Reversible Control of Gelatin Hydrogel Stiffness by Using DNA Crosslinkers**

Biomaterials with dynamically tunable properties are critical for a range of applications in regenerative medicine and basic biology. In this work, we show the reversible control of gelatin methacrylate (GelMA) hydrogel stiffness through the use of DNA crosslinkers. We replaced some of the inter-GelMA crosslinks with double-stranded DNA, allowing for their removal through toehold-mediated strand displacement. The crosslinks could be restored by adding fresh dsDNA with complementary handles to those on the hydrogel. The elastic modulus (G’) of the hydrogels could be tuned between 500 and 1000 Pa, reversibly, over two cycles without degradation of performance. By functionalizing the gels with a second DNA strand, it was possible to control the crosslink density and a model ligand in an orthogonal fashion with two different displacement strands. Our results demonstrate the potential for DNA to reversibly control both stiffness and ligand presentation in a protein-based hydrogel, and will be useful for teasing apart the spatiotemporal behavior of encapsulated cells.     

Effects of carbon nanotubes and interphase properties on the interfacial conductivity and electrical conductivity of polymer nanocomposites

L_c is the minimum length of carbon nanotubes (CNTs) required for efficient transfer of filler conductivity to polymer matrix in polymer CNT nanocomposites (PCNTs). In this work, L_c is correlated with the dimensions of the CNTs and the interphase thickness. Subsequently, the interfacial conductivity as well as the effective length and concentration of CNTs are expressed by CNT and interphase properties. Moreover, a simple model for the tunneling conductivity of PCNTs is developed with these effective terms. The impacts of all parameters on L_c, the interfacial conductivity, the fraction of CNTs in the networks and the conductivity of the PCNT are explained and justified. In addition, the predictions of the percolation threshold and conductivity are compared with the experimental results of several samples. The desirable values of interfacial conductivity are achieved by thin, short and super‐conductive CNTs, high waviness and a thick interphase. However, thin and long CNTs, low waviness, a thick interphase, poor tunneling resistivity due to the polymer matrix and a short tunneling distance advantageously affect the conductivity of PCNTs, because they produce large conductive networks. The predictions also show good agreement with the experimental measurements of percolation threshold and conductivity, which confirms the developed equations. © 2020 Society of Chemical Industry     

The role of Persian-language word exercise games in improving spelling of students with dyslexia: Word exercise games in improving spelling

Children with dyslexia have reduced sensitivity to phonological sounds and words, and this deficiency in lexical processing causes many problems for them. Word exercise games based on phonological awareness have emphasized the mistakes of students with dyslexia, attempting to help children avoid these mistakes. This study was conducted to investigate the effect of Persian-language word exercise games on the spelling of students with dyslexia. The design of the present study was quasi-experimental, with a pretest and posttest of an experimental group and a control group. Participants were 30 students with dyslexia from second grade to fifth grade in elementary schools in Bojnord, who completed the spelling test as a pretest and posttest. The experimental group played eleven 40-min sessions of word exercise games. The results showed that the word exercise programme improved the spelling of the children with dyslexia. This suggests that basing training on the phonological mistakes of students with dyslexia and using the word exercise games can improve their spelling.     

Modeling project criticality in IT project portfolios

Today's IT project portfolios (ITPP) contain many projects and varied interdependencies. Depending on a project's criticality to the ITPP, a failure can have massive consequences. However, existing methods usually only assess overall project portfolio risk and do not account for the criticality of single projects and their dependencies. Applying Bayesian network modeling to ITPPs, we bridge this gap and extend the current body of knowledge for the information systems and project management literatures. Our new method analyzes single projects' criticality in a portfolio context by considering both transitive dependencies and different dependency types in an integrated way. Since we demonstrate that single projects' criticality can vary substantially, being aware of which projects are critical is a key success factor for ITPP management. For practitioners, our method provides a straightforward procedure to enhance ITPP risk management.     

Highly efficient electrocatalysts fabricated via electrophoretic deposition for alcohol oxidation,oxygen reduction,hydrogen evolution,and oxygen evolution reactions

Electrophoretic deposition (EPD) is a versatile technique that has drawn attention due to its ease of use and performance in depositing high-quality layers at room temperature. This technique principle is based on the deposition of charged particles from a stable colloidal suspension on a conductive substrate using either a direct or alternating current. Using relatively simple and low-cost equipment, the EPD technique enables the deposition of layers with controlled microstructures at nanoscale. The EPD technique has been particularly successful in the fabrication of the electrocatalyst layers for low-temperature fuel cells, which are anchored on the top of the fuel cell electrodes. In comparison with other electrocatalyst layer deposition techniques such as drop-casting, the EPD technique offers clear advantages for the control of the thickness and packing density of the electrocatalyst layers. Owing to the dense packing density, electrocatalyst layers deposited by EPD could achieve enhanced conductivity and efficiency. The present review aims at comprehensively evaluating the recently published results on the electrocatalyst layers fabricated by EPD and applied in oxygen reduction reactions, alcohol electro-oxidation reactions, hydrogen evolution reactions, and oxygen evolution reactions.     

CFD Simulation of Deep-Bed Paddy Drying Process and Performance

Computational Fluid Dynamics (CFD) was applied three-dimensionally to simulate the drying behavior of paddy in a deep-bed dryer. The commercial CFD software Fluent 6.3.26 was used. The deep-bed paddy drying process and performance were studied by incorporating user-defined function (UDF) in Fluent written in C language. The predicted drying parameters were compared with experimental data of deep-bed drying of paddy. The values of mean relative deviation (MRD), standard error of prediction (SEP), and maximum error of prediction (MEP) for prediction of grain moisture content, air temperature, and absolute humidity were less than 6, 10, and 9%; 0.33% (d.b), 1.24°C, and 0.06% (kg/kg of dry air); and 2.25% (d.b), 6.8°C, and 0.37% (kg/kg of dry air), respectively, which reflect reasonable accuracy. Moreover, the energetic and exergetic performance of deep-bed paddy drying were simulated and analyzed. The effects of inlet air temperature and mass flow rate on the performance parameters were investigated. It was shown that the application of higher levels of inlet air temperature and lower mass flow rates yielded higher exergy efficiencies of deep-bed paddy drying.     

Short communication: Heritability estimates for susceptibility to Mycobacterium avium subspecies paratuberculosis infection defined by ELISA and fecal culture test results in Jersey cattle

Paratuberculosis (Johne’s disease), an enteric disorder in ruminants caused by Mycobacterium avium ssp. paratuberculosis, causes economic losses in excess of $200 million annually to the US dairy industry. Costly diagnostic testing, cumbersome control programs, incurability, and ineffective vaccination all make M. avium ssp. paratuberculosis susceptibility a good candidate for genetic studies and genetic selection a potentially useful adjunct to management-based control programs. No report has been published for heritability of susceptibility to M. avium ssp. paratuberculosis infection in Jersey cattle. The objective of this study was to estimate variance components and heritability for susceptibility to M. avium ssp. paratuberculosis infection in US Jersey cattle. Data consisted of complete serum ELISA and partial fecal culture results on a total of 2,861 Jersey cows from 23 commercial herds throughout the United States after editing. Four M. avium ssp. paratuberculosis susceptibility phenotypes were defined using (1) ELISA sample-to-positive ratios as a continuous trait, (2) ELISA results as a binary trait (positive = 1, negative = 0), (3) ELISA results as an ordered categorical trait, and (4) a combined test in which ELISA and fecal culture results were both taken into account in a binary analysis. Three statistical models, including linear, binary threshold, and ordered threshold sire models, were used to analyze the data. All analyses were executed using the restricted maximum likelihood method in ASReml 3 software. The heritability estimates were low to moderate and ranged from 0.08 (±0.03) to 0.27 (±0.11) based on different trait definitions. The nonzero heritability indicates that susceptibility to M. avium ssp. paratuberculosis infection in Jersey cattle is influenced by genetic factors. Therefore, selection of the least susceptible animals could decrease genetic predisposition to M. avium ssp. paratuberculosis infection in Jersey populations in future generations.     

Pulsed Plasma Neutron Accelerator

Adoptable beam of neutrons enables investigating neutron interaction with matter and extends its applications. Neutrons from a low energy Sb–Be source are accelerated to the desired energies by collision with high energy protons from a plasma focus device. Protons pass through a set of magnetic lenses and converged to a small point where the neutron source is located. Proton density is increased by narrowing the beam with a charge funnel. From a practical point of view, the source geometry of neutron is a half-cone, which enhances the number of forward peaked neutrons after collision with proton.     

Solving non-convex economic dispatch problem with valve point effects using modified group search optimizer method

This paper presents a novel solution based on the group search optimizer (GSO) methodology in order to determine the feasible optimal solution of the economic dispatch (ED) problem considering valve loading effects. The basic disadvantage of the original GSO algorithm is the fact that it gives a near-optimal solution rather than an optimal one in a limited runtime period. In this paper, a new modified group search optimizer (MGSO) is presented for improving the scrounger and ranger operators of GSO. The proposed MGSO is applied on different test systems and compared with most of the recent methodologies. The results show the effectiveness of the proposed method and prove that MGSO can be applicable for solving the power system economic load dispatch problem, especially in large scale power systems.     

Tool flank wear prediction in CNC turning of 7075 AL alloy SiC composite

Flank wear occurs on the relief face of the tool and the life of a tool used in a machining process depends upon the amount of flank wear; so predicting of flank wear is an important requirement for higher productivity and product quality. In the present work, the effects of feed, depth of cut and cutting speed on flank wear of tungsten carbide and polycrystalline diamond (PCD) inserts in CNC turning of 7075 AL alloy with 10 wt% SiC composite are studied; also artificial neural network (ANN) and co-active neuro fuzzy inference system (CANFIS) are used to predict the flank wear of tungsten carbide and PCD inserts. The feed, depth of cut and cutting speed are selected as the input variables and artificial neural network and co-active neuro fuzzy inference system model are designed with two output variables. The comparison between the results of the presented models shows that the artificial neural network with the average relative prediction error of 1.03% for flank wear values of tungsten carbide inserts and 1.7% for flank wear values of PCD inserts is more accurate and can be utilized effectively for the prediction of flank wear in CNC turning of 7075 AL alloy SiC composite. It is also found that the tungsten carbide insert flank wear can be predicted with less error than PCD flank wear insert using ANN. With Regard to the effect of the cutting parameters on the flank wear, it is found that the increase of the feed, depth of cut and cutting speed increases the flank wear. Also the feed and depth of cut are the most effective parameters on the flank wear and the cutting speed has lesser effect.