Sustainable Design Process and Factors Considered for Product Service System

"Sustainability" is apparently reflected in corporate management and product development. Technology development and industrial revolution not only provide human beings luxury living quality but also causes global environmental problems and natural resources crisis on earth. Therefore, quite a few environmental protection policies in the world have been made. "Reduce, reuse and recycle" definitely becomes a new life trend. Under this circumstance, Product Service System (PSS) is a new way to satisfy the customers' needs by means of a complete process in products and services. It can make resource usage become a closed loop, thus reducing total product quantity and enhancing resource usage sufficiency. PSS has a characteristic of lower environmental impacts. Therefore, the author incorporates the concept of PSS into sustainable design strategy. This study first utilizes Analytic Network Process (ANP) to analyze both service categories from PSS and ranking priority of life cycle structure to be the foundation of sustainable design. Secondly, this study adopts Modified Delphi?MD?method to inspect the sustainable design factors considered for the application of PSS on case study. Furthermore, the author develops evaluation guidance and check list to make sure the target achievement of product sustainable design. Finally, this research accomplishes an applied process of PSS's sustainable design. By employing PSS on sustainable design, the study improves the impact of product life cycle on environmental quality. As a result, this study provides the PSS's design factors considered of sustainable products for corporations, and supplies a continuous service to create an operation mechanism with higher profit and lower risk as well

DENDRITIC POLYMERS AS BIOCOMPATIBLE OIL SPILL DISPERSANTS: EFFECTIVENESS AND MECHANISIMS WITH CRUDE OIL

Dendritic polymers have recently been shown to entrap polycyclic aromatic hydrocarbons (PAHs) and other hydrophobic materials. Laboratory results have shown that poly(amidoamine) dendrimers and hyperbranched poly(ethyleneimine) polymers form complexes with linear (hexadecane) and polyaromatic (phenanthrene) hydrocarbons, increasing the dispersion of these model crude oil components. It is thus hypothesized that crude oil can be dispersed using these polymers. Compared with commercial dispersants, dendritic polymers have the potential to be more biocompatible and less toxic. The objective of this research was to gain a fundamental understanding of the interactions of dendritic polymers with crude oil. We used Louisians Sweet Crude oil to explore the dispersion effectiveness of the polymers and the mechanisms of oil-polymer interactions. Results were compared with Corexit 9500, the dispersant used in response to the Deepwater Horizon disaster of 2010. We investigated the factors that may influence the experimental results, such as dispersant to oil ratio (DOR), mixing and settling time, sample preparation methods and sample collection methods to establish experimental protocols that adequately characterized the effectiveness of the polymers. The effects of polymer size and surface groups on oil dispersion effectiveness were examined through an optimized effectiveness test. Five hyperbranched polyethylenimine polymers (HY-PEI) with molecular weight 1.2, 1.8, 10, 70 and 750 kDa and amino surface terminal groups were examined with the effectiveness test. The results showed the 10 kDa HY-PEI had the highest dispersion efficiency (58%) slightly larger than Corexit (56%). 70 kDa and 750 kDa HY-PEI also had a relatively high effectiveness, 48% and 40% respectively; however, the low molecular weight polymers, 1.2 kDa and 1.8 kDa had low dispersion efficiency, 11% and 17%, similar to the no dispersant scenario which had 13% oil dispersion. We also tested three dendrimers with different surface terminal groups: amino (positive charge), amidoethanol (neutral charge) and succinamic acid (negative charge). The results showed that G4-PAMAM-NH2 with positive surface charge had the highest efficiency of these three, 42%. G4-PAMAM-OH and G4-PAMAM-SA had lower dispersion capacity, with effectiveness of 16% and 19%. We concluded that the polymers with moderate size and positive charged surface groups are very capable in dispersing light sweet crude oil. Further exploring the interactions of dendritic polymers with crude oil, we conducted dynamic interfacial tension test and oil droplet size distribution test. The dynamic interfacial tension curves shows that all the polymers can reduce the interfacial tension and the larger polymers are more capable at decreasing the interfacial tension rapidly. The efficiency of polymer dispersion for oil has also been verified by drop size distribution measurements. Polymers with high performance in effectiveness test tend to create smaller droplets than polymers that show less effectiveness. Again, moderately sized polymers gave the smaller average droplet size and polydispersity. By further analyzing the data we developed a conceptual model for the oil dendritic polymer interaction, which is a hybrid surfactant and Pickering emulsion mechanism

AIC-AB NET: A Neural Network for Image Captioning with Spatial Attention and Text Attributes

Image captioning is a significant field across computer vision and natural language processing. We propose and present AIC-AB NET, a novel Attribute-Information-Combined Attention-Based Network that combines spatial attention architecture and text attributes in an encoder-decoder. For caption generation, adaptive spatial attention determines which image region best represents the image and whether to attend to the visual features or the visual sentinel. Text attribute information is synchronously fed into the decoder to help image recognition and reduce uncertainty. We have tested and evaluated our AICAB NET on the MS COCO dataset and a new proposed Fashion dataset. The Fashion dataset is employed as a benchmark of single-object images. The results show the superior performance of the proposed model compared to the state-of-the-art baseline and ablated models on both the images from MSCOCO and our single-object images. Our AIC-AB NET outperforms the baseline adaptive attention network by 0.017 (CIDEr score) on the MS COCO dataset and 0.095 (CIDEr score) on the Fashion dataset