三槽式切换氧化沟运行工况数学模式及应用

三槽式切换氧化沟运行工况呈周期性的变化。根据理论分析，建立了各槽活性污泥浓度变化及相互关系的数学模式，并进行了验证。同时就数字模式在周期程序设置及排泥方式选择时的应用做了说明。     

裂纹梁损伤识别的一种新型方法

在能量有限元法研究基础上,提出了基于能量密度的结构损伤识别方法。以工程结构基本构件梁为研究对象,以能量密度突变作为损伤识别指标,对损伤结构进行了仿真;在此基础上,对结构损伤中最常见的形式——裂纹进行损伤识别试验。结果表明基于能量密度的损伤识别方法适用于中高频场合,具有对结构微小缺陷敏感、操作简便、精确度高、无需复杂数据处理等优点,有较高的工程应用价值。     

矿工安全注意力影响因素的SEM研究

为探索矿工安全注意力的影响因素作用路径,寻求合理的干预对策,在文献研究和实地调研基础上,提取矿工安全注意力的衡量指标和影响因素,借助结构方程模型技术验证研究假设,构建修正后的矿工安全注意力影响因素作用路径概念模型图。研究表明:矿工安全注意力的影响因素,即矿工安全意识、矿工工作疲劳、安全信息刺激以及安全监管对矿工安全注意力的衡量指标,即注意专注度、注意广度以及注意转移度都存在路径关系,而且矿工安全注意力的衡量指标之间也存在路径关系。以期通过有效干预影响因素提高矿工安全注意力,降低人因失误导致煤矿安全事故发生的概率。     

铜对大鼠肝脏脂质过氧化作用的影响

观察了铜（CuSo₄）在离体大鼠肝微粒体脂质过氧化模型中的反应性及对在体大鼠肝脏脂质过氧化中的抑制作用,结果表明：离体模型中25、50μmol铜对Vc/Fe₂₊及ADP/Fe²⁺激发的肝微粒体脂质过氧化有抑制作用;在体内实验中5、10,20ppm铜灌胃可使大鼠肝匀浆及微粒体中丙二醛含量明显减少,且有剂量依从性,但同时高铜组仍出现铜中毒的病理改变。实验提示,本实验条件下铜与脂质过氧化的关系与给铜剂量、实验模型有关,而铜的抗氧化机制与Cu,Zn－SOD活性提高无关。     

矿井热宽温度环境人体热健康分区热调节模型*

为评价矿井热环境中工人职业健康安全状况,提出矿井热宽温度环境人体热健康状态的基本特征与生理要求,分析热宽温度环境人体分区热调节规律与热健康状态的对应关系。基于生物控制论的观点,提出热宽温度范围内不同热应力作用下人体分区热调节机制,建立人体分区热调节模型。结果表明:模型能实现对环境热应力作用下人体物理热平衡状态与生理状态的参数化描述,揭示多因素作用下人体热健康状态的热应力边界与变化规律。分区热调节模型提供了人体热健康状态定量模拟平台,通过参数调整可使模型适应研究需求,模型为井下热环境工人职业健康安全状况分析与评价提供了可参考的思路与方法。     

基于微生物相互作用机理的完全耦合活性污泥模型研究

根据微生物生长机理,推导出微生物的耦合作用机理并在该机理的基础上改进了ASM3 Bio-P模型.假设活性污泥系统中有机物氧化过程、生物硝化过程、生物反硝化过程、生物除磷过程可同时存在,在ASM3 Bio-P模型上添加相关的开关函数,推导出完全耦合活性污泥模型(FCASM).基于计算机程序进行数值模拟,并将FCASM模拟结果与实测值以及ASM3 Bio-P模型模拟值进行对比.结果表明,完全耦合活性污泥模型对氨氮模拟的稳态出水值为1.90 g·m-3,ASM3 Bio-P模型模拟的氨氮稳态出水值为0 g·m-3,而实测的氨氮稳态出水值为1.50 g·m-3,完全耦合活性污泥模型的结果更接近真实值.     

民用机场消防指挥员决策能力分析

为研究民用机场消防指挥员决策能力,根据专家意见及相关规章、文件的规定,筛选出文化素质、身体素质、心理素质等18项影响机场消防指挥员决策能力的要素,并采用问卷调查的方式进行调研。运用SPSS 21.0对问卷进行信度分析、效度分析及因子分析,确定民用机场消防指挥员决策能力指标体系。根据该指标体系构建民用机场消防指挥员决策能力结构方程模型,运用Amos 17.0计算各变量的路径系数,进而得到各指标的权重,避免了人为打分方法确定权重的主观性。研究结果为分析民用机场消防指挥员决策能力提供更加准确、合理、可信的评价方法。     

军用物资集装箱内部低温环境条件研究

以天津和齐齐哈尔两个低温地区军用物资集装箱运输为背景,分别对两个地区的集装箱内部温度变化及大气温度等数据进行测试并对比分析。对齐齐哈尔地区的数据进行回归分析,建立了集装箱内部温度模型,预测出集装箱内部最恶劣的低温极值范围,为危险军用物资集装箱安全储运提供理论依据,并提出了相应的对策及建议。     

Using empirical models of species colonization under multiple threatening processes to identify complementary threat‐mitigation strategies

Approaches to prioritize conservation actions are gaining popularity. However, limited empirical evidence exists on which species might benefit most from threat mitigation and on what combination of threats, if mitigated simultaneously, would result in the best outcomes for biodiversity. We devised a way to prioritize threat mitigation at a regional scale with empirical evidence based on predicted changes to population dynamics—information that is lacking in most threat‐management prioritization frameworks that rely on expert elicitation. We used dynamic occupancy models to investigate the effects of multiple threats (tree cover, grazing, and presence of an hyperaggressive competitor, the Noisy Miner (Manorina melanocephala) on bird‐population dynamics in an endangered woodland community in southeastern Australia. The 3 threatening processes had different effects on different species. We used predicted patch‐colonization probabilities to estimate the benefit to each species of removing one or more threats. We then determined the complementary set of threat‐mitigation strategies that maximized colonization of all species while ensuring that redundant actions with little benefit were avoided. The single action that resulted in the highest colonization was increasing tree cover, which increased patch colonization by 5% and 11% on average across all species and for declining species, respectively. Combining Noisy Miner control with increasing tree cover increased species colonization by 10% and 19% on average for all species and for declining species respectively, and was a higher priority than changing grazing regimes. Guidance for prioritizing threat mitigation is critical in the face of cumulative threatening processes. By incorporating population dynamics in prioritization of threat management, our approach helps ensure funding is not wasted on ineffective management programs that target the wrong threats or species.     

The Value of Using Feasibility Models in Systematic Conservation Planning to Predict Landholder Management Uptake

Understanding the social dimensions of conservation opportunity is crucial for conservation planning in multiple‐use landscapes. However, factors that influence the feasibility of implementing conservation actions, such as the history of landscape management, and landholders’ willingness to engage are often difficult or time consuming to quantify and rarely incorporated into planning. We examined how conservation agencies could reduce costs of acquiring such data by developing predictive models of management feasibility parameterized with social and biophysical factors likely to influence landholders’ decisions to engage in management. To test the utility of our best‐supported model, we developed 4 alternative investment scenarios based on different input data for conservation planning: social data only; biological data only; potential conservation opportunity derived from modeled feasibility that incurs no social data collection costs; and existing conservation opportunity derived from feasibility data that incurred collection costs. Using spatially explicit information on biodiversity values, feasibility, and management costs, we prioritized locations in southwest Australia to control an invasive predator that is detrimental to both agriculture and natural ecosystems: the red fox (Vulpes vulpes). When social data collection costs were moderate to high, the most cost‐effective investment scenario resulted from a predictive model of feasibility. Combining empirical feasibility data with biological data was more cost‐effective for prioritizing management when social data collection costs were low (<4% of the total budget). Calls for more data to inform conservation planning should take into account the costs and benefits of collecting and using social data to ensure that limited funding for conservation is spent in the most cost‐efficient and effective manner.