Chemical Recycling of PET by Glycolysis: Polymerization and Characterization of the Dimethacrylated Glycolysate

Summary: In the framework of chemical recycling of polymers, leading to the generation of secondary value‐added products, PET flakes taken from post‐consumer soft drink bottles, were glycolyzed with DEG. The oligomers obtained were analyzed for their molecular weight and characterized by FT‐IR and POM. Subsequently, dimethacrylated oligoesters of PET glycolysate (PET‐GLY‐DMA) were synthesized by methacrylation of the glycolyzed PET product. The resulted monomer PET‐GLY‐DMA was studied by FT‐IR, POM and DSC. Thermal polymerization of this monomer was carried out at 80 °C in the presence of benzoyl peroxide as initiator. A UV‐curable formulation was also prepared on the basis of neat PET‐GLY‐DMA, as well as by mixing PET‐GLY‐DMA with styrene, using DMPA as photoinitiator. Nanoparticles of SiO₂ were dispersed into PET‐GLY‐DMA/styrene copolymers as reinforcing agents and the mechanical properties of resins formed were studied.

Preparation of methacrylated PET glycolysate.     

Analysis of the receiver response for a noncoaxial lidar system with fiber-optic output

The return signal of a noncoaxial lidar system with fiber-optic output is examined. The dependence of the overlap regions and the overlap factor of the system on the fiber diameter is calculated for several inclination angles between the laser beam and the optical receiver axes. The effect of central obstruction is included and both cases of Gaussian and quasi-Gaussian laser beam profiles are treated. The irradiance spatial distribution on the focal plane of the system is calculated and experimentally determined. Finally, an alignment procedure of the lidar system is described based on the comparison between the range-corrected lidar signal and the range-corrected exponentially attenuated Rayleigh backscattered coefficient.     

Search personalization through query and page topical analysis

Thousands of users issue keyword queries to the Web search engines to find information on a number of topics. Since the users may have diverse backgrounds and may have different expectations for a given query, some search engines try to personalize their results to better match the overall interests of an individual user. This task involves two great challenges. First the search engines need to be able to effectively identify the user interests and build a profile for every individual user. Second, once such a profile is available, the search engines need to rank the results in a way that matches the interests of a given user. In this article, we present our work towards a personalized Web search engine and we discuss how we addressed each of these challenges. Since users are typically not willing to provide information on their personal preferences, for the first challenge, we attempt to determine such preferences by examining the click history of each user. In particular, we leverage a topical ontology for estimating a user’s topic preferences based on her past searches, i.e. previously issued queries and pages visited for those queries. We then explore the semantic similarity between the user’s current query and the query-matching pages, in order to identify the user’s current topic preference. For the second challenge, we have developed a ranking function that uses the learned past and current topic preferences in order to rank the search results to better match the preferences of a given user. Our experimental evaluation on the Google query-stream of human subjects over a period of 1 month shows that user preferences can be learned accurately through the use of our topical ontology and that our ranking function which takes into account the learned user preferences yields significant improvements in the quality of the search results.     

Collaborative reputation mechanisms for electronic marketplaces

Members of electronic communities are often unrelated to each other, they may have never met and have no information on each other's reputation. This kind of information is vital in Electronic Commerce interactions, where the potential counterpart's reputation can be a significant factor in the negotiation strategy. Collaborative reputation mechanisms can provide personalized evaluations of the various ratings assigned to each user to predict their reliabilities. While these reputation mechanisms are developed in the context of electronic commerce, they are applicable in other types of electronic communities such as chatrooms, newsgroups, mailing lists, etc.     

Development of a Green Process for the Preparation of Antioxidant and Pigment-Enriched Extracts from Winery Solid Wastes Using Response Surface Methodology and Kinetics

Red grape pomace (RGP), an abundant wine industry solid waste, was used for the recovery of polyphenols and anthocyanin pigments, using ultrasound-assisted extraction and water/glycerol mixtures as the solvent. Glycerol concentration (C_gl) and liquid-to-solid ratio (R_L/S) were first optimized by implementing Box?Behnken experimental design and the process was further studied through kinetics. The optimal conditions were found to be C_gl = 90% (w/v) and R_L/S = 90 mL g^?1, and under these conditions the extraction of total polyphenols (TP) and total pigments (TPm) obeyed first-order kinetics. Maximum diffusivity (D_e) values were 4.22 × 10^?12 and 12.59 × 10^?12 m² s^?1, for TP and TPm, respectively, and the corresponding activation energies were (E_a) 13.94 and 8.22 kJ mol^?1.     

Improving maximum margin matrix factorization

Collaborative filtering is a popular method for personalizing product recommendations. Maximum Margin Matrix Factorization (MMMF) has been proposed as one successful learning approach to this task and has been recently extended to structured ranking losses. In this paper we discuss a number of extensions to MMMF by introducing offset terms, item dependent regularization and a graph kernel on the recommender graph. We show equivalence between graph kernels and the recent MMMF extensions by Mnih and Salakhutdinov (Advances in Neural Information Processing Systems 20, 2008). Experimental evaluation of the introduced extensions show improved performance over the original MMMF formulation.     

Leiomyosarcoma of the inferior vena cava. Diagnosis and surgical management

Leiomyosarcoma of vascular origin are rare tumors arising most frequently from the inferior vena cava (IVC). We report on three patients one of whom underwent definitive resection. These tumors most commonly involve the upper segment of the IVC, and appear with manifestations of the Budd-Chiari syndrome. Lesions at this level are not amenable to surgical therapy. Tumors of the middle and lower segments of the IVC usually cause right-sided pain. Diagnosis is difficult, but is best approached preoperatively by angiography and vena cavography. Optimal therapy of lesions at these levels is surgical resection. Resection of the IVC below the hepatic veins is possible with renal function preserved by collateral drainage of the left renal vein.