Data mining applications in evaluating mine ventilation system

A mine’s ventilation system is an important component of an underground mining system. It provides a sufficient quantity of air to maintain suitable working environment. Therefore, the status of mine ventilation should be tracked and monitored as a timely matter. Based on former findings and in-depth analysis of mine ventilation systems, a proper early warning model is proposed in this paper for such considerations to improve the mine ventilation safety. The model itself is comprised of two sub-models, and two data mining techniques are used to assist in building each sub-model. One is the optimal indexes selection model which applies the Rough Set theory (RS) to assist the selection of best ventilation indexes. The other is the risk evaluation model based on the Support Vector Machine (SVM) to classify the risk ranks for the mine ventilation system. Testing cases have been used to demonstrate the applicability of this integrated model.     

Development of machine learning multi-city model for municipal solid waste generation prediction

● A database of municipal solid waste (MSW) generation in China was established. ● An accurate MSW generation prediction model (WGMod) was constructed. ● Key factors affecting MSW generation were identified. ● MSW trends generation in Beijing and Shenzhen in the near future are projected. Integrated management of municipal solid waste (MSW) is a major environmental challenge encountered by many countries. To support waste treatment/management and national macroeconomic policy development, it is essential to develop a prediction model. With this motivation, a database of MSW generation and feature variables covering 130 cities across China is constructed. Based on the database, advanced machine learning (gradient boost regression tree) algorithm is adopted to build the waste generation prediction model, i.e., WGMod. In the model development process, the main influencing factors on MSW generation are identified by weight analysis. The selected key influencing factors are annual precipitation, population density and annual mean temperature with the weights of 13%, 11% and 10%, respectively. The WGMod shows good performance with R² = 0.939. Model prediction on MSW generation in Beijing and Shenzhen indicates that waste generation in Beijing would increase gradually in the next 3–5 years, while that in Shenzhen would grow rapidly in the next 3 years. The difference between the two is predominately driven by the different trends of population growth.     

Machine learning and theoretical analysis release the non-linear relationship among ozone, secondary organic aerosol and volatile organic compounds

Fine particulate matter (PM_2.5) and ozone (O₃) pollutions are prevalent air quality issues in China. Volatile organic compounds (VOCs) have significant impact on the formation of O₃ and secondary organic aerosols (SOA) contributing PM_2.5. Herein, we investigated 54 VOCs, O₃ and SOA in Tianjin from June 2017 to May 2019 to explore the non-linear relationship among O₃, SOA and VOCs. The monthly patterns of VOCs and SOA concentrations were characterized by peak values during October to March and reached a minimum from April to September, but the observed O₃ was exactly the opposite. Machine learning methods resolved the importance of individual VOCs on O₃ and SOA that alkenes (mainly ethylene, propylene, and isoprene) have the highest importance to O₃ formation; alkanes (C_n, n ≥ 6) and aromatics were the main source of SOA formation. Machine learning methods revealed and emphasized the importance of photochemical consumptions of VOCs to O₃ and SOA formation. Ozone formation potential (OFP) and secondary organic aerosol formation potential (SOAFP) calculated by consumed VOCs quantitatively indicated that more than 80% of the consumed VOCs were alkenes which dominated the O₃ formation, and the importance of consumed aromatics and alkenes to SOAFP were 40.84% and 56.65%, respectively. Therein, isoprene contributed the most to OFP at 41.45% regardless of the season, while aromatics (58.27%) contributed the most to SOAFP in winter. Collectively, our findings can provide scientific evidence on policymaking for VOCs controls on seasonal scales to achieve effective reduction in both SOA and O₃.     

机械制造企业安全质量标准化初探

本文首先阐明了开展机械制造企业安全质量标准化工作的重要性.在明确机械制造企业安全标准化内涵的基础上,简述了其涉及的内容、分级标准以及评定程序.最后讨论了该项工作在实施过程中可能面临的问题.     

TBM 在不良地质地段的安全通过技术

主要论述了TBM (隧道掘进机 )安全通过不良地质地段的方法步骤及辅助措施 ,并介绍了坍塌、岩爆、涌水、软弱地层、膨胀性围岩等常见不良地质情况 ,对TBM施工安全的影响及相应处理措施 ,以供工程技术人员参考。     

事故树分析法在预防自动取款机犯罪活动中的应用

笔者将事故树分析法(FTA)运用到银行领域,预防作案人利用自动取款机(ATM)进行犯罪活动,定性分析其犯罪活动发生的过程,导出引起犯罪活动发生的基本原因事件;建立了相应的事故树图;得出了最小割集、最小径集,比较了各基本原因事件的割集重要系数。最后结合各基本原因事件,从人-机-环系统的角度出发(用户和银行方面、ATM自身设计、环境布置),为银行ATM系统安全性能方面的改善,控制犯罪活动的发生提出一些应对措施。     

喷丸亚光机的危害性分析与无害化设计

从安全设备工程学的观点出发 ,分析了喷丸亚光机这一特种机械的危险因素和有害因素 ,阐述了在防尘、降噪、安全、耐用等方面所采取的相应防护措施和关键技术。提出了使该设备达到安全化、无害化的设计方法 ,使机械设计制造与工业安全卫生科学紧密结合 ,不仅具有生产功能的实用性 ,而其加工性能和相关指标达到或优于国外同类设备。     

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.     

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%。