A new time synchronous average method for variable speed operating condition gearbox

Gearbox is a widely used component for power transmission and speed change. Time synchronous average (TSA) is one of the most effective methods for vibration monitoring and diagnosis of gearboxes. Traditional TSA technique requires key-phase signal and constant operating speed. So the application of TSA is difficult in many situations such as in the case of gearboxes used in wind power generators and automobiles. A new method to implement TSA without key-phase signal for variable speed condition gearbox is proposed in the paper. The reported method is based on the estimation of instantaneous speed with time-frequency domain filtering and equal angular interval re-sampling of vibration signal. Experimental investigation performed in a variable speed gearbox test rig indicates that the proposed method can eliminate the influence of large speed fluctuation of gearboxes and provide satisfactory TSA results

Monthly blue water footprint caps in a river basin to achieve sustainable water consumption:The role of reservoirs

The blue water footprint (WF) measures the consumption of runoff in a river basin. In order to ensure sustainable water consumption, setting a monthly blue WF cap, that is an upper-limit to the blue WF in a river basin each month, can be a suitable policy instrument. The blue WF cap in a river basin depends on the precipitation that becomes runoff and the need to maintain a minimum flow for sustaining ecosystems and livelihoods. Reservoirs along the river generally smooth runoff variability and thus raise the WF cap and reduce blue water scarcity during the dry season. Previous water scarcity studies, considering the ratio of actual blue WF to the blue WF cap under natural background conditions, have not studied this effect of reservoir storages. Here we assess how water reservoirs influence blue WF caps over time and how they affect the variability of blue water scarcity in a river basin. We take the Yellow River Basin over the period January 2002–July 2006 as case study and consider data on observed storage changes in five large reservoirs along the main stream. Results indicate that reservoirs redistribute the blue WF cap and blue water scarcity levels over time. Monthly blue WF caps were generally lowered by reservoir storage during the flood season (July–October) and raised by reservoir releases over the period of highest crop demand (March–June). However, with water storage exceeding 20% of natural runoff in most rainy months, reservoirs contribute to “scarcity in the wet months”, which is to be understood as a situation in which environmental flow requirements related to the occurrence of natural peak flows are no longer met

Versatile gold-silver-PB nanojujubes for multi-modal detection and photo-responsive elimination against bacteria

Bacterial infections have become a serious threat to global public health. Nanomaterials have shown promise in the development of bacterial biosensing and antibiotic-free antibacterial modalities, but single-component materials are often less functional and difficult to achieve dual bacterial detection and killing. Herein, we report a novel strategy based on the effective integration of multi-modal bacterial detection and elimination, by constructing the versatile gold-silver-Prussian blue nanojujubes (GSP NJs) via a facile template etching method. Such incorporation of multi-components involves the utilization of cores of gold nanobipyramids with strong surface-enhanced Raman scattering (SERS) activity, the shells of Prussian blue as both an efficient bio-silent SERS label and an active peroxidase-mimic, and functionalization of polyvinyl pyrrolidone and vancomycin, respectively endowing them with good colloidal dispersibility and specificity against S. aureus. The GSP NJs show operational convenience in the SERS detection and excellent peroxidase-like activity for the sensitive colorimetric detection. Meanwhile, they exhibit robust near-infrared photothermal/photodynamic effects, and the photo-promoted Ag+ ions release, ultimately achieving a high antibacterial efficiency over 99.9% in 5 min. The NJs can also effectively eliminate complex biofilms. The work provides new insights into the design of multifunctional core-shell nanostructures for the integrated bacterial detection and therapy

Enhanced Proteolysis of β-Amyloid in APP Transgenic Mice Prevents Plaque Formation, Secondary Pathology, and Premature Death

AbstractConverging evidence suggests that the accumulation of cerebral amyloid β-protein (Aβ) in Alzheimer's disease (AD) reflects an imbalance between the production and degradation of this self-aggregating peptide. Upregulation of proteases that degrade Aβ thus represents a novel therapeutic approach to lowering steady-state Aβ levels, but the consequences of sustained upregulation in vivo have not been studied. Here we show that transgenic overexpression of insulin-degrading enzyme (IDE) or neprilysin (NEP) in neurons significantly reduces brain Aβ levels, retards or completely prevents amyloid plaque formation and its associated cytopathology, and rescues the premature lethality present in amyloid precursor protein (APP) transgenic mice. Our findings demonstrate that chronic upregulation of Aβ-degrading proteases represents an efficacious therapeutic approach to combating Alzheimer-type pathology in vivo

Soil infiltration based on bp neural network and grey relational analysis Infiltração no solo com base em rede neural de retropropagação e análise relacional grey

Soil infiltration is a key link of the natural water cycle process. Studies on soil permeability are conducive for water resources assessment and estimation, runoff regulation and management, soil erosion modeling, nonpoint and point source pollution of farmland, among other aspects. The unequal influence of rainfall duration, rainfall intensity, antecedent soil moisture, vegetation cover, vegetation type, and slope gradient on soil cumulative infiltration was studied under simulated rainfall and different underlying surfaces. We established a six factor-model of soil cumulative infiltration by the improved back propagation (BP)-based artificial neural network algorithm with a momentum term and self-adjusting learning rate. Compared to the multiple nonlinear regression method, the stability and accuracy of the improved BP algorithm was better. Based on the improved BP model, the sensitive index of these six factors on soil cumulative infiltration was investigated. Secondly, the grey relational analysis method was used to individually study grey correlations among these six factors and soil cumulative infiltration. The results of the two methods were very similar. Rainfall duration was the most influential factor, followed by vegetation cover, vegetation type, rainfall intensity and antecedent soil moisture. The effect of slope gradient on soil cumulative infiltration was not significant.A infiltração da água no solo é um processo fundamental do ciclo natural de água. Estudos sobre a permeabilidade do solo contribuem para avaliar e estimar os recursos hídricos, a regulação e gestão de escoamento, a modelagem da erosão do solo e a poluição de origem difusa e de origem pontual de terras agrícolas, entre outros aspectos. A influência desigual da duração da chuva, a intensidade de precipitação pluvial, a umidade do solo, a cobertura vegetal, o tipo de vegetação e inclinação do terreno sobre a infiltração acumulada no solo foram estudados para diferentes superfícies subjacentes, sob chuva simulada. Um modelo de seis fatores de infiltração acumulada no solo foi estabelecido com base numa rede neural artificial melhorada, utilizando o algoritmo de retropropagação com um termo de momento e taxa de aprendizagem autoajustável. Comparadas com o método de regressão múltipla não linear, a estabilidade e exatidão do algoritmo de retropropagação melhorada foram superiores. Com base no modelo de retropropagação melhorada, o índice de sensibilidade desses seis fatores sobre a infiltração acumulada no solo foi investigado. Posteriormente, o método de análise relacional grey foi usado para estudar individualmente correlações grey entre os seis fatores do solo e a infiltração acumulada. Os resultados dos dois métodos foram muito semelhantes. A duração da chuva foi o fator mais importante, seguido de cobertura vegetal, tipo de vegetação, intensidade de precipitação pluvial e umidade do solo. O efeito do gradiente de inclinação sobre a infiltração acumulada no solo não foi significativo

The effects of no-tillage practice on soil physical properties

No-tillage (NT) is now widely recognized as a variable concept for practicing sustainable agriculture. The objectives of this study were to summarize the effects of no-tillage on soil physical properties and outline the environment capability of no-tillage practice. The effect of no-tillage on soil bulk density was a debated question, and in order to make it comparable, the study conditions (soil texture, climate conditions, planting system, straw covering conditions on soil surface, soil water content and the no-tillage practiced period) were first addressed. Total porosity, a measure of the porous space left in the soil for air and water movement, was inversely related to bulk density. When the conventional tillage practices were used, the volume of soil macropores (>0.05 cm) was higher than that under no-tillage practice. With time, it decreased greatly, but the conventional tillage treatment still kept the lead. As a result of soil agitation, the soil aggregate rate under conventional tillage cropland was generally lower than that under the no-tillage practiced cropland. The studies of no-tillage on soil temperature and on crop yield also have conflicting results because of the absence of systemically long term monitoring, and there was little information on the effects of no-tillage on crop quality. Therefore, future perspectives of no-tillage research were put forward

Estimating spatial mean soil water contents of sloping jujube orchards using temporal stability

Estimating spatial mean soil water contents from point-scale measurements is important to improve soil water management in sloping land of semiarid areas. Temporal stability analysis, as a statistical technique to estimate soil water content, is an effective tool in terms of facilitating the upscaling estimation of mean values. The objective of this study was to examine temporal stability of soil water profiles (0-20, 20-40, 40-60 and 0-60 cm) in sloping jujube (Zizyphus jujuba) orchards and to estimate field mean root-zone soil water based on temporal stability analysis in the Yuanzegou catchment of the Chinese Loess Plateau, using soil water observations under both dry and wet soil conditions. The results showed that different time-stable locations were identified for different depths and the temporal stability of soil water content in 20-40 cm was significantly (P < 0.05) weaker than that in other depths. Moreover, these time-stable locations had relatively high clay contents, relatively mild slopes and relatively planar surfaces compared to the corresponding field means. Statistical analysis revealed that the temporal stability of root zone soil water (0-60 cm) was higher in either dry or wet season than that including both, and soil water exhibited very low temporal stability during the transition period from dry to wet. Based on the temporal stability analysis, field mean soil water contents were estimated reasonably (R(2) from 0.9560 to 0.9873) from the point measurements of these time-stable locations. Since the terrains in this study are typical in the hilly regions of the Loess Plateau, the results presented here should improve soil water management in sloping orchards in the Loess Plateau. (C) 2011 Elsevier B.V. All rights reserved

Soil moisture variability along transects over a well-developed gully in the Loess Plateau, China

Knowledge of soil moisture distributions in gullies, which are highly variable spatially and temporally, is important for both restoring vegetation and controlling erosion in them, but little attention has been paid to this spatio-temporal variability to date. Therefore, we examined soil moisture profiles and their variability along three transects traversing sidewalls of a well-developed gully with steep slopes in a hilly area of the Chinese Loess Plateau. We took intensive measurements at 20-cm intervals from 0 to 160 cm depth, using a portable time domain reflectometer, from September 3 to October 20 2009 and from April 5 to July 20 2010. The results indicate that the mean, standard deviation and coefficient of variation of moisture content vary with time, their responses to precipitation vary at different depths, and moisture content is most variable when mean values are moderate (15-20%). Revised fitting functions developed and introduced by Famiglietti et al. (2008) captured with confidence the relationship between spatial variability (SD and CV) and spatial mean of moisture content (RMSE ranging from 0.0015 to 0.0293). Soil moisture clearly varied along the transects, the vertical distribution of soil moisture differed in different seasons, and correlation analysis showed that soil texture influenced the variability of surface soil moisture more strongly than terrain attributes (except during distinct rainfall events, when this pattern reversed). The results presented here should improve understanding of spatio-temporal variations in soil moisture profiles in well-developed gullies in the Loess Plateau, and potentially elsewhere. (C) 2011 Elsevier B.V. All rights reserved

A 6D Pose Estimation for Robotic Bin-Picking Using Point-Pair Features with Curvature (Cur-PPF)

Pose estimation is a particularly important link in the task of robotic bin-picking. Its purpose is to obtain the 6D pose (3D position and 3D posture) of the target object. In real bin-picking scenarios, noise, overlap, and occlusion affect accuracy of pose estimation and lead to failure in robot grasping. In this paper, a new point-pair feature (PPF) descriptor is proposed, in which curvature information of point-pairs is introduced to strengthen feature description, and improves the point cloud matching rate. The proposed method also introduces an effective point cloud preprocessing, which extracts candidate targets in complex scenarios, and, thus, improves the overall computational efficiency. By combining with the curvature distribution, a weighted voting scheme is presented to further improve the accuracy of pose estimation. The experimental results performed on public data set and real scenarios show that the accuracy of the proposed method is much higher than that of the existing PPF method, and it is more efficient than the PPF method. The proposed method can be used for robotic bin-picking in real industrial scenarios

Soil infiltration based on bp neural network and grey relational analysis

Soil infiltration is a key link of the natural water cycle process. Studies on soil permeability are conducive for water resources assessment and estimation, runoff regulation and management, soil erosion modeling, nonpoint and point source pollution of farmland, among other aspects. The unequal influence of rainfall duration, rainfall intensity, antecedent soil moisture, vegetation cover, vegetation type, and slope gradient on soil cumulative infiltration was studied under simulated rainfall and different underlying surfaces. We established a six factor-model of soil cumulative infiltration by the improved back propagation (BP)-based artificial neural network algorithm with a momentum term and self-adjusting learning rate. Compared to the multiple nonlinear regression method, the stability and accuracy of the improved BP algorithm was better. Based on the improved BP model, the sensitive index of these six factors on soil cumulative infiltration was investigated. Secondly, the grey relational analysis method was used to individually study grey correlations among these six factors and soil cumulative infiltration. The results of the two methods were very similar. Rainfall duration was the most influential factor, followed by vegetation cover, vegetation type, rainfall intensity and antecedent soil moisture. The effect of slope gradient on soil cumulative infiltration was not significant