This work proposes a microwave resonator built from gallium arsenide using integrated passive device (IPD) technology. It consists of a three-layered interlaced spiral structure with airbridges and inner interdigital structures. For integrated systems, IPD technology demonstrated outstanding performance, robustness, and a tiny size at a low cost. The airbridges were made more compact, with overall dimensions of 1590 × 800 µm (0.038 × 0.019 λg). The designed microwave resonator operated at 1.99 GHz with a return loss of 39 dB, an insertion loss of 0.07 dB, and a quality factor of 1.15. Additionally, an experiment was conducted on the properties of the airbridge and how they affected resistance, inductance, and S-parameters in the construction of the resonator. To investigate the impact of airbridges on the structure, E- and H-field distributions of the resonator were simulated. Furthermore, its use in sensing applications was explored. Various concentrations of glucose solutions were used in the experiment. The proposed device featured a minimum detectable concentration of 0.2 mg/mL; high sensitivity, namely, 14.58 MHz/mg·mL, with a linear response; and a short response time. Thus, this work proposes a structure that exhibits potential in integrated systems and real-time sensing systems with high sensitivity.
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http://dx.doi.org/10.3390/mi15030367 | DOI Listing |
JACS Au
December 2024
Biomacromolecules Research Team, RIKEN Center for Sustainable Resource Science, Saitama 351-0198, Japan.
The ability to quench reactive oxygen species (ROS) overproduced in plant chloroplasts under light stress conditions is essential for securing plant photosynthetic performance and agricultural yield. Although genetic engineering can enhance plant stress resistance, its widespread application faces limitations due to challenges in successful transformation across plant species and public acceptance concerns. This study proposes a nontransgenic chemical approach using a designed chimeric peptide that scavenges ROS within plant chloroplasts for managing light stress.
View Article and Find Full Text PDFHeliyon
December 2024
Department of Mechanical Engineering, National Cheng Kung University, Tainan, 701, Taiwan.
Machining optimization is crucial for determining cutting parameters that enhance machining economics. However, few studies address the significant variation in cutting tool wear and the complexities of discrete production, often leading to lower cutting parameters to prevent operational failures. Moreover, variations in part geometries lead to differing contact conditions between the cutting tool and workpiece, as well as variations in material removal.
View Article and Find Full Text PDFHeliyon
December 2024
Department of Mathematics, Bahria Foundation College, Peshawar Road Campus, Rawalpindi, Pakistan.
Improving efficiency has long been a focal challenge in sampling literature. However, simultaneously enhancing estimator efficacy and optimizing survey costs is a practical necessity across various fields such as medicine, agriculture, and transportation. In this study, we present a comprehensive family of generalized exponential estimators specifically designed for estimating population means within stratified sampling frameworks.
View Article and Find Full Text PDFNetw Neurosci
December 2024
Statistics and Operations Research, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA.
Despite the widespread exploration and availability of parcellations for the functional connectome, parcellations designed for the structural connectome are comparatively limited. Current research suggests that there may be no single "correct" parcellation and that the human brain is intrinsically a multiresolution entity. In this work, we propose the Continuous Structural Connectivitity-based, Nested (CoCoNest) family of parcellations-a fully data-driven, multiresolution family of parcellations derived from structural connectome data.
View Article and Find Full Text PDFProc IEEE Int Symp Biomed Imaging
May 2024
Department of Human Oncology, University of Wisconsin-Madison, Madison, WI, USA.
In medical image segmentation, although multi-modality training is possible, clinical translation is challenged by the limited availability of all image types for a given patient. Different from typical segmentation models, modality-agnostic (MAG) learning trains a single model based on all available modalities but remains input-agnostic, allowing a single model to produce accurate segmentation given any modality combinations. In this paper, we propose a novel frame-work, MAG learning through Multi-modality Self-distillation (MAG-MS), for medical image segmentation.
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