Jackknife for bias reduction in predictive regressions

One of the fundamental econometric models in finance is predictive regression. The standard least squares method produces biased coefficient estimates when the regressor is persistent and its innovations are correlated with those of the dependent variable. This article proposes a general and convenient method based on the jackknife technique to tackle the estimation problem. The proposed method reduces the bias for both single- and multiple-regressor models and for both short- and long-horizon regressions. The effectiveness of the proposed method is demonstrated by simulations. An empirical application to equity premium prediction using the dividend yield and the short rate highlights the differences between the results by the standard approach and those by the bias-reduced estimator. The significant predictive variables under the ordinary least squares become insignificant after adjusting for the finite-sample bias. These discrepancies suggest that bias reduction in predictive regressions is important in practical applications.

Photocatalytic reduction of carbon dioxide to formic acid, formaldehyde, and methanol using dye-sensitized TiO2 film

A bifunctionalized TiO2 film containing a dye-sensitized zone and a catalysis zone is designed for visible-light photocatalytic reduction of CO2 to chemicals continuously. Charge separation can be accomplished with electron transferring to catalysis zone and positive charge transforming to anode. Highly efficient conversion of CO2 to formic acid, formaldehyde, and methanol is achieved through the transferring electrons on conduction bands (CB) of TiO2. Reduction of CO2 and O2 evolution take place in separated solutions on different catalysts. The separated solution carried out in this photo-reactor system can avoid CO2 reduction products being oxidized by anode. The yields of reduction products were enhanced remarkably by external electrical power. This study provides not only a new photocatalytic system but also a potential of renewable energy source via carbon dioxide.

Empirical analysis of the effect of dimension reduction and word order on semantic vectors

The aim of this paper is to provide a comparison of various algorithms and parameters to build reduced semantic spaces. The effect of dimension reduction, the stability of the representation and the effect of word order are examined in the context of the five algorithms bearing on semantic vectors: Random projection (RP), singular value decom- position (SVD), non-negative matrix factorization (NMF), permutations and holographic reduced representations (HRR). The quality of semantic representation was tested by means of synonym finding task using the TOEFL test on the TASA corpus. Dimension reduction was found to improve the quality of semantic representation but it is hard to find the optimal parameter settings. Even though dimension reduction by RP was found to be more generally applicable than SVD, the semantic vectors produced by RP are somewhat unstable. The effect of encoding word order into the semantic vector representation via HRR did not lead to any increase in scores over vectors constructed from word co-occurrence in context information. In this regard, very small context windows resulted in better semantic vectors for the TOEFL test.

Identification of copper species present in Cu-ZSM-5 catalysts for NOx reduction

The structure of Cu-ZSM-5 catalysts that show activity for direct NO decomposition and selective catalytic reduction of NOx by hydrocarbons has been investigated by a multitude of modern surface analysis and spectroscopy techniques including X-ray photoelectron spectroscopy, thermogravimetric analysis, and in situ Fourier transform infrared spectroscopy. A series of four catalysts were prepared by exchange of Na-ZSM-5 with dilute copper acetate, and the copper loading was controlled by variation of the solution pH. Underexchanged catalysts contained isolated Cu2+OH-(H2O) species and as the copper loading was increased Cu2+ ions incorporated into the zeolite lattice appeared. The sites at which the latter two copper species were located were fundamentally different. The Cu2+OH-(H2O) moieties were bound to two lattice oxygen ions and associated with one aluminum framework species. In contrast, the Cu2+ ions were probably bound to four lattice oxygen ions and associated with two framework aluminum ions. Once the Cu-ZSM-5 samples attained high levels of exchange, the development of [Cu(μ-OH)2Cu]n2+OH-(H2O) species along with a small concentration of Cu(OH)2 was observed. On activation in helium to 500°C the Cu2+OH-(H2O) species transformed into Cu2+O- and Cu+ moieties, whereas the Cu2+ ions were apparently unaffected by this treatment (apart from the loss of ligated water molecules). Calcination of the precursors resulted in the formation of Cu2+O2- and a one-dimensional CuO species. Temperature-programmed desorption studies revealed that oxygen was removed from the latter two species at 407 and 575°C, respectively. © 1999 Academic Press.

Oxide reduction in NiO-containing solid solution systems during transmission electron microscopy

The effects of electron irradiation on NiO-containing solid solution systems are described. Partially hydrated NiO solid solutions, e. g. , NiO-MgO, undergo surface reduction to Ni metal after examination by TEM. This surface layer results in the formation of Moire interference patterns.

Classification of the Johnson Space Center stratospheric dust collection

A review of 291 catalogued particles on the bases of particle size, shape, bulk chemistry, and texture is used to establish a reliable taxonomy. Extraterrestrial materials occur in three defined categories: spheres, aggregates and fragments. Approximately 76% of aggregates are of probable extraterrestrial origin, whereas spheres contain the smallest amount of extraterrestrial material (approx 43%). -B.M.

Nanoporous Graphitic-C3N4@Carbon Metal-Free Electrocatalysts for Highly Efficient Oxygen Reduction

Based on theoretical prediction, a g-C3N4@carbon metal-free oxygen reduction reaction (ORR) electrocatalyst was designed and synthesized by uniform incorporation of g-C3N4 into a mesoporous carbon to enhance the electron transfer efficiency of g-C3N4. The resulting g-C3N4@carbon composite exhibited competitive catalytic activity (11.3 mA cm–2 kinetic-limiting current density at −0.6 V) and superior methanol tolerance compared to a commercial Pt/C catalyst. Furthermore, it demonstrated significantly higher catalytic efficiency (nearly 100% of four-electron ORR process selectivity) than a Pt/C catalyst. The proposed synthesis route is facile and low-cost, providing a feasible method for the development of highly efficient electrocatalysts.

Gradients of refractive index in the crystalline lens and transient changes in refraction among patients with diabetes

Transient hyperopic refractive shifts occur on a timescale of weeks in some patients after initiation of therapy for hyperglycemia, and are usually followed by recovery to the original refraction. Possible lenticular origin of these changes is considered in terms of a paraxial gradient index model. Assuming that the lens thickness and curvatures remain unchanged, as observed in practice, it appears possible to account for initial hyperopic refractive shifts of up to a few diopters by reduction in refractive index near the lens center and alteration in the rate of change between center and surface, so that most of the index change occurs closer to the lens surface. Restoration of the original refraction depends on further change in the refractive index distribution with more gradual changes in refractive index from the lens center to its surface. Modeling limitations are discussed.

Heteroscedastic probabilistic linear discriminant analysis for manifold learning in video-based face recognition

To recognize faces in video, face appearances have been widely modeled as piece-wise local linear models which linearly approximate the smooth yet non-linear low dimensional face appearance manifolds. The choice of representations of the local models is crucial. Most of the existing methods learn each local model individually meaning that they only anticipate variations within each class. In this work, we propose to represent local models as Gaussian distributions which are learned simultaneously using the heteroscedastic probabilistic linear discriminant analysis (PLDA). Each gallery video is therefore represented as a collection of such distributions. With the PLDA, not only the within-class variations are estimated during the training, the separability between classes is also maximized leading to an improved discrimination. The heteroscedastic PLDA itself is adapted from the standard PLDA to approximate face appearance manifolds more accurately. Instead of assuming a single global within-class covariance, the heteroscedastic PLDA learns different within-class covariances specific to each local model. In the recognition phase, a probe video is matched against gallery samples through the fusion of point-to-model distances. Experiments on the Honda and MoBo datasets have shown the merit of the proposed method which achieves better performance than the state-of-the-art technique.

Evaluation of primary epidermal lamellar density in the forefeet of near-term fetal Australian feral and domesticated horses

Objective: To investigate the density of the primary epidermal lamellae (PEL) around the solar circumference of the forefeet of near-term fetal feral and nonferal (ie, domesticated) horses. Sample: Left forefeet from near-term Australian feral (n = 14) and domesticated (4) horse fetuses. Procedures: Near-term feral horse fetuses were obtained from culled mares within 10 minutes of death; fetuses that had died in utero 2 weeks prior to anticipated birth date and were delivered from live Thoroughbred mares were also obtained. Following disarticulation at the carpus, the left forefoot of each fetus was frozen during dissection and data collection. In a standard section of each hoof, the stratum internum PEL density was calculated at the midline center (12 o'clock) and the medial and lateral break-over points (11 and 1 o'clock), toe quarters (10 and 2 o'clock), and quarters (4 and 6 o'clock). Values for matching lateral and medial zones were averaged and expressed as 1 density. Density differences at the 4 locations between the feral and domesticated horse feet were assessed by use of imaging software analysis. Results: In fetal domesticated horse feet, PEL density did not differ among the 4 locations. In fetal feral horse feet, PEL density differed significantly among locations, with a pattern of gradual reduction from the dorsal to the palmar aspect of the foot. The PEL density distribution differed significantly between fetal domesticated and feral horse feet. Conclusions and Clinical Relevance: Results indicated that PEL density distribution differs between fetal feral and domesticated horse feet, suggestive of an adaptation of feral horses to environment challenges.

Effects of eco-driving instructions using a vehicle with automatic transmission on urban roads

Eco-driving instructions could reduce fuel consumption to up to 20% (EcoMove, 2010). Participants (N=13) drove an instrumented vehicle (i.e. Toyota Camry 2007) with an automatic transmission. Fuel consumption of the participants were compared before and after they received eco-driving instructions. Participants drove the same vehicle on the same urban route under similar traffic conditions. Results show that, on free flow sections of the track, all participants drove slightly faster (on average, 0.7 Km/h faster), during the lap for which they were instructed to drive in an eco-friendly manner as compared to when they were not given the eco-driving instruction. Suprisingly, eco-driving instructions increased the RPM significantly in most cases. Fuel consumption slightly decreased (6%) after the eco-driving instructions. We have found strong evidence showing that the fuel saving observed in our experiment (urban environment, automatic transmission) fall short of the 20% reduction claimed in other international trials.

Reduction in resting plasma granulysin as a marker of increased training load

Granulysin is a cytolytic granule protein released by natural killer cells and activated cytotoxic T lymphocytes. The influence of exercise training on circulating granulysin concentration is unknown, as is the relationship between granulysin concentration, natural killer cell number and natural killer cell cytotoxicity. We examined changes in plasma granulysin concentration, natural killer cell number and cytotoxicity following acute exercise and different training loads. Fifteen highly trained male cyclists completed a baseline 40-km cycle time trial (TT401) followed by five weeks of normal training and a repeat time trial (TT402). The cyclists then completed four days of high intensity training followed by another time trial (TT403) on day five. Following one final week of normal training cyclists completed another time trial (TT404). Fasting venous blood was collected before and after each time trial to determine granulysin concentration, natural killer cell number and natural killer cell cytotoxicity. Granulysin concentration increased significantly after each time trial (P<0.001). Pre-exercise granulysin concentration for TT403 was significantly lower than pre-exercise concentration for TT401 (-20.3 +/- 7.5%, P<0.026), TT402 (-16.7 +/- 4.3%, P<0.003) and 7T404 (-21 +/- 4.2%, P<0.001). Circulating natural killer cell numbers also increased significantly post-exercise for each time trial (P<0.001), however there was no significant difference across TT40 (P>0.05). Exercise did not significantly alter natural killer cell cytotoxicity on a per cell basis, and there were no significant differences between the four time trials. In conclusion, plasma granulysin concentration increases following moderate duration, strenuous exercise and is decreased in response to a short-term period of intensified training.

IMO mandatory energy efficiency measures for international shipping : the first mandatory global greenhouse gas reduction instrument for an international industry

Bunker fuels used in the aviation and maritime sectors are responsible for nearly 10% of global greenhouse gas emissions.1 According to a scientific survey: ‘[s]hipping is estimated to have emitted 1,046 million tonnes of CO2 in 2007, which corresponds to 3.3% of the global emissions during 2007. International shipping is estimated to have emitted 870 million tonnes, or about 2.7% of the global emissions of CO2 in 2007’. The study also predicted that ‘by 2050, in the absence of policies, ship emissions may grow by 150% to 250% (compared to the emissions in 2007) as a result of the growth in shipping.’