Measurement and Management of Human-Induced Patterns of Forest Fragmentation: A Case Study

In many tropical developing countries, the twin pressures of population and poverty are resulting in substantial fragmentation of forests, increasing the probability of extinction for many species, Forest fragmentation occurs when large contiguous forests are perforated by small holes or broken up into edges and smaller patches to form a nonforested matrix of open spaces. Thus, forest fragmentation refers not only to the area of forest cleared, but also to the pattern of this clearance, the resulting forest’s spatial properties. Both characteristics are important for species survivability. Apart from opening up forests to many abiotic and biotic influences, fragmentation can affect species dispersal and migration through its effects on forest connectivity. Landscape ecology conceptualizes connectivity as a gradient of critical thresholds, ranging from the large intact forest to the small unconnected forest patch. This article reports results from a multiple-scale analysis of forest fragmentation in Jamaica’s Cockpit Country, an area of once contiguous forest now under threat from human encroachment. Spatial forest data derived from classification of ETM+ satellite imagery are used to measure fragmentation patterns representing various degrees of forest connectivity and density. The results suggest that, overall, 81% of the region is in forest. However, fragmentation patterns also suggest that this forest is riven with extensive perforations indicative of an early stage in the decline of contiguity. The results provided by the spatial fragmentation model are a first step in the design of effective conservation and rehabilitation plans for the area. The article concludes with a discussion of possible multiscale management options for the region.     

基于大数据挖掘技术的地震舆情感知研究

对地震舆情信息的深入感知和有效管理,能够保障社会和谐发展.提出一个基于大数据技术和深度学习的地震舆情感知平台,基于Hadoop和MongoDB大数据技术实现对海量实时地震舆情数据的处理和存储.基于Word2vec和LSTM的融合模型能够有效实现震后网民的情感识别,为舆情预警提供支持.以台湾5.8级地震舆情数据为例,对该...     

A critical overview of driver recording tools

Introduction: Technological advancements during recent decades have led to the development of a wide array of tools and methods in order to record driving behavior and measure various aspects of driving performance. The aim of the present study is to present and comparatively assess the various driver recording tools that researchers have at their disposal. Method: In order to achieve this aim, a multitude of published studies from the international literature have been examined based on the driver recording methodologies that have been implemented. An examination of more traditional survey methods (questionnaires, police reports, and direct observer methods) is initially conducted, followed by investigating issues pertinent to the use of driving simulators. Afterwards, an extensive section is provided for naturalistic driving data tools, including the utilization of on-board diagnostics (OBD) and in-vehicle data recorders (IVDRs). Lastly, in-depth incident analysis and the exploitation of smartphone data are discussed. Results: A critical synthesis of the results is conducted, providing the advantages and disadvantages of utilizing each tool and including additional knowledge regarding ease of experimental implementation, data handling issues, impacts on subsequent analyses, as well as the respective cost parameters. Conclusions: New technologies provide undeniably powerful tools that allow for seamless data handling, storage, and analysis, such as smartphones and in-vehicle data recorders. However, this sometimes comes at considerable costs (which may or may not pay off at a later stage), while legacy driver recording methods still have their own niches to fill in research. Practical Applications: The present research supports researchers when designing driver behavior monitoring studies. The present work enables better scheduling and pacing of research activities, but can also provide insights for the distribution of research funds.     

Experiment and simulation research of evolution process for LNG leakage and diffusion

The frequent occurrence of LNG leakage accidents has caused serious economic loss and environmental damage. Experiments and simulations can be combined to obtain the transient process of LNG leakage and diffusion. This paper analyzed LNG leakage diffusion rules with experiment results obtained by depleting 1.4t LNG. The vapor clouds and LNG concentration are measured, which can be a comparison with the simulation results. Computational fluid dynamics and gas diffusion theory were chosen as the theoretical basis, simulating the transient process of LNG gasification to obtain the diffusion concentration rules. The simulation of LNG diffusion is divided into two parts: LNG leakage at the source and atmospheric diffusion. The maximum concentration of methane in the experiment was 4.1%, and the maximum concentration in the simulation was 4.6%. The results show good agreement of the deviation statistics, which fall in the standard recommendation value range. Then we make a prediction of the dangerous concentration area and the flammability hazard zone of LNG leakage accident. The simulation results show that the range of the lower wind direction danger area firstly increases and then decreases, and the maximum distance of IDLH increases firstly and arrived at the peak of 52  m at 300s.     

基于第二次全国污染源普查质量保证的思考及体系建设初探

通过对清查数据的结果分析,确定第二次全国污染源入户普查的对象,基于企业填报、现场入户、资料收集审核、数据核查等具体工作的实施与开展,思考和归总普查工作中影响数据质量的现实问题和难点,初步构建污染源普查质量保证体系,以期为全国污染源普查数据质量保证提供有益参考。     

数据挖掘在预测组织事故防控效果中的应用*

为进一步探索数据挖据技术在组织事故预防工作中的融入性与适用性,基于24Model构建事故预控基础模型,通过预测准确率数值及接受者操作特性曲线（ROC曲线）对比分析随机森林（RF）、支持向量机(SVM)、决策树（DT）与神经网络（NN）4种方法对组织事故防控效果的预测性能。结果表明:针对事故率控制（Y1）、职业危害预防（Y2）、财产损失3类预测目标（Y3）,RF方法均能达到较高的准确率及稳定性,具有较优的预测性能。根据特征重要度（FI）排序,明确对组织事故水平影响最显著的因素为安全实践活动认知（SC5）及安全管理程序文件（SMS3）,FI值均大于0.150 0。研究结果可为有效预测组织事故防控效果提供方法依据,同时为企业安全工作的规划设计提供思路。     

探究环境监测数据在环境执法中的应用

伴随经济发展的迅速,人们对环境越来越重视,国家也制定了相应的环境执法与保护措施,保障环境质量。在环境执法的过程中,必须严格依照环境监测的数据,从而保障信息的准确性,使环境执法策略的开展能够顺应时代发展,符合目前发展的现状。因此,环境执法中环境监测数据占据重要的位置。基于此,本文分析了环境执法中环境监测数据的应用,笔者根据自身经验提出相应的建议。     

基于大数据技术构建生态环境信息化体系及其重点应用研究

本文首先分析了生态环境信息化体系的主要构成要素,其后基于大数据技术的生态环境信息化体系重点领域应用进行了深入研究。     

Spatial and Seasonal Response of Municipal Water Use to Weather across the Contiguous U.S.

Weather variability has the potential to influence municipal water use, particularly in dry regions such as the western United States (U.S.). Outdoor water use can account for more than half of annual household water use and may be particularly responsive to weather, but little is known about how the expected magnitude of these responses varies across the U.S. This nationwide study identified the response of municipal water use to monthly weather (i.e., temperature, precipitation, evapotranspiration [ET]) using monthly water deliveries for 229 cities in the contiguous U.S. Using city‐specific multiple regression and region‐specific models with city fixed effects, we investigated what portion of the variability in municipal water use was explained by weather across cities, and also estimated responses to weather across seasons and climate regions. Our findings indicated municipal water use was generally well‐explained by weather, with median adjusted R² ranging from 63% to 95% across climate regions. Weather was more predictive of water use in dry climates compared to wet, and temperature had more explanatory power than precipitation or ET. In response to a 1°C increase in monthly maximum temperature, municipal water use was shown to increase by 3.2% and 3.9% in dry cities in winter and summer, respectively, with smaller changes in wet cities. Quantifying these responses allows urban water managers to plan for weather‐driven variability in water use.