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我国职业噪声危害成因分析及总体控制对策 总被引:5,自引:2,他引:5
职业噪声不仅属于职业卫生问题 ,也是当今世界上主要的环境公害之一。有效控制职业噪声不仅能够保护劳动者的听力不受损害 ,使其身心安全与健康得到保障 ,同时也是消除安全隐患 ,实现安全生产的重要途径。笔者分析了我国职业噪声危害的现状 ,论述了我国职业噪声危害的严重性以及控制的重要性与迫切性。然后采用“人 -机 -环境”系统工程方法 ,分别从人的行为、物的状态以及工作环境等 3个方面 ,分析职业噪声危害的成因 ,构造了基于宏观管理维、中观人机环境维以及微观技术维等三维结构的我国职业噪声危害控制总体框架 ,并从政府、企业以及劳动者等角度 ,对职业噪声危害控制的运行机制进行了研究 ,以解决我国日益严重的职业噪声危害问题 ,不断改善我国安全生产现状并进一步提高劳动保护工作的整体水平。 相似文献
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社会噪声主要指营业性文化娱乐活动和商业经营活动中使用的设备、设施产生的噪声。本文主要从目前存在的社会噪声的各个方面论述居民受噪声污染的主要因素,并针对存在的问题提出整治措施和合理化的建议,使人们远离噪声污染。 相似文献
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根据伊宁市“十一五”期间环境噪声质量监测结果,分析了环境噪声的变化趋势,总结了经验,提出进一步提高环境质量的措施。 相似文献
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Species-specific tuning increases robustness to sampling bias in models of species distributions: An implementation with Maxent 总被引:3,自引:0,他引:3
Various methods exist to model a species’ niche and geographic distribution using environmental data for the study region and occurrence localities documenting the species’ presence (typically from museums and herbaria). In presence-only modelling, geographic sampling bias and small sample sizes represent challenges for many species. Overfitting to the bias and/or noise characteristic of such datasets can seriously compromise model generality and transferability, which are critical to many current applications - including studies of invasive species, the effects of climatic change, and niche evolution. Even when transferability is not necessary, applications to many areas, including conservation biology, macroecology, and zoonotic diseases, require models that are not overfit. We evaluated these issues using a maximum entropy approach (Maxent) for the shrew Cryptotis meridensis, which is endemic to the Cordillera de Mérida in Venezuela. To simulate strong sampling bias, we divided localities into two datasets: those from a portion of the species’ range that has seen high sampling effort (for model calibration) and those from other areas of the species’ range, where less sampling has occurred (for model evaluation). Before modelling, we assessed the climatic values of localities in the two datasets to determine whether any environmental bias accompanies the geographic bias. Then, to identify optimal levels of model complexity (and minimize overfitting), we made models and tuned model settings, comparing performance with that achieved using default settings. We randomly selected localities for model calibration (sets of 5, 10, 15, and 20 localities) and varied the level of model complexity considered (linear versus both linear and quadratic features) and two aspects of the strength of protection against overfitting (regularization). Environmental bias indeed corresponded to the geographic bias between datasets, with differences in median and observed range (minima and/or maxima) for some variables. Model performance varied greatly according to the level of regularization. Intermediate regularization consistently led to the best models, with decreased performance at low and generally at high regularization. Optimal levels of regularization differed between sample-size-dependent and sample-size-independent approaches, but both reached similar levels of maximal performance. In several cases, the optimal regularization value was different from (usually higher than) the default one. Models calibrated with both linear and quadratic features outperformed those made with just linear features. Results were remarkably consistent across the examined sample sizes. Models made with few and biased localities achieved high predictive ability when appropriate regularization was employed and optimal model complexity was identified. Species-specific tuning of model settings can have great benefits over the use of default settings. 相似文献
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