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  • 1
    In: Remote Sensing, MDPI AG, Vol. 15, No. 1 ( 2022-12-24), p. 97-
    Abstract: In this paper, a method for extracting the height of urban forest trees based on a smartphone was proposed to efficiently and accurately determine tree heights. First, a smartphone was used to obtain person–tree images, LabelImg was used to label the images, and a dataset was constructed. Secondly, based on a deep learning method called You Only Look Once v5 (YOLOv5) and the small-hole imaging and scale principles, a person–tree scale height measurement model was constructed. This approach supports recognition and mark functions based on the characteristics of a person and a tree in a single image. Finally, tree height measurements were obtained. By using this method, the heights of three species in the validation set were extracted; the range of the absolute error was 0.02 m–0.98 m, and the range of the relative error was 0.20–10.33%, with the RMSE below 0.43 m, the rRMSE below 4.96%, and the R2 above 0.93. The person–tree scale height measurement model proposed in this paper greatly improves the efficiency of tree height measurement while ensuring sufficient accuracy and provides a new method for the dynamic monitoring and investigation of urban forest resources.
    Type of Medium: Online Resource
    ISSN: 2072-4292
    Language: English
    Publisher: MDPI AG
    Publication Date: 2022
    detail.hit.zdb_id: 2513863-7
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  • 2
    Online Resource
    Online Resource
    MDPI AG ; 2017
    In:  Sustainability Vol. 9, No. 3 ( 2017-03-03), p. 370-
    In: Sustainability, MDPI AG, Vol. 9, No. 3 ( 2017-03-03), p. 370-
    Type of Medium: Online Resource
    ISSN: 2071-1050
    Language: English
    Publisher: MDPI AG
    Publication Date: 2017
    detail.hit.zdb_id: 2518383-7
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  • 3
    In: Diagnostics, MDPI AG, Vol. 13, No. 3 ( 2023-02-02), p. 560-
    Abstract: Objective: Absence of homozygosity (AOH) is a genetic characteristic known to cause human diseases mainly through autosomal recessive or imprinting mechanisms. The importance and necessity of accurate AOH detection has become more clinically significant in recent years. However, it remains a challenging task for sequencing-based methods thus far. Methods: In this study, we developed and optimized a new bioinformatic algorithm based on the assessment of minimum sequencing coverage, optimal bin size, the Z-score threshold of four types of allele count and the frequency for accurate genotyping using 28 AOH negative samples, and redefined the AOH detection cutoff value. We showed the performance of chromosome analysis by five-fold coverage whole genome sequencing (CMA-seq) for AOH identification in 27 typical prenatal/postnatal AOH positive samples, which were previously confirmed by chromosomal microarray analysis with single nucleotide polymorphism array (CMA/SNP array). Results: The blinded study indicated that for all three forms of AOH, including whole genomic AOH, single chromosomal AOH and segmental AOH, and all kinds of sample types, including chorionic villus sampling, amniotic fluid, cord blood, peripheral blood and abortive tissue, CMA-seq showed equivalent detection power to that of routine CMA/SNP arrays (750K). The subtle difference between the two methods is that CMA-seq is prone to detect small inconsecutive AOHs, while CMA/SNP array reports it as a whole. Conclusion: Based on our newly developed bioinformatic algorithm, it is feasible to detect clinically significant AOH using CMA-seq in prenatal diagnosis.
    Type of Medium: Online Resource
    ISSN: 2075-4418
    Language: English
    Publisher: MDPI AG
    Publication Date: 2023
    detail.hit.zdb_id: 2662336-5
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  • 4
    In: Remote Sensing, MDPI AG, Vol. 15, No. 1 ( 2022-12-25), p. 110-
    Abstract: The accurate classification of tree species is essential for the sustainable management of forest resources and the effective monitoring of biodiversity. However, a literature review shows that most of the previous unmanned aerial vehicle (UAV) light detection and ranging (LiDAR)-based studies on fine tree species classification have used only limited intensity features, accurately identifying relatively few tree species. To address this gap, this study proposes developing a new intensity feature—intensity frequency—for the LiDAR-based fine classification of eight tree species. Intensity frequency is defined as the number of times a certain intensity value appears in the individual tree crown (ITC) point cloud. In this study, we use UAV laser scanning to obtain LiDAR data from urban forests. Intensity frequency features are constructed based on the extracted intensity information, and a random forest (RF) model is used to classify eight subtropical forest tree species in southeast China. Based on four-point cloud density sampling schemes of 100%, 80%, 50% and 30%, densities of 230 points/m2, 184 points/m2, 115 points/m2 and 69 points/m2 are obtained. These are used to analyze the effect of intensity frequency on tree species classification accuracy under four different point cloud densities. The results are shown as follows. (1) Intensity frequencies of trees are not significantly different for intraspecies (p 〉 0.05) values and are significantly different for interspecies (p 〈 0.01) values. (2) The intensity frequency features of LiDAR can be used to classify different tree species with an overall accuracy (OA) of 86.7%. Acer Buergerianum achieves a user accuracy (UA) of over 95% and a producer accuracy (PA) of over 90% for four density conditions. (3) The OA varies slightly under different point cloud densities, but the sum of correct classification trees (SCI) and PA decreases rapidly as the point cloud density decreases, while UA is less affected by density with some stability. (4) The priori feature selected by mean rank (MR) covers the top 10 posterior features selected by RF. These results show that the new intensity frequency feature proposed in this study can be used as a comprehensive and effective intensity feature for the fine classification of tree species.
    Type of Medium: Online Resource
    ISSN: 2072-4292
    Language: English
    Publisher: MDPI AG
    Publication Date: 2022
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  • 5
    In: Catalysts, MDPI AG, Vol. 13, No. 5 ( 2023-05-16), p. 896-
    Abstract: Carbon dioxide (CO2) assisted oxidative dehydrogenation of propane over Ga-modified catalysts is highly sensitive to the identity of support, but the underlying cause of support effects has not been well established. In this article, SSZ-13, SSZ-39, ZSM-5, silica and γ-Al2O3 were used to load Ga species by incipient wet impregnation. The structure, textural properties, acidity of the Ga-based catalysts and the process of CO2-assisted oxidative dehydrogenation of propane were examined by X-ray diffraction (XRD), nitrogen physisorption (N2 physisorption), ammonia temperature-programmed desorption (NH3-TPD), pyridine chemisorbed Fourier transform infrared spectra (Py-FTIR), OH-FTIR and in situ FTIR. Evaluation of the catalytic performance combined with detailed catalyst characterization suggests that their dehydrogenation activity is positively associated with the number of acid sites in middle strength, confirming that the Lewis acid sites generated by Ga cations are the active species in the reaction. Ga/Na-SSZ-39(9) also has feasible acidic strength and a unique channel structure, which is conducive to the dissociative adsorption of propane and desorption of olefins. The Ga/Na-SSZ-39(9) catalysts showed superior olefins selectivity and catalytic stability at 600 ℃ compared to any other catalysts. This approach to quantifying support acid strength, and channel structure and applying it as a key catalytic descriptor of support effects is a useful tool to enable the rational design of next-generation CO2-assisted oxidative dehydrogenation catalysts.
    Type of Medium: Online Resource
    ISSN: 2073-4344
    Language: English
    Publisher: MDPI AG
    Publication Date: 2023
    detail.hit.zdb_id: 2662126-5
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  • 6
    Online Resource
    Online Resource
    MDPI AG ; 2022
    In:  Electronics Vol. 11, No. 4 ( 2022-02-18), p. 639-
    In: Electronics, MDPI AG, Vol. 11, No. 4 ( 2022-02-18), p. 639-
    Abstract: Annotations for image segmentation are expensive and time-consuming. In contrast to image segmentation, the task of object detection is in general easier in terms of the acquisition of labeled training data and the design of training models. In this paper, we combine the idea of unsupervised learning and a pretrained object-detection network to perform image segmentation, without using expensive segmentation labels. Specially, we designed a pretext task based on the sparse decomposition of object instances in videos to obtain the segmentation mask of the objects, which benefits from the sparsity of image instances and the inter-frame structure of videos. To improve the accuracy of identifying the ’right’ object, we used a pretrained object-detection network to provide the location information of the object instances, and propose an Object Location Segmentation (OLSeg) model of three branches with bounding box prior. The model is trained from videos and is able to capture the foreground, background and segmentation mask in a single image. The performance gain benefits from the sparsity of object instances (the foreground and background in our experiments) and the provided location information (bounding box prior), which work together to produce a comprehensive and robust visual representation for the input. The experimental results demonstrate that the proposed model boosts the performance effectively on various image segmentation benchmarks.
    Type of Medium: Online Resource
    ISSN: 2079-9292
    Language: English
    Publisher: MDPI AG
    Publication Date: 2022
    detail.hit.zdb_id: 2662127-7
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  • 7
    In: Diagnostics, MDPI AG, Vol. 13, No. 18 ( 2023-09-09), p. 2895-
    Abstract: Objective: Mosaicism is a common biological phenomenon in organisms and has been reported in many types of chromosome abnormalities, including the absence of heterozygosity (AOH). Due to the detection limitations of the sequencing approach, mosaic AOH events are rarely assessed in clinical cases. Herein, we report the performance of mosaic AOH identification using a low-pass (5~8-fold) WGS method (termed ‘CMA-seq’, an abbreviation for ‘Chromosome Analysis by Sequencing’) in fetal genetic diagnosis. Methods: Thirty AOH-negative, eleven constitutional AOH, and three mosaic AOH samples were collected as training data sets to develop the algorithm and evaluate the suitable thresholds for distinguishing mosaic AOH. Twenty-four new chromosomal aberrant cases, along with sixteen constitutional AOH samples, which were previously ascertained via the SNP-array-based method, were used as a validation data set to measure the performance in terms of sensitivity and specificity of this algorithm. Results: A new statistic, ‘D-value’, was implemented to identify and distinguish constitutional and mosaic AOH events. The reporting thresholds for constitutional and mosaic AOH were also established. In the validation set consisting of 24 new cases, seven constitutional AOH cases and 1 mosaic AOH case were successfully identified, indicating that the results were consistent with those of the SNP-array-based method. The results of all sixteen constitutional AOH validation samples also met the threshold requirements. Conclusions: In this study, we developed a new bioinformatic algorithm to accurately distinguish mosaic AOH from constitutional AOH by low-pass WGS. However, due to the small sample size of the training data set, the algorithm proposed in this manuscript still needs further refinements.
    Type of Medium: Online Resource
    ISSN: 2075-4418
    Language: English
    Publisher: MDPI AG
    Publication Date: 2023
    detail.hit.zdb_id: 2662336-5
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  • 8
    In: Remote Sensing, MDPI AG, Vol. 14, No. 24 ( 2022-12-16), p. 6375-
    Abstract: The estimation of characteristic parameters such as diameter at breast height (DBH), aboveground biomass (AGB) and stem volume (V) is an important part of urban forest resource monitoring and the most direct manifestation of the ecosystem functions of forests; therefore, the accurate estimation of urban forest characteristic parameters is valuable for evaluating urban ecological functions. In this study, the height and density characteristic variables of canopy point clouds were extracted as Scheme 1 and combined with the canopy structure variables as Scheme 2 based on unmanned aerial vehicle lidar (UAV-Lidar). We analyzed the spatial distribution characteristics of the canopies of different tree species, and multiple linear regression (MLR), support vector regression (SVR), and random forest (RF) models were used to estimate the DBH, AGB, and V of urban single trees. The estimation accuracy of different models was evaluated based on the field-measured data. The results indicated that the model accuracy of coupling canopy structure variables (R2 = 0.69–0.85, rRMSE = 9.87–24.67%) was higher than that of using only point-cloud-based height and density characteristic variables. The comparison of the results of different models shows that the RF model had the highest estimation accuracy (R2 = 0.76–0.85, rRMSE = 9.87–22.51%), which was better than that of the SVR and MLR models. In the RF model, the estimation accuracy of AGB was the highest (R2 = 0.85, rRMSE = 22.51%), followed by V, with an accuracy of R2 = 0.83, rRMSE = 18.51%, and the accuracy of DBH was the lowest (R2 = 0.76, rRMSE = 9.87%). The results of the study provide an important reference for the estimation of single-tree characteristic parameters in urban forests based on UAV-Lidar.
    Type of Medium: Online Resource
    ISSN: 2072-4292
    Language: English
    Publisher: MDPI AG
    Publication Date: 2022
    detail.hit.zdb_id: 2513863-7
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  • 9
    Online Resource
    Online Resource
    MDPI AG ; 2017
    In:  Sustainability Vol. 9, No. 11 ( 2017-11-03), p. 2011-
    In: Sustainability, MDPI AG, Vol. 9, No. 11 ( 2017-11-03), p. 2011-
    Type of Medium: Online Resource
    ISSN: 2071-1050
    Language: English
    Publisher: MDPI AG
    Publication Date: 2017
    detail.hit.zdb_id: 2518383-7
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  • 10
    In: Buildings, MDPI AG, Vol. 14, No. 1 ( 2024-01-11), p. 192-
    Abstract: The mode of urban renewal in China has changed from incremental expansion to a value-added stage, and the development of cities has increasingly focused on the human experience and quality improvement. However, Cultural Street Vending and the unique street spaces in the historic districts that bear important connotations to the city have shown a trend of rapid decline and even disappearance. Therefore, how to identify the characteristics and connotations of such street vending spaces, grasp the relationship between perception of locality and built environment elements in different states and formulate targeted protection and renewal strategies is presently an urgent problem to be solved. Many studies have now discussed in detail the identification of elements, street perception and preservation strategies for historic districts. However, the Cultural Street Vending space, which carries special urban memory connotations, has been neglected, especially the characterisation of this type of space in different time dimensions. This paper takes the Cultural Street Vending space as a research object and focuses on the perceptual differences between its market day and non-market day, aiming at grasping the perceptual characteristics and constituent elements of this type of street space in different time states, as well as the patterns of changes they undergo. This paper took the Baxian Temple, a typical Cultural Street Vending space in a historic district of Xi’an, as the research object and took 30 architecture students as the evaluators to score 33 pairs of adjectives and recall the elements of nine streets. This paper compared and analysed the differences in the structure of people’s psychological perception of the street in the state of market day and non-market day by SD method. It was found that the activities of the vendors obviously promoted people’s positive emotions, and three commonality factors including impression, vitality and morphosis were extracted through the factor analysis. Moreover, through the element recall method to understand the change in element perception on the market day, we found that the range of people’s perception was smaller on the market day and summarised the element map of the space and the element components of high, medium and low characteristics. The correlation analysis between psychological quantities and environmental elements revealed that people’s positive psychology has a positive correlation with vendors, goods and street components and a negative correlation with greening, colour and texture. This study can provide an important research basis for the development of conservation and renewal strategies for this type of Cultural Street Vending space.
    Type of Medium: Online Resource
    ISSN: 2075-5309
    Language: English
    Publisher: MDPI AG
    Publication Date: 2024
    detail.hit.zdb_id: 2661539-3
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