工业项目建设用地控制指标管理实践研究——以大连长兴岛经济技术开发区为例

工业项目建设用地控制指标作为一种政策标准,是落实节约优先战略的重要手段。大连长兴岛经济技术开发区在工业项目建设用地控制指标管理中进行了有益探索,构建了由投资强度等7项指标组成的立体控制体系。文章就建设用地控制体系的衍变、编制的原则和步骤、指标值的确定、管理和执行情况等进行了探讨,并提出应增加环境保护指标、定期开展评价等工作建议。     

关于提高金刚石钻头胎体工作能力的试验研究

在地质勘探中,金刚石钻进是一种主要的方法。在金刚石钻探生产中,钻探设备、钻头、钻探工艺、钻探生产操作技术和生产管理都是非常重要的。钻探技术经济指标与钻头胎体的工作能力关系更为密切,因此成了人们关心的问题。俄罗斯Кубасов В.В.博士等在提高钻头胎体工作能力方面做了大量的工作,取得了很好的技术经济效果。本文介绍了他们在低品级金刚石的选择性破碎处理、金刚石的热处理、金刚石钻头的低温处理和胎体性能的控制技术等方面的研究内容及取得的成果。     

皱纹盘鲍排氨率变化特征及代谢产物对水质的影响研究

为研究不同规格皱纹盘鲍(Haliotis discus hannai Ino)的排氨率变化特征和代谢产物对养殖水质的影响,于鲍摄食后的3、6、9、12、18、24 h时取水样对主要水质因子的变化情况进行测定,并根据总氨氮(TAN)浓度计算各规格组鲍的排氨率。实验结果表明,随着鲍体质量的增加,TAN浓度也随之增加(P0.05),大规格组和小规格组中TAN浓度均在9 h时出现第一个波峰。大、中、小规格组鲍的单位体质量排氨率最大值依次为16.79、29.96、48.56μg/(g·h)。且小规格组鲍的排氨率显著高于大、中等规格组(P0.05),各规格组中的NO_2~–-N和PO_4~(3–_-P浓度均在24 h时达到最大值,且除0 h外,大规格组中的NO_2~–-N和PO43–-P浓度均显著高于中、小规格组(P0.05)。9 h时,大规格组中的NO_3~–-N浓度达到最大值,而中等规格组则在24 h时达到最大值。18 h时,中等规格和小规格组COD均达到最大值,而大规格组则是在24 h时浓度达到最大。因此,基于皱纹盘鲍的排氨率变化特征和TAN等在水体中的累积情况,可以预测水质变化规律并根据实际情况及时采取换水等相关操作,对促进养殖生物健康生长、降低病害发生风险、提高养殖效益等均有借鉴意义。     

氨氮急性胁迫对许氏平鲉血液生化指标影响

采用96 h半静态毒性实验，研究了许氏平鲉幼鱼的氨氮耐受性和氨氮胁迫下血液生化指标的变化规律。结果表明，在水温（18.6±0.5）℃，pH 7.85，盐度30和溶氧（8.6±0.5） mg/L的条件下，平均体质量（63.60±10.14） g、平均全长（15.90±3.29） cm的许氏平鲉幼鱼总氨（TAN）和非离子氨（NH₃-N）96 h的半致死浓度（LC₅₀）分别为18.01 mg/L和0.27 mg/L，安全浓度（SC）分别为1.80 mg/L和0.03 mg/L。设定TAN为0、3.5、10和20 mg/L 4个浓度梯度，对同等规格苗种进行96 h急性胁迫实验，血液生化指标的双因素方差分析表明：氨氮浓度、暴露时间及两者交互作用对血浆血糖（GLU）、血氨（PA）、还原型谷胱甘肽（GSH）、碱性磷酸酶（AKP）和谷草转氨酶（AST）含量/活性存在显著影响，两者交互作用对血浆皮质醇（Cor）和谷丙转氨酶（ALT）含量/活性无显著影响，暴露时间对血浆胆固醇（TCH）无显著影响；其中，血浆GLU、GSH、AKP随处理时间延长呈先升后降趋势，随氨氮浓度升高响应时间提前；PA浓度与环境TAN浓度正相关。相关研究结果可为许氏平鲉幼鱼的养殖管理和苗种运输提供指导，血浆GLU、GSH、PA和AKP可作为许氏平鲉氨氮胁迫的敏感指标。     

Multi-hazard susceptibility mapping based on Convolutional Neural Networks

Multi-hazard susceptibility prediction is an important component of disasters risk management plan. An effective multi-hazard risk mitigation strategy includes assessing individual hazards as well as their interactions. However, with the rapid development of artificial intelligence technology, multi-hazard susceptibility prediction techniques based on machine learning has encountered a huge bottleneck. In order to effectively solve this problem, this study proposes a multi-hazard susceptibility mapping framework using the classical deep learning algorithm of Convolutional Neural Networks (CNN). First, we use historical flash flood, debris flow and landslide locations based on Google Earth images, extensive field surveys, topography, hydrology, and environmental data sets to train and validate the proposed CNN method. Next, the proposed CNN method is assessed in comparison to conventional logistic regression and k-nearest neighbor methods using several objective criteria, i.e., coefficient of determination, overall accuracy, mean absolute error and the root mean square error. Experimental results show that the CNN method outperforms the conventional machine learning algorithms in predicting probability of flash floods, debris flows and landslides. Finally, the susceptibility maps of the three hazards based on CNN are combined to create a multi-hazard susceptibility map. It can be observed from the map that 62.43% of the study area are prone to hazards, while 37.57% of the study area are harmless. In hazard-prone areas, 16.14%, 4.94% and 30.66% of the study area are susceptible to flash floods, debris flows and landslides, respectively. In terms of concurrent hazards, 0.28%, 7.11% and 3.13% of the study area are susceptible to the joint occurrence of flash floods and debris flow, debris flow and landslides, and flash floods and landslides, respectively, whereas, 0.18% of the study area is subject to all the three hazards. The results of this study can benefit engineers, disaster managers and local government officials involved in sustainable land management and disaster risk mitigation.