Reassessment on the monophyly of the fruit-piercing moth, Eudocima bilberg, 1820 (Lepidoptera: Erebidae) using molecular data

Eudocima Billberg, 1820 is a group of moths known for their fruit-piercing behaviour. Members of this group are easily distinguished for their bright colour, hind wing patterns, and the robustness of their body. However, the monophyly of this genus is still in dispute. Based on morphological characters, a current study on 48 species of this genus showed that Eudocima is not a monophyletic group. We conducted a preliminary analysis of 25 species of Eudocima based on 632-bp sequences of cytochrome oxidase 1 (COI) to reassess the monophyly of this genus. Using Maximum Parsimony (MP) method, we ran a number of data sets to reconstruct the most appropriate phylogenetic tree. The result showed that Eudocima is a monophyletic group based on a nucleotide weighting of transversion: transitions = 2:1, despite the very low Jackknife support. This result should not be taken as a final conclusion because only about 60% of Eudocima species were included in our analysis. An upcoming study involving all members of this genus is necessary in order to reassess the putative monophyly of this genus.

Monitoring Dynamic Deformation of Building Using Unmanned Aerial Vehicle

The height irregularity and complexity of steel structures bring difficulties to dynamic deformation monitoring. PDMS (photogrammetric dynamic monitoring system) can obtain the dynamic deformation of the steel structure, but the flexibility of monitoring is limited because the camera station can only be placed on the ground. In this study, UAV (unmanned aerial vehicle) -PDMS is innovatively proposed to be used in monitoring dynamic deformation of steel structures, and it is verified in the steel frame test and Jinan Olympic Sports Center Tennis Stadium test. To solve the problem that the attitude of UAV cannot be strictly maintained in the hovering process, the improved Z-MP (zero-centered motion parallax) method is used, and the monitoring results are compared with the original Z-MP method. The feasibility of UAV-PDMS applied to steel structure deformation monitoring and the feasibility of improving the Z-MP method to reduce UAV hovering error are verified. The monitoring results showed that the steel structures of the Jinan Olympic Sports Center Tennis Stadium were robust, and the deformations were elastic and within the permissible value.

An application of the multi-physical method of determining error of WRF models to simulate the track and intensity Usagi storm in 2018

This paper presents the test results of the WRF model error determination methods simulating the trajectory and intensity of storm Usagi in 2018. The study conducted three experiments: (1) The Combination of 11 options physical model, 21 composites, no increase in error correlation (MP); (2) Using a set of physical model, 21 composite components, multiplier growth factor l = 6.5 (MI); (3) Using a set of physical model, 21 compositions, no increase in error correlation (PF). Test results show that the multi-physics (MP) method has quite well simulated the intensity as well as the moving direction of the northern cold high pressure in the active Usagi storm area. As a result, The 2018 - Usagi 's trajectory and intensity is simulated in MP test better than in MI test and PF test. Specifically, at the 48-hour forecast term, the orbital prediction error of the MP test is below 350 km which is lower than the two tests (MI and PF), The orbital error in the MP test at the forecast term of 60 and 72 hours is 3-6% reduction in compared with the PF test, and in compared with the MI test, the orbital predictive error of the MP test decreased from 5% to 10% at the 12 hour to 72 hours forecast period. In terms of intensity, absolute error of Pmin (Vmax) in MP test always has lower value than two MI and PF tests. In particular, the absolute error of Vmax in the MP test decreased from 30-40% in compared to the other two trials at all forecasting terms, especially at the forecast term longer than 2 days. Thus, the multi-physics method can be a potential application of determining the error for the model to simulate the trajectory and intensity of storms affecting Vietnam.

REAL-TIME MONITORING DEFORMATION OF BUILDING USING PHOTOGRAPHY DYNAMIC MONITORING SYSTEM

The spatial structure building is a type of building system; it is necessary to monitor deformation to determine its stability and robustness. Under the dynamic deformation of structures, it is challenging to determine appropriate zero image (the reference image) if we use the PST-IM- MP (photograph scale transformation-image matching-motion parallax) method to obtain the deformation of structures. This paper offers the Z-MP (zero-centered motion parallax) method to solve these problems and offers PDMS (Photography Dynamic Monitoring System) based on the digital photography system to monitor the dynamic deformation of the tennis stadium located in Jinan Olympic Sports Center. The results showed that the spatial structures of the tennis stadium were robust, and the deformations were elastic and within the permissible value. Compared with the PST-IM-MP method, the Z-MP method is more suitable for deformation monitoring structures under real-time deformation. This paper indicates PDMS has advantages of the simplicity of operations, automation, and the ability of non-contact dynamic deformation monitoring for multiple points in a short period. In the future, it will have broader application prospects.

A comparison between manual and artificial intelligence–based automatic positioning in CT imaging for COVID-19 patients

Objective To analyze and compare the imaging workflow, radiation dose, and image quality for COVID-19 patients examined using either the conventional manual positioning (MP) method or an AI-based automatic positioning (AP) method. Materials and methods One hundred twenty-seven adult COVID-19 patients underwent chest CT scans on a CT scanner using the same scan protocol except with the manual positioning (MP group) for the initial scan and an AI-based automatic positioning method (AP group) for the follow-up scan. Radiation dose, patient positioning time, and off-center distance of the two groups were recorded and compared. Image noise and signal-to-noise ratio (SNR) were assessed by three experienced radiologists and were compared between the two groups. Results The AP operation was successful for all patients in the AP group and reduced the total positioning time by 28% compared with the MP group. Compared with the MP group, the AP group had significantly less patient off-center distance (AP 1.56 cm ± 0.83 vs. MP 4.05 cm ± 2.40, p < 0.001) and higher proportion of positioning accuracy (AP 99% vs. MP 92%), resulting in 16% radiation dose reduction (AP 6.1 mSv ± 1.3 vs. MP 7.3 mSv ± 1.2, p < 0.001) and 9% image noise reduction in erector spinae and lower noise and higher SNR for lesions in the pulmonary peripheral areas. Conclusion The AI-based automatic positioning and centering in CT imaging is a promising new technique for reducing radiation dose and optimizing imaging workflow and image quality in imaging the chest. Key Points • The AI-based automatic positioning (AP) operation was successful for all patients in our study. • AP method reduced the total positioning time by 28% compared with the manual positioning (MP). • AP method had less patient off-center distance and higher proportion of positioning accuracy than MP method, resulting in 16% radiation dose reduction and 9% image noise reduction in erector spinae.

Decimeter Level Indoor Localization Using WiFi Channel State Information

<div>Indoor localization using WiFi signal parameters is challenging, with encouraging decimeter localization results available with enough line-of-sight coverage and hardware infrastructure. This paper proposes a new 2-dimensional multiple packets based matrix pencil (2D M-MP) method to estimate the Angle of Arrival (AoA) and Time of Flight (ToF) based on WiFi channel state information (CSI). Compared with the conventional parameter estimation algorithms, this method has two advantages. First, 2D M-MP method uses the discrete Fourier transform (DFT) to convert the complex computation into real computation to reduce the computational complexity significantly without losing accuracy. Second, it accumulates multiple CSI packets to improve the parameter estimation accuracy effectively, especially at low values of signal-to-noiseratio (SNR) environment. To verify the practicability of our proposed 2D M-MP method, we set up a localization system in an actual scenario using commodity WiFi cards which demonstrates that the performance of 2D M-MP method is better than conventional parameter estimation algorithms and can achieve a localization accuracy of 42 cm in indoor hall deployment.</div>

Decimeter Level Indoor Localization Using WiFi Channel State Information

<div>Indoor localization using WiFi signal parameters is challenging, with encouraging decimeter localization results available with enough line-of-sight coverage and hardware infrastructure. This paper proposes a new 2-dimensional multiple packets based matrix pencil (2D M-MP) method to estimate the Angle of Arrival (AoA) and Time of Flight (ToF) based on WiFi channel state information (CSI). Compared with the conventional parameter estimation algorithms, this method has two advantages. First, 2D M-MP method uses the discrete Fourier transform (DFT) to convert the complex computation into real computation to reduce the computational complexity significantly without losing accuracy. Second, it accumulates multiple CSI packets to improve the parameter estimation accuracy effectively, especially at low values of signal-to-noiseratio (SNR) environment. To verify the practicability of our proposed 2D M-MP method, we set up a localization system in an actual scenario using commodity WiFi cards which demonstrates that the performance of 2D M-MP method is better than conventional parameter estimation algorithms and can achieve a localization accuracy of 42 cm in indoor hall deployment.</div>

A Novel Method to Denoise Images Based on a Meta-heuristic Algorithm and Pre-learned Dictionary

Noise may affect images in many ways during different processes. Such as during obtaining, distribution, processing, or compressing. The Sparse Representation (SR) algorithm is one of the best strategies for noise reduction. One meta-heuristic algorithm is the Particle Swarm Optimization (PSO). This research demonstrates excellent results in noise reduction in the Fast PSO version while utilizing the SRs as well as meta-heuristic algorithms to gain. This method is known as FPSO-MP and it depends on the Pursuit Algorithm (MP) that matches. In this proposed study, a Dynamic-Multi-Swarm (DMS) method and a pre-learned dictionary (FPSO-MP) approach is presented to reduce the time for the learning dictionary calculations. The output of the denoising algorithm QPSO-MP is dependable on dictionary learning because of the dictionary size or increased number of patches. Similar to this work, a Non-locally Estimated Sparse Coefficient (NESC) is one explanation for the low efficiency of the original algorithm. Compared to the original PSO-MP method, these enhancements have achieved substantial gains in computational efficiency of approximately 92% without sacrosanct image quality. After modification, the proposed FPSO-MP technique is in contrast with the original PSO-MP method. The scientific findings demonstrate that the FPSO-MP algorithm is much more efficient and faster than the original algorithm, without affecting image quality. The proposed method follows the original technique and therefore reduces during run-time. The result of this study demonstrates that the bestdenoised images can always be accessed from the pre-learned dictionary rather than the learning dictionary developed across the noisy image during runtime. We constructed images dataset from the BSD500 collection and performed a statistical test on these images. The actual findings reveal that the suggested method is excellent for noise reduction (noise elimination) as well as highly efficient during runtime. The analytical findings indicate that both quantitative and image performance outcomes are obtained with the proposed FPSO-MP approach during its contradiction with when denoising algorithms.

Application of Artificial Neural Network to Predict Load Bearing Capacity and Stiffness of Perforated Masonry Walls

Perforations adversely affect the structural response of unreinforced masonry walls (UMW) by reducing the wall’s load bearing capacity, which can cause serious structural damage. In the absence of a reliable procedure to accurately predict the load bearing capacity and stiffness of perforated masonry walls subjected to in-plane loadings, this study presents a novel approach to measure these parameters by developing simple but practical equations. In this regard, the Multi-Pier (MP) method as a numerical approach was employed along with the application of an Artificial Neural Network (ANN). The simulated responses of centrally perforated UMW by the MP method were validated utilizing full-scale experimental walls. The validated MP model was used to generate a simulated database. The simulated database includes results of analyses for 49 different configurations of perforated masonry walls and their corresponding solid masonry walls. The effect of the area and shape of the perforations on the UMW’s behavior was evaluated by the MP method. Following the outcomes of the verified MP method, the ANN is trained to develop empirical equations to accurately predict the reduction in the load bearing capacity and initial stiffness due to the perforation of UMW. The results of this study indicate that the perforations have a significant effect on the structural capacity of the UMW subjected to in-plane loadings.