Optimization of thermal performance of the parabolic trough solar collector systems based on GA-BP neural network model

The aim of this paper is to optimize the thermal performance (system output energy, thermal efficiency, and heat loss of cavity absorber) of parabolic trough solar collector (PTC) systems in order to improve its thermal performance, based on the genetic algorithm-back propagation (GA-BP) neural network model. There are a number of undefined problems, fuzzy or incomplete information and a complex thermal performance of the PTC systems. Therefore, the thermal performance prediction of the PTC systems based on GA-BP neural network model was developed. Subsequently, the metrics performances have been adopted to comprehensively understand the algorithm and evaluate the prediction accuracy. Results revealed that the GA-BP neural network model can be successfully used to predict the complex nonlinear relationship between the input variables and thermal performance of the PTC systems. The cosine effect has a great influence on the thermal performance; thereby the geometrical structure of the PTC systems was optimized. It was found that the optimized geometrical structure was beneficial to improve the thermal performance of the PTC system. In conclusion, the GA-BP neural network model has higher prediction accuracy than the other algorithm and it can be feasible and reliable.     

Non-linear autoregressive neural network approach for inside air temperature prediction of a pillar cooler

The volcanic plate made pillar cooler system is designed for outdoor spaces as a heat exchanging medium and reduces the outcoming air temperature which flows through the exhaust port. This paper proposes the use of artificial neural networks (ANNs) to predict inside air temperature of a pillar cooler. For this purpose, at first, three statistically significant factors (outside temperature, airing and watering) influencing the inside air temperature of the pillar cooler are identified as input parameters for predicting the output (inside air temperature) and then an ANN was employed to predict the output. In addition, 70%, 15% and 15% data was chosen from a previously obtained data set during the field trial of the pillar cooler for training, testing and validation, respectively, and then an ANN was employed to predict inside air temperature. The training (0.99918), testing (0.99799) and validation errors (0.99432) obtained from the model indicate that the artificial neural network model (NARX) may be used to predict inside air temperature of pillar cooler. This study reveals that, an ANN approach can be used successfully for predicting the performance of pillar cooler.     

Evaluating efforts to build sustainable WEEE reverse logistics network design: comparison of regulatory and non-regulatory approaches

Many developing countries such as Turkey are still making an effort on building an infrastructure for waste of electrical and electronic equipment (WEEE) reverse logistic network design (RLND) processes. It is obvious that policies/laws/regulations related to WEEE management provide a sustainable framework for implementation in the RLND. The question is here: Does the implementation of WEEE directives make sense in terms of reducing the total cost of the network in the long term? This study aims to compare regulatory and non-regulatory situations of WEEE RLND in developing countries by formulating two models named as ‘regulatory’ and ‘non-regulatory’. Model 1 is considered as sustainable with economic, environmental and social goals, and the quotas imposed by the environmental directive are taken into consideration as the data of product return amount. In Model 2, only economic goal is considered, and product return amount is forecasted using Artificial Neural Network (ANN). A case study is conducted in a recycling company in order to evaluate performance of the proposed models. This study contributes to the relevant literature by (1) comparing the regulatory and non-regulatory situations RL models explicitly and (2) proposing ANN model to forecast EEE product return or WEEE quantity for non-regulatory situation.     

Addressing challenges for future strategic‐level emergency management: reframing,networking, and capacity‐building

The mounting frequency and intensity of natural hazards, alongside growing interdependencies between social‐technical and ecological systems, are placing increased pressure on emergency management. This is particularly true at the strategic level of emergency management, which involves planning for and managing non‐routine, high‐consequence events. Drawing on the literature, a survey, and interviews and workshops with Australia's senior emergency managers, this paper presents an analysis of five core challenges that these pressures are creating for strategic‐level emergency management. It argues that emphasising ‘emergency management’ as a primary adaptation strategy is a retrograde step that ignores the importance of addressing socio‐political drivers of vulnerabilities. Three key suggestions are presented that could assist the country's strategic‐level emergency management in tackling these challenges: (i) reframe emergency management as a component of disaster risk reduction rather than them being one and the same; (ii) adopt a network governance approach; and (iii) further develop the capacities of strategic‐level emergency managers.     

斜井送排式通风隧道的火灾救援通风研究

公路隧道发生火灾开启横通道后,形成通风网络。本文以重庆地区吕家梁隧道为工程依托,进行了斜井前后不同位置发生火灾的通风网络模拟。结果表明,斜井前发生火灾时,受斜井浮力效应、风流密度变化等的影响,主风机的工作压力降低,而体积流量增加;当火灾发生在斜井前隧道时,采用斜井单抽方式可实现全部火灾烟流从竖井中排除,斜井前设射流风机增压调节、斜井后设射流风机增阻调节是一较好方案;对于斜井后发生火灾,采用全射流火灾通风和斜井压入两种通风方案均能满足火灾风流控制要求,虽然后者需要开启轴流风机,通风能耗大,但火灾通风的时效性要好于前者。     

长江（万州段）水体溶解性有机物的荧光光谱分析

按照河床形态、水文条件、排污口分布、支流状况等特点从长江万州段及长江支流苎溪河选取8个断面进行采样,对水样中溶解性有机物（DOM）的荧光光谱特征进行了分析和研究。利用日立F 4500荧光分光光度计对长江水、普通自来水和纯水在紫外光激励下产生的荧光光谱及其特性进行了比较研究。同时,研究了不同水样、不同取样点的水体DOM的荧光特性和三维荧光光谱图。结果表明：长江水在波长290 nm左右的紫外光激励下能产生较强的荧光。荧光峰是350~550 nm 范围的宽谱峰,荧光峰值波长在450 nm左右。但是不同取样点水样的荧光峰强度明显有差异,在4号、5号、2号取样点的水样荧光强度明显高于其他取样点,这与采样点附近污染源排放有明显的关系。该研究方法可作为鉴别水质污染的一种有效的手段.     

城市景观生态网络规划的空间模式应用探讨

城市景观生态网络空间模式研究是生态城市和景观规划设计现今研究的重要领域之一,目的在于依据景观生态网络模式的应用建立起生态城市规划建设的基本框架和技术途径。在探讨城市景观生态系统特征和城市景观生态网络结构的基础上,总结景观生态网络空间模式并重点分析了水景树的空间生态模式、森林 道路 住宅复合网络模式、平原城市农田 灌木丛 河流交叉网络模式、岛屿城市的绿地 道路生态网络模式4种城市景观生态网络典型模式的图示特征及其生态意义。依此为理论指导基础,以都江堰为例,在继承都江堰大地景观格局并结合都江堰市灾后重建需求的前提下,研究都江堰市城市景观生态格局和生态过程,探讨在不同尺度和不同维度上运用4种网络图示进行都江堰城市景观生态网络优化调整的空间模式和对策。(1)依据“水景树图式”优化调整纵向维度景观生态格局;(2)依据“森林 道路 住宅”和“农田 灌木丛 河流”网络调整横向维度景观生态格局;(3)依据岛屿城市的“绿地 道路”景观生态网络调整城市组团景观生态格局.     

线路交换开关级联失效的评估

本文主要针对信号线路交换网络设计中的失效问题展开讨论,对输出信号在传输网络中失效性进行了评估,根据失效评估在设备可靠性设计方面做了相应的考虑。     

Trophic structure of ecosystems and ecotoxicology of soil organisms

Studies on the ecotoxicology of soil organisms have led to the revision of views on the trophic structure of ecosystems. It was found that the microbial link is obligatory and controls the migration of toxicants and their effects in the food chain. Differences in effects are accounted for by both the physiological stability of organisms and their affliation with relatively independent and biogeochemically closed ecosystems differing in their spatiotemporal scales. The latter form a hierarchical three-level structure: ecosystems of unicellular organisms—ecosystems of small multicellular organisms—the ecosystem of large multicellular organisms, or of biogeocenosis. Trophic networks within the structure are united by ecosystemophagy as the type of feeding of large multicellular organisms, and this accounts for the importance of the latter as indicators of long-term changes in a biogeocenosis.     

基于神经网络模型的房屋震害易损性估计方法

结构易损性分析是震害预测工作中的一个重要环节.本文提出了一种基于人工神经网络模型的房屋震害易损性估计方法,文中以多层砖房结构的建筑为例,给出了易损性估计神经网络模型的输入向量设计方案和具有输出约束控制的BP神经网络模型.