A framework based on fuzzy AHP-TOPSIS for prioritizing solutions to overcome the barriers in the implementation of ecodesign practices in SMEs

Small and medium enterprises (SMEs) are under pressure to adopt sustainable practices in their business due to increasing environmental challenges. This study aims to identify and prioritize the solutions to overcome the barriers in the implementation of ecodesign practices in SMEs. This study helps the companies to develop and implement the strategies on priority using higher ranked solutions. In this study, a framework is proposed that is based on fuzzy analytical hierarchy process and fuzzy technique for order preference by similarity to ideal solution to rank the solutions that overcome the barriers in ecodesign implementation in SMEs. The proposed framework is illustrated by conducting a case study of an Indian ring and piston manufacturing company. This framework provides an effective support in the decision-making of the company for producing eco-friendly products through the implementation of ecodesign practices. Results of this study emphasize that training of designers to use different ecodesign tools is an essential practice for SMEs.     

Bayesian areal wombling via adjacency modeling

Recently, public health professionals and other geostatistical researchers have shown increasing interest in boundary analysis, the detection or testing of zones or boundaries that reveal sharp changes in the values of spatially oriented variables. For areal data (i.e., data which consist only of sums or averages over geopolitical regions), Lu and Carlin (Geogr Anal 37: 265–285, 2005) suggested a fully model-based framework for areal wombling using Bayesian hierarchical models with posterior summaries computed using Markov chain Monte Carlo (MCMC) methods, and showed the approach to have advantages over existing non-stochastic alternatives. In this paper, we develop Bayesian areal boundary analysis methods that estimate the spatial neighborhood structure using the value of the process in each region and other variables that indicate how similar two regions are. Boundaries may then be determined by the posterior distribution of either this estimated neighborhood structure or the regional mean response differences themselves. Our methods do require several assumptions (including an appropriate prior distribution, a normal spatial random effect distribution, and a Bernoulli distribution for a set of spatial weights), but also deliver more in terms of full posterior inference for the boundary segments (e.g., direct probability statements regarding the probability that a particular border segment is part of the boundary). We illustrate three different remedies for the computing difficulties encountered in implementing our method. We use simulation to compare among existing purely algorithmic approaches, the Lu and Carlin (2005) method, and our new adjacency modeling methods. We also illustrate more practical modeling issues (e.g., covariate selection) in the context of a breast cancer late detection data set collected at the county level in the state of Minnesota.     

Risk-Based Viable Population Monitoring

Abstract:  We describe risk-based viable population monitoring, in which the monitoring indicator is a yearly prediction of the probability that, within a given timeframe, the population abundance will decline below a prespecified level. Common abundance-based monitoring strategies usually have low power to detect declines in threatened and endangered species and are largely reactive to declines. Comparisons of the population's estimated risk of decline over time will help determine status in a more defensible manner than current monitoring methods. Monitoring risk is a more proactive approach; critical changes in the population's status are more likely to be demonstrated before a devastating decline than with abundance-based monitoring methods. In this framework, recovery is defined not as a single evaluation of long-term viability but as maintaining low risk of decline for the next several generations. Effects of errors in risk prediction techniques are mitigated through shorter prediction intervals, setting threshold abundances near current abundance, and explicitly incorporating uncertainty in risk estimates. Viable population monitoring also intrinsically adjusts monitoring effort relative to the population's true status and exhibits considerable robustness to model misspecification. We present simulations showing that risk predictions made with a simple exponential growth model can be effective monitoring indicators for population dynamics ranging from random walk to density dependence with stable, decreasing, or increasing equilibrium. In analyses of time-series data for five species, risk-based monitoring warned of future declines and demonstrated secure status more effectively than statistical tests for trend.     

对现行监测结果报告规则的粗浅看法

监测结果的报告是整个环境监测工作的结晶。它要求必须实事求是，才能为环境管理提供科学依据。而现行的监测结果报告规则。存在着人为夸大和缩小两方面的弊端，低于检出限的监测结果的表述模糊。不能真实的反映监测对象的实际情况，对人们了解环境质量状况及环境容量不能提供准确依据。给环境管理与环境规划等工作造成误导，本文以实例作了说明，并提出解决的方法。     

采空区地面塌陷危险性两级模糊综合评判

根据门城镇采空区实地勘查资料,采用两级模糊综合评判的理论与方法,建立了地面塌陷危险性评判模型及评判指标,对采空区地面塌陷危险性进行了分区预测,其结果与实际情况吻合较好,可作为该区的开发建设规划及地质灾害防治工作的依据。     

大气对人体硒、碘、氟的直接影响研究

对一个磷肥厂周围12个村庄的人发、粮食和饮水硒、碘、氟进行了测定,利用改进的模糊数学中的群体识别法对以上数据进行分析,所得结果表明,受污染较重的几个村庄人体硒、碘、氟含量确实受到来自磷肥厂的大气高浓度硒、碘、氟的直接影响.     

中国环境中氮循环的动态模式

对中国960万km~2范围内氮的生物地球化学循环作了初步探讨。该工作建立在对大气、土、生物及水圈界面氮的流通量的研究基础上,以数学方式模拟氮的生物地球化学循环规律,并预测各圈中氮的库存量和流通量的变化趋势。经过验证,模式的收敛性、稳定性及可信度均是好的。     

矿区开发对环境的影响与评价方法

分析了矿区开发对环境的影响，在此基础上建立了评价矿区环境质量的分层次指标体系和模糊综合评价方法，并将矿区环境质量分为５个等级：清洁，较清洁，轻污染，中污染和重污染。通过利用模糊评价使复杂的定性问题定量化，更直观简单地掌握矿区环境的治理状态，并利用此方法对潞安矿区的环境质量进行了评价。     

海岸地区三维风场与污染物输送扩散模式

首先建立了一个复杂地形条件下的中尺度三维边界层预报模式,模拟研究了局地海陆风环流条件下的三维风场。并将其输出直接作为一个三维欧拉数值模式的输入,以模拟沿海地区海陆风演变过程中,空气污染物(SO_2)浓度的时空分布。为更接近实际,还专门建立了一个只需少量常规气象资料输入的背景风诊断模式。较好地完成了这三个模式间的联接。实例模拟计算的结果表明,此联接模式是行之有效的。