Although increased risk for adverse birth outcomes has been associated with neighborhood socioeconomic disadvantage, most studies have used cross-sectional measures to account for neighborhood context. Consequently, d...Although increased risk for adverse birth outcomes has been associated with neighborhood socioeconomic disadvantage, most studies have used cross-sectional measures to account for neighborhood context. Consequently, dynamic neighborhood processes that may influence adverse birth outcomes are not fully understood. In this study, a longitudinal measure of socioeconomic change was used to explore variation in low birth weight (LBW) rates between 1990 and 2006 in Chicago neighborhoods. A crosss-ectional measure of neighborhood socioeconomic characteristics was then used to compare the LBW rates across Chicago neighborhoods during the same time frame to determine whether the cross-sectional measure would capture the same nuances in LBW variation as the longitudinal measure. Consistent with previous studies, both measures identified higher low birth weight rates in neighborhoods entrenched in poverty during the study period. However, the longitudinal measure showed that mothers residing in low income neighborhoods with high concentrations of immigrants had LBW rates that were lower than mothers residing in high income neighborhoods. Our results suggest that while cross-sectional measures of neighborhood socioeconomic context may capture global variations in low birth weight rates, longitudinal measures may illuminate subtleties between neighborhoods that might provide an opportunity for targeted policies to reduce adverse maternal and child health outcomes.展开更多
Most of the machineries in small or large-scale industry have rotating elementsupported by bearings for rigid support and accurate movement. For proper functioning ofmachinery, condition monitoring of the bearing is v...Most of the machineries in small or large-scale industry have rotating elementsupported by bearings for rigid support and accurate movement. For proper functioning ofmachinery, condition monitoring of the bearing is very important. In present study soundsignal is used to continuously monitor bearing health as sound signals of rotatingmachineries carry dynamic information of components. There are numerous studies inliterature that are reporting superiority of vibration signal of bearing fault diagnosis.However, there are very few studies done using sound signal. The cost associated withcondition monitoring using sound signal (Microphone) is less than the cost of transducerused to acquire vibration signal (Accelerometer). This paper employs sound signal forcondition monitoring of roller bearing by K-star classifier and k-nearest neighborhoodclassifier. The statistical feature extraction is performed from acquired sound signals. Thentwo-layer feature selection is done using J48 decision tree algorithm and random treealgorithm. These selected features were classified using K-star classifier and k-nearestneighborhood classifier and parametric optimization is performed to achieve the maximumclassification accuracy. The classification results for both K-star classifier and k-nearestneighborhood classifier for condition monitoring of roller bearing using sound signals werecompared.展开更多
The yield map is generated by fitting the yield surface shape of yield monitor data mainly using paraboloid cones on floating neighborhoods. Each yield map value is determined by the fit of such a cone on an elliptica...The yield map is generated by fitting the yield surface shape of yield monitor data mainly using paraboloid cones on floating neighborhoods. Each yield map value is determined by the fit of such a cone on an elliptical neighborhood that is wider across the harvest tracks than it is along them. The coefficients of regression for modeling the paraboloid cones and the scale parameter are estimated using robust weighted M-estimators where the weights decrease quadratically from 1 in the middle to zero at the border of the selected neighborhood. The robust way of estimating the model parameters supersedes a procedure for detecting outliers. For a given neighborhood shape, this yield mapping method is implemented by the Fortran program paraboloidmapping.exe, which can be downloaded from the web. The size of the selected neighborhood is considered appropriate if the variance of the yield map values equals the variance of the true yields, which is the difference between the variance of the raw yield data and the error variance of the yield monitor. It is estimated using a robust variogram on data that have not had the trend removed.展开更多
文摘Although increased risk for adverse birth outcomes has been associated with neighborhood socioeconomic disadvantage, most studies have used cross-sectional measures to account for neighborhood context. Consequently, dynamic neighborhood processes that may influence adverse birth outcomes are not fully understood. In this study, a longitudinal measure of socioeconomic change was used to explore variation in low birth weight (LBW) rates between 1990 and 2006 in Chicago neighborhoods. A crosss-ectional measure of neighborhood socioeconomic characteristics was then used to compare the LBW rates across Chicago neighborhoods during the same time frame to determine whether the cross-sectional measure would capture the same nuances in LBW variation as the longitudinal measure. Consistent with previous studies, both measures identified higher low birth weight rates in neighborhoods entrenched in poverty during the study period. However, the longitudinal measure showed that mothers residing in low income neighborhoods with high concentrations of immigrants had LBW rates that were lower than mothers residing in high income neighborhoods. Our results suggest that while cross-sectional measures of neighborhood socioeconomic context may capture global variations in low birth weight rates, longitudinal measures may illuminate subtleties between neighborhoods that might provide an opportunity for targeted policies to reduce adverse maternal and child health outcomes.
文摘Most of the machineries in small or large-scale industry have rotating elementsupported by bearings for rigid support and accurate movement. For proper functioning ofmachinery, condition monitoring of the bearing is very important. In present study soundsignal is used to continuously monitor bearing health as sound signals of rotatingmachineries carry dynamic information of components. There are numerous studies inliterature that are reporting superiority of vibration signal of bearing fault diagnosis.However, there are very few studies done using sound signal. The cost associated withcondition monitoring using sound signal (Microphone) is less than the cost of transducerused to acquire vibration signal (Accelerometer). This paper employs sound signal forcondition monitoring of roller bearing by K-star classifier and k-nearest neighborhoodclassifier. The statistical feature extraction is performed from acquired sound signals. Thentwo-layer feature selection is done using J48 decision tree algorithm and random treealgorithm. These selected features were classified using K-star classifier and k-nearestneighborhood classifier and parametric optimization is performed to achieve the maximumclassification accuracy. The classification results for both K-star classifier and k-nearestneighborhood classifier for condition monitoring of roller bearing using sound signals werecompared.
文摘The yield map is generated by fitting the yield surface shape of yield monitor data mainly using paraboloid cones on floating neighborhoods. Each yield map value is determined by the fit of such a cone on an elliptical neighborhood that is wider across the harvest tracks than it is along them. The coefficients of regression for modeling the paraboloid cones and the scale parameter are estimated using robust weighted M-estimators where the weights decrease quadratically from 1 in the middle to zero at the border of the selected neighborhood. The robust way of estimating the model parameters supersedes a procedure for detecting outliers. For a given neighborhood shape, this yield mapping method is implemented by the Fortran program paraboloidmapping.exe, which can be downloaded from the web. The size of the selected neighborhood is considered appropriate if the variance of the yield map values equals the variance of the true yields, which is the difference between the variance of the raw yield data and the error variance of the yield monitor. It is estimated using a robust variogram on data that have not had the trend removed.
文摘货位分配(storage location assignment problem,SLAP),即在存储区域为物料分配货位的过程。当仓库布局、拣货路径、订单组合等其他因素确定时,货位分配策略对订单拣货效率有很大影响。本文研究实际生产型仓库中的关联物料区位分配问题。生产中使用的相对稳定的BOM(bill of material)使得仓库中的物料具有稳定的相关性,因此,本文考虑将具有需求关联的物料存储在同一区域,以尽可能地减少在拣选物料时所需要的区域访问次数。此外,该仓库还存在两个重要特征,即存在两类不同尺寸货架构成的两类不同容量的区域及采用严格的重物下置原则。本文建立了以最小化区域访问次数为目标的数学规划模型,给出了求解该问题的一种聚类启发式方法与自适应大邻域搜索算法(adaptive large neighborhood search,ALNS),并设计了能够反映物料关联特征的小规模和大规模算例用于测试两种算法的性能。将两个算法结果与随机策略、CPLEX求解结果对比,结果显示聚类启发式方法与ALNS在大规模算例中表现明显优于随机策略和CPLEX的求解结果。
