Filtering antenna with two-octave harmonic suppression

A novel 2nd–order antenna with filtering performance and two-octave harmonic suppression is proposed. In order to reduce the effects of the harmonics of the antenna, two types of antennas (PIFA and patch) with different resonant characteristics are integrated into the design. Compared with the traditional patch antennas, this integrated work can not only eliminate the high-order harmonics of the antenna but also improve the in-band bandwidth and frequency selectivity. The 2nd and 4th–order harmonics of the patch are suppressed because of the detuned harmonic performance of the PIFA and patch. The 3rd-order harmonic is eliminated by integrating notch resonators in the PIFA. A prototype works at 2.4 GHz is developed to demonstrate the PIFA-patch integration scheme. Measured and simulated results of antennas agree well with each other, demonstrating good performance of bandwidth, 2nd-order filtering, radiation and wideband harmonic suppression (up to 11 GHz)

SCA-PVNet: Self-and-Cross Attention Based Aggregation of Point Cloud and Multi-View for 3D Object Retrieval

To address 3D object retrieval, substantial efforts have been made to generate highly discriminative descriptors of 3D objects represented by a single modality, e.g., voxels, point clouds or multi-view images. It is promising to leverage the complementary information from multi-modality representations of 3D objects to further improve retrieval performance. However, multi-modality 3D object retrieval is rarely developed and analyzed on large-scale datasets. In this paper, we propose self-and-cross attention based aggregation of point cloud and multi-view images (SCA-PVNet) for 3D object retrieval. With deep features extracted from point clouds and multi-view images, we design two types of feature aggregation modules, namely the In-Modality Aggregation Module (IMAM) and the Cross-Modality Aggregation Module (CMAM), for effective feature fusion. IMAM leverages a self-attention mechanism to aggregate multi-view features while CMAM exploits a cross-attention mechanism to interact point cloud features with multi-view features. The final descriptor of a 3D object for object retrieval can be obtained via concatenating the aggregated features from both modules. Extensive experiments and analysis are conducted on three datasets, ranging from small to large scale, to show the superiority of the proposed SCA-PVNet over the state-of-the-art methods

Sustainable Solution for Shoring Method of Cross-Creek Bridge in Ankeng MRT System in New Taipei City: A Case Study

In the Ankeng Light Rail MRT system (ALRMS) project, the U7 box girder passes crossing the Erbads creek and needs a temporary supporting system for the construction work.  In this study, three temporary shoring system options were proposed to be the construction method.  The D-B Contractor, New Asia construction and Development Corporation, evaluated and selected the optimal choice, The Steel truss frame with supporting beams, to serve as the temporary supporting system.  Compare the deflection of Δmax and Δactual, which are 1.609 cm and 1.59 cm, respectively.  This result presented that the shoring system composed of the H912*302*18*37 supporting beams and steel truss frame had achieved outstanding performance and work to construct the U7 box girder.  This paper presents how the three options are evaluated and the detailed construction processes along with the survey verification for the method

Detection of Tooth Position by YOLOv4 and Various Dental Problems Based on CNN With Bitewing Radiograph

Periodontitis is a high prevalence dental disease caused by bacterial infection of the bone that surrounds the tooth. Early detection and precision treatment can prevent more severe symptoms such as tooth loss. Traditionally, periodontal disease is identified and labeled manually by dental professionals. The task requires expertise and extensive experience, and it is highly repetitive and time-consuming. The aim of this study is to explore the application of AI in the field of dental medicine. With the inherent learning capabilities, AI exhibits remarkable proficiency in processing extensive datasets and effectively managing repetitive tasks. This is particularly advantageous in professions demanding extensive experiential knowledge, such as dentistry. By harnessing AI, the potential arises to amplify process efficiency and velocity. In this study, bitewing radiographs are used as the image source, and there are two major steps to detect the dental symptoms including 1) tooth position identification; and 2) symptom identification. The study combines image enhancement techniques and tooth position identification using Gaussian filtering and adaptive binarization for data preprocessing, facilitated by the YOLOv4 model to precisely mark tooth positions. The subsequent step enhances symptom area visibility via contrast enhancement, utilizing a CNN model, particularly the AlexNet model, with significant improvements in caries recognition accuracy (92.85%) and restorations recognition accuracy (96.55%) compared to prior research. Moreover, the inclusion of periodontal disease symptoms achieves an accuracy of 91.13%. By harnessing deep learning techniques based on CNN models, this research enhances diagnostic precision, reduces errors, and increases efficiency for dentists, thereby providing meticulous and swift patient care. This innovation not only saves time but also has the potential for widespread implementation in remote and preventive medicine, aligning with the aspiration of universal health care accessibility

5,6,7-Trichloro-2-meth­oxy-8-hy­droxy­quinoline

In the title compound, C10H6Cl3NO2, a mean plane fitted through all non-H atoms has an r.m.s. deviation of 0.035 Å. In the crystal, adjacent mol­ecules are connected by O—H⋯O hydrogen bonds and π–π stacking inter­actions [centroid–centroid distance = 3.650 (1) Å], resulting in an infinite chain which propagates in the b-axis direction

Tracking the nematicity in cuprate superconductors: a resistivity study under uniaxial pressure

Overshadowing the superconducting dome in hole-doped cuprates, the pseudogap state is still one of the mysteries that no consensus can be achieved. It has been suggested that the rotational symmetry is broken in this state and may result in a nematic phase transition, whose temperature seems to coincide with the onset temperature of the pseudogap state $T^*$ around optimal doping level, raising the question whether the pseudogap results from the establishment of the nematic order. Here we report results of resistivity measurements under uniaxial pressure on several hole-doped cuprates, where the normalized slope of the elastoresistivity $\zeta$ can be obtained as illustrated in iron-based superconductors. The temperature dependence of $\zeta$ along particular lattice axis exhibits kink feature at $T_{k}$ and shows Curie-Weiss-like behavior above it, which may suggest a spontaneous nematic transition. While $T_{k}$ seems to be the same as $T^*$ around the optimal doping and in the overdoped region, they become very different in underdoped La$_{2-x}$Sr$_{x}$CuO$_4$. Our results suggest that the nematic order, if indeed existing, is an electronic phase within the pseudogap state.Comment: 6 pages, 4 figure