Far-field intensity distribution of beam generated by Gaussian mirror resonator

Based on scalar diffraction theory, we investigated far-field intensity distribution (FFID) of beam generated by Gaussian mirror resonator. We found usable analytical expressions of diffracted field with respect to variation of diffraction parameters. Particular attention was paid to the parameters such as mirror spot size and radius of the Gaussian mirror, which determine the FFID. All analyses were limited to TEM00 fundamental mode. (c) 2004 Elsevier B.V. All rights reserved.

华南热带亚热带森林生态系统恢复/演替过程碳、氮、水演变机理

项目针对热带亚热带森林生态系统服务功能及所属区域社会发展的需求，结合全球变化（CO2浓度上升导致的全球变暖、氮沉降、降水格局演变）所面临的关键科学问题，系统开展森林生态系统碳、氮、水等过程演变规律的研究。经过十多年的努力，取得如下创新性理论：⑴成熟森林土壤可持续积累有机碳。发现亚热带成熟森林表土层（0-20cm）有机碳以0.61t/hm2•a的速度增加，为确认成熟森林作为新的碳汇奠定基础。⑵成熟森林趋于氮饱和。发现热带亚热带成熟森林生态系统趋于氮饱和，氮沉降增加将导致系统养分平衡的破坏。⑶退化生态系统恢复限制因子理论。水热季节分配不均限制了退化生态系统的恢复。⑷森林生态系统恢复/演替过程中其结构与功能、地上和地下不同步理论。创新方法：计算土壤C贮量长期变化的新方法；建立森林地下NPP关联估算模型；基于C/N确定森林土壤硝化与反硝化作用速率；推出降水动能及其受林冠分配调控的理论计算方法；提出任意时空尺度的生态系统水热状况量度指标及计算公式。发表Science等SCI论文52篇，核心期刊论文185篇，专著3部，被SCI论文引用312篇次，核心期刊引用2277篇次，核心内容之一被评为“2006年度中国基础研究十大新闻”。该成果催生生态系统非平衡理论框架的建立，引起国际同行的极大关注并获得高度评价，达到了世界领先水平。

Use of SARIMA models to assess data-poor fisheries: a case study with a sciaenid fishery off Portugal

Research on assessment and monitoring methods has primarily focused on fisheries with long multivariate data sets. Less research exists on methods applicable to data-poor fisheries with univariate data sets with a small sample size. In this study, we examine the capabilities of seasonal autoregressive integrated moving average (SARIMA) models to fit, forecast, and monitor the landings of such data-poor fisheries. We use a European fishery on meagre (Sciaenidae: Argyrosomus regius), where only a short time series of landings was available to model (n=60 months), as our case-study. We show that despite the limited sample size, a SARIMA model could be found that adequately fitted and forecasted the time series of meagre landings (12-month forecasts; mean error: 3.5 tons (t); annual absolute percentage error: 15.4%). We derive model-based prediction intervals and show how they can be used to detect problematic situations in the fishery. Our results indicate that over the course of one year the meagre landings remained within the prediction limits of the model and therefore indicated no need for urgent management intervention. We discuss the information that SARIMA model structure conveys on the meagre lifecycle and fishery, the methodological requirements of SARIMA forecasting of data-poor fisheries landings, and the capabilities SARIMA models present within current efforts to monitor the world’s data-poorest resources.