novel phosphoric acid loaded quaternary 1,4-diazabicyclo-[2.2.2]-octane polysulfone membrane for intermediate temperature fuel cells

Phosphoric acid loaded quaternary 1,4-diazabicyclo-[2.2.2]-octane (DABCO) polysulfone was synthesised with different degrees of substitution (DS) and the membranes were characterized. The polymer structure was investigated using NMR and FT-IR spectra. The effect of DS on ionic conductivity and fuel cell performance are described. Conductivities of 0.12 and 0.064 S cm⁻¹ were achieved for the high degree of substitution membrane (DS106) and low degree of substitution membrane (DS58), respectively. Fuel cell tests gave a high power output of 400 mW cm⁻² using H₂ and O₂ at 150 °C and atmospheric pressure. Therefore, PA/QDPSU membrane has potential applications for intermediate temperature fuel cells (ITFCs).