Integration of life cycle assessment and life cycle costing for the eco-design of rubber products

Rubber hoses are a category of rubber products that are widely and intensively employed in construction sites for concrete conveying. There has been lack of study to investigate the life cycle environmental and economic impacts of the rubber hoses as an industrial product. In this study, we analyze four types of rubber hoses with the inner layer made of different rubber composites to resist abrasion, i.e., Baseline, S-I, S-II and S-III. Tests of the wear resistance are carried out in the laboratory and S-III shows high abrasion resisting performance with the concrete conveying volume up to 20,000 m3 during the service life. Life cycle assessment (LCA) and life cycle costing (LCC) models are established for evaluating the four types of rubber hoses. A target function is developed to integrate LCA and LCC by converting the LCA results to the environmental costs. It is found that S-III can save 13% total cost comparing to Baseline. The production stage is the largest contributor to the environmental single score, while the use stage is the largest contributor to the life cycle cost. Sensitivity analyses are conducted and the results of this study are validated with the previous studies. The integrated method of LCA and LCC developed in this study paves a way for the eco-design of industrial rubber hoses and is potentially applicable to other rubber products.

LA PALABRA IMAGEN: ESTUDIO SEMIÓTICO-COGNITIVO DE LA POESÍA VISUAL DE FERNANDO MILLÁN, FELIPE BOSO Y CLARA JANÉS

El presente trabajo propone un estudio de la llamada poesía visual mediante un método integrado que hará confluir las herramientas de análisis de la teoría semiótica con las consideraciones de la Poética Cognitiva respecto a este tipo de objetos intermediales. De esta manera, de un estudio teórico inicial que permitirá extraer estas herramientas cognitivo-semióticas de análisis, se pasará a un estudio práctico, mediante el método previamente desarrollado, de obras concretas y variadas de poesía visual, seleccionadas entre la creación de Fernando Millán, Felipe Boso y Clara Janés. Abstract: The present work develops a study on the so-called visual poetry by means of an integrated method which will fuse the analytical tools of the semiotic theory with considerations of Cognitive Poetics about this kind of intermedial objects. Therefore, from a theoretical study which will provide us with this cognitive-semiotic analytical tools, a practical study will follow with the analysis of specific and varied works of visual poetry, which will be selected within the works of Fernando Millán, Felipe Boso and Clara Janés.

An Integrated Method for Modular Design Based on Auto-Generated Multi-Attribute DSM and Improved Genetic Algorithm

Modular architecture is very conducive to the development, maintenance, and upgrading of electromechanical products. In the initial stage of module division, the design structure matrix (DSM) is a crucial measure to concisely express the component relationship of electromechanical products through the visual symmetrical structure. However, product structure modeling, as a very important activity, was mostly carried out manually by engineers relying on experience in previous studies, which was inefficient and difficult to ensure the consistency of the model. To overcome these problems, an integrated method for modular design based on auto-generated multi-attribute DSM and improved genetic algorithm (GA) is presented. First, the product information extraction algorithm is designed based on the automatic programming structure provided by commercial CAD software, to obtain the assembly, degrees of freedom, and material information needed for modeling. Secondly, based on the evaluation criteria of product component correlation strength, the structural correlation DSM and material correlation DSM of components are established, respectively, and the comprehensive correlation DSM of products is obtained through weighting processing. Finally, the improved GA and the modularity evaluation index Q are used to complete the product module division and obtain the optimal modular granularity. Based on a model in published literature and a bicycle model, comparative studies are carried out to verify the effectiveness and practicality of the proposed method.

Measuring the Similarity of Metro Stations Based on the Passenger Visit Distribution

The distribution of passengers reflects the characteristics of urban rail stations. The automatic fare collection system of rail transit collects a large amount of passenger trajectory data tracking the entry and exit continuously, which provides a basis for detailed passenger distributions. We first exploit the Automatic Fare Collection (AFC) data to construct the passenger visit pattern distribution for stations. Then we measure the similarity of all stations using Wasserstein distance. Different from other similarity metrics, Wasserstein distance takes the similarity between values of quantitative variables in the one-dimensional distribution into consideration and can reflect the correlation between different dimensions of high-dimensional data. Even though the computational complexity grows, it is applicable in the metro stations since the scale of urban rail transit stations is limited to tens to hundreds and detailed modeling of the stations can be performed offline. Therefore, this paper proposes an integrated method that can cluster multi-dimensional joint distribution considering similarity and correlation. Then this method is applied to cluster the rail transit stations by the passenger visit distribution, which provides some valuable insight into the flow management and the station replanning of urban rail transit in the future.

Determining Plant Diversity within Interconnected Natural Habitat Remnants (Ecological Network) in an Agricultural Landscape: A Matter of Sampling Design?

In intensively used and human-modified landscapes, biodiversity is often confined to remnants of natural habitats. Thus, identifying ecological networks (ENs) necessary to connect these patches and maintain high levels of biodiversity, not only for conservation but also for the effective management of the landscape, is required. However, ENs are often defined without a clear a-priori evaluation of their biodiversity and are seldom even monitored after their establishment. The objective of this study was to determine the adequate number of replicates to effectively characterize biodiversity content of natural habitats within the nodes of an EN in north-eastern Italy, based on vascular plant diversity. Plant communities within habitat types of the EN’s nodes were sampled through a hierarchical sampling design, evaluating both species richness and compositional dissimilarity. We developed an integrated method, consisting of multivariate measures of precision (MultSE), rarefaction curves and diversity partitioning approaches, which was applied to estimate the minimum number of replicates needed to characterize plant communities within the EN, evaluating also how the proposed optimization in sampling size affected the estimations of the characteristics of habitat types and nodes of the EN. We observed that reducing the total sampled replicates by 85.5% resulted to sufficiently characterize plant diversity of the whole EN, and by 72.5% to exhaustively distinguish plant communities among habitat types. This integrated method helped to fill the gap regarding the data collection to monitor biodiversity content within existing ENs, considering temporal and economic resources. We therefore suggest the use of this quantitative approach, based on probabilistic sampling, to conduct pilot studies in the context of ENs design and monitoring, and in general for habitat monitoring.

Assessment of groundwater potential zones using GIS and AHP techniques: a case study of the Lafia district, Nasarawa State, Nigeria

In the Lafia district, rising population has increased the need for groundwater resources for economic growth. Sustainable groundwater resource management demands accurate quantitative assessment, which may be accomplished using scientific theories and innovative methods. In present study, an integrated method has been employed to assess the groundwater potential zones in the Lafia district utilizing remote sensing (RS), geographic information system (GIS), and analytic hierarchy method (AHP). For this aim, eight thematic maps regulating to occurrence and transportation of groundwater (i.e., geology, rainfall, geomorphology, slope, drainage density, soil, land use/land cover and lineament density) were generated and converted into raster format utilizing ArcGIS tool. Weights were assigned to these eight thematic maps based on their importance. Moreover, the final normalized weights of these parameters were calculated adopting pairwise comparison matrix of the AHP. To create the groundwater potential zones (GWPZs) map of the research area, we employed the overlay weighted sum approach to combine the parameters. The map has been divided into four zones (good, moderate, poor and very poor), each of which represents 19.3, 12.9, 57.8, and 10% of the study area. Lastly, the GWPZs map was validated utilizing borehole data obtained from 50 wells scattered throughout the study area to examine the performance of the approach. The validation results demonstrate that the adopted procedure produces highly reliable results that can aid in long-term development and strategic use of groundwater resources in this area.

Mapping Large-Scale Forest Disturbance Types with Multi-Temporal CNN Framework

Forests play a vital role in combating gradual developmental deficiencies and balancing regional ecosystems, yet they are constantly disturbed by man-made or natural events. Therefore, developing a timely and accurate forest disturbance detection strategy is urgently needed. The accuracy of traditional detection algorithms depends on the selection of thresholds or the formulation of complete rules, which inevitably reduces the accuracy and automation level of detection. In this paper, we propose a new multitemporal convolutional network framework (MT-CNN). It is an integrated method that can realize long-term, large-scale forest interference detection and distinguish the types (forest fire and harvest/deforestation) of disturbances without human intervention. Firstly, it uses the sliding window technique to calculate an adaptive threshold to identify potential interference points, and then a multitemporal CNN network is designed to render the disturbance types with various disturbance duration periods. To illustrate the detection accuracy of MT-CNN, we conducted experiments in a large-scale forest area (about 990 km2) on the west coast of the United States (including northwest California and west Oregon) with long time-series Landsat data from 1986 to 2020. Based on the manually annotated labels, the evaluation results show that the overall accuracies of disturbance point detection and disturbance type recognition reach 90%. Also, this method is able to detect multiple disturbances that continuously occurred in the same pixel. Moreover, we found that forest disturbances that caused forest fire repeatedly appear without a significant coupling effect with annual temporal and precipitation variations. Potentially, our method is able to provide large-scale forest disturbance mapping with detailed disturbance information to support forest inventory management and sustainable development.

First Application of the Integrated Karst Aquifer Vulnerability (IKAV) method. Potential and Actual vulnerability in Yucatan, Mexico

Groundwater vulnerability maps are important decision support tools for water resources protection against pollution and helpful to minimize environmental damage. However, these tools carry a high subjectivity along the multiple steps taken for the development of such maps. Additionally, the theoretical models on which they are based do not consider important parameters such as pollutant concentration or pollutant residence time in a given section of the aquifer, solely focusing the analysis on a theoretical travel time from a release point towards a target. In this work, an integrated methodology for the evaluation of potential (intrinsic) and actual vulnerability is presented. This integrated method, named IKAV, was developed after the analysis of several study cases and the application of multiple intrinsic groundwater vulnerability methods in a selected study area. Also, a solute transport model served as the basis to define additional parameters for vulnerability analysis for areas severely affected by anthropogenic practices. A defined workflow and several criteria for parameters and attributes selection, rating and weighting assignment, and vulnerability classification are presented. The first application of the IKAV method was carried out in the Yucatan karst, demonstrating to be a reliable method for vulnerability estimation. Results demonstrated the scope of the IKAV method to highlight important regional conditions, minimizing the subjectivity, and expanding the analysis of vulnerability.