Workers’ compensation claim counts and rates by injury event/exposure among state-insured private employers in Ohio, 2007–2017

Introduction: This study analyzed workers’ compensation (WC) claims among private employers insured by the Ohio state-based WC carrier to identify high-risk industries by detailed cause of injury. Methods: A machine learning algorithm was used to code each claim by U.S. Bureau of Labor Statistics (BLS) event/exposure. The codes assigned to lost-time (LT) claims with lower algorithm probabilities of accurate classification or those LT claims with high costs were manually reviewed. WC data were linked with the state’s unemployment insurance (UI) data to identify the employer’s industry and number of employees. BLS data on hours worked per employee were used to estimate full-time equivalents (FTE) and calculate rates of WC claims per 100 FTE. Results: 140,780 LT claims and 633,373 medical-only claims were analyzed. Although counts and rates of LT WC claims declined from 2007 to 2017, the shares of leading LT injury event/exposures remained largely unchanged. LT claims due to Overexertion and Bodily Reaction (33.0%) were most common, followed by Falls, Slips, and Trips (31.4%), Contact with Objects and Equipment (22.5%), Transportation Incidents (7.0%), Exposure to Harmful Substances or Environments (2.8%), Violence and Other Injuries by Persons or Animals (2.5%), and Fires and Explosions (0.4%). These findings are consistent with other reported data. The proportions of injury event/exposures varied by industry, and high-risk industries were identified. Conclusions: Injuries have been reduced, but prevention challenges remain in certain industries. Available evidence on intervention effectiveness was summarized and mapped to the analysis results to demonstrate how the results can guide prevention efforts. Practical Applications: Employers, safety/health practitioners, researchers, WC insurers, and bureaus can use these data and machine learning methods to understand industry differences in the level and mix of risks, as well as industry trends, and to tailor safety, health, and disability prevention services and research.     

小波支持向量机在大气污染物浓度预测中的应用

用小波分解重构和支持向量机相结合的方法,建立大气污染物浓度预测模型。通过小波分解,将大气污染物浓度序列分解为不同频段的小波系数序列,再对各层的小波系数序列重构到原尺度上。利用相关分析的方法构建出低频小波系数a3和中频小波系数d3的支持向量机模型输入因子为前一天小波系数a3和7个气象因子;高频小波系数d2和d1以前三日的小波系数为输入因子,然后对各小波系数序列采用相应的支持向量机模型进行预测,各小波系数均使用ν-支持向量回归机(ν-SVR)算法和径向基函数,最后通过小波重构合成大气污染物浓度序列的最终预测结果。通过对大气SO2浓度预测实例证明,该大气污染物浓度预测模型具有推广能力较强、预测精度较高、训练速度快、便于建模等优点,具有良好的应用前景。     

洗槽机清洗头升降故障原因分析及对策

针对洗槽机清洗头运行中的升降故障进行了原因分析,找出了解决方法,为今后类似的故障检查和排除提供了借鉴。     

Automated detection of frog calls and choruses by pulse repetition rate

Anurans (frogs and toads) are among the most globally threatened taxonomic groups. Successful conservation of anurans will rely on improved data on the status and changes in local populations, particularly for rare and threatened species. Automated sensors, such as acoustic recorders, have the potential to provide such data by massively increasing the spatial and temporal scale of population sampling efforts. Analyzing such data sets will require robust and efficient tools that can automatically identify the presence of a species in audio recordings. Like bats and birds, many anuran species produce distinct vocalizations that can be captured by autonomous acoustic recorders and represent excellent candidates for automated recognition. However, in contrast to birds and bats, effective automated acoustic recognition tools for anurans are not yet widely available. An effective automated call-recognition method for anurans must be robust to the challenges of real-world field data and should not require extensive labeled data sets. We devised a vocalization identification tool that classifies anuran vocalizations in audio recordings based on their periodic structure: the repeat interval-based bioacoustic identification tool (RIBBIT). We applied RIBBIT to field recordings to study the boreal chorus frog (Pseudacris maculata) of temperate North American grasslands and the critically endangered variable harlequin frog (Atelopus varius) of tropical Central American rainforests. The tool accurately identified boreal chorus frogs, even when they vocalized in heavily overlapping choruses and identified variable harlequin frog vocalizations at a field site where it had been very rarely encountered in visual surveys. Using a few simple parameters, RIBBIT can detect any vocalization with a periodic structure, including those of many anurans, insects, birds, and mammals. We provide open-source implementations of RIBBIT in Python and R to support its use for other taxa and communities.     

Butterfly effects in mimicry? Combining signal and taste can twist the relationship of Müllerian co-mimics

Müllerian co-mimics are aposematic species that resemble each other; sharing a warning signal is thought to be mutually beneficial for the co-mimics by reducing per capita predation risk. In Batesian mimicry, edible mimics avoid predation by resembling an aposematic model species. The protection of both the model and the mimic is weakened when the mimics are abundant compared to the models. The quasi-Batesian view suggests that defended (Müllerian) co-mimics, when unequal in their defences, could also show a Batesian-like trend of increasing mortality with increasing abundance of a less defended “mimic”. We manipulated frequencies of unequally distasteful artificial co-mimics that were prey for great tits. The co-mimics had different signals (imperfect mimicry) but were equally preferred by the birds when palatable. Unexpectedly, when unpalatable, one of the signals was easier for the birds to learn to avoid. Consequently, during predator learning, the signal design of the prey strongly affected mortality of the co-mimics; there was an interaction between the signal and frequency treatments, but increasing the frequency of a less defended “mimic” did not increase co-mimic mortalities as predicted. In contrast, in a memory test that followed, the effect of signal design disappeared; if the birds had experienced high frequency of “mimics” during learning, co-mimic mortalities did subsequently increase. Since the effect of co-mimic frequencies on mortalities changed depending on the signal design of the prey and predator experience, the results suggest that mimetic relationship may be an unpredictable interplay of several factors in addition to taste and abundance.     

Grasslands, People, and Conservation: Over-the-Horizon Learning Exchanges between African and American Pastoralists

Abstract:  The world's grasslands and large migratory populations of wildlife have been disproportionately lost or disrupted by human activities, yet are poorly represented in protected areas. The major threats they face are land subdivision and the loss of large-scale dynamic processes such as wildlife migrations and fire. The large-scale dynamical processes and ubiquity of livestock economies and cultures across the grasslands calls for an integrated ecosystem approach to conservation to make up the shortfall in protected-area coverage. Ranchers and pastoralists will be more inclined to adopt an integrated landscape approach to conservation if they also see the threats to wildlife and grassland ecosystems as affecting their livelihoods and way of life. We arranged a series of learning exchanges between African and American pastoralists, ranchers, scientists, and conservationists aimed at building the collaboration and consensus needed to conserve grasslands at a landscape level. There was broad agreement on the threat of land fragmentation to livelihoods, wildlife, and grasslands. The exchanges also identified weaknesses in prevailing public, private, and community modes of ownership in halting fragmentation. New collaborative approaches were explored to attain the benefits of privatization while keeping the landscape open. The African–U.S. exchanges showed that learning exchanges can anticipate over-the-horizon problems and speed up the feedback loops that underlie adaptive management and build social and ecological resilience.     

基于机器学习遥感算法的大型漂浮藻面积提取方法研究

采用基于机器学习的多层感知机算法,利用GOCI(Geostationary Ocean Color Imager)传感器获取的瑞利校正反射率数据,对东中国海大型漂浮藻进行遥感自动识别,采用线性混合像元分解来计算大型漂浮藻的覆盖面积,并利用膨胀和侵蚀法进行大型漂浮藻的分布面积计算。利用L8/OLI(Landsat 8/Operational Land Imager)高空间分辨率资料进行验证,结果表明,基于机器学习遥感算法针对GOCI提取的大型漂浮藻覆盖面积,与L8/OLI结果十分接近,R2达到0. 959,平均绝对误差和平均相对误差分别为39. 32 km2和18. 15%。     

Tackling wicked problems and tricky transitions: change and continuity in Cape Town's environmental policy landscape

This paper offers a reflection on 15 years of policy change in the City of Cape Town aimed at fostering sustainability from the perspective of a City practitioner. The persistent continuation of unsustainable outcomes, despite ongoing policy reforms, is understood as a combination of the emergence of wicked problems, within a changing local government mandate, in the absence of a transformation of institutional structures, tools and approaches. While the approach to policy reform in Cape Town has focussed on reducing substantive uncertainty through its knowledge-based approach, we show that in the context of an expanding local government mandate, sustainability becomes an aspect of many departments’ directive resulting in strategic uncertainty. The untransformed traditional line-function-based structure of local government in turn works against integration between departments (fundamental for addressing non-linear wicked problems), thus promoting institutional uncertainty. In addressing this combination of strategic and institutional uncertainty, our findings indicate that integration has to happen in the policy stage in order for sustainability principles to be implemented in relevant departments; that implementation requires resourcing across the institution, and ought to be included in departments’ targets; and that competing and conflicting rationalities underpin the policy–practice gap. It is suggested that a first step in breaking down the strategic and institutional uncertainties would be to foster shared values through creating deliberative spaces within the City in which debate, discussion and learning can occur.     

中国钢铁行业NO_X减排情景预测

钢铁行业是NOX污染减排的重点领域。在预测钢铁行业未来发展趋势的基础上,基于情景分析法,设置3个NOX排放情景,估算出2020年不同情景下钢铁行业NOX排放量及不同控制措施的减排贡献。研究结果表明,未来钢铁行业产品产量的快速上涨加大了NOX减排压力。在不实施额外NOX控制措施的情况下,2020年钢铁行业NOX排放量约为108万t,相比2013年上涨9%。实施钢铁行业NOX排放控制措施可取得显著的减排效果,根据两种控制情景预计NOX的减排比例分别为5%和13%。淘汰落后产能和烧结烟气循环技术是NOX减排的最有效手段。     

Estimation of daily sunshine duration using support vector machines

Although sunshine duration (SD) is one of the most frequently measured meteorological parameters, there is a lack of measurements in some parts of the world. Hence, it should be estimated accurately for areas where no reliable measurement is possible. The main objective of this study is to evaluate the potential of support vector machine (SVM) approach for estimating daily SD. For this purpose, three different kernels of SVM, such as linear, polynomial, and radial basis function (RBF), were used. Different combinations of five related meteorological parameters, namely cloud cover, maximum temperature (T_max), minimum temperature (T_min), relative humidity (RH), and wind speed (WS), and one astronomic parameter, day length, were considered as the inputs of the models, and the output was obtained as daily SD. Simulated values of the models were compared with ground measured values, and concluded that the usage of the SVM-RBF estimator with combination of all input attributes produced the best results. The coefficient of determination, root mean square error, and mean absolute error were found to be 0.8435, 1.5105 h, and 1.0771 h, respectively, for the pooled four-year daily data set of 14 stations in Turkey. It was also deduced that accuracy increased as the number of attributes increased and the major contribution to this came from RH as compared with T_max, T_min, and WS. This study has shown that the SVM methodology can be a good alternative for conventional and artificial neural network methods for estimating daily SD.