Fire safe,sustainable loose furnishing

The aim of this exploratory study has been to investigate the fire properties and environmental aspects of different upholstery material combinations, mainly for domestic applications. An analysis of the sustainability and circularity of selected textiles, along with lifecycle assessment, is used to qualitatively evaluate materials from an environmental perspective. The cone calorimeter was the primary tool used to screen 20 different material combinations from a fire performance perspective. It was found that textile covers of conventional fibres such as wool, cotton and polyester, can be improved by blending them with fire resistant speciality fibres. A new three‐dimensional web structure has been examined as an alternative padding material, showing preliminary promising fire properties with regard to ignition time, heat release rates and smoke production.     

Monthly Streamflow Modeling Based on Self-Organizing Maps and Satellite-Estimated Rainfall Data

Hydrological data provide valuable information for the decision-making process in water resources management, where long and complete time series are always desired. However, it is common to deal with missing data when working on streamflow time series. Rainfall-streamflow modeling is an alternative to overcome such a difficulty. In this paper, self-organizing maps (SOM) were developed to simulate monthly inflows to a reservoir based on satellite-estimated gridded precipitation time series. Three different calibration datasets from Três Marias Reservoir, composed of inflows (targets) and 91 TRMM-estimated rainfall data (inputs), from 1998 to 2019, were used. The results showed that the inflow data homogeneity pattern influenced the rainfall-streamflow modeling. The models generally showed superior performance during the calibration phase, whereas the outcomes varied depending on the data homogeneity pattern and the chosen SOM structure in the testing phase. Regardless of the input data homogeneity, the SOM networks showed excellent results for the rainfall-runoff modeling, presenting Nash–Sutcliffe coefficients greater than 0.90.

Graphical Abstract

     

Nanogrooved carbon microtubes for wet three‐dimensional printing of conductive composite structures

Recent advances in three‐dimensional (3D) printing have enabled the fabrication of interesting structures which are not achievable using traditional fabrication approaches. The 3D printing of carbon microtube composite inks allows fabrication of conductive structures for practical applications in soft robotics and tissue engineering. However, it is challenging to achieve 3D printed structures from solution‐based composite inks, which requires an additional process to solidify the ink. Here, we introduce a wet 3D printing technique which uses a coagulation bath to fabricate carbon microtube composite structures. We show that through a facile nanogrooving approach which introduces cavitation and channels on carbon microtubes, enhanced interfacial interactions with a chitosan polymer matrix are achieved. Consequently, the mechanical properties of the 3D printed composites improve when nanogrooved carbon microtubes are used, compared to untreated microtubes. We show that by carefully controlling the coagulation bath, extrusion pressure, printing distance and printed line distance, we can 3D print composite lattices which are composed of well‐defined and separated printed lines. The conductive composite 3D structures with highly customised design presented in this work provide a suitable platform for applications ranging from soft robotics to smart tissue engineering scaffolds. © 2019 Society of Chemical Industry     

Modification of narrow‐spectrum peptidomimetic polyurethanes with fatty acid chains confers broad‐spectrum antibacterial activity

Each year, thousands of patients die from antimicrobial‐resistant bacterial infections that fail to respond to conventional antibiotic treatment. Antimicrobial polymers are a promising new method of combating antibiotic‐resistant bacterial infections. We have previously reported the synthesis of a series of narrow‐spectrum peptidomimetic antimicrobial polyurethanes that are effective against Gram‐negative bacteria, such as Escherichia coli; however, these polymers are not effective against Gram‐positive bacteria, such as Staphylococcus aureus. With the aim of understanding the correlation between chemical structure and antibacterial activity, we have subsequently developed three structural variants of these antimicrobial polyurethanes using post‐polymerization modification with decanoic acid and oleic acid. Our results show that such modifications converted the narrow‐spectrum antibacterial activity of these polymers into broad‐spectrum activity against Gram‐positive species such as S. aureus, however, also increasing their toxicity to mammalian cells. Mechanistic studies of bacterial membrane disruption illustrate the differences in antibacterial action between the various polymers. The results demonstrate the challenge of balancing antimicrobial activity and mammalian cell compatibility in the design of antimicrobial polymer compositions. © 2019 Society of Chemical Industry     

Bayesian Inference for ARFIMA Models

This article develops practical methods for Bayesian inference in the autoregressive fractionally integrated moving average (ARFIMA) model using the exact likelihood function, any proper prior distribution, and time series that may have thousands of observations. These methods utilize sequentially adaptive Bayesian learning, a sequential Monte Carlo algorithm that can exploit massively parallel desktop computing with graphics processing units (GPUs). The article identifies and solves several problems in the computation of the likelihood function that apparently have not been addressed in the literature. Four applications illustrate the utility of the approach. The most ambitious is an ARFIMA(2,d,2) model for the Campito tree ring time series (length 5405), for which the methods developed in the article provide an essentially uncorrelated sample of size 16,384 from the exact posterior distribution in under four hours. Less ambitious applications take as little as 4 minutes without exploiting GPUs.     

The use of recycled low-density polyethylene films from protective prepreg for the development of nanocomposites with bentonite clay

Prepreg is a pre-impregnated composite fiber where a thermoset polymer matrix material is present. Before being used, these materials are kept at low temperatures and have a low-density polyethylene (LDPE) film for your protection. The increase in the use of structural composites based on prepregs causes an increase in the amount of protective LDPE film. This material is usually discarded or incinerated and can cause great damage to the environment. Thus, the present work aimed to study the feasibility of recycling up to 100% of protective LDPE (rLDPE) to develop blends-based nanocomposites with rLDPE/virgin LDPE (60/40, 70/30, 80/20, 90/10 and 100/0) with the addition of 5 wt% of compatibilizer agent (maleic anhydride grafted LDPE, LDPE-g-MA), and 1 wt% of bentonite (BNT) using a co-rotational twin-screw extruder, followed by hot pressing in a hydropneumatic press and die-cutting. Water absorption test, thermal aging resistance, morphological characterization by scanning electron microscopy, mechanical properties by Izod impact strength and tensile tests, and differential scanning calorimetry were performed. By the results, it was possible to verify the viability of 100% of the recycling of rLDPE. The results showed a good distribution of 1 wt% of BNT in the matrix, better mechanical properties when compared with virgin LDPE. Furthermore, the thermal properties, water absorption test, and thermal aging resistance showed no statistical differences between the samples. These results confirm the effectiveness and the environmental gain in the use of the recycled material.     

Assessing students' conceptual understanding using an online three‐tier diagnostic test

When solving a mathematical problem, students who do not have sufficient conceptual understanding may perform poorly and exhibit misconceptions. This study was aimed to examine students' conceptual understanding and significant misconceptions when solving number sense‐related problems. An online three‐tier diagnostic test was administered to 125 fifth‐grade students with varied socio‐economic backgrounds in Hong Kong. Only 14.40% of the students exhibited high performance with high confidence, indicating that these students had a profound conceptual understanding of number sense. In addition, the majority of the students (66.40%) did not demonstrate number sense; these students exhibited several significant misconceptions and could solve the questions only by using a rule‐based method or guessing. Accordingly, most students performed unsatisfactorily on number sense‐related problems. This study is imperative in identifying early predictors and provides information for further compatible interventions in the teaching and learning of number sense in Hong Kong in particular and worldwide in general.