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- Zhao, Yuxuan, et al.
(författare)
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The CHECH study : A prospective pregnancy cohort study on CHemical exposure and children’s health in Tianjin, China
- 2024
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Ingår i: Hygiene and Environmental Health Advances. - : Elsevier. - 2773-0492. ; 9
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Tidskriftsartikel (refereegranskat)abstract
- The CHemical Exposure and Children’s Health (CHECH) study is an ongoing pregnancy cohort study in Tianjin, China. This paper describes the background, aim and the study design, which can be followed by future researchers to design and conduct similar studies. The abundance and the potential adverse health outcomes of endocrine disrupting chemicals (EDCs) is concerning. More notably, developing fetuses and infants are more vulnerable to EDCs exposure. The CHECH study aims to investigate the importance of early life exposure to multiple EDCs (phthalates and their metabolites, bisphenol A and their substitutes, perfluorinated compounds and poly brominated diphenyl ethers) for multiple health outcomes in Chinese children, namely sexual development, neurodevelopment, metabolism and growth, as well as asthma and allergy. A total of 2238 pregnant women were recruited in Tianjin from May 2017 to April 2021 with a response rate of 90 %. Among these women, 2255 children were born with available information, including 47 pairs of twins. Urine samples were collected from pregnant women and children, while air and dust samples were obtained from the home environment during pregnancy and infancy periods. Information on children’s health was gathered through physical examinations and questionnaires. The CHECH study, which collected exposure information and health outcomes at multiple time points, will contribute to the understanding of prenatal exposure to EDCs and their impact on children’s health, thereby facilitating the development of risk assessments aimed at reducing exposure and associated health risks.
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| 2. |
- Liang, Hanwei, et al.
(författare)
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Supporting zero-waste building: A novel spatial explicit material flow analysis model for construction waste
- 2024
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Ingår i: Journal of Cleaner Production. - 0959-6526. ; 456
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Tidskriftsartikel (refereegranskat)abstract
- The accurate quantification of construction waste (CW) is prerequisite for achieving zero-waste in the building sector. This study employs a Spatial Material Flow Analysis model considering building life cycle to estimate the spatio-temporal dynamics of CW in Nanjing, China, over two decades. Additionally, it employs an ARIMA model for forecasting CW generation over the forthcoming decade. During 2000 to 2020, Nanjing's CW witnessed a CW increase from 0.8 to 59.0 Tg, predominantly comprising non-metallic materials. Meanwhile, the spatial distribution of CW has widened, accompanied by the emergence of numerous CW hotspots, in parallel with economic and population growth. Future projections suggest a continuous yet decelerating increase in CW generation, highlighting a significant opportunity for resource recovery. The application of this hybrid approach not only enhances CW management accuracy but also supports the broader goal of fostering sustainable building practices. This research expects to enlighten the stakeholders committed to advancing sustainable construction and waste management strategies in the context of zero-waste buildings.
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| 3. |
- Wang, Kang, et al.
(författare)
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Luminescent metal-halide perovskites: fundamentals, synthesis, and light-emitting devices
- 2024
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Ingår i: Science in China Series B. - : SCIENCE PRESS. - 1674-7291 .- 1869-1870. ; 67:6, s. 1776-1838
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Forskningsöversikt (refereegranskat)abstract
- Metal-halide perovskites have garnered considerable research attention as highly efficient light emitters in recent years due to their outstanding optoelectronic properties with remarkable tunability and excellent solution processabilities. Substantial advancements have been achieved in the development of novel halide perovskites, and the exploitations of these materials in light-emitting devices. This review comprehensively outlines recent breakthroughs in metal-halide perovskites, encompassing the rational design of perovskite materials with tunable light emission properties, the controllable growth of single crystal for a deeper understanding of their structure-property relationships, as well as the fundamental insights into the photophysics and carrier dynamics in perovskite systems. Additionally, it provides an overview of recent applications of perovskite materials in high-performance light-emitting diodes (LEDs) and lasers.
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| 4. |
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| 5. |
- Xiong, Weiyi, et al.
(författare)
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LXL: LiDAR Excluded Lean 3D Object Detection with 4D Imaging Radar and Camera Fusion
- 2024
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Ingår i: IEEE Transactions on Intelligent Vehicles. - 2379-8858. ; 9:1, s. 79-92
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Tidskriftsartikel (refereegranskat)abstract
- As an emerging technology and a relatively affordable device, the 4D imaging radar has already been confirmed effective in performing 3D object detection in autonomous driving. Nevertheless, the sparsity and noisiness of 4D radar point clouds hinder further performance improvement, and in-depth studies about its fusion with other modalities are lacking. On the other hand, as a new image view transformation strategy, “sampling” has been applied in a few image-based detectors and shown to outperform the widely applied “depth-based splatting” proposed in Lift-Splat-Shoot (LSS), even without image depth prediction. However, the potential of “sampling” is not fully unleashed. This paper investigates the “sampling” view transformation strategy on the camera and 4D imaging radar fusion-based 3D object detection. LiDAR Excluded Lean (LXL) model, predicted image depth distribution maps and radar 3D occupancy grids are generated from image perspective view (PV) features and radar bird's eye view (BEV) features, respectively. They are sent to the core of LXL, called “radar occupancy-assisted depth-based sampling”, to aid image view transformation. We demonstrated that more accurate view transformation can be performed by introducing image depths and radar information to enhance the “sampling” strategy. Experiments on VoD and TJ4DRadSet datasets show that the proposed method outperforms the state-of-the-art 3D object detection methods by a significant margin without bells and whistles. Ablation studies demonstrate that our method performs the best among different enhancement settings.
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| 6. |
- Xiong, Weiyi, et al.
(författare)
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LXL: LiDAR Excluded Lean 3D Object Detection with 4D Imaging Radar and Camera Fusion
- 2024
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Ingår i: IEEE Intelligent Vehicles Symposium, Proceedings. - 1931-0587 .- 2642-7214. ; , s. 3142-
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Konferensbidrag (refereegranskat)abstract
- As an emerging technology and a relatively affordable device, the 4D imaging radar has already been confirmed effective in performing 3D object detection in autonomous driving [1]. Nevertheless, the sparsity and noisiness of 4D radar point clouds hinder further performance improvement, and in-depth studies about its fusion with other modalities are lacking. On the other hand, as a new image view transformation strategy, sampling has been applied in a few image-based detectors and shown to outperform the widely applied depth-based splatting proposed in Lift-Splat-Shoot (LSS) [2] , even without image depth prediction [3]. However, the potential of sampling is not fully unleashed. As a result, this paper investigates the sampling strategy on the camera and 4D imaging radar fusion-based 3D object detection. In the proposed LiDAR Excluded Lean (LXL) model, predicted image depth distribution maps and radar 3D occupancy grids are generated from image perspective view (PV) features and radar bird's eye view (BEV) features, respectively. They are sent to the core of LXL, called radar occupancy-assisted depth-based sampling , to aid image view transformation.
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