An Analysis of Food Grain Consumption in Urban Jiangsu Province of China

The impacts of economic and demographic variables on the demand for food grain commodities in urban Jiangsu province of China are estimated, using both the QUAIDS and the AIDS models. Results show that the demands for wheat flour and coarse grains are price-elastic while the demands for rice and grain products are price-inelastic. Certain demographic variables show as having a significant impact on food grain demand. Finally, a decomposition of causes of changes in rice consumption over the period of 1995–2007 is performed.AIDS model, China food grain demand, China urban household demand, Agribusiness, Community/Rural/Urban Development, Crop Production/Industries, Demand and Price Analysis, Farm Management, Food Consumption/Nutrition/Food Safety, International Development, Production Economics, Public Economics, Research Methods/ Statistical Methods, D12, Q18, Q13,

Herramienta de apoyo al diagnóstico basada en el análisis de historias clínicas

Actualmente una de las tareas más importantes a las que se enfrenta un médico es encontrar el diagnóstico de un paciente en el menor tiempo posible para poder hacer frente a la situación. Este proyecto tiene como objetivo ayudar al personal médico ofreciéndoles la posibilidad de comparar un gran número de informes en busca de aquellos más similares respecto a la información extraída de cada informe. Para ello, hay que conseguir representar todos los informes de una manera similar, tal y como se describe en este proyecto, de forma que la comparación sea lo más sencilla y precisa posible. Esta aplicación se divide en tres partes: las dos primeras llevan a cabo la construcción de las representaciones extrayendo la mayor información posible de cada informe médico y la tercera parte se encarga de realizar la búsqueda por similitud en dichas representaciones

A Fast Method to Measure the Volume of a Large Cavity

How to quickly and accurately measure the volume of a large cavity is challenging. This paper presents an efficient method to measure the volume of a large conducting cavity. The proposed method is based on statistical wave theory. By measuring the Q factor in the time and frequency domains, the volume of the cavity can be extracted. In the time domain, the Q factor can be extracted directly from the time domain response, while in the frequency domain, the Q factor depends on the volume of the cavity and the transferred power; the transferred power can be measured directly. By correcting the frequency domain Q with the radiation efficiency of antennas, the Q factors obtained from both the time and frequency domains are equal in a well-stirred chamber; this provides an opportunity to measure the volume of the cavity. Measurements are conducted to verify the proposed method. Although the measurement is conducted using electromagnetic waves, acoustic waves can also be used; in this case, the approach can be applied to any cavity, not limited to a conducting cavity. The advantages and the limitations of the proposed method are also discussed

Synaptic Cleft Segmentation in Non-Isotropic Volume Electron Microscopy of the Complete Drosophila Brain

Neural circuit reconstruction at single synapse resolution is increasingly recognized as crucially important to decipher the function of biological nervous systems. Volume electron microscopy in serial transmission or scanning mode has been demonstrated to provide the necessary resolution to segment or trace all neurites and to annotate all synaptic connections. Automatic annotation of synaptic connections has been done successfully in near isotropic electron microscopy of vertebrate model organisms. Results on non-isotropic data in insect models, however, are not yet on par with human annotation. We designed a new 3D-U-Net architecture to optimally represent isotropic fields of view in non-isotropic data. We used regression on a signed distance transform of manually annotated synaptic clefts of the CREMI challenge dataset to train this model and observed significant improvement over the state of the art. We developed open source software for optimized parallel prediction on very large volumetric datasets and applied our model to predict synaptic clefts in a 50 tera-voxels dataset of the complete Drosophila brain. Our model generalizes well to areas far away from where training data was available

Arsenic Release Batch Test for Sulfate-Reducing Bacteria

The United States Environmental Protection Agency revised regulations related to the drinking water content of arsenic (As) in 2006 lowering the Maximum Contaminant Level (MCL) from 50 μg/L to10 μg/L. The concentration of arsenic in ground water is associated with iron-reducing, sulfate-reducing and methanogenic conditions. In highly reducing systems, the dominant pathway for arsenic release is poorly understood, but reflects contributions from sulfide minerals (e.g., pyrite) and acid-volatile monosulfides (AVS). This research will evaluate arsenic release and sequestration processes under highly-reducing conditions, with the goal of identifying mechanisms and pathways responsible for arsenic release from aquifer solids under such conditions using a single culture of sulfate-reducing organisms (insert name). During the 4 week anaerobic incubation, sampling was conducted over 20 days. Samples were analyzed to determine dissolved concentrations of inorganic elements (e.g. As, S,), and concentration of anion such as acetate. Based on the first 20 days data, the result shows that the iron started being reduced, but sulfate reduction still cannot be clearly observed due to the slow rate of anaerobic bacteria growth. This suggests, more time of sampling and analytical experiments are still needed to be conducted in the following two months.USGSNo embarg

Experimental and Numerical Study of Macro-Cell Corrosion Between Crossed Steel Bars

Reinforcing steel bars embedded in concrete are always intersected with each other to form rebar mesh or three-dimensional steel cage. The present study aims to investigate the phenomenon of severe corrosion observed at stirrups or some intersections of steel rebar mesh, which has not been well studied before. Macro-cell corrosion between crossed steel bars was considered to be the major cause for this phenomenon. In this regard, crossed steel bars were divided into intersected zone (IZ) and non-intersected zone (NIZ). The macro-cell current at the face-to-face IZ was calculated by Ohm’s law. A one-dimensional model based on transmission line method was employed to obtain the distribution of macro-cell current on the NIZ juxtaposed to the IZ. Experiments of steel bars in aqueous solutions and concrete were undertaken to verify the numerical model. The results demonstrated a good match between experiments and numerical model. It was also shown that the distribution of macro-cell current on the non-intersected areas was influenced by the resistivity of electrolyte. Based on the corrosion rate model presented in this study, the severe corrosion observed at stirrups or some intersection zones of rebar mesh can be explained and quantified

Enhancing Deep Traffic Forecasting Models with Dynamic Regression

Deep learning models for traffic forecasting often assume the residual is independent and isotropic across time and space. This assumption simplifies loss functions such as mean absolute error, but real-world residual processes often exhibit significant autocorrelation and structured spatiotemporal correlation. This paper introduces a dynamic regression (DR) framework to enhance existing spatiotemporal traffic forecasting models by incorporating structured learning for the residual process. We assume the residual of the base model (i.e., a well-developed traffic forecasting model) follows a matrix-variate seasonal autoregressive (AR) model, which is seamlessly integrated into the training process through the redesign of the loss function. Importantly, the parameters of the DR framework are jointly optimized alongside the base model. We evaluate the effectiveness of the proposed framework on state-of-the-art (SOTA) deep traffic forecasting models using both speed and flow datasets, demonstrating improved performance and providing interpretable AR coefficients and spatiotemporal covariance matrices