Investigation and improvement of zinc electrodes for electrochemical cells quarterly report no. 2, oct. - dec. 1964

Influence of separator and surfactant on growth rate of zinc deposits in electrochemical cell

Firm dynamics in the Cambodian garment industry: firm turnover, productivity growth, and wage profile under trade liberalization

The international garment trade was liberalized in 2005 following the termination of the MFA (Multifibre Arrangement) and ever since then, price competition has intensified. Employing a unique firm dataset collected by the authors, this paper examines the changes in the performance of Cambodian garment firms between 2002/03 and 2008/09. During the period concerned, frequent firm turnover led to an improvement of the industry’s productivity, and the study found that the average total-factor productivity (TFP) of new entrants was substantially higher than that of exiting firms. Furthermore, we observed that thanks to productivity growth, an improvement in workers’ welfare, including a rise in the relative wages of the low-skilled, was taking place. These industrial dynamics differ considerably from those indicated by the “race to the bottom†argument as applied to labor-intensive industrialization in low income countries.Cambodia, Firm turnover, Garments, Labor-intensive industrialization, Multifibre arrangement, Race to the bottom, Total factor productivity, Apparel industry, Productivity, Wages, Employment

Multi-Person Brain Activity Recognition via Comprehensive EEG Signal Analysis

An electroencephalography (EEG) based brain activity recognition is a fundamental field of study for a number of significant applications such as intention prediction, appliance control, and neurological disease diagnosis in smart home and smart healthcare domains. Existing techniques mostly focus on binary brain activity recognition for a single person, which limits their deployment in wider and complex practical scenarios. Therefore, multi-person and multi-class brain activity recognition has obtained popularity recently. Another challenge faced by brain activity recognition is the low recognition accuracy due to the massive noises and the low signal-to-noise ratio in EEG signals. Moreover, the feature engineering in EEG processing is time-consuming and highly re- lies on the expert experience. In this paper, we attempt to solve the above challenges by proposing an approach which has better EEG interpretation ability via raw Electroencephalography (EEG) signal analysis for multi-person and multi-class brain activity recognition. Specifically, we analyze inter-class and inter-person EEG signal characteristics, based on which to capture the discrepancy of inter-class EEG data. Then, we adopt an Autoencoder layer to automatically refine the raw EEG signals by eliminating various artifacts. We evaluate our approach on both a public and a local EEG datasets and conduct extensive experiments to explore the effect of several factors (such as normalization methods, training data size, and Autoencoder hidden neuron size) on the recognition results. The experimental results show that our approach achieves a high accuracy comparing to competitive state-of-the-art methods, indicating its potential in promoting future research on multi-person EEG recognition.Comment: 10 page

A generalized least-squares framework for rare-variant analysis in family data.

Rare variants may, in part, explain some of the hereditability missing in current genome-wide association studies. Many gene-based rare-variant analysis approaches proposed in recent years are aimed at population-based samples, although analysis strategies for family-based samples are clearly warranted since the family-based design has the potential to enhance our ability to enrich for rare causal variants. We have recently developed the generalized least squares, sequence kernel association test, or GLS-SKAT, approach for the rare-variant analyses in family samples, in which the kinship matrix that was computed from the high dimension genetic data was used to decorrelate the family structure. We then applied the SKAT-O approach for gene-/region-based inference in the decorrelated data. In this study, we applied this GLS-SKAT method to the systolic blood pressure data in the simulated family sample distributed by the Genetic Analysis Workshop 18. We compared the GLS-SKAT approach to the rare-variant analysis approach implemented in family-based association test-v1 and demonstrated that the GLS-SKAT approach provides superior power and good control of type I error rate

Water resources transfers through southern African food trade:water efficiency and climate signals

Temporal and spatial variability of precipitation in southern Africa is particularly high. The associated drought and flood risks, combined with a largely rain-fed agriculture, pose a challenge for water and food security in the region. As regional collaboration strengthens through the Southern Africa Development Community and trade with other regions increases, it is thus important to understand both how climate variability affects agricultural productivity and how food trade (regional and extra-regional) can contribute to the region's capacity to deal with climate-related shocks. We combine global hydrological model simulations with international food trade data to quantify the water resources embedded in international food trade in southern Africa and with the rest of the world, from 1986-2011. We analyze the impacts of socio-economic, political, and climatic changes on agricultural trade and embedded water resources during this period. We find that regional food trade is efficient in terms of water use but may be unsustainable because water-productive exporters, like South Africa, rely on increasingly stressed water resources. The role of imports from the rest of the world in the region's food supply is important, in particular during severe droughts. This reflects how trade can efficiently redistribute water resources across continents in response to a sudden gap in food production and water productivity. In a context of regional and global integration, our results highlight opportunities for improved water-efficiency and sustainability of the region's food supply via trade

Influence of water vapour on the height distribution of positive ions, effective recombination coefficient and ionisation balance in the quiet lower ionosphere

Mesospheric water vapour concentration effects on the ion composition and electron density in the lower ionosphere under quiet geophysical conditions were examined. Water vapour is an important compound in the mesosphere and the lower thermosphere that affects ion composition due to hydrogen radical production and consequently modifies the electron number density. Recent lower-ionosphere investigations have primarily concentrated on the geomagnetic disturbance periods. Meanwhile, studies on the electron density under quiet conditions are quite rare. The goal of this study is to contribute to a better understanding of the ionospheric parameter responses to water vapour variability in the quiet lower ionosphere. By applying a numerical D region ion chemistry model, we evaluated efficiencies for the channels forming hydrated cluster ions from the NO⁺ and O₂⁺ primary ions (i.e. NO⁺.H₂O and O₂⁺.H₂O, respectively), and the channel forming H⁺(H₂O)_<i>n</i> proton hydrates from water clusters at different altitudes using profiles with low and high water vapour concentrations. Profiles for positive ions, effective recombination coefficients and electrons were modelled for three particular cases using electron density measurements obtained during rocket campaigns. It was found that the water vapour concentration variations in the mesosphere affect the position of both the Cl₂⁺ proton hydrate layer upper border, comprising the NO⁺(H₂O)_<i>n</i> and O₂⁺(H₂O)_<i>n</i> hydrated cluster ions, and the Cl₁⁺ hydrate cluster layer lower border, comprising the H⁺(H₂O)_<i>n</i> pure proton hydrates, as well as the numerical cluster densities. The water variations caused large changes in the effective recombination coefficient and electron density between altitudes of 75 and 87 km. However, the effective recombination coefficient, &alpha;_eff, and electron number density did not respond even to large water vapour concentration variations occurring at other altitudes in the mesosphere. We determined the water vapour concentration upper limit at altitudes between 75 and 87 km, beyond which the water vapour concentration ceases to influence the numerical densities of Cl₂⁺ and Cl₁⁺, the effective recombination coefficient and the electron number density in the summer ionosphere. This water vapour concentration limit corresponds to values found in the H₂O-1 profile that was observed in the summer mesosphere by the Upper Atmosphere Research Satellite (UARS). The electron density modelled using the H₂O-1 profile agreed well with the electron density measured in the summer ionosphere when the measured profiles did not have sharp gradients. For sharp gradients in electron and positive ion number densities, a water profile that can reproduce the characteristic behaviour of the ionospheric parameters should have an inhomogeneous height distribution of water vapour