Electrohydrodynamic Enhancement of Phase Change Material Melting in Circular-Elliptical Annuli

Phase change material (PCM) has received significant attention due to its great potential for thermal energy storage. However, the major undesirable property of PCM is related to its low thermal conductivity. In this work, the electrohydrodynamic (EHD) enhancement of PCM melting in circular-elliptical annuli is investigated numerically by using the lattice Boltzmann method (LBM). The key motivation for our choice of the elliptical shape is due to the fact that the more curved elliptical surface corresponds to stronger charge injection strength, which may lead to stronger flow field, and the consequent increase of heat transfer rate. The influences of several non-dimensional parameters, including electric Rayleigh number T, thermal Rayleigh number (Ra) and the aspect ratio (AR) of the inner ellipse are investigated in detail. Based on the numerical results, it is found that the radial electro-convective flow induced by the external electric field makes a significant contribution to the enhancement of melting heat transfer, and specially, the maximum time saving in some cases is more than 85%. Moreover, we observe that when the Coulomb force is dominant over the buoyancy force, no matter the inner elliptical tube is oriented horizontally or vertically, the total melting times in these two cases are nearly the same, and the melting performance obtained for the circular electrode is usually better than the other cases. However, when the flow regime is dominated by the buoyancy force, the use of a slender vertical-oriented elliptical electrode instead of the circular one is more efficient.

Evaluating Bayesian adaptive randomization procedures with adaptive clip methods for multi-arm trials

Bayesian adaptive randomization is a heuristic approach that aims to randomize more patients to the putatively superior arms based on the trend of the accrued data in a trial. Many statistical aspects of this approach have been explored and compared with other approaches; yet only a limited number of works has focused on improving its performance and providing guidance on its application to real trials. An undesirable property of this approach is that the procedure would randomize patients to an inferior arm in some circumstances, which has raised concerns in its application. Here, we propose an adaptive clip method to rectify the problem by incorporating a data-driven function to be used in conjunction with Bayesian adaptive randomization procedure. This function aims to minimize the chance of assigning patients to inferior arms during the early time of the trial. Moreover, we propose a utility approach to facilitate the selection of a randomization procedure. A cost that reflects the penalty of assigning patients to the inferior arm(s) in the trial is incorporated into our utility function along with all patients benefited from the trial, both within and beyond the trial. We illustrate the selection strategy for a wide range of scenarios.

The sticky behavior of pulp and paper mill biosludge during drying

Pulp and paper mill biosludge becomes sticky after being dried to a certain solids content. As biosludge becomes sticky, it agglomerates and adheres to the heat transfer surfaces of the dryer. This undesirable property can lower the dryer efficiency and cause the drying equipment to fail. A systematic study was conducted to examine the sticky behavior of biosludge. The stickiness was evaluated by measuring the adhesive force between a sludge cake and a stainless steel substrate, and the cohesive force between a sludge cake and a sludge substrate. The results show that: i) both adhesive and cohesive forces increase markedly as the solids content increases, reaching a maximum value at about 13% solids, and then decrease steadily at a higher solids content; ii) cohesive force is stronger than adhesive force, implying that biosludge tends to agglomerate rather than adhere to smooth equipment surfaces; and iii) mixing wood fines or fly ash from a biomass boiler reduces the stickiness of the mixture. These findings may help mills improve the thermal efficiency of biosludge dryers and to turn biosludge into a more attractive fuel for burning in biomass boilers.

DeePaC: Predicting pathogenic potential of novel DNA with a universal framework for reverse-complement neural networks

Motivation:We expect novel pathogens to arise due to their fast-paced evolution, and new species to be discovered thanks to advances in DNA sequencing and metagenomics. What is more, recent developments in synthetic biology raise concerns that some strains of bacteria could be modified for malicious purposes. Traditional approaches to open-view pathogen detection depend on databases of known organisms, limiting their performance on unknown, unrecognized, and unmapped sequences. In contrast, machine learning methods can infer pathogenic phenotypes from single NGS reads even though the biological context is unavailable. However, modern neural architectures treat DNA as a simple character string and may predict conflicting labels for a given sequence and its reverse-complement. This undesirable property may impact model performance.Results:We present DeePaC, a Deep Learning Approach to Pathogenicity Classification. It includes a universal, extensible framework for neural architectures ensuring identical predictions for any given DNA sequence and its reverse-complement. We implement reverse-complement convolutional neural networks and LSTMs, which outperform the state-of-the-art methods based on both sequence homology and machine learning. Combining a reverse-complement architecture with integrating the predictions for both mates in a read pair results in cutting the error rate almost in half in comparison to the previous state-of-the-art.Availability:The code and the models are available at: https://gitlab.com/rki_bioinformatics/DeePaC

Additive and Non-additive Biomass Equations for Black Wattle

ABSTRACT The objectives of this work were to propose additive equations for biomass components (stem and crown) and total biomass for black wattle (Acacia mearnsii De Wild.) and show the inconsistency of independently adjusted biomass equations. Two procedures were used to fit nonlinear equations of biomass: i) independent and ii) systems of equations. The second procedure, defined by the application of the seemingly unrelated regression model, has better biological and statistical properties to estimate allometric equations of biomass components and total biomass when compared with the independent estimation. An effective property of this procedure is the additivity, i.e., the estimates of component biomass are compatible with those of total biomass. Independent fitted adjusted equations do not consider the dependence between the biomass components, thus, besides the estimates being non-additive, which is an undesirable property, they will result in estimates with larger variance.

Upper Gastrointestinal Bleed Embolization with Onyx®: The “Tattoo Effect”

Endoscopic intervention is well validated for the control of upper gastrointestinal bleeding (UGIB). In cases of refractory bleeding, transarterial embolization is a safe and effective alternative. Ethylene vinyl alcohol (EVOH) commonly known as Onyx® is an inherently black liquid embolic approved for use in cerebrovascular arteriovenous malformations and is increasingly used as an embolic agent in multiple peripheral and visceral territories. Onyx® has a uniquely undesirable property of causing black color discoloration when used in peripheral applications adjacent to the skin akin to a “tattoo effect.” Knowledge of the agents used by the interventional radiologist, clinical correlation, and close follow-up can be of paramount importance to avoid unnecessary surgical intervention. Here, we report a case demonstrating this “tattoo effect” on the gastric mucosa following UGIB embolization.

Effect of Filament Fineness on Composite Yarn Residual Torque

Yarn residual torque or twist liveliness occurs when the twist is imparted to spin the fibers during yarn formation. It causes yarn snarling, which is an undesirable property and can lead the problems for further processes such as weaving and knitting. It affects the spirality of knitted fabrics and skewness of woven fabrics. Generally, yarn residual torque depends on yarn twist, yarn linear density, and fiber properties used. Composite yarns are widely produced to exploit two yarns with different properties such on optimum way at the same time and these yarns can be produced by wrapping sheath fibers around filament core fiber with a certain twist. In this study, the effect of filament fineness used as core component of composite yarn on residual torque was analyzed. Thus, the false twist textured polyester filament yarns with different filament fineness were used to produce composite yarns with different yarn count. The variance analysis was performed to determine the significance of twist liveliness of filament yarns and yarn count on yarn twist liveliness. Results showed that there is a statistically significant differences at significance level of α=0.05 between filament fineness and yarn residual torque of composite yarns.

Parikh Matrices and Strong M-Equivalence

Parikh matrices have been a powerful tool in arithmetizing words by numerical quantities. However, the dependence on the ordering of the alphabet is inherited by Parikh matrices. Strong M-equivalence is proposed as a canonical alternative to M-equivalence to get rid of this undesirable property. Some characterization of strong M-equivalence for a restricted class of ternary words is obtained. Finally, the existential counterpart of strong M-equivalence is introduced as well.

Ray Effect Mitigation Through Reference Frame Rotation

The discrete ordinates method is a popular and versatile technique for solving the radiative transport equation, a major drawback of which is the presence of ray effects. Mitigation of ray effects can yield significantly more accurate results and enhanced numerical stability for combined mode codes. When ray effects are present, the solution is seen to be highly dependent upon the relative orientation of the geometry and the global reference frame. This is an undesirable property. A novel ray effect mitigation technique of averaging the computed solution for various reference frame orientations is proposed.

QSBR Study of Bitter Taste of Peptides: Application of GA-PLS in Combination with MLR, SVM, and ANN Approaches

Detailed information about the relationships between structures and properties/activities of peptides as drugs and nutrients is useful in the development of drugs and functional foods containing peptides as active compounds. The bitterness of the peptides is an undesirable property which should be reduced during drug/nutrient production, and quantitative structure bitter taste relationship (QSBR) studies can help researchers to design less bitter peptides with higher target efficiency. Calculated structural parameters were used to develop three different QSBR models (i.e., multiple linear regression, support vector machine, and artificial neural network) to predict the bitterness of 229 peptides (containing 2–12 amino acids, obtained from the literature). The developed models were validated using internal and external validation methods, and the prediction errors were checked using mean percentage deviation and absolute average error values. All developed models predicted the activities successfully (with prediction errors less than experimental error values), whereas the prediction errors for nonlinear methods were less than those for linear methods. The selected structural descriptors successfully differentiated between bitter and nonbitter peptides.