基于粒子群算法的城市垃圾中转站的空间布局研究

随着城市化的进程，垃圾处理设施成为影响城市化质量的重要指标。依据人们实际所关注的垃圾处理设施的总成本和负效应两个因素，在前人研究的基础上，建立了垃圾收运多目标模型，使得总成本和负效应最优化。根据模型设计了粒子群算法，该算法属于智能算法，且精度高，收敛快，在许多领域也得到广泛应用。用设计的粒子群算法并结合案例求出了Pareto最优解，给出三种决策方案，验证了模型和算法的有效性和可行性，为决策者提供了一种理论参考。     

Estimated Injury Risk for Specific Injuries and Body Regions in Frontal Motor Vehicle Crashes

Objective: Injury risk curves estimate motor vehicle crash (MVC) occupant injury risk from vehicle, crash, and/or occupant factors. Many vehicles are equipped with event data recorders (EDRs) that collect data including the crash speed and restraint status during a MVC. This study's goal was to use regulation-required data elements for EDRs to compute occupant injury risk for (1) specific injuries and (2) specific body regions in frontal MVCs from weighted NASS-CDS data.

Methods: Logistic regression analysis of NASS-CDS single-impact frontal MVCs involving front seat occupants with frontal airbag deployment was used to produce 23 risk curves for specific injuries and 17 risk curves for Abbreviated Injury Scale (AIS) 2+ to 5+ body region injuries. Risk curves were produced for the following body regions: head and thorax (AIS 2+, 3+, 4+, 5+), face (AIS 2+), abdomen, spine, upper extremity, and lower extremity (AIS 2+, 3+). Injury risk with 95% confidence intervals was estimated for 15–105 km/h longitudinal delta-Vs and belt status was adjusted for as a covariate.

Results: Overall, belted occupants had lower estimated risks compared to unbelted occupants and the risk of injury increased as longitudinal delta-V increased. Belt status was a significant predictor for 13 specific injuries and all body region injuries with the exception of AIS 2+ and 3+ spine injuries. Specific injuries and body region injuries that occurred more frequently in NASS-CDS also tended to carry higher risks when evaluated at a 56 km/h longitudinal delta-V. In the belted population, injury risks that ranked in the top 33% included 4 upper extremity fractures (ulna, radius, clavicle, carpus/metacarpus), 2 lower extremity fractures (fibula, metatarsal/tarsal), and a knee sprain (2.4–4.6% risk). Unbelted injury risks ranked in the top 33% included 4 lower extremity fractures (femur, fibula, metatarsal/tarsal, patella), 2 head injuries with less than one hour or unspecified prior unconsciousness, and a lung contusion (4.6–9.9% risk). The 6 body region curves with the highest risks were for AIS 2+ lower extremity, upper extremity, thorax, and head injury and AIS 3+ lower extremity and thorax injury (15.9–43.8% risk).

Conclusions: These injury risk curves can be implemented into advanced automatic crash notification (AACN) algorithms that utilize vehicle EDR measurements to predict occupant injury immediately following a MVC. Through integration with AACN, these injury risk curves can provide emergency medical services (EMS) and other patient care providers with information on suspected occupant injuries to improve injury detection and patient triage.     

On-Scene Injury Severity Prediction (OSISP) Algorithm for Truck Occupants

Objective: The aim of this study is to develop an on-scene injury severity prediction (OSISP) algorithm for truck occupants using only accident characteristics that are feasible to assess at the scene of the accident. The purpose of developing this algorithm is to use it as a basis for a field triage tool used in traffic accidents involving trucks. In addition, the model can be valuable for recognizing important factors for improving triage protocols used in Sweden and possibly in other countries with similar traffic environments and prehospital procedures.

Methods: The scope is adult truck occupants involved in traffic accidents on Swedish public roads registered in the Swedish Traffic Accident Data Acquisition (STRADA) database for calendar years 2003 to 2013. STRADA contains information reported by the police and medical data on injured road users treated at emergency hospitals. Using data from STRADA, 2 OSISP multivariate logistic regression models for deriving the probability of severe injury (defined here as having an Injury Severity Score [ISS] > 15) were implemented for light and heavy trucks; that is, trucks with weight up to 3,500 kg and ??16,500 kg, respectively. A 10-fold cross-validation procedure was used to estimate the performance of the OSISP algorithm in terms of the area under the receiver operating characteristic curve (AUC).

Results: The rate of belt use was low, especially for heavy truck occupants. The OSISP models developed for light and heavy trucks achieved cross-validation AUC of 0.81 and 0.74, respectively. The AUC values obtained when the models were evaluated on all data without cross-validation were 0.87 for both light and heavy trucks. The difference in the AUC values with and without use of cross-validation indicates overfitting of the model, which may be a consequence of relatively small data sets. Belt use stands out as the most valuable predictor in both types of trucks; accident type and age are important predictors for light trucks.

Conclusions: The OSISP models achieve good discriminating capability for light truck occupants and a reasonable performance for heavy truck occupants. The prediction accuracy may be increased by acquiring more data. Belt use was the strongest predictor of severe injury for both light and heavy truck occupants. There is a need for behavior-based safety programs and/or other means to encourage truck occupants to always wear a seat belt.     

回采工作面瓦斯涌出量遗传投影寻踪回归预测

为快速、准确预测回采工作面瓦斯涌出量,基于投影降维思想,建立一种遗传算法(GA)投影寻踪回归预测方法。选取煤层瓦斯原始含量、埋藏深度、煤层厚度、煤层倾角、工作面长度、推进速度、采出率、临近层瓦斯含量、临近层厚度、临近层层间距、岩层岩性、开采深度作为评价因子,对某矿15个学习样本进行训练,建立GA投影寻踪回归预测模型。利用该矿3个实测样本对模型进行检验,并与主成分分析和BP神经网络方法结果进行对比。研究表明:利用GA投影寻踪回归预测回采工作面瓦斯涌出量,平均误差为3.43%,最大误差为5.7%,精度优于其他2种方法。     

沿海城市多灾种耦合危险性评估的初步研究——以福建泉州为例

如何处理区域内多灾种之间的相互作用关系是当前多灾种综合风险评估研究的热点和难点之一。在梳理沿海城市多灾种之间相互作用关系的基础上,提出一种基于触发关系的多灾种耦合危险性评估方法。首先选取多指标建立单灾种分级方案,计算单灾种初始危险性指数;然后构建基于触发关系的多灾种耦合规则,结合GIS技术进行空间耦合,计算耦合后的危险性指数;最后对耦合后的多灾种危险性的综合进行了探讨。以福建省泉州市为例进行了案例研究,结果表明:该方法在多灾种耦合危险性评估中具有可行性,为后续多脆弱性和多灾种综合风险的研究提供支持,为决策者进行科学风险管理提供依据。     

孑遗植物四合木(Tetraenamongolica)迁地保护中的光合作用日变化特征与生理生态适应性

为了进一步廓清迁地保护条件下孑遗植物四合木（Tetraena mongolica）的光合生理生态适应性,在分析了瞬时光合效率的基础上,应用LI-6400光合作用测定系统测定了迁地保护试验区的四合木以及原生境伴生种白刺（Nitraria tangutorum）的光合作用日变化,并测定了其生长量。结果表明：四合木实生苗的生长南北冠幅大小依次为乌海四合木核心区实生苗（26.48 cm×27.26 cm）>鄂尔多斯实生苗（21.27 cm×21.75 cm）>阿拉善实生苗（19.25 cm×18.27 cm）。在原生境地乌海四合木核心区种植的实生苗与阿拉善实生苗之间的生长量存在显著差异（P≤0.01）,与鄂尔多斯实生苗之间的生长量存在显著差异（P≤0.05）。迁地保护条件下四合木生实生苗植株叶片光合速率Pn日变化均呈“双峰”曲线。不同试验区四合木光合作用日变化（Pn）、蒸腾速率（Tr）、气孔导度（Gs）和胞间 CO2浓度（Ci）均表现出明显的分异。迁地保护四合木条件下原生境地栽培的四合木实生苗的光合速率>鄂尔多斯栽培的四合木实生苗的光合速率>阿拉善栽培的四合木实生苗。鉴此可以作进一步推论,孑遗濒危植物四合木从原生境地西鄂尔多斯核心区（乌海）东移进行迁地保护具有更高的生理生态适应性和生境适宜性。但完成“从种子到种子”进而实现“保存性和代表性”,最终实现四合木迁地保护的“保持性和防止性”,保持其遗传多样性和遗传稳定性,最终成功实现四合木的迁地保护仍有待作进一步探索和深入研究。