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Microstructure changes of on the extruded high-amylose bionanocomposites as affected by moisture content via synchrotron radiation studies

- Liu, Huihua, Chaudhary, Deeptangshu

  • Authors: Liu, Huihua , Chaudhary, Deeptangshu
  • Date: 2014
  • Type: Text , Journal article
  • Relation: Solid State Communications Vol. 192, no. (August 2014 2014), p. 10-14
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  • Description: The crystalline domain changes and lamellar structure observations of sorbitol-plasticized starch nanocomposite had been investigated via synchrotron. Strong interactions were found between amylose-sorbitol, resulting in reduced inter-helix spacing of the starch polymer. Achievable dspacing of nanoclay was confirmed to be correlated to the moisture content (mc) within the nanocomposites. SAXS diffraction patterns changed from circular (high mc samples) to elliptical (low mc samples), indicating the formation of long periodic structure and increased heterogeneities of the electron density within the samples. Two different domains sized at around 90 Ã… and 350 Ã… were found for the low mc samples. However, only the ~90 Ã… domain was observed in high mc samples. Formation of the 380 Ã… domain is attributed to the retrogradation behaviour in the absence of water molecules. Meanwhile, the nucleation effect of nanoclay is another factor leading to the emergence of the larger crystalline domain.

Artificial Neural Network application for predicting in-flight particle characteristics of an atmospheric plasma spray process

- Choudhury, Tanveer, Hosseinzadeh, Nasser, Berndt, Christopher

  • Authors: Choudhury, Tanveer , Hosseinzadeh, Nasser , Berndt, Christopher
  • Date: 2011
  • Type: Text , Journal article
  • Relation: Surface and Coatings Technology Vol. 205, no. 21-22 (2011), p. 4886-4895
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  • Description: Thermal spray consists of a group of coating processes that are used to apply metal or non-metallic coatings to protect a functional surface or to improve its performance. There are some 40 processing parameters that define the overall coating quality and these must be selected in an optimized fashion to manufacture a coating that exhibits desirable properties. The proper combination of processing variables is critical since these influence the cost as well as the coating characteristics.Because of this high number of processing parameters, a major challenge is to have full control over the system and to understand parameter interdependencies, correlations and their individual effects on the in-flight particle characteristics, which have significant influence on the in service coating properties. This paper proposes an approach, based on the Artificial Neural Network (ANN) method, to play this role and illustrates the model's design, network optimization procedures, the database handling and expansion steps, and analysis of the predicted values, with respect to the experimental ones, in order to evaluate the network's performance. © 2011 Elsevier B.V.

Enhanced profitability of photovoltaic plants by utilizing cryptocurrency-based mining load

- Eid, Bilal, Islam, Md Rabiul, Shah, Rakibuzzaman, Nahid, Abdullah, Kouzani, Abbas, Mahmud, M.

  • Authors: Eid, Bilal , Islam, Md Rabiul , Shah, Rakibuzzaman , Nahid, Abdullah , Kouzani, Abbas , Mahmud, M.
  • Date: 2021
  • Type: Text , Journal article
  • Relation: IEEE Transactions on Applied Superconductivity Vol. 31, no. 8 (2021), p.
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  • Description: The grid connected photovoltaic (PV) power plants (PVPPs) are booming nowadays. The main problem facing the PV power plants deployment is the intermittency which leads to instability of the grid. In order to stabilize the grid, either energy storage device - mainly batteries - or a power curtailment technique can be used. The additional cost on utilizing batteries make it not preferred solution, because it leads to a drop in the return on investment (ROI) of the project. A good alternative, is using a customized load (such as; cryptocurrency-based loads) which consumes the surplus energy. This paper investigating the usage of a customized load - cryptocurrency mining rig - to create an added value for the owner of the plant and increase the ROI of the project. These devices are widely used to perform the required calculations for validating the transactions on the network of the Blockchain. A comparison between the ROI of the mining rig and the battery have been conducted in this study. Based on this study the mining rig has superior ROI of 7.7% - in the case with the lowest ROI - compared to 4.5% for battery. Moreover, an improved controlling strategy is developed to combine both the battery and mining rig in the same system. The developed strategy is able to keep the profitability as high as possible during the fluctuation of the mining network. © 2002-2011 IEEE.

From general language understanding to noisy text comprehension

- Kasthuriarachchy, Buddhika, Chetty, Madhu, Shatte, Adrian, Walls, Darren

  • Authors: Kasthuriarachchy, Buddhika , Chetty, Madhu , Shatte, Adrian , Walls, Darren
  • Date: 2021
  • Type: Text , Journal article
  • Relation: Applied Sciences (Switzerland) Vol. 11, no. 17 (2021), p.
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  • Description: Obtaining meaning-rich representations of social media inputs, such as Tweets (unstructured and noisy text), from general-purpose pre-trained language models has become challenging, as these inputs typically deviate from mainstream English usage. The proposed research establishes effective methods for improving the comprehension of noisy texts. For this, we propose a new generic methodology to derive a diverse set of sentence vectors combining and extracting various linguistic characteristics from latent representations of multi-layer, pre-trained language models. Further, we clearly establish how BERT, a state-of-the-art pre-trained language model, comprehends the linguistic attributes of Tweets to identify appropriate sentence representations. Five new probing tasks are developed for Tweets, which can serve as benchmark probing tasks to study noisy text comprehension. Experiments are carried out for classification accuracy by deriving the sentence vectors from GloVe-based pre-trained models and Sentence-BERT, and by using different hidden layers from the BERT model. We show that the initial and middle layers of BERT have better capability for capturing the key linguistic characteristics of noisy texts than its latter layers. With complex predictive models, we further show that the sentence vector length has lesser importance to capture linguistic information, and the proposed sentence vectors for noisy texts perform better than the existing state-of-the-art sentence vectors. © 2021 by the authors. Licensee MDPI, Basel, Switzerland.
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