Undercutting of defects in thin film protective coatings on polymer surfaces exposed to atomic oxygen

Protection for polymeric surfaces is needed to make them durable in the low Earth orbital environment, where oxidation by atomic oxygen is the predominant failure mechanism. Thin film coatings of oxides such as silicon dioxide are viable candidates to provide this protection, but concern has been voiced over the ability of these coatings to protect when defects are present in the coating due to surface anomalies occurring during the deposition process, handling, or micrometeoroid and debris bombardment in low Earth orbit. When a defected coating protecting a polymer substrate is exposed to atomic oxygen, the defect provides a pathway to the underlying polymer allowing oxidation and subsequent undercutting to occur. Defect undercutting was studied for sputter deposited coatings of silicon dioxide on polyimide Kapton. Preliminary results indicate that undercutting may be limited as long as the coating remains intact with the substrate. Therefore, coatings may not need to be defect free to give protection to the underlying surface

Life cycle approach for evaluating sanitation projects - case study: biogas latrine

This paper applies a Life Cycle Assessment framework for the evaluation of water and sanitation projects to a biogas latrine constructed in Uganda. This will be the first time this assessment tool is applied to a sanitation project in the East African Region. While using this tool, one takes into consideration five life stages of a development project and five sustainability factors (socio cultural respect, community participation, political cohesion, economic sustainability, and environmental sustainability). By using this tool during planning, implementation and evaluation of a project, the sustainability of a project can be increased and lessons can be learned and implemented in similar future projects. In this case study the tool was used to evaluate the biogas project and create a starting point to rehabilitate the system

Improving Energy Efficiency and Environmental Sustainability of Commercial Insulation

With increasingly stringent energy standards set in place by the Department of Energy, energy efficiency is becoming a paramount concern to manufacturers of appliances. Additionally, the production and disposal of the voluminous amount of polyurethane foam commonly utilized as insulation in refrigeration units poses a significant environmental challenge. In this context, this study investigated an alternative insulation for use in commercial refrigerator/freezer units. A prototype exploring the use of evacuated packets of pyrogenic silica substituting for conventional insulation was assessed. Assessment criteria included experimental comparison of heat transfer characteristics and the energy efficiency of the new insulation as well as its life cycle as it is related to environmental sustainability. Results indicate that in the new insulation design applied to the unit’s cover, heat flux decreased by an average of 36%, and energy efficiency improved by 5.1% over a 24 hour period. The new insulation design also resulted in improved environmental sustainability, resulting in a savings of 0.257 metric tons of CO2e over 20 years for a single unit. Results provide an alternative insulation design for use in commercial refrigerator and freezers, and a framework by which to assess the efficiency and environmental performance of similar products

Similarities in the Use of Dramatic Recitative Style in the Music of Claudio Monteverdi and Giuseppe Verdi, with Some Performance-Practice Issues

The objective of this dissertation, inspired by performance experience, was to establish the similarities in the use of recitative style in the music of Claudio Monteverdi and Giuseppe Verdi. To achieve this objective, their use of recitative style was examined through comparative analysis of four scenes from their operas: “Arianna's Lament” from L'Arianna and “Disprezzata regina” from L'incoronazione di Poppea by Monteverdi, and “Condotta ell'era in ceppi” from Il trovatore and “Judgment Scene” from Aida by Verdi. The examination of the similarities included a discussion of the following: (a) the historical influences and cultural backgrounds of the composers; (b) general similarities in their compositional approaches to recitative style; (c) comparable characteristics of the dramatic recitative style in the early Baroque monody and in Verdi's operas; (d) similarities in musical characterization and expression of affective and emotional content through stylistic musical devices; (e) similarities in the composers' approaches to vocal and acting issues with special emphasis on the problems of diction; and (f) some related performance-practice issues. A discussion of the poetic lament and the influence of its form and content on musical setting was also a part of this research. The comparative research revealed numerous similarities in the historical circumstances influencing Monteverdi's and Verdi's choice of musical styles; their motivation; formal and stylistic characteristics of their dramatic recitative scenes; their choice of libretto; their use of the elements of lament; their musical treatment of emotional content of the text; and their prerogatives in vocal and acting issues. Numerous similar characteristics were also established regarding vibrato, tempo, rhythm, and ornamentation in the performance practice of the early Baroque recitative soliloquy and Verdi's dramatic recitative scenes. The similarities of the four scenes' functions, topics, form, and characterization through devices of musical style indicated a fundamental continuity in the development of Italian opera from its inception to the end of the nineteenth century

Challenges and Opportunities of Using Transformer-Based Multi-Task Learning in NLP Through ML Lifecycle: A Survey

The increasing adoption of natural language processing (NLP) models across industries has led to practitioners' need for machine learning systems to handle these models efficiently, from training to serving them in production. However, training, deploying, and updating multiple models can be complex, costly, and time-consuming, mainly when using transformer-based pre-trained language models. Multi-Task Learning (MTL) has emerged as a promising approach to improve efficiency and performance through joint training, rather than training separate models. Motivated by this, we first provide an overview of transformer-based MTL approaches in NLP. Then, we discuss the challenges and opportunities of using MTL approaches throughout typical ML lifecycle phases, specifically focusing on the challenges related to data engineering, model development, deployment, and monitoring phases. This survey focuses on transformer-based MTL architectures and, to the best of our knowledge, is novel in that it systematically analyses how transformer-based MTL in NLP fits into ML lifecycle phases. Furthermore, we motivate research on the connection between MTL and continual learning (CL), as this area remains unexplored. We believe it would be practical to have a model that can handle both MTL and CL, as this would make it easier to periodically re-train the model, update it due to distribution shifts, and add new capabilities to meet real-world requirements

In-silico identification of phenotype-biased functional modules

<p>Abstract</p> <p>Background</p> <p>Phenotypes exhibited by microorganisms can be useful for several purposes, e.g., ethanol as an alternate fuel. Sometimes, the target phenotype maybe required in combination with other phenotypes, in order to be useful, for e.g., an industrial process may require that the organism survive in an anaerobic, alcohol rich environment and be able to feed on both hexose and pentose sugars to produce ethanol. This combination of traits may not be available in any existing organism or if they do exist, the mechanisms involved in the phenotype-expression may not be efficient enough to be useful. Thus, it may be required to genetically modify microorganisms. However, before any genetic modification can take place, it is important to identify the underlying cellular subsystems responsible for the expression of the target phenotype.</p> <p>Results</p> <p>In this paper, we develop a method to identify statistically significant and phenotypically-biased functional modules. The method can compare the organismal network information from hundreds of phenotype expressing and phenotype non-expressing organisms to identify cellular subsystems that are more prone to occur in phenotype-expressing organisms than in phenotype non-expressing organisms. We have provided literature evidence that the phenotype-biased modules identified for phenotypes such as hydrogen production (dark and light fermentation), respiration, gram-positive, gram-negative and motility, are indeed phenotype-related.</p> <p>Conclusion</p> <p>Thus we have proposed a methodology to identify phenotype-biased cellular subsystems. We have shown the effectiveness of our methodology by applying it to several target phenotypes. The code and all supplemental files can be downloaded from (<url>http://freescience.org/cs/phenotype-biased-biclusters/</url>).</p

Synthesis and Preliminary Evaluation Steroidal AntiestrogenGeldanamycin Conjugates

Three novel steroidal antiestrogen-geldanamycin conjugates were prepared using a convergent strategy. The antiestrogenic component utilized the 11β-(4-functionalized-oxyphenyl) estradiol scaffold, while the geldanamycin component was derived by replacement of the 17-methoxy group with an appropriately functionalized amine. Ligation was achieved in high yield using azide alkyne cyclization reactions. Evaluation of the products against two breast cancer cell lines indicated that the conjugates retained significant antiproliferative activity

DENSE: efficient and prior knowledge-driven discovery of phenotype-associated protein functional modules

<p>Abstract</p> <p>Background</p> <p>Identifying cellular subsystems that are involved in the expression of a target phenotype has been a very active research area for the past several years. In this paper, <it>cellular subsystem </it>refers to a group of genes (or proteins) that interact and carry out a common function in the cell. Most studies identify genes associated with a phenotype on the basis of some statistical bias, others have extended these statistical methods to analyze functional modules and biological pathways for phenotype-relatedness. However, a biologist might often have a specific question in mind while performing such analysis and most of the resulting subsystems obtained by the existing methods might be largely irrelevant to the question in hand. Arguably, it would be valuable to incorporate biologist's knowledge about the phenotype into the algorithm. This way, it is anticipated that the resulting subsytems would not only be related to the target phenotype but also contain information that the biologist is likely to be interested in.</p> <p>Results</p> <p>In this paper we introduce a fast and theoretically guranteed method called <it>DENSE </it>(Dense and ENriched Subgraph Enumeration) that can take in as input a biologist's <it>prior </it>knowledge as a set of query proteins and identify all the dense functional modules in a biological network that contain some part of the query vertices. The density (in terms of the number of network egdes) and the enrichment (the number of query proteins in the resulting functional module) can be manipulated via two parameters γ and <it>μ</it>, respectively.</p> <p>Conclusion</p> <p>This algorithm has been applied to the protein functional association network of <it>Clostridium acetobutylicum </it>ATCC 824, a hydrogen producing, acid-tolerant organism. The algorithm was able to verify relationships known to exist in literature and also some previously unknown relationships including those with regulatory and signaling functions. Additionally, we were also able to hypothesize that some uncharacterized proteins are likely associated with the target phenotype. The DENSE code can be downloaded from <url>http://www.freescience.org/cs/DENSE/</url></p