Polyurethane foams made from bio-based polyols

Title from PDF of title page (University of Missouri--Columbia, viewed on May 25, 2012).The entire thesis text is included in the research.pdf file; the official abstract appears in the short.pdf file; a non-technical public abstract, appears in the public.pdf file.Dissertation advisor: Dr. Fu-Hung HsiehVita.Ph. D. University of Missouri--Columbia 2011."December 2011"Polyurethane (PU) foams have great applications in industry. The raw materials of PU, polyol and isocyanate, are conventionally derived from petroleum. Bio-based polyols are promising substitutes for petrochemical polyols due to their sustainability. This project studied water-blown polyurethane (PU) foams made from soy-polyols. The flexible bio-based PU foams were successfully produced by mixing petroleum polyol and commercial soy-polyols with different hydroxyl numbers and functionalities. The effect of hydroxyl number and functionality of soy-polyols, and the effect of tin catalyst, cross-linker levels and isocyanate index on foam properties were identified. Water-blown rigid polyurethane (PU) foams were made from 0-50% soy-phosphate polyol (SPP) and 2-4% water as the blowing agent. The effects of water content and isocyanate index on physical properties of SPP PU foams were investigated. Low density soy-polyol based rigid PU foams were modified with different concentrations of glass microspheres and nanoclay. The physical properties, especially the mechanical properties, were studied. The effects of high viscosity soy-polyols (13,000 cP to 31,000 cP) on water-blown rigid polyurethane foams (SBO PU foams) containing 1-50% high viscosity soy-polyols were investigated. With regard to density-compressive strength, foams made from high viscosity (21,000 to 31,000 cP) soy-polyols demonstrated comparable or superior value to the control foam.Includes bibliographical reference

Leveraging Datapath Propagation in IC3 for Hardware Model Checking

IC3 is a famous bit-level framework for safety verification. By incorporating datapath abstraction, a notable enhancement in the efficiency of hardware verification can be achieved. However, datapath abstraction entails a coarse level of abstraction where all datapath operations are approximated as uninterpreted functions. This level of abstraction, albeit useful, can lead to an increased computational burden during the verification process as it necessitates extensive exploration of redundant abstract state space. In this paper, we introduce a novel approach called datapath propagation. Our method involves leveraging concrete constant values to iteratively compute the outcomes of relevant datapath operations and their associated uninterpreted functions. Meanwhile, we generate potentially useful datapath propagation lemmas in abstract state space and tighten the datapath abstraction. With this technique, the abstract state space can be reduced, and the verification efficiency is significantly improved. We implemented the proposed approach and conducted extensive experiments. The results show promising improvements of our approach compared to the state-of-the-art verifiers

Uncertainty-Aware Performance Prediction for Highly Configurable Software Systems via Bayesian Neural Networks

Configurable software systems are employed in many important application domains. Understanding the performance of the systems under all configurations is critical to prevent potential performance issues caused by misconfiguration. However, as the number of configurations can be prohibitively large, it is not possible to measure the system performance under all configurations. Thus, a common approach is to build a prediction model from a limited measurement data to predict the performance of all configurations as scalar values. However, it has been pointed out that there are different sources of uncertainty coming from the data collection or the modeling process, which can make the scalar predictions not certainly accurate. To address this problem, we propose a Bayesian deep learning based method, namely BDLPerf, that can incorporate uncertainty into the prediction model. BDLPerf can provide both scalar predictions for configurations' performance and the corresponding confidence intervals of these scalar predictions. We also develop a novel uncertainty calibration technique to ensure the reliability of the confidence intervals generated by a Bayesian prediction model. Finally, we suggest an efficient hyperparameter tuning technique so as to train the prediction model within a reasonable amount of time whilst achieving high accuracy. Our experimental results on 10 real-world systems show that BDLPerf achieves higher accuracy than existing approaches, in both scalar performance prediction and confidence interval estimation

CG-DIQA: No-reference Document Image Quality Assessment Based on Character Gradient

Document image quality assessment (DIQA) is an important and challenging problem in real applications. In order to predict the quality scores of document images, this paper proposes a novel no-reference DIQA method based on character gradient, where the OCR accuracy is used as a ground-truth quality metric. Character gradient is computed on character patches detected with the maximally stable extremal regions (MSER) based method. Character patches are essentially significant to character recognition and therefore suitable for use in estimating document image quality. Experiments on a benchmark dataset show that the proposed method outperforms the state-of-the-art methods in estimating the quality score of document images.Comment: To be published in Proc. of ICPR 201

Evaluation of putative reference genes for gene expression normalization in soybean by quantitative real-time RT-PCR

<p>Abstract</p> <p>Background</p> <p>Real-time quantitative reverse transcription PCR (RT-qPCR) data needs to be normalized for its proper interpretation. Housekeeping genes are routinely employed for this purpose, but their expression level cannot be assumed to remain constant under all possible experimental conditions. Thus, a systematic validation of reference genes is required to ensure proper normalization. For soybean, only a small number of validated reference genes are available to date.</p> <p>Results</p> <p>A systematic comparison of 14 potential reference genes for soybean is presented. These included seven commonly used (<it>ACT2, ACT11, TUB4, TUA5, CYP, UBQ10, EF1b</it>) and seven new candidates (<it>SKIP16, MTP, PEPKR1, HDC, TIP41, UKN1, UKN2</it>). Expression stability was examined by RT-qPCR across 116 biological samples, representing tissues at various developmental stages, varied photoperiodic treatments, and a range of soybean cultivars. Expression of all 14 genes was variable to some extent, but that of <it>SKIP16, UKN1 </it>and <it>UKN2 </it>was overall the most stable. A combination of <it>ACT11, UKN1 </it>and <it>UKN2 </it>would be appropriate as a reference panel for normalizing gene expression data among different tissues, whereas the combination SKIP16, UKN1 and MTP was most suitable for developmental stages. <it>ACT11, TUA5 </it>and <it>TIP41 </it>were the most stably expressed when the photoperiod was altered, and <it>TIP41, UKN1 </it>and <it>UKN2 </it>when the light quality was changed. For six different cultivars in long day (LD) and short day (SD), their expression stability did not vary significantly with <it>ACT11, UKN2 </it>and <it>TUB4 </it>being the most stable genes. The relative gene expression level of <it>GmFTL3</it>, an ortholog of Arabidopsis <it>FT </it>(<it>FLOWERING LOCUS T</it>) was detected to validate the reference genes selected in this study.</p> <p>Conclusion</p> <p>None of the candidate reference genes was uniformly expressed across all experimental conditions, and the most suitable reference genes are conditional-, tissue-specific-, developmental-, and cultivar-dependent. Most of the new reference genes performed better than the conventional housekeeping genes. These results should guide the selection of reference genes for gene expression studies in soybean.</p

ESG disclosure facilitator: How do the multiple large shareholders affect firms’ ESG disclosure? evidence from China

The Environmental, social, and governance (ESG) disclosure is an important aspect of firms’ strategies. Therefore, exploring how to facilitate the firms’ ESG disclosure is necessary. This paper examines the role of multiple large shareholders (MLS, hereafter) in facilitating a firm’s ESG disclosure. Using a sample of Chinese listed firms during 2011–2020, we compare the ESG disclosure of firms having MLS with that of firms having a single large shareholder (SLS, hereafter) and find that having MLS associated with significantly higher ESG disclosure. After addressing endogeneity and altering the measurement of MLS, the benchmark results still hold after. Additional analysis shows that MLS exerts a more prominent positive effect on ESG disclosure in SOEs. We also examine the role of the other large shareholders in facilitating firms’ ESG disclosure. Our findings reveal a bright side of MLS: it facilitates ESG disclosure by monitoring. Therefore, this paper’s conclusion sheds new light on the bright side of MLS from the perspective of firms’ ESG disclosure and provides insights into how to improve ESG disclosure

Improving Performance Estimation for Design Space Exploration for Convolutional Neural Network Accelerators

Contemporary advances in neural networks (NNs) have demonstrated their potential in different applications such as in image classification, object detection or natural language processing. In particular, reconfigurable accelerators have been widely used for the acceleration of NNs due to their reconfigurability and efficiency in specific application instances. To determine the configuration of the accelerator, it is necessary to conduct design space exploration to optimize the performance. However, the process of design space exploration is time consuming because of the slow performance evaluation for different configurations. Therefore, there is a demand for an accurate and fast performance prediction method to speed up design space exploration. This work introduces a novel method for fast and accurate estimation of different metrics that are of importance when performing design space exploration. The method is based on a Gaussian process regression model parametrised by the features of the accelerator and the target NN to be accelerated. We evaluate the proposed method together with other popular machine learning based methods in estimating the latency and energy consumption of our implemented accelerator on two different hardware platforms targeting convolutional neural networks. We demonstrate improvements in estimation accuracy, without the need for significant implementation effort or tuning