Perfluoroalkyl compounds in groundwater alter the spatial pattern of health risk in an arsenic­cadmium contaminated region.

Integrated health risk assessment strategies for emerging organic pollutants and heavy metals that coexist in water/soil media are lacking. Contents of perfluoroalkyl compounds and potentially toxic elements in multiple media were determined by investigating a county where a landfill and a tungsten mine coexist. The spatial characteristics and sources of contaminants were predicted by Geostatistics-based and multivariate statistical analysis, and their comprehensive health risks were assessed. The average contents of perfluorooctane acid, perfluorooctanesulfonic acid, arsenic, and cadmium in groundwater were 3.21, 0.77, 1.69, and 0.14 µg L-1, respectively; the maximum content of cadmium in soils and rice highly reached 2.12 and 1.52 mg kg-1, respectively. In soils, the contribution of mine lag to cadmium was 99 %, and fertilizer and pesticide to arsenic was 59.4 %. While in groundwater, arsenic, cadmium and perfluoroalkyl compounds near the landfill mainly came from leachate leakage. Significant correlations were found between arsenic in groundwater and arsenic and cadmium in soils, as well as perfluoroalkyl compounds in groundwater and pH and sulfate. Based on these correlations, the geographically optimal similarity model predicted high-level arsenic in groundwater near the tungsten mine and cadmium/perfluoroalkyl compounds around the landfill. The combination of analytic network process, entropy weighting method and game theory-based trade-off method with risk assessment model can assess the comprehensive risks of multiple pollutants. Using this approach, a high health-risk zone located around the landfill, which was mainly attributed to the presence of arsenic, cadmium and perfluorooctanesulfonic acid, was found. Overall, perfluoroalkyl compounds in groundwater altered the spatial pattern of health risks in an arsenic­cadmium contaminated area.

Origami metamaterials for ultra-wideband and large-depth reflection modulation.

The dynamic control of electromagnetic waves is a persistent pursuit in modern industrial development. The state-of-the-art dynamic devices suffer from limitations such as narrow bandwidth, limited modulation range, and expensive features. To address these issues, we fuse origami techniques with metamaterial design to achieve ultra-wideband and large-depth reflection modulation. Through a folding process, our proposed metamaterial achieves over 10-dB modulation depth over 4.96 - 38.8 GHz, with a fractional bandwidth of 155% and tolerance to incident angles and polarizations. Its ultra-wideband and large-depth reflection modulation performance is verified through experiments and analyzed through multipole decomposition theory. To enhance its practical applicability, transparent conductive films are introduced to the metamaterial, achieving high optical transparency (>87%) from visible to near-infrared light while maintaining cost-effectiveness. Benefiting from lightweight, foldability, and low-cost properties, our design shows promise for extensive satellite communication and optical window mobile communication management.

Advanced liquid crystal-based switchable optical devices for light protection applications: principles and strategies.

With the development of optical technologies, transparent materials that provide protection from light have received considerable attention from scholars. As important channels for external light, windows play a vital role in the regulation of light in buildings, vehicles, and aircrafts. There is a need for windows with switchable optical properties to prevent or attenuate damage or interference to the human eye and light-sensitive instruments by inappropriate optical radiation. In this context, liquid crystals (LCs), owing to their rich responsiveness and unique optical properties, have been considered among the best candidates for advanced light protection materials. In this review, we provide an overview of advances in research on LC-based methods for protection against light. First, we introduce the characteristics of different light sources and their protection requirements. Second, we introduce several classes of light modulation principles based on liquid crystal materials and demonstrate the feasibility of using them for light protection. In addition, we discuss current light protection strategies based on liquid crystal materials for different applications. Finally, we discuss the problems and shortcomings of current strategies. We propose several suggestions for the development of liquid crystal materials in the field of light protection.

Hybrid Deep Feature Fusion of 2D CNN and 3D CNN for Vestibule Segmentation from CT Images.

The accurate vestibule segmentation from CT images is essential to the quantitative analysis of the anatomical structure of the ear. However, it is a challenging task due to the tiny size, blur boundary, and drastic variations in shape and size. In this paper, according to the specific characteristics and segmentation requirements of the vestibule, a vestibule segmentation network with a hybrid deep feature fusion of 2D CNN and 3D CNN is proposed. First, a 2D CNN is designed to extract the intraslice features through multiple deep feature fusion strategies, including a convolutional feature fusion strategy for different receptive fields, a feature channel fusion strategy based on channel attention mechanism, and an encoder-decoder feature fusion strategy. Next, a 3D DenseUNet is designed to extract the interslice features. Finally, a hybrid feature fusion module is proposed to fuse the intraslice and interslice features to effectively exploit the context information, thus achieving the accurate segmentation of the vestibule structure. At present, there is no publicly available dataset for vestibule segmentation. Therefore, the proposed segmentation method is validated on two self-established datasets, namely, VestibuleDataSet and IEBL-DataSet. It has been compared with several state-of-the-art methods on the datasets, including the general DeeplabV3+ method and specific 3D DSD vestibule segmentation method. The experimental results show that our proposed method can achieve superior segmentation accuracy.

Visible and infrared optical modulation of PSLC smart films doped with ATO nanoparticles.

In recent years, smart windows have been gradually applied and developed in the fields of environmental protection, energy management, smart camouflage and display. With the continuous upgrading of market demand, smart windows with both visible and infrared band modulation functions are the future development trends. However, the optical modulation of smart windows is usually limited in the visible light band (380-780 nm). In this paper, we report a novel smart window by doping antimony doped tin oxide (ATO) nanoparticles (NPs) into polymer stabilized liquid crystal (PSLC) films. These films have the widest waveband modulation function among those reported so far, covering the visible and infrared regions (380-5500 nm). The transmittance of the as-prepared smart films can be changed reversibly from highly transparent (78.5%) state to a strong light scattering (10%) state in the visible region. In addition, due to the localized surface plasmon resonance of ATO NPs, up to 80.7% of the infrared invisible light can be effectively shielded. The significance of this research is to provide theoretical and technical support for the broadband optical modulation of novel smart windows.

Vestibule segmentation from CT images with integration of multiple deep feature fusion strategies.

Vestibule Segmentation is of great significance for the clinical diagnosis of congenital ear malformations and cochlear implants. However, automated segmentation is a challenging task due to the tiny size, blur boundary, and drastic changes in shape and size. In this paper, a vestibule segmentation method from CT images has been proposed specifically, which exploits different deep feature fusion strategies, including convolutional feature fusion for different receptive fields, channel attention based feature channel fusion, and encoder-decoder feature fusion. The experimental results on the self-established vestibule segmentation dataset show that, compared with several state-of-the-art methods, our method can achieve superior segmentation accuracy.