Demonstration of Line Transect Methodologies to Estimate Urban Gray Squirrel Density

Sciurus carolinensis ) have been labor intensive and costly, I demonstrate the use of line transect surveys to estimate gray squirrel density and determine the costs of conducting surveys to achieve precise estimates. Density estimates are based on four transects that were surveyed five times from 30 June to 9 July 1994. Using the program DISTANCE, I estimated there were 4.7 (95% CI = 1.86–11.92) gray squirrels/ha on the Clemson University campus. Eleven additional surveys would have decreased the percent coefficient of variation from 30% to 20% and would have cost approximately $114. Estimating urban gray squirrel density using line transect surveys is cost effective and can provide unbiased estimates of density, provided that none of the assumptions of distance sampling theory are violated.     

The effect of contextual information on professional judgment: Reliability and biasability of expert workplace safety inspectors

Introduction: A critical aspect of occupational safety is workplace inspections by experts, in which hazards are identified. Scientific research demonstrates that expectation generated by context (i.e., prior knowledge and experience) can bias the judgments of professionals and that individuals are largely unaware when their judgments are affected by bias. Method: The current research tested the reliability and biasability of expert safety inspectors’ judgments. We used a two-study design (Study 1, N = 83; Study 2, N = 70) to explore the potential of contextual, task-irrelevant, information to bias professionals’ judgments. We examined three main issues: (1) the effect that biasing background information (safe and unsafe company history) had on professional regulatory safety inspectors’ judgments of a worksite; (2) the reliability of those judgments amongst safety inspectors and (3) inspectors’ awareness of bias in their judgments and confidence in their performance. Results: Our findings establish that: (i) inspectors’ judgments were biased by historical contextual information, (ii) they were not only biased, but the impact was implicit: they reported being unaware that it affected their judgments, and (iii) independent of our manipulations, inspectors were inconsistent with one another and the variations were not a product of experience. Conclusion: Our results are a replication of findings from a host of other professional domains, where honest, hardworking professionals underappreciate the biasing effect of context on their decision making. The current paper situates these findings within the relevant research on safety inspection, cognitive bias and decision making, as well as provides suggestions for bias mitigation in workplace safety inspection. Practical Application: Our results have implications for occupational health and safety given that inspection is an integral aspect of an effective safety system. In addition to our findings, this study contributes to the literature by providing recommendations regarding how to mitigate the effect of bias in inspection.     

Impact of bias in predicted height on tree volume estimation: A case-study of intrinsic nonlinearity

Bias originating from intrinsic nonlinearity in nonlinear models is caused by excess curvature in the solution locus of parameter estimates derived from least squares procedures. Bias due to intrinsic nonlinearity varies according to sample size as well as model specification. This paper analyses consequences of fractionising data into smaller sub-samples. Based on measurements of stem diameter and total tree height from the first Danish national forest inventory, it is demonstrated how data splitting at random may cause the intrinsic nonlinear curvature to exceed the critical F-value. Application of a Taylor-series expansion shows that, for all practical purposes, the bias in predictions of individual tree volume (based on stem diameter and tree height) is negligible. To minimize residual variance, intrinsic curvature and, in turn, prediction bias, it is recommended that data be stratified according to site conditions, stand characteristics or other relevant criteria. Finally, the preferred model should exhibit close-to-linear behaviour.     

一种改善工作环境利于人身健康的自动监控系统

有些特定的工作环境，在不能够确定空气状态参数的情况下，不允许对其进行通风处理。本文介绍了一种可以在线检测空气状态，并根据判定标志，自动开启换气设备的智能仪器。它对于工作环境的改善，人身健康的保护，仪器使用及物品存放的安全都具有较高的实用和推广价值。     

Cheap talk efficacy under potential and actual Hypothetical Bias: A meta-analysis

Cheap Talk (CT) is a common technique employed in stated preference methods of nonmarket valuation to reduce Hypothetical Bias (HB). However, multiple studies have documented its mixed usefulness. Using meta-analysis of 67 studies identified after reviewing over 400 articles, we first show the efficacy of CT while controlling for publication bias. We then investigate when CT is likely to be effective. Our results indicate that on average CT is significant in reducing estimated economic values by about 20% compared to the baseline treatment without implementing CT. Further analysis demonstrates that using CT in the context of public goods, using a budget/substitute reminder, and using CT in conjunction with other HB mitigation strategies improves its efficacy. In addition, we show that CT works better when the actual HB is larger thus one potential reason for previous studies failing to identify positive reduction in potential HB through CT may be because actual HB is small instead of CT being ineffective.     

GG型高压电源常见故障分析及维护

根据多年来从事GG型高压电源的设计生产及现场调试的经验,对该产品在实际应用中常见的故障进行了认真的分析,对现场调试中出现的偏励磁及反电晕现象从理论到实际问题作了深入的探讨,并根据应用中容易出现的问题对现场操作和维护人员提出了几点注意事项。     

地表水自动监测站高锰酸盐指数在线监测仪测定误差原因分析及解决办法

从仪器量程及温度、加热时间、溶液的酸度、校准试剂选择等实验条件方面对引起我国地表水自动监测站的高锰酸盐指数在线监测仪测定误差的原因进行了分析。由于高锰酸盐指数是一个条件性指标,实验条件对测定结果的影响较大,在线监测仪必须严格控制实验条件,其测定结果与手工方法才具有可比性。对仪器实验条件进行优化选择,对于保证高锰酸盐指数在线监测仪测定结果的准确性,更好地发挥水质自动站的预警监视作用有一定的指导意义。     

不同疏密度条件下纸堆燃烧特性研究

利用热释放速率测量平台,开展了纸堆燃烧特性的实验研究。其中纸堆体积为0.45m×0.45m×0.2m~0.45m×0.45m×0.6m,质量为0.3kg~2.7kg,疏密度为7.4kg/m3~22.2kg/m3。结果表明,当纸堆的密度变大时,纸堆的有效燃烧热值下降,未燃尽部分增多。在t平方增长火模型中,纸堆也不再总是适用于快速增长火,当纸堆疏密度变大,其火灾强度系数α减小,并拟合了疏密度与火灾强度系数的经验公式。     

Modeling soil and plant phosphorus within DSSAT

The crop models in the Decision Support System for Agrotechnology Transfer (DSSAT) have served worldwide as a research tool for improving predictions of relationships between soil and plant nitrogen (N) and crop yield. However, without a phosphorus (P) simulation option, the applicability of the DSSAT crop models in P-deficient environments is limited. In this study, a soil-plant P model integrated to DSSAT was described, and results showing the ability of the model to mimic wide differences in maize responses to P in Ghana are presented as preliminary attempts to testing the model on highly weathered soils. The model simulates P transformations between soil inorganic labile, active and stable pools and soil organic microbial and stable pools. Plant growth is limited by P between two concentration thresholds that are species-specific optimum and minimum concentrations of P defined at different stages of plant growth. Phosphorus stress factors are computed to reduce photosynthesis, dry matter accumulation and dry matter partitioning. Testing on two highly weathered soils from Ghana over a wide range of N and P fertilizer application rates indicated that the P model achieved good predictability skill at one site (Kpeve) with a final grain yield root mean squared error (RMSE) of 535 kg ha⁻¹and a final biomass RMSE of 507 kg ha⁻¹. At the other site (Wa), the RMSE was 474 kg ha⁻¹ for final grain yield and 1675 kg ha⁻¹ for final biomass. A local sensitivity analysis indicated that under P-limiting conditions and no P fertilizer application, crop biomass, grain yield, and P uptake could be increased by over 0.10% due to organic P mineralization resulting from a 1% increase in organic carbon. It was also shown that the modeling philosophy that makes P in a root-free zone unavailable to plants resulted in a better agreement of simulated crop biomass and grain yield with field measurements. Because the complex soil P chemistry makes the availability of P to plants extremely variable, testing under a wider range of agro-ecological conditions is needed to complement the initial evaluation presented here, and extend the use of the DSSAT-P model to other P-deficient environments.     

Quantifying sample biases of inland lake sampling programs in relation to lake surface area and land use/cover

We quantified potential biases associated with lakes monitored using non-probability based sampling by six state agencies in the USA (Michigan, Wisconsin, Iowa, Ohio, Maine, and New Hampshire). To identify biases, we compared state-monitored lakes to a census population of lakes derived from the National Hydrography Dataset. We then estimated the probability of lakes being sampled using generalized linear mixed models. Our two research questions were: (1) are there systematic differences in lake area and land use/land cover (LULC) surrounding lakes monitored by state agencies when compared to the entire population of lakes? and (2) after controlling for the effects of lake size, does the probability of sampling vary depending on the surrounding LULC features? We examined the biases associated with surrounding LULC because of the established links between LULC and lake water quality. For all states, we found that larger lakes had a higher probability of being sampled compared to smaller lakes. Significant interactions between lake size and LULC prohibit us from drawing conclusions about the main effects of LULC; however, in general lakes that are most likely to be sampled have either high urban use, high agricultural use, high forest cover, or low wetland cover. Our analyses support the assertion that data derived from non-probability-based surveys must be used with caution when attempting to make generalizations to the entire population of interest, and that probability-based surveys are needed to ensure unbiased, accurate estimates of lake status and trends at regional to national scales.