NLRP3 inflammasome activation determines the fibrogenic potential of PM2.5 air pollution particles in the lung

Airborne fine particulate matter(PM_2.5) is known to cause respiratory inflammation such as chronic obstructive pulmonary disease and lung fibrosis. NLRP3 inflammasome activation has been implicated in these diseases; however, due to the complexity in PM_2.5compositions, it is difficult to differentiate the roles of the components in triggering this pathway. We collected eight real-life PM_2.5samples for a comparative analysis of their effects on NLRP3 inflammasome ...     

Predictive control based double-electrode submerged arc welding for filet joints

Double-electrode submerged arc welding (DE-SAW) is an innovative process to achieve the same metal deposition rate at reduced heat input. To maximally take advantage of this new capability, the heat input needed to produce acceptable welds needs to be reduced. A joint gap is successfully introduced to reduce the required heat input but it introduces variations affecting currents/heat input. To minimize the resultant variations in currents and resultant welds, the process is modeled and a multivariable predictive control algorithm is developed to control the currents at desired levels. Experiments verified the effectiveness of the resultant feedback controlled DE-SAW process.     

Using qualitative models to define sustainable management for the commons in data poor conditions

Nearly 50 years after Hardin’s “tragedy of the commons” we have not yet found predictive tools to guide us towards sustainable management of common-pool resources (CPR). We often have a good understanding of the qualitative relationships between the principal actors in socioecological systems (SESs), but classical quantitative approaches require a tremendous amount of data to understand the drivers of SESs sustainability. Here we show that qualitative modelling approaches can provide important governance insights for SESs that are understood but not quantified.We used Loop Analysis to test the outcomes of different management regimes on a simple nature-based tourism SES described by economic, social and environmental variables. We tested the sustainability of different management scenarios on this system and we identified the necessary conditions to achieve it.We found that management regimes where property rights and responsibilities are shared between different stakeholders are more likely to be successful. However, the system is generally highly unstable and it is important to tune each strategy to each particular situation.The conditions for sustainability found across the different systems tested were: a low reinvestment rate of tourist revenues into new infrastructures and a low growth rate of the environment. Management strategies based on maximum sustainable yield, which keep the environment far from its carrying capacity, have less chance to be sustainable.Qualitative models of SESs are powerful diagnostic tools; they can help identifying variables that play an important role in determining socioecological sustainability in data-poor circumstances and guide the design of efficient data collection programmes. Our results highlight the importance of careful planning when designing management strategies for nature-based tourism. The application of one-size-fits-all solutions to every situation is likely to lead to the failure of the commons; however tourism-based SESs can be sustainable if management strategies are tuned to each particular case.     

人工神经网络模型在地震安全评价中的应用

地震影响系数是地震安全评价的一个重要参数 ,其分布受到场地条件和基岩条件的共同影响 ,对场地影响的考虑相对比较成熟 ,对基岩的影响则相对考虑较少 ,难以满足城市和重大工程设防的要求。综合考虑基岩条件和场地条件的地震影响系数计算 ,关键在于确定基岩裂缝、能量、位移、场地等指标的分布与地震影响系数之间的关系 ,由于这种非线性关系需要构建多元非线性模型 ,故通过应用人工神经网络方法 ,得到地震影响系数计算模型。以唐山市为实例 ,进行了计算并分析了计算结果 ,为城市规划和工程建设提出了几点建议     

A new general mechanistic river model for radionuclides from single pulse fallouts which can be run by readily accessible driving variables

This paper presents a new general, process-based river model for substances such as radionuclides from single pulse fallouts. The new model has been critically tested using data from 13 European rivers contaminated by radiocesium from the Chernobyl accident. This modelling approach gives radionuclide concentrations in water (total, dissolved and particulate phases; and also concentrations in sediments and fish, but the latter aspects are not discussed in this paper) at defined river sites. The model is based on processes in the upstream river stretch and in the upstream catchment area. The catchment area is differentiated into inflow ( approximately dry land) areas and outflow ( approximately wetland) areas. The model also accounts for time-dependent fixation of substances in the catchment. The catchment area sub-model is based on a previous catchment model, which has been tested with very good results for radiocesium, radiostrontium and Ca-concentrations (from liming operations). The new river model is simple to apply in practice since all driving variables may be readily accessed from maps and standard monitoring programs. The driving variables are: latitude, altitude, continentality, catchment area, mean annual precipitation, soil type (percentages or organic and sandy soils), fallout and month of fallout. Modelled values have been compared to independent empirical data from 10 rivers sites (91 data on radiocesium in water) covering a wide domain (catchment areas from 4000 to 180 000 km(2), precipitation from 500 to 960 mm/yr and fallout from 1700 to 660 000 Bq/m(2)). The new model predicts very well--when modelled values are compared to empirical data, the slope is perfect (1.0) and the r(2)-value is 0.90. This is good giving the fact that there are also uncertainties in the empirical data, which set a limit to the achieved predictive power, as expressed by the r(2)-value.     

The effects of military tank traffic on prairie: A management model

The effect of various frequencies and seasons of military tank traffic on native mixed-grass prairie was examined in a randomized and replicated field experiment. Vegetation (in 10×10 m plots) was subjected to tank traffic at the following rates: (a) one pass per day of training from May until August; (b) one pass per day in May and June; (c) one pass per day in July and August; (d) one pass every three weeks from May until August; (e) zero (control). Species composition and the amount of bare ground were found to vary significantly with traffic frequency. Plant species alien to North America invaded plots subjected to spring driving. Regression analysis showed spring driving to produce more bare ground than summer driving. The regression models suggested that much higher intensities of training could be conducted without damage if spring driving were avoided. Regression models were also used to estimate the frequency of traffic associated with a significant change in species composition, where species composition was expressed as the ratio ofBouteloua gracilis toStipa spartea, an indicator of disturbance-induced change in prairie vegetation. This relationship predicted the capacity of the vegetation of a training area of any given width to support tank traffic without changing species composition. The predictive ability of the model was tested by comparing predicted traffic capacities with the amount of traffic actually applied to two training areas in 1986. Where traffic capacity was exceeded, the model successfully predicted a significantly higher frequency of bare ground and ratio ofBouteloua gracilis toStipa spartea.     

A challenge to the assumed generalizability of prediction and countermeasure for risky driving: different factors predict different risky driving behaviors

In road safety, it may be debated whether all risky behaviors are sufficiently similar to be explained by similar factors. The often assumed generalizability of the factors that influence risky driving behaviors has been inadequately tested. Study 1 (N=116) examined the role of demographic, personality and attitudinal factors in the prediction of a range of risky driving behaviors, for young drivers. Results illustrated that different driving behaviors were predicted by different factors (e.g., speeding was predicted by authority--rebellion, while drink driving was predicted by sensation seeking and optimism bias). Study 2 (N=127) examined the generalizability of these results to the general driving population. Study 1 results did not generalize. Predictive factors remained behavior-specific, but different predictor-behavior relationships were observed in the community sample. Overall, results suggest that future research and practice should focus on a multi-factor framework for specific risky driving behaviors, rather than assuming generalizability across behaviors and driving populations.     

New approaches to modelling fish-habitat relationships

Ecologists often develop models that describe the relationship between faunal communities and their habitat. Coral reef fishes have been the focus of numerous such studies, which have used a wide range of statistical tools to answer an equally wide range of questions. Here, we apply a series of both conventional statistical techniques (linear and generalized additive regression models) and novel machine-learning techniques (the support vector machine and three ensemble techniques used with regression trees) to predict fish species richness, biomass, and diversity from a range of habitat variables. We compare the techniques in terms of their predictive performance, and we compare a subset of the models in terms of the influence each habitat variable has for the predictions. Prediction errors are estimated by cross-validation, and variable importance is assessed using permutations of individual variable values. For predictions of species richness and diversity the tree-based models generally and the random forest model specifically are superior (produce the lowest errors). These model types are all able to model both nonlinear and interaction effects. The linear model, unable to model either effect type, performs the worst (produces the highest errors). For predictions of biomass, the generalized additive model is superior, and the support vector machine performs the worst. Depth range, the difference between maximum and minimum water depth at a given site, is identified as the most important variable in the majority of models predicting the three fish community variables. However, variable importance is highly dependent upon model type, which leads to questions regarding the interpretation of variable importance and its proper use as an indicator of causality. The representation of ecological relationships by tree-based ensemble learners will improve predictive performance, and provide a new avenue for exploring ecological relationships, both statistical and causal.     

中短波广播发射台电磁辐射环境影响预测模型

为预测中短波广播发射台的电磁辐射环境影响,根据国家相关电磁辐射标准,研究和分析了中短波广播发射台电磁辐射环境影响理论预测模型,编辑了中短波发射台电磁辐射环境影响计算分析程序,验证发现实际监测符合理论预测.实例计算了中短波发射台周围的电磁辐射影响,表明程序的计算结果可用于指导中短波发射台周围的建设.     

Mapping eucalypt forest susceptible to dieback associated with bell miners (Manorina melanophys) using laser scanning, SPOT 5 and ancillary topographical data

Mapping the location and extent of forest at risk from damaging agents or processes assists forest managers in prioritizing their planning and operational mitigation activities. In Australia, Bell Miner Associated Dieback (BMAD) refers to a form of canopy decline observed in eucalypt crowns occupied by colonies of bell miners (Manorina melanophrys). High densities of bell miners are associated with decreased avian abundance and diversity and an increase in psyllid abundance on crown foliage. BMAD has recently been nominated as a key threatening process in New South Wales (NSW). Consequently, a modelling system for predicting bell miner distribution in coastal eucalypt forests of NSW has been developed. The presence or absence of bell miners was recorded in 130 plots located within a 12,800 ha catchment study area containing a range of eucalypt forest types. The modelling system was produced by integrating a machine learning software suite (WEKA), and the statistical software R within the geographic resources analysis support system (GRASS) geographical information system (GIS). The variable modelled was the binary variable: presence or absence of bell minors. Six modelling techniques (Logistic regression; generalised additive models; two tree-based ensemble classification algorithms, random forest and Adaboost and Neural Networks) were integrated with airborne laser scanning; SPOT 5 and topographic derived variables. Model evaluation and parameter selection were measured by three threshold dependent measures (sensitivity, specificity and kappa) and the threshold independent Receiver Operator Curve (ROC) analysis. The final presence and absence maps were obtained through maximisation of the kappa statistic and applied at a resolution of 10 m across the entire catchment study area. For this data set, the most accurate algorithm for predicting the distribution of bell miner colonies was random forest (kappa = 0.84; ROC area under curve = 0.97). Variables most commonly selected in the six models were the laser scanning metrics; coefficient of variation, skewness, and the 10th and 90th percentiles derived from the shape of the height frequency distribution which, in turn, is directly influenced by vertical structure of the forest. An image textural statistic based on the shortwave infrared (SWIR) band of SPOT 5 was also commonly selected by the models. The SWIR band is sensitive to vegetation and soil moisture content. These models predicted that forest stands with a sparse eucalypt canopy over a moist, dense understorey were susceptible to being colonised by bell miners and hence BMAD.