Who should wear mask against airborne infections? Altering the contact network for controlling the spread of contagious diseases

There are several ways of controlling the propagation of a contagious disease. For instance, to reduce the spreading of an airborne infection, individuals can be encouraged to remain in their homes and/or to wear face masks outside their domiciles. However, when a limited amount of masks is available, who should use them: the susceptible subjects, the infective persons or both populations? Here we employ susceptible-infective-recovered (SIR) models described in terms of ordinary differential equations and probabilistic cellular automata in order to investigate how the deletion of links in the random complex network representing the social contacts among individuals affects the dynamics of a contagious disease. The inspiration for this study comes from recent discussions about the impact of measures usually recommended by health public organizations for preventing the propagation of the swine influenza A (H1N1) virus. Our answer to this question can be valid for other eco-epidemiological systems.     

基于遥感和GIS的华东区域洪涝灾害动态监测系统的研究和建立

以NOAA卫星资料为主要信息源,以地面高程、上地利用等地形地貌资料为辅助信息源,研制了华东区域多重因子空间分析模式,在GIS平台上建立洪涝灾害动态监测和分析系统,从而提高了洪涝灾害判识的准确性和客观性。     

生命线网络系统抗震拓扑优化的Benchmark模型

生命线系统拓扑优化问题的Benchmark模型是评测新型算法正确性和适用性的重要手段。基于此,首先以生命线网络系统抗震拓扑优化分析模型为背景,建立了该优化问题的三个Benchmark模型,并在Visual Compaq Fortran开发环境下,通过穷举法统计出解空间的所有网络,进而甄选出不同节点可靠度约束下的最优网络和若干次优网络,最后利用上述Benchmark模型对生命线网络系统抗震拓扑优化中的蚁群算法进行测试。结果表明,当网络规模较小时,蚁群算法能精确地搜索到最优解;当网络规模增大后,蚁群算法也能以较大概率搜索到最优解或次优解。     

安全与应急管理专业学科发展

为使各高校能有效应对工程教育与应急管理的新一轮发展,在新工科建设与应急管理部成立背景下研究安全与应急管理专业学科的现状与发展趋势。阐述专业建设与学科建设的联系、区别和作用,在此基础上分析安全与应急管理专业学科发展现状、凝练专业建设核心4要素、完善安全学科体系、总结应急管理一级学科体系构建思路,分别提出基于“现在完成进行时”与“将来完成进行时”状态的安全、应急管理专业学科发展趋势。结果表明:安全专业发展趋势分行业安全与“大安全”2类,安全学科仍需完善学科体系和科学分支;应急管理的长远发展趋势是设立一级学科,从而推动应急管理专业高质量创新型发展。     

A methodology to model causal relationships on offshore safety assessment focusing on human and organizational factors

INTRODUCTION: Focusing on people and organizations, this paper aims to contribute to offshore safety assessment by proposing a methodology to model causal relationships. METHOD: The methodology is proposed in a general sense that it will be capable of accommodating modeling of multiple risk factors considered in offshore operations and will have the ability to deal with different types of data that may come from different resources. Reason's "Swiss cheese" model is used to form a generic offshore safety assessment framework, and Bayesian Network (BN) is tailored to fit into the framework to construct a causal relationship model. The proposed framework uses a five-level-structure model to address latent failures within the causal sequence of events. The five levels include Root causes level, Trigger events level, Incidents level, Accidents level, and Consequences level. To analyze and model a specified offshore installation safety, a BN model was established following the guideline of the proposed five-level framework. A range of events was specified, and the related prior and conditional probabilities regarding the BN model were assigned based on the inherent characteristics of each event. RESULTS: This paper shows that Reason's "Swiss cheese" model and BN can be jointly used in offshore safety assessment. On the one hand, the five-level conceptual model is enhanced by BNs that are capable of providing graphical demonstration of inter-relationships as well as calculating numerical values of occurrence likelihood for each failure event. Bayesian inference mechanism also makes it possible to monitor how a safety situation changes when information flow travel forwards and backwards within the networks. On the other hand, BN modeling relies heavily on experts' personal experiences and is therefore highly domain specific. IMPACT ON INDUSTRY: "Swiss cheese" model is such a theoretic framework that it is based on solid behavioral theory and therefore can be used to provide industry with a roadmap for BN modeling and implications. A case study of the collision risk between a Floating Production, Storage and Offloading (FPSO) unit and authorized vessels caused by human and organizational factors (HOFs) during operations is used to illustrate an industrial application of the proposed methodology.     

The role of optimality in characterizing CO2 seepage from geologic carbon sequestration sites

Storage of large amounts of carbon dioxide (CO₂) in deep geologic formations for greenhouse-gas mitigation is gaining momentum and moving from its conceptual and testing stages towards widespread application. In this work we explore various optimization strategies for characterizing surface leakage (seepage) using near-surface measurement approaches such as accumulation chambers and eddy covariance towers. Seepage characterization objectives and limitations need to be defined carefully from the outset especially in light of large natural background variations that can mask seepage. The cost and sensitivity of seepage detection are related to four critical length scales pertaining to the size of the: (1) region that needs to be monitored; (2) footprint of the measurement approach, and (3) main seepage zone; (4) region in which concentrations or fluxes are influenced by seepage. Seepage characterization objectives may include one or all of the tasks of detecting, locating, and quantifying seepage. Each of these tasks has its own optimal strategy. Detecting and locating seepage in a region in which there is no expected or preferred location for seepage nor existing evidence for seepage requires monitoring on a fixed grid, e.g., using eddy covariance towers. The fixed-grid approaches needed to detect seepage are expected to require large numbers of eddy covariance towers for large-scale geologic CO₂ storage. Once seepage has been detected and roughly located, seepage zones and features can be optimally pinpointed through a dynamic search strategy, e.g., employing accumulation chambers and/or soil-gas monitoring. Quantification of seepage rates can be done through measurements on a localized fixed grid once the seepage is pinpointed. Background measurements are essential for seepage detection in natural ecosystems. Artificial neural networks are considered as regression models useful for distinguishing natural system behavior from anomalous behavior suggestive of CO₂ seepage without need for detailed understanding of natural system processes. Because of the local extrema in CO₂ fluxes and concentrations in natural systems, simple steepest-descent algorithms are not effective and evolutionary computation algorithms are proposed as a paradigm for dynamic monitoring networks to pinpoint CO₂ seepage areas.     

Integration of Wireless Sensor Networks into Cyberinfrastructure for Monitoring Hawaiian “Mountain-to-Sea” Environments

Monitoring the complex environmental relationships and feedbacks of ecosystems on catchment (or mountain)-to-sea scales is essential for social systems to effectively deal with the escalating impacts of expanding human populations globally on watersheds. However, synthesis of emerging technologies into a robust observing platform for the monitoring of coupled human-natural environments on extended spatial scales has been slow to develop. For this purpose, the authors produced a new cyberinfrastructure for environmental monitoring which successfully merged the use of wireless sensor technologies, grid computing with three-dimensional (3D) geospatial data visualization/exploration, and a secured internet portal user interface, into a working prototype for monitoring mountain-to-sea environments in the high Hawaiian Islands. A use-case example is described in which native Hawaiian residents of Waipa Valley (Kauai) utilized the technology to monitor the effects of regional weather variation on surface water quality/quantity response, to better understand their local hydrologic cycle, monitor agricultural water use, and mitigate the effects of lowland flooding.     

Ensemble Streamflow Forecast: A GLUE‐Based Neural Network Approach1

Abstract: While training a Neural Network to model a rainfall‐runoff process, generally two aspects are considered: its capability to be able to describe the complex nature of the processes being modeled and the ability to generalize so that novel samples could be mapped correctly. The general conclusion is that, the smallest size network capable of representing the sample distribution is the best choice, as far as generalization is concerned. Oftentimes input variables are selected a priori in what is called an explanatory data analysis stage and are not part of the actual network training and testing procedures. When they are, the final model will have only a “fixed” type of inputs, lag‐space, and/or network structure. If one of these constituents was to change, one would obtain another equally “optimal” Neural Network. Following Beven and others' generalized likelihood uncertainty estimate approach, a methodology is introduced here that accounts for uncertainties in network structures, types of inputs, and their lag‐space relationships by looking at a population of Neural Networks rather than target in getting a single “optimal” network. It is shown that there is a wide array of networks that provide “similar” results, as seen by a likelihood measure, for different types of inputs, lag‐space, and network size combinations. These equally optimal networks expose the range of uncertainty in streamflow predictions and their expected value results in a better performance than any of the single network predictions.     

The strength of networks: the local NGO response to the tsunami in India

This paper examines the role played by a network of 12 local non-governmental organisations (NGOs)--the East Coast Development Forum (ECDF)-in the response to the Indian Ocean tsunami ('Asian tsunami') of 26 December 2004, which devastated the east coast of India. It examines how the ECDF sought to meet the needs of affected people through a direct relief programme, a rehabilitation programme focused on the restoration of livelihoods, and through advocacy to press for changes to government programmes to make them inclusive and to ensure that they satisfy the priority needs of the people most affected. The paper concludes that it was the trust and capacity built up through past network activities of the fisher, dalit, and tribal communities that enabled the ECDF to launch an effective response to the tsunami. A lesson to emerge is that the use of similar existing networks could be employed in other disaster responses around the world.     

水质综合评价的人工神经网络模型

为探讨水质综合评价的客观方法,以成都市金堂县东风水库水质资料为例,建立了地面水水质综合评价的BP网络和Hopfield网络模型。BP网络模型以单输出代替多输出可保证评价结果的唯一性。Hopfield网络更优于BP网络,既适用于定量指标的水质参数又适用于定性指标的水质参数,而且使水质评价形象化