| 1. |
- Beal, Jacob, et al.
(författare)
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Robust estimation of bacterial cell count from optical density
- 2020
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Ingår i: Communications Biology. - : Springer Science and Business Media LLC. - 2399-3642. ; 3:1
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Tidskriftsartikel (refereegranskat)abstract
- Optical density (OD) is widely used to estimate the density of cells in liquid culture, but cannot be compared between instruments without a standardized calibration protocol and is challenging to relate to actual cell count. We address this with an interlaboratory study comparing three simple, low-cost, and highly accessible OD calibration protocols across 244 laboratories, applied to eight strains of constitutive GFP-expressing E. coli. Based on our results, we recommend calibrating OD to estimated cell count using serial dilution of silica microspheres, which produces highly precise calibration (95.5% of residuals <1.2-fold), is easily assessed for quality control, also assesses instrument effective linear range, and can be combined with fluorescence calibration to obtain units of Molecules of Equivalent Fluorescein (MEFL) per cell, allowing direct comparison and data fusion with flow cytometry measurements: in our study, fluorescence per cell measurements showed only a 1.07-fold mean difference between plate reader and flow cytometry data.
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| 2. |
- Kristan, Matej, et al.
(författare)
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The Sixth Visual Object Tracking VOT2018 Challenge Results
- 2019
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Ingår i: Computer Vision – ECCV 2018 Workshops. - Cham : Springer Publishing Company. - 9783030110086 - 9783030110093 ; , s. 3-53
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Konferensbidrag (refereegranskat)abstract
- The Visual Object Tracking challenge VOT2018 is the sixth annual tracker benchmarking activity organized by the VOT initiative. Results of over eighty trackers are presented; many are state-of-the-art trackers published at major computer vision conferences or in journals in the recent years. The evaluation included the standard VOT and other popular methodologies for short-term tracking analysis and a “real-time” experiment simulating a situation where a tracker processes images as if provided by a continuously running sensor. A long-term tracking subchallenge has been introduced to the set of standard VOT sub-challenges. The new subchallenge focuses on long-term tracking properties, namely coping with target disappearance and reappearance. A new dataset has been compiled and a performance evaluation methodology that focuses on long-term tracking capabilities has been adopted. The VOT toolkit has been updated to support both standard short-term and the new long-term tracking subchallenges. Performance of the tested trackers typically by far exceeds standard baselines. The source code for most of the trackers is publicly available from the VOT page. The dataset, the evaluation kit and the results are publicly available at the challenge website (http://votchallenge.net).
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| 3. |
- Luo, Yifei, et al.
(författare)
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Technology Roadmap for Flexible Sensors
- 2023
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Ingår i: ACS Nano. - : American Chemical Society. - 1936-0851 .- 1936-086X. ; 17:6, s. 5211-5295
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Forskningsöversikt (refereegranskat)abstract
- Humans rely increasingly on sensors to address grand challenges and to improve quality of life in the era of digitalization and big data. For ubiquitous sensing, flexible sensors are developed to overcome the limitations of conventional rigid counterparts. Despite rapid advancement in bench-side research over the last decade, the market adoption of flexible sensors remains limited. To ease and to expedite their deployment, here, we identify bottlenecks hindering the maturation of flexible sensors and propose promising solutions. We first analyze challenges in achieving satisfactory sensing performance for real-world applications and then summarize issues in compatible sensor-biology interfaces, followed by brief discussions on powering and connecting sensor networks. Issues en route to commercialization and for sustainable growth of the sector are also analyzed, highlighting environmental concerns and emphasizing nontechnical issues such as business, regulatory, and ethical considerations. Additionally, we look at future intelligent flexible sensors. In proposing a comprehensive roadmap, we hope to steer research efforts towards common goals and to guide coordinated development strategies from disparate communities. Through such collaborative efforts, scientific breakthroughs can be made sooner and capitalized for the betterment of humanity.
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| 4. |
- Kanoni, Stavroula, et al.
(författare)
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Implicating genes, pleiotropy, and sexual dimorphism at blood lipid loci through multi-ancestry meta-analysis.
- 2022
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Ingår i: Genome biology. - : Springer Science and Business Media LLC. - 1474-760X .- 1465-6906 .- 1474-7596. ; 23:1
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Tidskriftsartikel (refereegranskat)abstract
- Genetic variants within nearly 1000 loci are known to contribute to modulation of blood lipid levels. However, the biological pathways underlying these associations are frequently unknown, limiting understanding of these findings and hindering downstream translational efforts such as drug target discovery.To expand our understanding of the underlying biological pathways and mechanisms controlling blood lipid levels, we leverage a large multi-ancestry meta-analysis (N=1,654,960) of blood lipids to prioritize putative causal genes for 2286 lipid associations using six gene prediction approaches. Using phenome-wide association (PheWAS) scans, we identify relationships of genetically predicted lipid levels to other diseases and conditions. We confirm known pleiotropic associations with cardiovascular phenotypes and determine novel associations, notably with cholelithiasis risk. We perform sex-stratified GWAS meta-analysis of lipid levels and show that 3-5% of autosomal lipid-associated loci demonstrate sex-biased effects. Finally, we report 21 novel lipid loci identified on the X chromosome. Many of the sex-biased autosomal and X chromosome lipid loci show pleiotropic associations with sex hormones, emphasizing the role of hormone regulation in lipid metabolism.Taken together, our findings provide insights into the biological mechanisms through which associated variants lead to altered lipid levels and potentially cardiovascular disease risk.
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| 5. |
- Liu, Tiefeng, et al.
(författare)
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Ground-state electron transfer in all-polymer donor:acceptor blends enables aqueous processing of water-insoluble conjugated polymers
- 2023
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Ingår i: Nature Communications. - : NATURE PORTFOLIO. - 2041-1723. ; 14:1
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Tidskriftsartikel (refereegranskat)abstract
- Water-based conductive inks are vital for the sustainable manufacturing and widespread adoption of organic electronic devices. Traditional methods to produce waterborne conductive polymers involve modifying their backbone with hydrophilic side chains or using surfactants to form and stabilize aqueous nanoparticle dispersions. However, these chemical approaches are not always feasible and can lead to poor material/device performance. Here, we demonstrate that ground-state electron transfer (GSET) between donor and acceptor polymers allows the processing of water-insoluble polymers from water. This approach enables macromolecular charge-transfer salts with 10,000x higher electrical conductivities than pristine polymers, low work function, and excellent thermal/solvent stability. These waterborne conductive films have technological implications for realizing high-performance organic solar cells, with efficiency and stability superior to conventional metal oxide electron transport layers, and organic electrochemical neurons with biorealistic firing frequency. Our findings demonstrate that GSET offers a promising avenue to develop water-based conductive inks for various applications in organic electronics. Chemical approaches to improve aqueous dispersions of conjugated polymers are limited by the feasibility of modifying the backbone or lead to poor performance. Here, Liu et al. show that ground-state electron transfer in donor:acceptor blends aids aqueous dispersion, for high conductivity and solubility.
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| 6. |
- Adin, Veysi, et al.
(författare)
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Tiny Machine Learning for Damage Classification in Concrete Using Acoustic Emission Signals
- 2023
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Ingår i: 2023 IEEE International Instrumentation and Measurement Technology Conference (I2MTC). - : IEEE. - 9781665453837
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Konferensbidrag (refereegranskat)abstract
- Acoustic emission (AE) is a widely used non-destructive test method in structural health monitoring applications to identify the damage type in the material. Usually, the analysis of the AE signal is done by using traditional parameter-based methods. Recently, machine learning methods showed promising results for the analysis of AE signals. However, these machine learning models are complex, slow, and consume significant amounts of energy. To address these limitations and to explore the trade-off between model complexity and the classification accuracy, this paper presents a lightweight artificial neural network model to classify damage types in concrete material using raw acoustic emission signals. The model consists of one hidden layer with four neurons and is trained on a public acoustic emission signal dataset. The created model is deployed to several microcontrollers and the performance of the model is evaluated and compared with a state-of-the-art machine learning model. The model achieves 98.4% accuracy on the test data with only 4019 parameters. In terms of evaluation metrics, the proposed tiny machine learning model outperforms previously proposed models 10 to 1000 times. The proposed model thus enables machine learning in real-time structural health monitoring applications.
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| 7. |
- Adin, Veysi, et al.
(författare)
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Tiny Machine Learning for Real-Time Postural Stability Analysis
- 2023
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Ingår i: 2023 IEEE Sensors Applications Symposium (SAS). - : IEEE conference proceedings. - 9798350323078
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Konferensbidrag (refereegranskat)abstract
- Postural sway is a critical measure for evaluating postural control, and its analysis plays a vital role in preventing falls among the elderly. Typically, physiotherapists assess an individual's postural control using tests such as the Berg Balance Scale, Tinetti Test, and time up-and-go test. Sensor-based analysis is available based on devices such as force plates or inertial measurement units. Recently, machine learning methods have demonstrated promising results in the sensor-based analysis of postural control. However, these models are often complex, slow, and energy-intensive. To address these limitations, this study explores the design space of lightweight machine learning models deployable to microcontrollers to assess postural stability. We developed an artificial neural network (ANN) model and compare its performance to that of random forests, gaussian naive bayes, and extra tree classifiers. The models are trained using a sway dataset with varying input sizes and signal-to-noise ratios. The dataset comprises two feature vectors extracted from raw accelerometer data. The developed models are deployed to an ARM Cortex M4-based microcontroller, and their performance is evaluated and compared. We show that the ANN model has 99.03% accuracy, higher noise immunity, and the model performs better with a window size of one second with 590.96 us inference time.
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| 8. |
- Cui, Chenggang, et al.
(författare)
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Implementation of Transferring Reinforcement Learning for DC-DC Buck Converter Control via Duty Ratio Mapping
- 2023
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Ingår i: IEEE Transactions on Industrial Electronics. - : Institute of Electrical and Electronics Engineers (IEEE). - 0278-0046 .- 1557-9948. ; 70:6, s. 6141-6150
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Tidskriftsartikel (refereegranskat)abstract
- The reinforcement learning (RL) control approach with application to power electronics systems has become an emerging topic, while the sim-to-real issue remains a challenging problem as very few results can be referred to in the literature. Indeed, due to the inevitable mismatch between simulation models and real-life systems, offline-trained RL control strategies may sustain unexpected hurdles in practical implementation during the transfer procedure. In this article, a transfer methodology via a delicately designed duty ratio mapping is proposed for a dc-dc buck converter. Then, a detailed sim-to-real process is presented to enable the implementation of a model-free deep reinforcement learning controller. As the main contribution of this article, the proposed methodology is able to endow the control system to achieve: 1) voltage regulation and 2) adaptability and optimization abilities in the presence of uncertain circuit parameters and various working conditions. The feasibility and efficacy of the proposed methodology are demonstrated by comparative experimental studies.
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| 9. |
- Hu, Chang-Kang, et al.
(författare)
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Native Conditional iSWAP Operation with Superconducting Artificial Atoms
- 2023
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Ingår i: Physical Review Applied. - 2331-7019. ; 20:3
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Tidskriftsartikel (refereegranskat)abstract
- Controlling the flow of quantum information is a fundamental task for quantum computers, which is unfeasible to realize on classical devices. Coherent devices, which can process quantum states are thus required to route the quantum states that encode information. In this paper we demonstrate experimentally the smallest quantum transistor with a superconducting quantum processor, which is composed of a collector qubit, an emitter qubit, and a coupler (transistor gate). The interaction strength between the collector and emitter qubits is controlled by the frequency and state of the coupler, effectively implementing a quantum switch. Through the coupler-state-dependent Heisenberg (inherent) interaction between the qubits, a single-step (native) conditional iSWAP operation can be applied. To this end, we find that it is useful to take into consideration the higher-energy level for achieving a native and high-fidelity transistor operation. By reconstructing the quantum process tomography, we obtain an operation fidelity of 92.36% when the transistor gate is open (iSWAP implementation) and 95.23% in the case of closed gate (identity gate implementation). The architecture has strong potential in quantum information processing applications with superconducting qubits.
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| 10. |
- Hu, Chang-Kang, et al.
(författare)
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Optimal charging of a superconducting quantum battery
- 2022
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Ingår i: Quantum Science and Technology. - : IOP Publishing. - 2058-9565. ; 7:4
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Tidskriftsartikel (refereegranskat)abstract
- Quantum batteries are miniature energy storage devices and play a very important role in quantum thermodynamics. In recent years, quantum batteries have been extensively studied, but limited in theoretical level. Here we report the experimental realization of a quantum battery based on superconducting qutrit. Our model explores dark and bright states to achieve stable and powerful charging processes, respectively. Our scheme makes use of the quantum adiabatic brachistochrone, which allows us to speed up the battery ergotropy injection. Due to the inherent interaction of the system with its surrounding, the battery exhibits a self-discharge, which is shown to be described by a supercapacitor-like self-discharging mechanism. Our results paves the way for proposals of new superconducting circuits able to store extractable work for further usage.
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| 11. |
- Huang, Chengjie, et al.
(författare)
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Tiny-Machine-Learning-Based Supply Canal Surface Condition Monitoring
- 2024
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Ingår i: Sensors. - : MDPI AG. - 1424-8220. ; 24:13
-
Tidskriftsartikel (refereegranskat)abstract
- The South-to-North Water Diversion Project in China is an extensive inter-basin water transfer project, for which ensuring the safe operation and maintenance of infrastructure poses a fundamental challenge. In this context, structural health monitoring is crucial for the safe and efficient operation of hydraulic infrastructure. Currently, most health monitoring systems for hydraulic infrastructure rely on commercial software or algorithms that only run on desktop computers. This study developed for the first time a lightweight convolutional neural network (CNN) model specifically for early detection of structural damage in water supply canals and deployed it as a tiny machine learning (TinyML) application on a low-power microcontroller unit (MCU). The model uses damage images of the supply canals that we collected as input and the damage types as output. With data augmentation techniques to enhance the training dataset, the deployed model is only 7.57 KB in size and demonstrates an accuracy of 94.17 ± 1.67% and a precision of 94.47 ± 1.46%, outperforming other commonly used CNN models in terms of performance and energy efficiency. Moreover, each inference consumes only 5610.18 μJ of energy, allowing a standard 225 mAh button cell to run continuously for nearly 11 years and perform approximately 4,945,055 inferences. This research not only confirms the feasibility of deploying real-time supply canal surface condition monitoring on low-power, resource-constrained devices but also provides practical technical solutions for improving infrastructure security.
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| 12. |
- Muthumala, Uditha, et al.
(författare)
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Comparison of Tiny Machine Learning Techniques for Embedded Acoustic Emission Analysis
-
Annan publikation (övrigt vetenskapligt/konstnärligt)abstract
- This paper compares machine learning approaches with different input data formats for the classification of acoustic emission (AE) signals. AE signals are a promising monitoring technique in many structural health monitoring applications. Machine learning has been demonstrated as an effective data analysis method, classifying different AE signals according to the damage mechanism they represent. These classifications can be performed based on the entire AE waveform or specific features that have been extracted from it. However, it is currently unknown which of these approaches is preferred. With the goal of model deployment on resource-constrained embedded Internet of Things (IoT) systems, this work evaluates and compares both approaches in terms of classification accuracy, memory requirement, processing time, and energy consumption. To accomplish this, features are extracted and carefully selected, neural network models are designed and optimized for each input data scenario, and the models are deployed on a low-power IoT node. The comparative analysis reveals that all models can achieve high classification accuracies of over 99\%, but that embedded feature extraction is computationally expensive. Consequently, models utilizing the raw AE signal as input have the fastest processing speed and thus the lowest energy consumption, which comes at the cost of a larger memory requirement.
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| 13. |
- Ossler, Frederik, et al.
(författare)
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Dynamics of hydrogen loss and structural changes in pyrolyzing biomass utilizing neutron imaging
- 2021
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Ingår i: Carbon. - : Elsevier BV. - 0008-6223. ; 176, s. 511-529
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Tidskriftsartikel (refereegranskat)abstract
- We present results from neutron-imaging studies of slow, vacuum pyrolysis of beech, poplar and conifer wood, and pelletized biomass from room temperature up to 1000 °C. A detailed and quantitative method to extract 2D (in situ neutron radiography, NR) and 3D (ex situ neutron computed tomography, NCT) information on structural transformation and elemental hydrogen content has been developed. NCT and X-ray tomography (XCT) experiments on a carbonized beech twig permitted comparison of the spatial distribution of hydrogen, better sensed by NCT, and carbon, oxygen, and heavier elements, better sensed by XCT. We have developed a methodology to directly compare structure and hydrogen-loss dynamics measured using neutron imaging with thermogravimetric analysis and differential thermogravimetry and thus can better understand the correlations between hydrogen and carbon release dynamics. While the methodology has been developed for the carbonization of biomass, we expect that it could be applied to in situ dynamic monitoring of other hydrogenous reacting systems with the appropriate spatial and temporal scales.
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| 14. |
- Stokey, Megan, et al.
(författare)
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Optical phonon modes, static and high-frequency dielectric constants, and effective electron mass parameter in cubic In2O3
- 2021
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Ingår i: Journal of Applied Physics. - : AMER INST PHYSICS. - 0021-8979 .- 1089-7550. ; 129:22
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Tidskriftsartikel (refereegranskat)abstract
- A complete set of all optical phonon modes predicted by symmetry for bixbyite structure indium oxide is reported here from a combination of far-infrared and infrared spectroscopic ellipsometry, as well as first principles calculations. Dielectric function spectra measured on high quality, marginally electrically conductive melt grown single bulk crystals are obtained on a wavelength-by-wavelength (also known as point-by-point) basis and by numerical reduction of a subtle free charge carrier Drude model contribution(. )A four-parameter semi-quantum model is applied to determine all 16 pairs of infrared-active transverse and longitudinal optical phonon modes, including the high-frequency dielectric constant, epsilon(infinity) = 4.05 +/- 0.05. The Lyddane-Sachs-Teller relation then gives access to the static dielectric constant, epsilon(DC) = 10.55 +/- 0.07. All experimental results are in excellent agreement with our density functional theory calculations and with previously reported values, where existent. We also perform optical Hall effect measurements and determine for the unintentionally doped n-type sample a free electron density of n = (2.81 +/- 0.01) x 10(17) cm(-3), a mobility of mu = (112 +/- 3) cm(2)/(Vs), and an effective mass parameter of (0.208 +/- 0.006)m(e). Density and mobility parameters compare very well with the results of electrical Hall effect measurements. Our effective mass parameter, which is measured independently of any other experimental technique, represents the bottom curvature of the Gamma point in In2O3 in agreement with previous extrapolations. We use terahertz spectroscopic ellipsometry to measure the quasi-static response of In2O3, and our model validates the static dielectric constant obtained from the Lyddane-Sachs-Teller relation. Published under an exclusive license by AIP Publishing.
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| 15. |
- Xie, Yang, et al.
(författare)
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Cervical Spondylosis Diagnosis Based on Convolutional Neural Network with X-ray Images
- 2024
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Ingår i: Sensors. - : MDPI AG. - 1424-8220. ; 24:11
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Tidskriftsartikel (refereegranskat)abstract
- The increase in Cervical Spondylosis cases and the expansion of the affected demographic to younger patients have escalated the demand for X-ray screening. Challenges include variability in imaging technology, differences in equipment specifications, and the diverse experience levels of clinicians, which collectively hinder diagnostic accuracy. In response, a deep learning approach utilizing a ResNet-34 convolutional neural network has been developed. This model, trained on a comprehensive dataset of 1235 cervical spine X-ray images representing a wide range of projection angles, aims to mitigate these issues by providing a robust tool for diagnosis. Validation of the model was performed on an independent set of 136 X-ray images, also varied in projection angles, to ensure its efficacy across diverse clinical scenarios. The model achieved a classification accuracy of 89.7%, significantly outperforming the traditional manual diagnostic approach, which has an accuracy of 68.3%. This advancement demonstrates the viability of deep learning models to not only complement but enhance the diagnostic capabilities of clinicians in identifying Cervical Spondylosis, offering a promising avenue for improving diagnostic accuracy and efficiency in clinical settings.
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| 16. |
- Zhang, Yuxuan, et al.
(författare)
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A Lightweight Convolutional Neural Network Model for Concrete Damage Classification using Acoustic Emissions
- 2022
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Ingår i: 2022 IEEE Sensors Applications Symposium, SAS 2022 - Proceedings. - : IEEE. - 9781665409810
-
Konferensbidrag (refereegranskat)abstract
- In this study, a convolutional neural network (CNN) model was developed for non-destructive damage classification of concrete materials based on acoustic emission techniques. The raw acoustic emission signal is used as the network model input, while the damage type is used as the output. In the study, 15,000 acoustic emission signals were used as the dataset, of which 12,000 signals were used for training, 1,500 signals for validation, and 1,500 signals for testing. Adaptive moment estimation (Adam) was used as the learning algorithm. Batch normalization and dropout layers were used to solve the overfitting problem generated in earlier versions of the model. The proposed model achieves an accuracy of 99.70% with 20,243 parameters, which provides a significant improvement over previous models. As a result, the classification of damages and decisions based upon them in non-destructive structural health monitoring applications can be improved.
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| 17. |
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| 18. |
- Zhang, Yuxuan, et al.
(författare)
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Leveraging Acoustic Emission and Machine Learning for Concrete Materials Damage Classification on Embedded Devices
- 2023
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Ingår i: IEEE Transactions on Instrumentation and Measurement. - : IEEE. - 0018-9456 .- 1557-9662. ; 72
-
Tidskriftsartikel (refereegranskat)abstract
- For the field of structural health monitoring (SHM), acoustic emission (AE) technology is important as a damage identification technique that does not cause secondary damage to concrete. Nowadays, applications of non-destructive concrete damage identification are mostly limited to commercial software or identification algorithms running on desktop computers. It has so far not been deployed in low-power embedded devices. In this study, a lightweight convolutional neural network (CNN) model for online non-destructive damage type recognition of concrete materials is presented and deployed on a resource-constrained microcontroller unit as a tiny machine learning (TinyML) application. The CNN model uses raw acoustic emission signals as input and damage recognition types as output. 15,000 acoustic emission signals are used as data sets divided into training, validation, and test sets in the ratio of 8:1:1. The experimental results show that an accuracy of 99.6% is achieved on the nRF52840 microcontroller (ARM Cortex M4) with only 166.822 ms and 0.555mJ for a single inference using only 20K parameters and 30.5KB model size. This work demonstrates the effectiveness and feasibility of the proposed model, which achieves a trade-off between high classification accuracy and deployability on resource-constrained MCUs. Consequently, it provides strong support for online continuous non-destructive structural health monitoring.
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| 19. |
- Zhang, Yuxuan
(författare)
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Tiny Machine Learning for Structural Health Monitoring with Acoustic Emissions
- 2024
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Licentiatavhandling (övrigt vetenskapligt/konstnärligt)abstract
- Acoustic Emission (AE) technology, as one of the non-destructive Structural Health Monitoring (SHM) methods, is increasingly utilized for the damage prediction, classification, maintenance, and real-time monitoring of infrastructure. Addressing the need for low latency, power consumption and high portability, a novel approach has been adopted where processing algorithms are embedded close to the sensors on these devices. Continuous data monitoring and collection, coupled with data processing and interpretation comparable to human experts, are anticipated from the next generation of the Internet of Things and smart sensing systems. While Machine Learning (ML) and Deep Learning (DL) has been successfully applied in a number of domains including SHM, resource-constrained, low-power devices pose a challenge for computationally complex ML algorithm execution.To explore the feasibility of deploying ML and DL algorithms on edge devices, this study first proposes a lightweight CNN model based on raw AE signals for concrete damage classification and evaluates its performance on an ultra-low-power microcontroller unit (MCU). Subsequently, to further simplify the algorithm and explore the adaptability across various MCU platforms, a raw AE signal-based Artificial Neural Network (ANN) model is proposed, and its deployment performance on multiple MCUs is assessed. Additionally, the study assesses the impact of feature extraction on ANN performance with raw AE signals on MCUs, finding that using raw data directly is more resource and time-efficient. Lastly, the study investigates the generalization ability of the aforementioned CNN on a carbon fiber panel AE dataset, as well as the performance of 13 traditional ML algorithms on this dataset and their final deployment performance on MCUs. Due to the small size of the dataset, various data augmentation methods were also introduced and their impact on model robustness and accuracy was evaluated.This thesis demonstrates for the first time that real-time inference on edge devices using AE signals for SHM is feasible. It also effectively demonstrates how to balance the critical trade-offs between accuracy, resource demands, and power consumption. Different MCUs and signal preprocessing methods are evaluated, and the impact of various data augmentation techniques on the accuracy of different ML algorithms and their inference robustness is explored in response to the challenge of collecting AE data, which is crucial for the next generation of SHM devices.
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| 20. |
- Zhao, Yuxuan, et al.
(författare)
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Design of Wideband Dual-Circularly Polarized Endfire Antenna Array on Gap Waveguide
- 2019
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Ingår i: 13th European Conference on Antennas and Propagation, EuCAP 2019.
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Konferensbidrag (refereegranskat)abstract
- A wideband dual-circularly polarized (CP) linear antenna array is presented in this paper. Firstly, a dual-CP endfire antenna based on septum polarizer is designed as the element for the array. Secondly, the feeding network is realized by ridge gap waveguide. Then a 1×8 linear antenna array is built up by the elements. The proposed array antenna achieves wide impedance bandwidth of 44.6% with the reflection coefficient below -10 dB, the isolation between ports greater than 15 dB, and a wide 3-dB axial ratio (AR) bandwidth of 46.2%.
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| 21. |
- 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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