The impact of electronic environmental knowledge on the environmental behaviors of people

Nowadays, we examined the connection between individuals' relationships with the natural environment, their environmental behaviors, and their environmental knowledge with a focus on environmental attitudes, environmental awareness, environmental values, public information, environmental skills, and environmental responsibility is very interesting. Environmental knowledge involves human discourse about interrelationships with the environment. The impact of people to perceive and interpret the relative health of environmental systems and to take appropriate action to maintain, restore, or improve the health of those systems is very important. Therefore, environmental teaching and learning is very necessary and can help to improve the environment behaviors of the people. The purpose of this paper is to investigate the impact of e-learning system the environmental of the behavior. Data were collected from 330 students of Tabriz Branch, Islamic Azad University. After performing satisfactory reliability and validity checks, the structural model was tested with the use of smart PLS 2.0. Results indicate that electronic environmental knowledge learning has a direct effect on person's environmental behaviors.     

Identification and characterization of information-networks in long-tail data collections

Scientists' ability to synthesize and reuse long-tail scientific data lags far behind their ability to collect and produce these data. Many Earth Science Cyberinfrastructures enable sharing and publishing their data over the web using metadata standards. While profiling data attributes advances the Linked Data approach, it has become clear that building information-networks among distributed data silos is essential to increase their integration and reusability. In this research, we developed a Long-Tail Information-Network (LTIN) model, which uses a metadata-driven approach to build semantic information-networks among datasets published over the web and aggregate them around environmental events. The model identifies and characterizes the spatial and temporal contextual association links and dependencies among datasets. This paper presents the design and application of the LTIN model, and an evaluation of its performance. The model capabilities were demonstrated by inferring the information-network of a stream discharge located at the downstream end of the Illinois River.     

The use of SVM-FFA in estimating fatigue life of polyethylene terephthalate modified asphalt mixtures

To predict fatigue life of Polyethylene Terephthalate (PET) modified asphalt mixture, various soft computing methods such as Genetic Programming (GP), Artificial Neural Network (ANN), and Fuzzy Logic-based methods have been employed. In this study, an application of Support Vector Machine Firefly Algorithm (SVM-FFA) is implemented to predict fatigue life of PET modified asphalt mixture. The inputs are PET percentages, stress levels and environmental temperatures. The performance of proposed method is validated against observed experiment data. The results of the prediction using SVM-FFA are then compared to those of applying ANN and GP approach and it is concluded that SVM-FFA leads to more accurate results when compared to observed experiment data.     

Renewable and nuclear electricity: Comparison of environmental impacts

Given the widely acknowledged negative impacts of fossil fuels, both on human health and on potential climate change, it is of interest to compare the impacts of low carbon alternative energy sources such as nuclear energy, hydropower, solar, wind and biomass. In this paper, we review the literature in order to summarise the impacts of the different technologies in terms of their materials and energy requirements, their emissions during operation, their health effects during operation, the accident risks, and the associated waste streams. We follow up these comparisons with some more anecdotal evidence on selected impacts that are either particularly topical or are important but less commonly addressed. These include impacts of wind turbines on persons and on bird life, the underestimated problems with biomass, and concerns about biodiversity reduction. Finally we address the public attitudes towards both renewable energy technologies and to nuclear power. The conclusion is drawn that energy policies of many countries are perhaps more strongly influenced by public and political perceptions of available technologies than they are by rational assessment of the actual benefits and drawbacks. Policy recommendations follow from this conclusion.     

Comparative analysis of discretization methods in Bayesian networks

A key step in implementing Bayesian networks (BNs) is the discretization of continuous variables. There are several mathematical methods for constructing discrete distributions, the implications of which on the resulting model has not been discussed in literature. Discretization invariably results in loss of information, and both the discretization method and the number of intervals determines the level of such loss. We designed an experiment to evaluate the impact of commonly used discretization methods and number of intervals on the developed BNs. The conditional probability tables, model predictions, and management recommendations were compared and shown to be different among models. However, none of the models did uniformly well in all comparison criteria. As we cannot justify using one discretization method against others, we recommend caution when discretization is used, and a verification process that includes evaluating alternative methods to ensure that the conclusions are not an artifact of the discretization approach.     

Fatigue behaviour of nanoclay reinforced epoxy resin composites

Nanoparticle filling is a feasible way to increase the mechanical properties of polymer matrices. Abundant research work has been published in the last number of years concerning the enhancement of the mechanical properties of nanoparticle filled polymers, but only a reduced number of studies have been done focusing on the fatigue behaviour. This work analyses the influence of nanoclay reinforcement and water presence on the fatigue behaviour of epoxy matrices. The nanoparticles were dispersed into the epoxy resin using a direct mixing method. The dispersion and exfoliation of nanoparticles was characterised by X-ray diffraction (XRD) and transmission electron microscopy (TEM). Fatigue strength decreased with the nanoclay incorporation into the matrix. Fatigue life of nanoclay filled composites was significantly reduced by the notch effect and by the immersion in water.     

Economic value and environmental impact (EVEI) analysis of biorefinery systems

The selection of product portfolios, processing routes and the combination of technologies to obtain a sustainable biorefinery design according to economic and environmental criteria represents a challenge to process engineering. The aim of this research is to generate a robust methodology that assists process engineers to conceptually optimise the environmental and economic performances of biorefinery systems. A novel economic value and environmental impact (EVEI) analysis methodology is presented in this paper. The EVEI analysis is a tool that emerges from the combination of the value analysis method for the evaluation of economic potential with environmental footprinting for impact analysis. The methodology has been effectively demonstrated by providing insights into the performance of a bioethanol plant as a case study. The systematisation of the methodology allowed its implementation and integration into a computer-aided process engineering (CAPE) tool in the spreadsheet environment.     

Eco-efficiency of the world cement industry: A data envelopment analysis

Chemical reactions and the combustion of dirty fuels, such as coal and petroleum coke (petcoke), that are used in cement production processes generate a significant amount of CO₂ emissions. In this paper, we provide an eco-efficiency measure for 21 prototypes of cement industries operating in many countries by applying both a data envelopment analysis (DEA) and a directional distance function approach, which are particularly suitable for models where several production inputs and desirable and undesirable outputs are taken into account. To understand whether this eco-efficiency is due to a rational utilization of inputs or to a real carbon dioxide reduction as a consequence of environmental regulation, we analyze the cases where CO₂ emissions can either be considered as an input or as an undesirable output. Empirical results show that countries where cement industries invest in technologically advanced kilns and adopt alternative fuels and raw materials in their production processes are eco-efficient. This gives a comparative advantage to emerging countries, such as India and China, which are incentivized to modernize their production processes.     

Occurrence and traceability of Listeria monocytogenes strains isolated from sheep's milk cheese-making plants environment

The aim of the study was to conduct an extensive survey on Listeria monocytogenes and Listeria spp. environmental contamination in 13 cheese-making plants. A total of 409 environmental and food samples were collected during years 2011–2013. Listeria spp. contamination was observed in all the facilities, while L. monocytogenes was recovered from 12 facilities with a prevalence ranging between 3.0% and 22.6%. Floor drains were the most contaminated sampling sites (48.8% of positive samples), serving as harbourage site for subsequent contamination. Out of 616 isolates, 277 (45.0%) were Listeria innocua, 274 (44.5%) L. monocytogenes, 41 (6.6%) Listeria ivanovii, 14 (2.3%) Listeria welshimeri and 10 (1.6%) Listeria gravyi. Serotyping carried out by PCR and agglutination method for L. monocytogenes revealed that 169 strains (61.7%) were serotype 1/2a, 65 (23.7%) 4b, 20 (7.3%) 1/2b, 10 (3.6%) 3a, 7 (2.5%) 1/2c and 3 (1.1%) 3b. PFGE conducted on L. monocytogenes isolates using AscI and ApaI restriction enzymes, yielded 6 clusters. Two predominant PFGE clusters were observed including respectively 36 and 32 strains. Within cheese-making plants, L. monocytogenes showed wide variability with strains distributed up to 4 different clusters. Pulsotypes isolated from raw milk filter were never detected in the processing environment, indicating that the contamination originated from sources other than raw milk. The isolation of strains with similar profile from different sampling sites, within and among cheese-making plants, indicated the possible transfer of L. monocytogenes contamination along production lines and from one facility to another. Strains recovered from food were confirmed as originating from the processing environment.