生命周期评价清单分析的算法研究

清单分析是生命周期评价的步骤之一,是LCA（Life Cycle Assessment）基本数据的一种表达,数据计算是整个清单分析阶段的重点和难点,清单分析数据结果的准确性直接决定了影响评价的可靠性。研究了清单分析的具体步骤,提出了一种适于求解LCA清单数据的算法,阐明了算法基于清单分析的理论模型,运用高斯消元法的原理以及详细的求解过程。用实例验证了算法的正确和可行,而且计算结果精确和快速。     

Web technologies for environmental Big Data

Recent evolutions in computing science and web technology provide the environmental community with continuously expanding resources for data collection and analysis that pose unprecedented challenges to the design of analysis methods, workflows, and interaction with data sets. In the light of the recent UK Research Council funded Environmental Virtual Observatory pilot project, this paper gives an overview of currently available implementations related to web-based technologies for processing large and heterogeneous datasets and discuss their relevance within the context of environmental data processing, simulation and prediction. We found that, the processing of the simple datasets used in the pilot proved to be relatively straightforward using a combination of R, RPy2, PyWPS and PostgreSQL. However, the use of NoSQL databases and more versatile frameworks such as OGC standard based implementations may provide a wider and more flexible set of features that particularly facilitate working with larger volumes and more heterogeneous data sources.     

CRT and LCD monitor and process materials evaluated for environmental improvement

Decision rules have been developed and applied to the bills of materials of a color cathode‐ray tube (CRT) and a liquid‐crystal‐display (LCD) desktop monitor to determine which product and process materials will be evaluated in an environmental life‐cycle assessment. Materials of significant mass, of technological importance, and of potential environmental impact are targeted. The list of materials identified are those for which life‐cycle inventory data will be obtained for the materials extraction and materials processing life‐cycle stages of a CRT and an LCD. Additionally, materials identified will also be used to represent life‐cycle impact in terms of resource consumption, as well as surrogates for occupational health impacts.     

Evaluating the sustainability of electronic media: Strategies for life cycle inventory data collection and their implications for LCA results

This paper compares two Life Cycle Assessment (LCA) studies independently carried out to assess the environmental impacts of electronic versus print media. Although the two studies lead to the same overall conclusion for the case of a news magazine – namely that the tablet version of the magazine has environmental advantages over the print version – there are significant differences in the details of the LCA results. We show how these differences can be explained by differences in the methodological approaches used for life cycle inventory (LCI) modelling, in particular the use of rough average data versus the attempt to use the most specific and detailed data as possible. We conclude that there are several issues in LCA practice (at least when applied in the domain of media) that can significantly influence the results already at the LCI level: The data collection strategy used (e.g. relying on desk-based research or dismantling a given device) and the decisions made at inventory level with regard to parameters with significant geographic variability, such as the electricity mix or recycling quotas.     

Expert system support for environmental assessment of manufacturing products and facilities

The goal of environmentally conscious design for manufacturing is to select materials and processes that minimize environmental impact. This paper describes a general and uniform way to analyze the environmental impact of manufacturing based on the product decomposition, the materials used in the manufacturing processes, and the particular view of the environment. To accomplish this task, we developed a computer program, called EcoSys, that assists manufacturing engineers and environmental reviewers in assessing the environmental consequences of their manufacturing decisions.     

Selecting among five common modelling approaches for integrated environmental assessment and management

The design and implementation of effective environmental policies need to be informed by a holistic understanding of the system processes (biophysical, social and economic), their complex interactions, and how they respond to various changes. Models, integrating different system processes into a unified framework, are seen as useful tools to help analyse alternatives with stakeholders, assess their outcomes, and communicate results in a transparent way. This paper reviews five common approaches or model types that have the capacity to integrate knowledge by developing models that can accommodate multiple issues, values, scales and uncertainty considerations, as well as facilitate stakeholder engagement. The approaches considered are: systems dynamics, Bayesian networks, coupled component models, agent-based models and knowledge-based models (also referred to as expert systems). We start by discussing several considerations in model development, such as the purpose of model building, the availability of qualitative versus quantitative data for model specification, the level of spatio-temporal detail required, and treatment of uncertainty. These considerations and a review of applications are then used to develop a framework that aims to assist modellers and model users in the choice of an appropriate modelling approach for their integrated assessment applications and that enables more effective learning in interdisciplinary settings.     

An adaptive neuro-fuzzy inference system for bridge risk assessment

Bridge risks are often evaluated periodically so that the bridges with high risks can be maintained timely. This paper develops an adaptive neuro-fuzzy system (ANFIS) using 506 bridge maintenance projects for bridge risk assessment, which can help Highways Agency to determine the maintenance priority ranking of bridge structures more systematically, more efficiently and more economically in comparison with the existing bridge risk assessment methodologies which require a large number of subjective judgments from bridge experts to build the complicated nonlinear relationships between bridge risk score and risk ratings. The ANFIS proves to be very effective in modelling bridge risks and performs better than artificial neural networks (ANN) and multiple regression analysis (MRA).     

An oscillation-driven neural network for the simulation of an olfactory system

Understanding the nonlinear dynamics of an olfactory bulb (OB) is essential for the modelling of the brain and nervous system. We have analysed the nature of odour-receptor interactions and the conditions controlling neural oscillations. This analysis is the basis for the proposed biologically plausible three-tiered model of an oscillation-driven neural network (ODNN) with three non-linearities. The layered architecture of the bulb is viewed as a composition of different processing stages performing specific computational tasks. The presented three-tiered model of the olfactory system (TTOS) contains the sensory, olfactory bulb and anterior nucleus tiers. The number of excitatory (mitral/tufted) cells differs from the number of inhibitory (granule) cells, which improves the cognitive ability of the model. The odour molecules are first received at the sensory layer, where receptor neurons spatio-temporally encode them in terms of spiking frequencies. Neurons expressing a specific receptor project to two or more topographically fixed glomeruli in the OB and create a sensory map. Excitatory postsynaptic potentials are formed in the primary dendrite of mitral cells and are encoded in an exclusive way to present them to the coupled non-linear oscillatory model of the next mitral-granule layer. In a noisy background, our model functions as an associative memory, although it operates in oscillatory mode. While feed-forward networks and recurrent networks with symmetric connections always converge to static states, learning and pattern retrieval in an asymmetrically connected neural network based on oscillations are not well studied. We derive the requirements under which a state is stable and test whether a given equilibrium state is stable against noise. The ODNN demonstrates its capability to discriminate odours by using nonlinear dendro-dendritic interactions between neurons. This model allows us to visualise and analyse how the brain is able to encode information from countless molecules with different odour receptors.     

An environmental evaluation learning apprentice system

In engineering problems with a weak domain theory, such as environmental evaluation, machine learning techniques should bring to bear any existing background knowledge so as to guide the knowledge-acquisition process. This paper proposes an Interactive Inductive Learning System (IILS) which can use background knowledge provided by experts to avoid incorrect or incomplete induced heuristics. Through the specification of guidance relations, the expert can force the rule-induction system to focus on a subset of relevant attributes and training instances. A guidance relation consists of a set of attribute constraints, where each constraint instructs IILS on the role of an attribute for the induction of a concept. IILS has been linked to the database of an Environmental Evaluation Support System (EESS) for the induction of impact estimation and comparison heuristics. The induced heuristics are generalizations of past assessments stored in the database and permit the prediction of impact levels for cases not previously encountered by EESS. The integration of IILS transforms EESS into an environmental impact assessment learning apprentice system capable of acquiring and improving evaluation heuristics through its normal use.     

A comprehensive system for semantic spatiotemporal assessment of risk in urban areas

Risk assessment of urban areas aims at limiting the impact of harmful events by increasing awareness of their possible consequences. Qualitative risk assessment allows to figure out possible risk situations and to prioritize them, whereas quantitative risk assessment is devoted to measuring risks from data, in order to improve preparedness in case of crisis situations. We propose an automatic approach to comprehensive risk assessment. This leverages on a semantic and spatiotemporal representation of knowledge of the urban area and relies on a software system including: a knowledge base; two components for quantitative and qualitative risk assessments, respectively; and a WebGIS interface. The knowledge base consists of the TERMINUS domain ontology, to represent urban knowledge, and of a geo‐referenced database, including geographical, environmental and urban data as well as temporal data related to the levels of operation of city services. CIPcast DSS is the component devoted to quantitative risk assessment, and WS‐CREAM is the component supporting qualitative risk assessment based on computational creativity techniques. Two case studies concerning the city of Rome (Italy) show how this approach can be used in a real scenario for crisis preparedness. Finally, we discuss issues related to plausibility of risks and objectivity of their assessment.     

An ergonomic expert system for risk assessment of work-related musculo-skeletal disorders

A computer-based expert system (SONEX) was developed to identify ergonomic risks for work-related musculoskeletal disorders (WRMSDs) in a wide variety of jobs and provide expert prevention advice. SONEX uses a rule base and 6 knowledge base modules: WRMSD risk factors are grouped into two main knowledge base modules (symptoms, engaged body part) with four supplementary knowledge base modules (work environment, work chair, work tools, organization factors). SONEX uses a menu-based interface and a series of simple questions that lead a user through each of the two main modules. Based on user responses it then recommends other knowledge base modules that are relevant for a detailed analysis of work risks. The SONEX rule base has over 140 questions, the knowledge base includes over 200 risk factors, and around 500 possible answers can be generated. SONEX relates ergonomic shortcomings in the job with worker's subjective symptoms; it predicts possible WRMSDs; and it offers preventive suggestions for ergonomic improvements to the job to prevent WRMSDs. It has been tested in a variety of work places with known ergonomic problems and with known employee WRMSDs by comparing its performance with conventional analytical methods and results show that it accurately predicts possible WRMSD risks and identifies ergonomic shortcomings. The advantages of SONEX are that it is much faster than other ergonomic analysis methods and it can be used by ergonomists and other professionals and also by workers themselves.     

Sustainable supply chain optimisation: An industrial case study

Sustainability plays a key role in the management of a successful and responsible business. When trying to improve the sustainability performance of a business, there are three major challenges that need to be addressed. First, assessment of sustainability requires consideration of not just economic, but also environmental and social impacts. Second, we need to find appropriate sustainability indicators and gather the necessary data in order to quantify sustainability performance. Finally, sustainability has to be seen in the context of the whole system, i.e. it has to include all activities along the supply chain. In this work, we consider all three aspects and propose a multi-objective optimisation framework for the optimisation of a sustainable supply chain. Three sustainability indicators have been considered, namely the total cost, GHG emissions and lead time. We apply this framework to an industrial test case using real-world data drawn from a Dow Chemical business. The results show clear trade-offs between the three different objectives. However, we can also observe that typically a considerable decrease in GHG emissions or lead time can already be achieved by only a relatively small increase in cost. The proposed framework enables us to determine such trade-off relations and consequently make decisions that improve the sustainability performance of the supply chain.     

An inexact fuzzy-queue programming model for environmental systems planning

In this study, an inexact fuzzy-queue programming (IFQP) model is developed for municipal solid waste (MSW) management under uncertainty, where fuzzy queue (FQ) model is introduced into the interval-fuzzy linear programming (IFLP) framework. The proposed IFQP model can not only handle uncertainties that are presented in terms of fuzzy sets and discrete intervals, but also reflect the influence of FQ in decision-making problems. Moreover, it can help quantify the satisfaction degrees of the system cost and constraints under uncertainty. It improves upon the existing optimization model with advantages in data availability, uncertainty reflection and result analysis. In its solution process, the model is transformed into two deterministic submodels based on an interactive algorithm. Interval solutions which are stable in the decision space can be obtained by solving the two submodels sequentially. The developed model is applied to a case of long-term planning of MSW management systems to demonstrate its applicability. It can effectively reflect dynamic, interactive, and uncertain characteristics of municipal waste management systems. The decision variable solutions provide optimal schemes for waste flow allocation corresponding to lower and upper bounds of system cost. The results indicate that reasonable solutions have been generated. They are helpful for supporting: (i) decision making or justification of waste-allocation patterns of MSW management system, and (ii) in-depth analysis of tradeoffs among system cost, satisfaction degree, and environmental constraint under uncertainty.     

网络终端安全状况评估指标体系的研究

为了准确和全面地评估网络终端安全状况,完善网络信息安全保障体系,提出了一套评估网络终端安全状况的指标体系.该指标体系包含了网络终端资产、威胁和脆弱性的各个方面,减少了人为的主观因素影响.基于信息安全风险评估方法,建立了网络终端安全状况评估模型,利用层次模糊综合评价法及定性分析与定量计算相结合的评估技术对网络终端安全状况进行评估,并结合实例证明了该指标体系的合理性和可行性.     

An OVAL-based active vulnerability assessment system for enterprise computer networks

Many security problems are caused by vulnerabilities hidden in enterprise computer networks. It is very important for system administrators to have knowledge about the security vulnerabilities. However, current vulnerability assessment methods may encounter the issues of high false positive rates, long computational time, and requirement of developing attack codes. Moreover, they are only capable of locating individual vulnerabilities on a single host without considering correlated effect of these vulnerabilities on a host or a section of network with the vulnerabilities possibly distributed among different hosts. To address these issues, an active vulnerability assessment system NetScope with C/S architecture is developed for evaluating computer network security based on open vulnerability assessment language instead of simulating attacks. The vulnerabilities and known attacks with their prerequisites and consequences are modeled based on predicate logic theory and are correlated so as to automatically construct potential attack paths with strong operation power of relational database management system. The testing results from a series of experiments show that this system has the advantages of a low false positive rate, short running periods, and little impact on the performance of audited systems and good scalability. The security vulnerabilities, undetectable if assessed individually in a network, are discovered without the need to simulate attacks. It is shown that the NetScope system is well suited for vulnerability assessment of large-scale computer networks such as campus networks and enterprise networks. Moreover, it can also be easily integrated with other security tools based on relational databases.

FCV氢源系统生命周期评价及其软件实现

合理选择燃料电池汽车氢源系统,是发展燃料电池汽车的前提。论文首先介绍了生命周期评价方法(LCA),并将这一方法用于经济、能源和环境(EEE)领域。论文还阐述了如何进行EEE-LCA清单分析,继而进行EEE综合评价的步骤。根据：EEE-LCA的思想,编制了用于氢源系统方案综合评价的专用软件,方便了多方案的比较评价和敏感性分析。     

Regional or global? The question of low-emission food sourcing addressed with spatial optimization modelling

Does producing staple food locally cause fewer greenhouse gas emissions than food sourced through imports from another continent? To address this question we used a spatial optimization approach that minimized greenhouse gas emissions from production and transport of five food commodities (barley, maize, oil, sugar and wheat) and compared this to a setting of local production where distances between production and consumption were minimized. We focused on the example of two countries – Brazil and Germany – in order to allow modelling at high spatial resolution. In the model, a minimization of greenhouse gas emissions led to an allocation of large shares of production to locations abroad. In contrast, the local production case, optimized on distance only, resulted in higher greenhouse gas emissions. Our findings show that despite additional transport needs for imports, specialization of countries on the production of specified crops can represent a low climate impact strategy.     

An expert system for the identification of environmental impact based on a geographic information system

The identification and assessment of the environmental impacts of engineering projects is an essential step in studies on environmental impact (IES). There are methods that allow both tasks to be performed and methods that allow each of them to be carried out separately. Normally, traditional methods are used to identify and evaluate environmental impacts, such as matrices, cause-effect network diagrams or check lists. Here we report the configuration of an expert system as a tool that allows environmental impacts to be identified. The expert system is based on a geographic information system to configure the knowledge base, the inference motor and the user interface. The knowledge base comprises declarative knowledge (structured in an alphanumeric and spatial database from official cartographic information) and procedural knowledge (via heuristic rules that superimpose project actions over environmental factors). We then describe the application of the expert system to the study of the environmental impact of the R-3 motorway in the Community of Madrid, Spain. As results, running the expert system allows the identification of environmental impacts on environmental factors defined at the 1:5000 and 1:25000 cartographic scales. Finally, analysis of the results or conclusions allows the validity of the use of graphic expert systems to be compared for the identification of environmental impacts.     

Uncertain optimization model for multi-echelon spare parts supply system

The optimization of spare parts inventory for equipment system is becoming a dominant support strategy, especially in the defense industry. Tremendous researches have been made to achieve optimal support performance of the supply system. However, the lack of statistical data brings limitations to these optimization models which are based on probability theory. In this paper, personal belief degree is adopted to compensate the data deficiency, and the uncertainty theory is employed to characterize uncertainty arising from subjective personal cognition. A base-depot support system is taken into consideration in the presence of uncertainty, supplying repairable spare parts for equipment system. With some constraints such as costs and supply availability, the minimal expected backorder model and the minimal backorder rate model will be presented based on uncertain measure. Genetic algorithm is adopted in this paper to search for optimal solution. Finally, a numerical example is employed to illustrate the feasibility of the optimization models.     

Data-driven manufacturing: An assessment model for data science maturity

Today, data science presents immense opportunities by turning raw data into manufacturing intelligence in data-driven manufacturing that aims to improve operational efficiency and product quality together with reducing costs and risks. However, manufacturing firms face difficulties in managing their data science endeavors for reaping these potential benefits. Maturity models are developed to guide organizations by providing an extensive roadmap for improvement in certain areas. Therefore, this paper seeks to address this problem by proposing a theoretically grounded Data Science Maturity Model (DSMM) for manufacturing organizations to assess their existing strengths and weaknesses, perform a gap analysis, and draw a roadmap for continuous improvements in their progress towards data-driven manufacturing. DSMM comprises six maturity levels from “Not Performed” to” Innovating” and twenty-eight data science processes categorized under six headings: Organization, Strategy Management, Data Analytics, Data Governance, Technology Management, and Supporting. The applicability and usefulness of DSMM are validated through multiple case studies conducted in manufacturing organizations of various sizes, industries, and countries. The case study results indicate that DSMM is applicable in different settings and is able to reflect the organizations’ current data science maturity levels and provide significant insights to improve their data science capabilities.