Polyimide foams from powder: Experimental analysis of competitive diffusion phenomena

In the present study, various diffusive processes have been investigated during foaming of powdered precursors of polyimide. A detailed analysis of the powdered precursor's characteristics allows for an enhanced morphological understanding of the resulting microstructures and foam unit cell. Parameters that are central to the foaming process such as particle morphology, volatile concentration and sorption-desorption processes are evaluated. Isothermal and non-isothermal desorption experiments have been carried out by thermogravimetric analysis (TGA), and specific diffusive processes have been correlated to thermodynamic and kinetic transitions by means of modulated differential scanning calorimetry (MDSC) of the corresponding materials. It was found that two primary fluxes of volatiles, one out of the external surface of the particles (responsible for volatile desorption) and the other into the growing bubble (responsible for vapor supersaturation inside the bubble) compete against each other creating a competitive scenario that becomes the controlling factor for potential inflation within the precursor particles.     

Optimal control of auxiliary heating of passive-solar-heated buildings

Previous papers have demonstrated that low-cost off-peak electricity can be used to provide auxiliary heating for a passive-solar-heated residence. This paper describes the development of optimal control strategies for the operation of this auxiliary heating system. The temperature in the residence throughout the day as determined by computer simulation is presented for several optimal and conventional control strategies. The optimal control strategies improved the temperature control in the building compared to the conventional strategies. All strategies were shown to depend heavily on weather prediction. Computer simulations showed that errors in weather prediction had a small effect on heating cost and a large effect on being able to maintain the desired indoor temperature.     

Relation between depressive symptoms and stressful life events in a sample of disadvantaged mothers.

The bidirectional relation between life events and self-reported depression was examined across a 1-year period. With Time 1 depression controlled, Time 2 stress accounted for an additional 10% of Time 2 depressive symptoms. Health-related stress, family violence, and financial stress at Time 2 predicted Time 2 depression after control for Time 1 depression. With Time 1 stress controlled, Time 2 depression accounted for 8% of the variance in Time 2 stress. Time 2 depression predicted Time 2 health-related stress, financial stress, household changes, spouse–partner stress, family violence stress, and substance abuse stress, controlling for each of these stressors at Time 1. The results describe a complex relation between stress and depression and suggest that the relation between stress and depression is moderated by the type of stress. (PsycINFO Database Record (c) 2010 APA, all rights reserved)     

Usher Syndrome in the Inner Ear: Etiologies and Advances in Gene Therapy

Hearing loss is the most common sensory disorder with ~466 million people worldwide affected, representing about 5% of the population. A substantial portion of hearing loss is genetic. Hearing loss can either be non-syndromic, if hearing loss is the only clinical manifestation, or syndromic, if the hearing loss is accompanied by a collage of other clinical manifestations. Usher syndrome is a syndromic form of genetic hearing loss that is accompanied by impaired vision associated with retinitis pigmentosa and, in many cases, vestibular dysfunction. It is the most common cause of deaf-blindness. Currently cochlear implantation or hearing aids are the only treatments for Usher-related hearing loss. However, gene therapy has shown promise in treating Usher-related retinitis pigmentosa. Here we review how the etiologies of Usher-related hearing loss make it a good candidate for gene therapy and discuss how various forms of gene therapy could be applied to Usher-related hearing loss.     

Improving class probability estimates for imbalanced data

Obtaining good probability estimates is imperative for many applications. The increased uncertainty and typically asymmetric costs surrounding rare events increase this need. Experts (and classification systems) often rely on probabilities to inform decisions. However, we demonstrate that class probability estimates obtained via supervised learning in imbalanced scenarios systematically underestimate the probabilities for minority class instances, despite ostensibly good overall calibration. To our knowledge, this problem has not previously been explored. We propose a new metric, the stratified Brier score, to capture class-specific calibration, analogous to the per-class metrics widely used to assess the discriminative performance of classifiers in imbalanced scenarios. We propose a simple, effective method to mitigate the bias of probability estimates for imbalanced data that bags estimators independently calibrated over balanced bootstrap samples. This approach drastically improves performance on the minority instances without greatly affecting overall calibration. We extend our previous work in this direction by providing ample additional empirical evidence for the utility of this strategy, using both support vector machines and boosted decision trees as base learners. Finally, we show that additional uncertainty can be exploited via a Bayesian approach by considering posterior distributions over bagged probability estimates.     

Superposition of the notched strength of composite laminates

An improved macroscopic model for predicting the strength of a composite laminate containing a circular notch is developed. Two constants are introduced which uniquely determine the notch sensitivity of a given material. A superposition method for the notched strength of composite laminates is developed which allows data for arbitrary materials and laminate configurations to be superimposed upon a single master curve. The influence of material orthotropy upon notched strength is discussed. A relative notch sensitivity parameter is introduced which allows quantification of the notch sensitivity of a given composite material system, stacking sequence, or laminate configuration.     

AutoRELAX: automatically RELAXing a goal model to address uncertainty

Dynamically adaptive systems (DAS) must cope with system and environmental conditions that may not have been fully understood or anticipated during development. RELAX is a fuzzy logic-based specification language for identifying and assessing sources of environmental uncertainty, thereby making DAS requirements more tolerant of unanticipated conditions. This paper presents AutoRELAX, an approach that automatically generates RELAXed goal models to address environmental uncertainty. Specifically, AutoRELAX identifies goals to RELAX, which RELAX operators to apply, and the shape of the fuzzy logic function that establishes the goal satisfaction criteria. AutoRELAX generates different solutions by making tradeoffs between minimizing the number of RELAXed goals and maximizing delivered functionality by reducing the number of adaptations triggered by minor and adverse environmental conditions. In a recent extension, AutoRELAX uses a stepwise adaptation of weights to balance these two competing concerns and thereby further improve the utility of AutoRELAX. We apply it to two industry-based applications involving network management and a robotic controller, respectively.     

Electrostatic capillary collector for in-situ spectroscopic analysis of aerosols

Identification of particulate matter is important in assessing an individual’s exposure to potentially harmful particles, such as aeroallergens, toxins, and emissions from combustion sources, which can contribute to cardio-pulmonary diseases. Efficient collection of aerosols is essential for aerosol exposure studies such as analysis of chemical and biological components. We present the design and evaluation of a capillary collector that collects PM_2.5 onto the outer surface of a capillary for in-situ spectroscopic analysis. The capillary collector uses a needle-to-ring corona generator to charge particles; the electric field between a cylinder and a wire inserted into the bore of a capillary is used to collect the charged particles. Corona and repelling voltages are optimized for maximum collection of ambient PM_2.5 particles and fluorescent polystyrene latex microspheres in the PM_2.5 size range, on the capillary. The capillary collection efficiency of ambient PM_2.5 at 3 slpm operating flow rate and optimal operating voltages is 63%. Fluorescence spectroscopy is used to quantify the collection of polystyrene latex microspheres. The fluorescence-based capillary collection efficiency is in close agreement with the capillary collection efficiency of ambient PM_2.5. The collection and analysis methodology can be used to develop a compact, low-cost sensor for in-situ spectroscopic analysis of aerosols to determine their chemical composition for source apportionment.

Copyright © 2019 American Association for Aerosol Research     

Multi-label classification via learning a unified object-label graph with sparse representation

Automatic annotation is an essential technique for effectively handling and organizing Web objects (e.g., Web pages), which have experienced an unprecedented growth over the last few years. Automatic annotation is usually formulated as a multi-label classification problem. Unfortunately, labeled data are often time-consuming and expensive to obtain. Web data also accommodate much richer feature space. This calls for new semi-supervised approaches that are less demanding on labeled data to be effective in classification. In this paper, we propose a graph-based semi-supervised learning approach that leverages random walks and ? ₁ sparse reconstruction on a mixed object-label graph with both attribute and structure information for effective multi-label classification. The mixed graph contains an object-affinity subgraph, a label-correlation subgraph, and object-label edges with adaptive weight assignments indicating the assignment relationships. The object-affinity subgraph is constructed using ? ₁ sparse graph reconstruction with extracted structural meta-text, while the label-correlation subgraph captures pairwise correlations among labels via linear combination of their co-occurrence similarity and kernel-based similarity. A random walk with adaptive weight assignment is then performed on the constructed mixed graph to infer probabilistic assignment relationships between labels and objects. Extensive experiments on real Yahoo! Web datasets demonstrate the effectiveness of our approach.     

A novel two-box search paradigm for query disambiguation

Precision-oriented search results such as those typically returned by the major search engines are vulnerable to issues of polysemy. When the same term refers to different things, the dominant sense is preferred in the rankings of search results. In this paper, we propose a novel two-box technique in the context of Web search that utilizes contextual terms provided by users for query disambiguation, making it possible to prefer other senses without altering the original query. A prototype system, Bobo, has been implemented. In Bobo, contextual terms are used to capture domain knowledge from users, help estimate relevance of search results, and route them towards a user-intended domain. A vast advantage of Bobo is that a wide range of domain knowledge can be effectively utilized, where helpful contextual terms do not even need to co-occur with query terms on any page. We have extensively evaluated the performance of Bobo on benchmark datasets that demonstrates the utility and effectiveness of our approach.