Is technological change biased toward energy? A multi-sectoral analysis for the French economy

Since the adoption and implementation of new technologies has an important influence on the structure and performance of the economy in both developed and developing countries, many research papers are devoted to the technology–economy nexus. Motivated by the fact that the impact of technical progress on the demand for different production factors may vary depending on the bias of the technological change, in this paper, by estimating a translog cost-share system and using state-space modeling technique, we investigate to what extent the direction of technical change is biased toward energy and away from other factors. By applying this methodology to the French economy for the period 1978–2006 the obtained results suggest that: first, technical change has a non-neutral impact on factor demands; second, capital-saving technical progress is present in the majority of the sectors studied; third, energy demand has increased in all sectors but electricity and gas. These findings may have important policy implications for environmental and energy issues in France.     

Estimation of real GDP and unrecorded economy in Turkey based on environmental data

This paper estimates the real gross domestic product (GDP) and unrecorded economy for Turkey using the Kalman filter technique. Using different tests, most of the research articles on energy policy investigate the causal relationship between energy consumption and GDP for different countries. On the other hand, other studies on climate change try to show the effects of both energy consumption and GDP on carbon dioxide (CO₂) emission. Since the unreported economy has an important weight in developing countries where the recorded (or official) GDP suffers from considerable measurement problems, investigation of the relationship between the recorded GDP and energy consumption may lead to biased results. In this paper, the economic variables (GDP, country population) as well as environmental variables (CO₂ emission, forest area) are used in order to estimate GDP, which is an unobserved variable in our model. The results clearly indicate that: first, the true GDP in Turkey, that our model estimates, is higher than the observed (recorded) GDP in the whole period of observation (1973–2003) and the size of unrecorded economy varies between 12 and 30 percent of the observed GDP; second, the gap between the true GDP and the observed GDP has an increasing trend; third, if the change in GDP per primary energy supply is smaller than the change in CO₂ per primary energy supply, then there may exist unrecorded economy.     

The effects of dissolved natural organic matter on the adsorption of synthetic organic chemicals by activated carbons and carbon nanotubes

Understanding the influence of natural organic matter (NOM) on synthetic organic contaminant (SOC) adsorption by carbon nanotubes (CNTs) is important for assessing the environmental implications of accidental CNT release and spill to natural waters, and their potential use as adsorbents in engineered systems. In this study, adsorption of two SOCs by three single-walled carbon nanotubes (SWNTs), one multi-walled carbon nanotube (MWNT), a microporous activated carbon fiber (ACF) [i.e., ACF10] and a bimodal porous granular activated carbon (GAC) [i.e., HD4000] was compared in the presence and absence of NOM. The NOM effect was found to depend strongly on the pore size distribution of carbons. Minimal NOM effect occurred on the macroporous MWNT, whereas severe NOM effects were observed on the microporous HD4000 and ACF10. Although the single-solute adsorption capacities of the SWNTs were much lower than those of HD4000, in the presence of NOM the SWNTs exhibited adsorption capacities similar to those of HD4000. Therefore, if released into natural waters, SWNTs can behave like an activated carbon, and will be able to adsorb, carry, and transfer SOCs to other systems. However, from an engineering application perspective, CNTs did not exhibit a major advantage, in terms of adsorption capacities, over the GAC and ACF. The NOM effect was also found to depend on molecular properties of SOCs. NOM competition was more severe on the adsorption of 2-phenylphenol, a nonplanar and hydrophilic SOC, than phenanthrene, a planar and hydrophobic SOC, tested in this study. In terms of surface chemistry, both adsorption affinity to SOCs and NOM effect on SOC adsorption were enhanced with increasing hydrophobicity of the SWNTs.     

Effect of Alloy Content in Hardox450 + FeW Composites Alloyed by PTAW on Wear Characteristic

Protection of Metals and Physical Chemistry of Surfaces - In this study, Hardox450 + FeW alloys were coated on AISI1020 substrate material by using plasma transferred arc welding (PTAW) technique....     

Importance and applications of DOE/optimization methods in PEM fuel cells: A review

Although proton exchange membrane (PEM) fuel cells are seen as one of the energy conversion technologies of the future due to their high energy conversion efficiency, low levels of emissions, low temperature operation, and compact systems, studies continue to reduce their cost, which is the biggest obstacle to commercialization. Design of experiment (DOE) methods are frequently used in optimization of PEM fuel cells to reduce their cost by decreasing experimental runs. This paper reviews the main gains subsuming the usage of several DOE and optimization methods in PEM fuel cell components, design, operation conditions, and model parameters. It firstly focuses on the Taguchi method and response surface methodology (RSM) known to be applied usually in PEM fuel cell studies. In addition to these known methods, other experimental design and optimization methods used in PEM fuel cells are discussed, and the results are summarized.     

Controlled Micro/Nanodome Formation in Proton‐Irradiated Bulk Transition‐Metal Dichalcogenides

At the few‐atom‐thick limit, transition‐metal dichalcogenides (TMDs) exhibit strongly interconnected structural and optoelectronic properties. The possibility to tailor the latter by controlling the former is expected to have a great impact on applied and fundamental research. As shown here, proton irradiation deeply affects the surface morphology of bulk TMD crystals. Protons penetrate the top layer, resulting in the production and progressive accumulation of molecular hydrogen in the first interlayer region. This leads to the blistering of one‐monolayer thick domes, which stud the crystal surface and locally turn the dark bulk material into an efficient light emitter. The domes are stable (>2‐year lifetime) and robust, and host strong, complex strain fields. Lithographic techniques provide a means to engineer the formation process so that the domes can be produced with well‐ordered positions and sizes tunable from the nanometer to the micrometer scale, with important prospects for so far unattainable applications.     

Weighting factors design in model predictive direct torque control based on cascaded neural network

An innovative control strategy is introduced in this paper for model predictive direct torque control (MPDTC) of a permanent magnet synchronous motor (PMSM) inverter, which is based on a cascaded neural network for the automatic selection and online tuning of weighting factors. Specifically, the lower network in the proposed two-stage cascaded network is trained by utilizing the weighting factors and the corresponding converter parameters under a specific working condition to establish data substitution models under that working condition and determine the fitness function for obtaining the optimal weighting factors. The upper network of the cascaded network is trained using various working conditions and their corresponding optimal weighting factors calculated by the lower network, with the goal of achieving real-time tuning of the weighting factors under variable working conditions. By employing a data-driven method to design the weighting factors at each working condition offline, this method reduces online tuning to a single calculation in the upper network and minimizes the online calculation burden. This approach enables the weighting factors to be adjusted in real-time according to changes in the working conditions, leading to enhanced accuracy of weighting factor optimization. Experimental results demonstrate that the proposed strategy improves the overall performance of the inverters.     

Trichloroethylene adsorption by fibrous and granular activated carbons: aqueous phase, gas phase, and water vapor adsorption studies

The important adsorption components involved in the removal of trichloroethylene (TCE) by fibrous and granular activated carbons from aqueous solutions were systematically examined. Namely, adsorption of TCE itself (i.e., TCE vapor isotherms), water molecules (i.e., water vapor isotherms), and TCE in water (i.e., TCE aqueous phase isotherms) were studied, side-by-side, using 20 well-characterized surface-modified activated carbons. The results showed that TCE molecular size and geometry, activated carbon surface hydrophilicity, pore volume, and pore size distribution in micropores control adsorption of TCE at relatively dilute aqueous solutions. TCE adsorption increased as the carbon surface hydrophilicity decreased and the pore volume in micropores of less than 10 A, especially in the 5-8 A range, increased. TCE molecules appeared to access deep regions of carbon micropores due to their flat geometry. The results indicated that characteristics of both adsorbate (i.e., the molecular structure, size, and geometry) and activated carbon (surface hydrophilicity, pore volume, and pore size distribution of micropores) control adsorption of synthetic organic compounds from water and wastewaters. The important micropore size region for a target compound adsorption depends on its size and geometry.     

Internal Validity Index for Fuzzy Clustering Based on Relative Uncertainty

Unsupervised clustering and clustering validity are used as essential instruments of data analytics. Despite clustering being realized under uncertainty, validity indices do not deliver any quantitative evaluation of the uncertainties in the suggested partitionings. Also, validity measures may be biased towards the underlying clustering method. Moreover, neglecting a confidence requirement may result in over-partitioning. In the absence of an error estimate or a confidence parameter, probable clustering errors are forwarded to the later stages of the system. Whereas, having an uncertainty margin of the projected labeling can be very fruitful for many applications such as machine learning. Herein, the validity issue was approached through estimation of the uncertainty and a novel low complexity index proposed for fuzzy clustering. It involves only uni-dimensional membership weights, regardless of the data dimension, stipulates no specific distribution, and is independent of the underlying similarity measure. Inclusive tests and comparisons returned that it can reliably estimate the optimum number of partitions under different data distributions, besides behaving more robust to over partitioning. Also, in the comparative correlation analysis between true clustering error rates and some known internal validity indices, the suggested index exhibited the highest strong correlations. This relationship has been also proven stable through additional statistical acceptance tests. Thus the provided relative uncertainty measure can be used as a probable error estimate in the clustering as well. Besides, it is the only method known that can exclusively identify data points in dubiety and is adjustable according to the required confidence level.