最小生成树法在环境背景值识别中的应用

本文用最小生成树法解决环境中的聚类分析问题。该法用x~2距离系数矩阵描述样本变量间的相似关系,同时用Kruskal法构造最小生成树,最后以分类图的形式给出分类结果。     

Spatial memory, habitat auto-facilitation and the emergence of fractal home range patterns

Animals interact with their habitat in a manner which involves both negative and positive feedback mechanisms. We apply a specific modeling approach, “multi-scaled random walk”, for the scenario where a spatially explicit positive feedback process emerges from a combination of a spatial memory-dependent tendency to return to familiar patches and a consequently objective or subjective improvement of the quality of these patches (habitat auto-facilitation). In addition to the potential for local resource improvement from physically altering a patch, primarily known from the ecology of grazing ungulates, auto-facilitation from site fidelity may also embed more subtle subjective, individual-specific advantages from patch familiarity. Under the condition of resource superabundance, fitness gain from intra-home range patch fidelity creates a self-reinforcing use of the preferred patches on expense of a broader foraging in a priori equally favorable patches. Through this process, our simulations show that a spatially fractal dispersion of accumulated locations of the individual will emerge under the given model assumptions. Based on a conjecture that intra-home range patch fidelity depends on spatial memory we apply the multi-scaled random walk model to construct a spatially explicit habitat suitability parameter H_ij, which quantifies the dispersion of the generally most constraining resource from the individual's perspective. An intra-home range set of observed H-scores, H_obs, can then be estimated from a simple 2-scale calculation that is derived from the local dispersion of fixes. We show how the spatially explicit habitat utilization index H_obs not necessarily correlates positively with the local density fluctuations of fixes. The H-index solves some well-known problems from using the pattern of local densities of telemetry fixes - the classic utilization distribution - as a proxy variable for relative intra-home range habitat quality and resource selection. A pilot study on a set of telemetry fixes collected from a herd of free-ranging domestic sheep with overlapping summer home ranges illustrates how the H-index may be estimated and interpreted as a first-level approach towards a more extensive analysis of intra-home range habitat resource availability and patch preferences. Spatial memory in combination with site fidelity requires a modeling framework that explicitly describes the property of positive feedback mechanism under auto-facilitation in a spatio-temporally explicit manner.     

Atmospheric stability and PM10 concentrations at far distance from elevated point sources in complex terrain: worst-case episode study

Atmospheric stability is the most important parameter affecting dilution of air pollutants. It plays a very important role in the investigation of parameters that affect ambient pollutant concentrations, especially in the case of complex terrain areas. In this study, the classification of atmospheric stability by Pasquill-Turner classes and any associated variation of ambient PM10 (particles with aerodynamic diameter<10 microm) concentrations for a region of complex terrain is investigated. Real experimental meteorological and PM10 data are used for a 2-year period from one observation station far distant from the main stack sources and they are related with the classified atmospheric stability categories in an hourly and monthly based distribution. A more detailed analysis is carried out during PM10 episodes for the same period in order to reveal the governing worst-case atmospheric conditions.     

Nonlinear time series analysis of ground-level ozone dynamics in Southern Taiwan

The ozone pollution at ground level in rural and urban areas has been a long-standing problem in the world. This paper focuses on estimating self-affined nature of nonlinearity of ground-level peak ozone time series, which is analyzed by two nonlinear fractal statistical methods, including R/S analysis and BDS test. To explore the underlying structure of ozone observations at ground level and improve the forecasting capacity in urban region, practical implementation was assessed by a case study via collecting and analyzing the monitoring data at Chaojhou and Zenwu in the Kaohsiung metropolitan region, Taiwan. Based on R/S analysis, the time series can be identified as persistent and long-memory processes with Hurst exponents of both about 0.75. In addition, the V statistics specifies possible fluctuation cycle lengths of 32, 170, and 420 day simultaneously. Such results are consistent with the regional meteorological conditions leading to help characterize the regional scale ozone behavioral trend. Furthermore, the BDS test results confirm a strong nonlinearity of both time series associated with these two cities. Yet in both cases, nonlinearity implies chaos. The R/S analysis and BDS test provide strong evidence for nonlinearity and fractality of ozone time series due to noisy chaos, and we could not rule out the possibility of deterministic chaos in tropospheric ozone system.     

复杂背景环境下基于SSD_MobileNet深度学习模型的火焰图像识别研究

针对现有的火灾火焰图像识别方法在光照和红花等类似火焰干扰的复杂环境下存在错检和漏检的问题, 提出一种基于SSD_MobileNet的复杂环境火焰区域标记方法。首先,将深度卷积神经网络SSD300的基础卷积网络VGG16替换为MobileNet网络,应用深度可分离卷积,降低网络参数,进而构建一种火焰图像检测的SSD_MobileNet模型;然后,迁移第一次训练模型所有的卷积层参数,初始化新的待训练模型;最后,加入新的数据样本用于削弱光照、红花等干扰对象的影响。通过与SSD300、以及深度学习的目标检测算法Faster R-CNN和YOLOv3-tiny对比,实验结果表明,构建的火焰检测和火焰区域标记SSD_MobileNet模型的综合性能优于Faster R-CNN和YOLOv3-tiny模型,更适用于实时火焰检测领域。     

Taking advantage of storm and waste water retention basins as part of water use minimization in industrial sites

A methodology for water use minimization has been developed by the Clean Technology Network of Bahia over a 10 year period in joint cooperative programs with the chemical, petrochemical and copper metallurgy industries located in the largest Industrial Complex in Latin America, in Camaçari, Bahia, Brazil. The methodology comprises a set of tools including reconciled aqueous stream balances, database of aqueous streams; large scale training leading to the identification of water minimization alternatives in the processes, water reuse optimization approaches; geographical information systems as well as, consideration of the region's hydro and hydro-geological characteristics. The results of a study carried out to assess the possibility of using storm and wastewaters for industrial use is presented in this paper. The inorganic system is composed by three water reservoirs (basins) receiving stormwater contaminated with inorganic effluents, and occasionally with organics. These basins have been operated to control water flow inputs according to the capacity of the pumping outlet systems before their discharge to a submarine outfall. A mass balance was performed with historical updated data to assess water availability from the basins based on the daily volume variation and flow rate of inorganic effluent from 2001 to 2007. The study identified the possibility of recovering about 1140 m³/h of the overall 5400 m³/h consumed by the Industrial Complex at the moment. Organizational changes in the present effluent disposal and stormwater harvest systems will be required in order to maximize water recovery for industrial use.     

Quantifying ecological thresholds from response surfaces

Ecological thresholds are abrupt changes of ecological state. While an ecological threshold is a widely accepted concept, most empirical methods detect them in time or across geographic space. Although useful, these approaches do not quantify the direct drivers of threshold response. Causal understanding of thresholds detected empirically requires their investigation in a multi-factor domain containing the direct drivers (often referred to as state space). Here, we present an approach to quantify thresholds from response surfaces modeled empirically in state space. We present two indices of shape attributes measured from response surfaces. The response surfaces are built using a regression method in state space. The indices are threshold strength (T) and diagonality (D). We use 48 simulated response surfaces of different shapes to test the efficacy of the indices in 3D. Our results show that T is sensitive to the steepness of the transition from one state to the next, with various forms of abrupt, centralized thresholds yielding the highest values among the simulated surfaces. D represents the orientation of the response surface or the simultaneous influence of more than one predictor in eliciting the response gradient. Strongly diagonal surfaces have the most diagonal surface area demonstrated by sharply undulating diagonal surfaces. Given that the success of T and D requires a regression method to accurately capture any shape of complex data structure, we also test the accuracy of empirical regression methods known to be tractable with complex data. We test classification and regression trees (CART), Random Forest, and non-parametric multiplicative regression (NPMR) for binary and continuous responses. We use the 48 simulated response surfaces to test the methods, and we find that prediction accuracy depends on both the T and D of the simulated data for each method. We choose the most accurate method among those we test for capturing any shape of response surface from real data, NPMR. Finally, we use NPMR to build response surfaces and quantify T and D from real ecological data sets. We demonstrate how measuring threshold strength and diagonality from multi-factor response surfaces can advance ecology.     

Using the multiple capitals framework to connect indicators of regional cumulative impacts of mining and pastoralism in the Murray Darling Basin,Australia

It is commonly recognized that there are constraints to successful regional-scale assessment and monitoring of cumulative impacts because of challenges in the selection of coherent and measurable indicators of the effects. It has also been sensibly declared that the connections between components in a region are as important as the state of the elements themselves. These have previously been termed “linked” cumulative impacts/effects. These connections can be difficult to discern because of a complicated set of interactions and unexpected linkages. In this paper we diagnose that a significant cause of these constraints is the selection of indicators without due regard for their inter-relationships in the formulation of the indicator set. The paper examines whether the common “forms of capital”, i.e., natural (renewable and non-renewable), manufactured, social, human and financial capitals, framework is a potential organizing structure. We examine a large region in western NSW Australia where the predominant production systems are mining and grazing for production of wool, beef and lamb. Production in both is driven by consumption of a non-renewable resource, i.e., ore for mining and topsoil for grazing, the latter on the basis that loss rate estimates far exceed soil formation rates. We propose that the challenge of identifying connections of components within and between capital stores can be approached by explicitly separating stores of capital and the flows of capital between stores and between elements within stores, so-called capital fluxes. We attempt to acquire data from public sources for both capital stores and fluxes. The question of whether these data are a sufficient base for regional assessment, with particular reference to connections, is discussed. The well-described challenge of a comparative common currency for stores and fluxes is also discussed. We conclude that the data acquisition is relatively successful for stores and fluxes. A number of linked impacts are identified and discussed. The potential use of money as the common currency for stores and fluxes of capital is considered. The basic proposition is that replacement or preservation costs be used for this. We conclude that the study is sufficiently positive to consider further research in fully-coupled models of capital stores and fluxes.     

Macroethical systems and sustainability science

The Industrial Revolution and associated economic, demographic, technological and cultural changes have resulted in what many scientists are beginning to refer to as “the Anthropocene” – roughly translated, the Age of Humans. One response to this development is the nascent field of “sustainability science,” a multidisciplinary and systemic attempt to perceive and understand this new era. In doing so, however, methodologies and intellectual frameworks must be developed which extend beyond existing, dominantly reductionist, approaches, and are intended to address emergent characteristics of complex systems that integrate cultural and social systems, the engineered and built environment and natural systems. In the area of ethics, this requires developing a capability for “macroethics,” or ethical systems and processes capable of addressing issues arising from the emergent behavior of the complicated systems that characterize the Anthropocene.     

A multi-approach strategy in climate attribution studies: Is it possible to apply a robustness framework?

Attribution studies investigate the causes of recent global warming. For a few decades the scientific community generally adopted dynamical models – the so-called Global Climate Models (GCMs) – for such an investigation. These models show the essential role of anthropogenic forcings in driving the temperature behaviour of the last half century. In the last period even other (data-driven) methodological approaches were adopted for attribution studies. This allows the scientific community to compare the results coming from these different approaches and to possibly increase their robustness. For such a purpose, the paper explores the possibility of applying a robustness framework, so far used only in the case of multi-model GCM ensembles, to a strategy including models from different methodological orientations, assessing such an application especially in the light of the independence issue.