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.     

Improving Properties of Poly(vinylidene fluoride) by Adding Expanded Graphite without Surface Modification via Water‐Assisted Mixing Extrusion

Expanded graphite (EG) is introduced into poly(vinylidene fluoride) (PVDF) by melt mixing extrusion with water injection. The results demonstrate that the unfunctionalized EG in composite prepared with water injection exbibits better dispersion than that in the one prepared without water injection due to the promoting role of water during extrusion. Thus, the PVDF/EG composite with loading of 4 wt% prepared by water‐assisted mixing extrusion (WAME) exhibits electrical conductivity of about three orders of magnitude higher than the neat PVDF and one order of magnitude higher than the one prepared without water injection. Comparing to the neat PVDF, the thermal conductivity of the composites prepared with and without water injection is increased by 101.5% and 75.0%, respectively. The introduced EG leads to increased Young’s modulus and tensile strength especially for the composite prepared by WAME. The present work indicates that WAME can promote the dispersion of EG in PVDF matrix without any extra functionalization.     

Chia Seed Oil Prevents High Fat Diet Induced Hyperlipidemia and Oxidative Stress in Mice

Hyperlipidemia is a common cardiovascular disease. At present, the influence of high fat diet (HFD) on this is being explored. Recently, vegetable oils rich in omega‐3 have been reported that can treat hyperlipidemia caused by HFD. However, the effects of chia seed oil (CSO) on HFD‐induced hyperlipidemia and oxidative stress are poorly studied. Hence, in this study, the effects of CSO on hyperlipidemia and oxidative stress induced by HFD in mice are analyzed by various commercial kits, section staining, and protein expression. The results show that CSO decreases body weight and organ index. Meanwhile, CSO reduces serum lipid levels of total cholesterol, triglyceride, and low‐density lipoprotein cholesterol. It can also elevate superoxide dismutase and glutathione peroxidase activities and reduce malondialdehyde content in serum and liver. The results of histopathological analysis prove that CSO improves hepatic steatosis and reduces lipid deposition. Further, the results of western blot demonstrate that CSO upregulates the expression of peroxisome proliferator‐activated receptor alpha and carnitine palmitoyltransferase 1a in the liver. As a result, CSO may be a potential lipid‐lowering oil to prevent and treat HFD‐induced hyperlipidemia and oxidative stress. Practical Applications CSO, as a byproduct of chia seed processing, is a rich source of α‐linolenic acid. This study investigates the effects of CSO on HFD‐induced hyperlipidemia and oxidative stress in mice. It is concluded that dietary CSO can improve the hyperlipidemia in HFD‐induced mice via analysis of lipid parameters, histopathology study of the liver, and lipid metabolism related genes. In addition, supplementation of CSO also can improve the oxidative stress in mice. Therefore, CSO can be used for the prevention of hyperlipidemia and oxidative stress. This research provides a theoretical basis for the comprehensive development and utilization of functional chia seed oil.     

Sulfonylguanidine Derivatives as Potential Antimelanoma Agents

Sulfonylguanidines are interesting bioactive compounds with a broad range of applications in the treatment of different pathologies. 2-Aminobenzazole-based structures are well employed in the development of new anticancer drugs. Two series of novel N-benzazol-2-yl-N′-sulfonyl guanidine derivatives were synthesized with the sulfonylguanidine in either an extra- or intracyclic frame. They were evaluated for their antiproliferative activity against malignant melanoma tumor cells, thus allowing structure-activity relationships to be defined. Additionally, NCI-60 screening was performed for the best analogue to study its efficiency against a panel of other cancer cell lines. The stability profile of this promising compound was then validated. During the synthetic process, an unexpected new deamidination of the sulfonylguanidine towards sulfonamide function was also identified.     

Delay compensated control of the Stefan problem and robustness to delay mismatch

This paper presents a control design for the one‐phase Stefan problem under actuator delay via a backstepping method. The Stefan problem represents a liquid‐solid phase change phenomenon which describes the time evolution of a material's temperature profile and the interface position. The actuator delay is modeled by a first‐order hyperbolic partial differential equation (PDE), resulting in a cascaded transport‐diffusion PDE system defined on a time‐varying spatial domain described by an ordinary differential equation (ODE). Two nonlinear backstepping transformations are utilized for the control design. The setpoint restriction is given to guarantee a physical constraint on the proposed controller for the melting process. This constraint ensures the exponential convergence of the moving interface to a setpoint and the exponential stability of the temperature equilibrium profile and the delayed controller in the norm. Furthermore, robustness analysis with respect to the delay mismatch between the plant and the controller is studied, which provides analogous results to the exact compensation by restricting the control gain.     

An efficient method of generating vortex electromagnetic wave based on double‐layer transmissive metasurface

A broadband double‐layer transmissive metasurface (TMS) for effective generate vortex electromagnetic wave is presented in this paper. The proposed TMS consists of two types of elements. The first element is composed of a multi‐resonant dipole and four metal vias. The metal vias increase the coupling strength between the upper and lower layers to improve transmission efficiency. On the basis of the first element, the second element adds stubs to ensure sufficient phase shift. The far‐field cross polarisation is eliminated by special element arrangement. Then, a centre‐fed linear polarisation TMS is designed to generate orbital angular momentum beams with mode l = ? 1. The proposed TMS is designed, manufactured and measured to verify the proposed design. The measured results indicate that a maximum gain of 20.8 dBi and narrow divergence angle of ±5° are achieved at 18 GHz. Furthermore, mode purity is higher than 86.1% within the 17 to 19 GHz band. The proposed double‐layer TMS saved costs, reduced weight and without assembly error is a good candidate for OAM generator.     

Transfer learning between crop types for semantic segmentation of crops versus weeds in precision agriculture

Agricultural robots rely on semantic segmentation for distinguishing between crops and weeds to perform selective treatments and increase yield and crop health while reducing the amount of chemicals used. Deep‐learning approaches have recently achieved both excellent classification performance and real‐time execution. However, these techniques also rely on a large amount of training data, requiring a substantial labeling effort, both of which are scarce in precision agriculture. Additional design efforts are required to achieve commercially viable performance levels under varying environmental conditions and crop growth stages. In this paper, we explore the role of knowledge transfer between deep‐learning‐based classifiers for different crop types, with the goal of reducing the retraining time and labeling efforts required for a new crop. We examine the classification performance on three datasets with different crop types and containing a variety of weeds and compare the performance and retraining efforts required when using data labeled at pixel level with partially labeled data obtained through a less time‐consuming procedure of annotating the segmentation output. We show that transfer learning between different crop types is possible and reduces training times for up to 80%. Furthermore, we show that even when the data used for retraining are imperfectly annotated, the classification performance is within 2% of that of networks trained with laboriously annotated pixel‐precision data.