超声波探伤技术中的波高百分比法的认识

对波高百分比探伤方法的认识,本文将该方法的基本理论、标准规定波幅关系以及在超声探伤中该方法的技术做一概略介绍。     

中国航油HSE管理体系建设实践

本文阐述了HSE管理体系建立的过程,对中国航油建立HSE管理体系的组织方法和质量控制措施进行了陈述,探讨了科学控制和精细化管理在企业HSE管理体系建设过程中的作用。提出适合国内石油企业的HSE管理体系建设控制方法,以最大限度的建立适宜有效的HSE管理体系。     

微塑料对环境中有机污染物吸附解吸的研究进展

微塑料已成为新的全球性环境污染问题。作为强吸附剂,微塑料可以吸附共存的有机污染物,进而改变其环境行为和毒性;也可以通过解吸作用促进污染物在不同介质中的迁移。因而,微塑料与有机污染物的相互作用强度和机理是全面评估两者的环境风险和深度研究微塑料毒性机制的必要信息。目前微塑料研究处于快速发展的起始阶段,加之微塑料本身成分、粒径、表面风化情况的复杂性及共存有机污染物的多样性使两者的相互作用十分复杂,亟需理清微塑料吸附解吸作用的影响因素和相关机制。因而,本文详细综述了微塑料对有机污染物吸附解吸作用的研究进展,并着重从微塑料性质(成分、粒径和表面风化)、有机污染物性质和水环境介质性质方面探讨了吸附的影响因素和相互作用机制,希望为微塑料吸附有机污染物及吸附的后续影响研究提供借鉴与参考。     

The business case for SDGs: an analysis of inclusive business models in emerging economies

In September 2015, the Sustainable Development Goals (SDGs) were endorsed by the United Nations and adopted by all 193 Member States. The SDGs integrate the 5P’s: People, Planet, Prosperity, Peace, and Partnership and clearly stress the need for all stakeholders to collaborate to create a sustainable world. Most importantly, the SDGs appeal to the central and diverse role that the business sector can play to deliver on the SDGs. This paper provides an analysis of inclusive business (IB) models as market-based solutions to contribute to the achievement of the SDGs and benefit those at the Base of the Pyramid (BoP). We investigate the IB models and their social impact in 20 organizations from emerging economies across five different sectors. The findings should help increase the uptake and scale of quality IB models and practices among the private sector, development communities, and governments to promote inclusive economic growth and social impact.     

Dynamics in the global protected-area estate since 2004

Nations of the world have committed to a number of goals and targets to address global environmental challenges. Protected areas have for centuries been a key strategy in conservation and play a major role in addressing current challenges. The most important tool used to track progress on protected-area commitments is the World Database on Protected Areas (WDPA). Periodic assessments of the world's protected-area estate show steady growth over the last 2 decades. However, the current method, which uses the latest version of the WDPA, does not show the true dynamic nature of protected areas over time and does not provide information on sites removed from the WDPA. In reality, this method can only show growth or remain stable. We used GIS tools in an approach to assess protected-area change over time based on 12 temporally distinct versions of the WDPA that quantify area added and removed from the WDPA annually from 2004 to 2016. Both the narrative of continual growth of protected area and the counter-narrative of protected area removal were overly simplistic. The former because growth was almost entirely in the marine realm and the latter because some areas removed were reprotected in later years. On average 2.5 million km² was added to the WDPA annually and 1.1 million km² was removed. Reasons for the inclusion and removal of protected areas in the WDPA database were in part due to data-quality issues but also to on-the-ground changes. To meet the 17% protected-area component of Aichi Biodiversity Target 11 by 2020, which stood at 14.7% in 2016, either the rate of protected-area removal must decrease or the rate of protected-area designation and addition to the WDPA must increase.     

Quantifying biases in marine-protected-area placement relative to abatable threats

Marine protected areas (MPAs) are a critical defense against biodiversity loss in the world's oceans, but to realize near-term conservation benefits, they must be established where major threats to biodiversity occur and can be mitigated. We quantified the degree to which MPA establishment has targeted stoppable threats (i.e., threats that can be abated through effectively managed MPAs alone) by combining spatially explicit marine biodiversity threat data in 2008 and 2013 and information on the location and potential of MPAs to halt threats. We calculated an impact metric to determine whether countries are protecting proportionally more high- or low-threat ecoregions and compared observed values with random protected-area allocation. We found that protection covered <2% of ecoregions in national waters with high levels of abatable threat in 2013, which is ∼59% less protection in high-threat areas than if MPAs had been placed randomly. Relatively low-threat ecoregions had 6.3 times more strict protection (International Union for Conservation of Nature categories I–II) than high-threat ecoregions. Thirty-one ecoregions had high levels of stoppable threat but very low protection, which presents opportunities for MPAs to yield more significant near-term conservation benefits. The extent of the global MPA estate has increased, but the establishment of MPAs where they can reduce threats that are driving biodiversity loss is now urgently needed.     

Microtargeting for conservation

Widespread human action and behavior change is needed to achieve many conservation goals. Doing so at the requisite scale and pace will require the efficient delivery of outreach campaigns. Conservation gains will be greatest when efforts are directed toward places of high conservation value (or need) and tailored to critical actors. Recent strategic conservation planning has relied primarily on spatial assessments of biophysical attributes, largely ignoring the human dimensions. Elsewhere, marketers, political campaigns, and others use microtargeting—predictive analytics of big data—to identify people most likely to respond positively to particular messages or interventions. Conservationists have not yet widely capitalized on these techniques. To investigate the effectiveness of microtargeting to improve conservation, we developed a propensity model to predict restoration behavior among 203,645 private landowners in a 5,200,000 ha study area in the Chesapeake Bay Watershed (U.S.A.). To isolate the additional value microtargeting may offer beyond geospatial prioritization, we analyzed a new high-resolution land-cover data set and cadastral data to identify private owners of riparian areas needing restoration. Subsequently, we developed and evaluated a restoration propensity model based on a database of landowners who had conducted restoration in the past and those who had not (n = 4978). Model validation in a parallel database (n = 4989) showed owners with the highest scorers for propensity to conduct restoration (i.e., top decile) were over twice as likely as average landowners to have conducted restoration (135%). These results demonstrate that microtargeting techniques can dramatically increase the efficiency and efficacy of conservation programs, above and beyond the advances offered by biophysical prioritizations alone, as well as facilitate more robust research of many social–ecological systems.     

Estimating resource utilization demands in implementing statewide screening,brief intervention,and referral to treatment for alcohol-impaired drivers

Objectives: The Alcohol Use Disorders Identification Test (AUDIT) is used to assess the level of alcohol use/misuse and to inform the intensity of intervention delivered within screening, brief intervention, and referral to treatment (SBIRT) programs. Policy initiatives are recommending delivery of SBIRT within health care settings to reduce alcohol misuse and prevent alcohol-impaired driving. Recent reports are considering extending delivery of SBIRT to criminal justice settings. One consideration in implementing SBIRT delivery is the question of resource utilization; the amount of effort required in delivering the 4 different intensities of intervention in SBIRT: Alcohol education, simple advice, brief counseling and continued monitoring, and brief counseling and referral to specialist (from least to most intense in terms of delivery time, the skill level of the provider, and personnel resources).

Methods: In order to inform expectations about intervention intensity, this article describes the AUDIT scores from 982 adults recently arrested for alcohol-impaired driving. The distribution of scores is extrapolated to state rates for individuals arrested for alcohol-impaired driving by intervention level.

Results: Though alcohol education was the most common intervention category, about one quarter of the sample scored in a range corresponding with the more intensive interventions using the brief counseling, continued monitoring for ongoing alcohol use, and/or referral to specialist for diagnostic evaluation and treatment.

Conclusions: This article provides local distribution of AUDIT scores and state estimates for the number of individuals scoring in each level of risk (AUDIT risk zone) and corresponding intervention type. Routine criminal justice practice is well positioned to deliver alcohol screening, education, simple advice, and continued alcohol monitoring, making delivery of SBIRT feasible for the majority of alcohol-impaired drivers. Challenges to implementing the full range of SBIRT services include resource demands of brief counseling, identifying the appropriate providers within a criminal justice context, and availability of community providers for referral to diagnostic and specialty care. Solutions may vary by state due to differences in population density and incidence rates of alcohol-impaired driving.     

Factors associated with driving under the influence of alcohol

Objective: This study examined the risk factors of driving under the influence of alcohol (DUI) among drivers of specific vehicle categories (DSC). On the basis of this research, the variables related to DUI and involvement in traffic crashes were defined. The analysis was conducted for car drivers, bicyclists, motorcyclists, bus drivers, and truck drivers.

Method: The research sample included drivers involved in traffic crashes on the territory of Serbia in 2016 (60,666). Two types of analyses were conducted in this study. Logistic regression established the correlation between DUI and DSC and the The Technique for Order of Preference by Similarity to Ideal Solution (Multi-criteria decision making) method was applied to consider the scoring and explore the potential for the prevalence of DUI on the basis of 2 data sets (DUI and non DUI).

Results: The study results showed that driver error and male drivers were the 2 most significant risk factors for DUI, with the highest scores and potential for prevalence. The nonuse of restraint systems, driver experience, and driver age are the factors with a significant prediction of involvement in an accident and an insignificant prediction of DUI.

Conclusions: Following the development of the logistic prediction models for DUI drivers, testing of the model was conducted for 3 control driver groups: Car, motorcycle, and bicycle. The prediction model with a probability greater than 50% showed that 77% of car drivers were under the influence of alcohol. Similarly, the prediction percentage for motorcyclists and bicyclists amounted to 71 and 67%, respectively. The recommendation of the study is that drivers whose DUI probability is above 50% should be potentially suspected of DUI. The results of this study can help to understand the problem of DUI among specific driver categories and detect DUI drivers, with the aim of creating successful traffic safety policy.     

Minimizing the cost of translocation failure with decision‐tree models that predict species’ behavioral response in translocation sites

The high number of failures is one reason why translocation is often not recommended. Considering how behavior changes during translocations may improve translocation success. To derive decision‐tree models for species’ translocation, we used data on the short‐term responses of an endangered Australian skink in 5 simulated translocations with different release conditions. We used 4 different decision‐tree algorithms (decision tree, decision‐tree parallel, decision stump, and random forest) with 4 different criteria (gain ratio, information gain, gini index, and accuracy) to investigate how environmental and behavioral parameters may affect the success of a translocation. We assumed behavioral changes that increased dispersal away from a release site would reduce translocation success. The trees became more complex when we included all behavioral parameters as attributes, but these trees yielded more detailed information about why and how dispersal occurred. According to these complex trees, there were positive associations between some behavioral parameters, such as fight and dispersal, that showed there was a higher chance, for example, of dispersal among lizards that fought than among those that did not fight. Decision trees based on parameters related to release conditions were easier to understand and could be used by managers to make translocation decisions under different circumstances. Minimizar el Costo del Fracaso de la Reubicación con Modelos de Árboles de Decisión que Predigan la Respuesta Conductual de la Especie en los Sitios de Reubicación