Improved Cylinder-Based Tree Trunk Detection in LiDAR Point Clouds for Forestry Applications by Shaobo Ma, Yongkang Chen, Zhefan Li, Junlin Chen, Xiaolan Zhong

Subjects: “…individual tree trunk detection…”
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3D Object Detection Based on Graph Network Fusion Sampling Strategy by LI Wenju, CHEN Zhilin, QU Jiantao, CUI Liu, CHU Wanghui, GAO Hui

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A weakly supervised method for 3D object detection with partially annotated samples by Bin Lu, Qing Li, Yanju Liang

“…To overcome the limitations of limited sample annotations, we propose an innovative weakly supervised learning methodology that utilizes reciprocal knowledge transfer between image detection models and 3D point cloud detection models. …”
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A “Sconce” Trap for Sampling Egg Masses of Spotted Lanternfly, <i>Lycorma delicatula</i> by Sarah M. Devine, Everett G. Booth, Miriam F. Cooperband, Emily K. L. Franzen, Phillip A. Lewis, Kelly M. Murman, Joseph A. Francese

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Tree stocking affects winter bird densities across a gradient of savanna, woodland, and forest in the Missouri Ozarks by Sarah W. Kendrick, Frank R. Thompson III

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eDNA Sampling Detects Early Colonization of Spotted Lanternfly Lycorma delicatula Better Than In‐Person Scouting in an Urban Landscape by Daiyanera Kelsey, Jonathan Lee‐Rodriguez, Andy Michel, Christopher M. Ranger, Luis Canas, Ashley Leach

“…Our results showed eDNA outperformed visual detection by a magnitude of 2× (average of 20.4% scouting samples were positive compared to a mean of 46.3% eDNA samples). …”
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Development of gross motor skills in children under the age of 3 years: a decision tree approach by Yuxiang Xiong, Xuhuai Hu, Jindan Cao, Li Shang, Yibei Yao, Ben Niu, Ben Niu

“…This study utilized decision tree modeling to examine the influences on gross motor development in children under the age of 3 years and to rank the key factors affecting their development.MethodsBased on randomized stratified sampling, 9,507 children aged 0–3 years in Shenzhen were included in this study. …”
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PVLF: point-voxel local feature fusion for 3D detection by Haowei Zhao, Zhuolei Xiao

Subjects: “…3D object detection…”
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Nutrition of Florida Citrus Trees, 3rd Edition: Chapter 3. General Soil Fertility and Citrus Tree Nutrition by Thomas A. Obreza, Mongi Zekri, Stephen H. Futch

“… The information provided in the 2008 2nd edition is still sound for healthy citrus trees under Florida production conditions. Much of the information provided in this document on nutrients, application methods, leaf and soil sampling and irrigation scheduling are also effective for huanglongbing (HLB) affected citrus trees. …”
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Nutrition of Florida Citrus Trees, 3rd Edition: Chapter 3. General Soil Fertility and Citrus Tree Nutrition by Thomas A. Obreza, Mongi Zekri, Stephen H. Futch

“… The information provided in the 2008 2nd edition is still sound for healthy citrus trees under Florida production conditions. Much of the information provided in this document on nutrients, application methods, leaf and soil sampling and irrigation scheduling are also effective for huanglongbing (HLB) affected citrus trees. …”
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An Improved Software Source Code Vulnerability Detection Method: Combination of Multi-Feature Screening and Integrated Sampling Model by Xin He, Asiya, Daoqi Han, Shuncheng Zhou, Xueliang Fu, Honghui Li

“…The key innovations include (i) utilizing abstract syntax tree (AST) representation of source code to extract potential vulnerability-related features through multiple feature screening techniques; (ii) conducting analysis of variance (ANOVA) and evaluating feature selection techniques to identify representative and discriminative features; (iii) addressing class imbalance by applying an integrated over-sampling strategy to create synthetic samples from vulnerable code to expand the minority class sample size; (iv) employing outlier detection technology to filter out abnormal synthetic samples, ensuring high-quality synthesized samples. …”
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The fluorescent dyes TO-PRO-3 and TOTO-3 iodide allow detection of microbial cells in soil samples without interference from background fluorescence by Ruth Henneberger, Debra Birch, Peter Bergquist, Malcolm Walter, Roberto P. Anitori

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Detection limitations of bacteria in tissue samples by Tim Holm Jakobsen, Julius Bier Kirkegaard, Mads Lichtenberg, Lasse Andersson Kvich, Hans Gottlieb, Martin McNally, Thomas Bjarnsholt

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Water-Driven Cascade Specific Detection of Al(III)/Hg(II) and CN–/GSH in Real Water Samples by Rabia Ardahanlı, Abdullah S. Hussein, Ferruh Lafzi, Sinan Bayindir, Haydar Kilic

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A novel rhodamine 6G-based chemosensor for Fe3+ detection in water samples and cellular imaging by Bao-Long Hou, Wenyu Li, Ruirui Feng, Xiumei Yang, Jianli Liu, Cuiling Wang

“…Moreover, confocal laser scanning microscopy was employed to confirm the effectiveness of RIC for detecting Fe3+ in living cells. Additionally, RIC proved effective in detecting Fe3+ in aqueous samples, with favorable relative recovery and standard deviations, thus offering a new method for Fe3+ detection in practical applications.…”
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SPS-RCNN: Semantic-Guided Proposal Sampling for 3D Object Detection from LiDAR Point Clouds by Hengxin Xu, Lei Yang, Shengya Zhao, Shan Tao, Xinran Tian, Kun Liu

Subjects: “…3D object detection…”
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IoT intrusion detection method for unbalanced samples by ANTONG P, Wen CHEN, Lifa WU

“…In recent years, network traffic increases exponentially with the iteration of devices, while more and more attacks are launched against various applications.It is significant to identify and classify attacks at the traffic level.At the same time, with the explosion of Internet of Things (IoT) devices in recent years, attacks on IoT devices are also increasing, causing more and more damages.IoT intrusion detection is able to distinguish attack traffic from such a large volume of traffic, secure IoT devices at the traffic level, and stop the attack activity.In view of low detection accuracy of various attacks and sample imbalance at present, a random forest based intrusion detection method (Resample-RF) was proposed, which consisted of three specific methods: optimal sample selection algorithm, feature merging algorithm based on information entropy, and multi-classification greedy transformation algorithm.Aiming at the problem of unbalanced samples in the IoT environment, an optimal sample selection algorithm was proposed to increase the weight of small samples.Aiming at the low efficiency problem of random forest feature splitting, a feature merging method based on information entropy was proposed to improve the running efficiency.Aiming at the low accuracy problem of random forest multi-classification, a multi-classification greedy transformation method was proposed to further improve the accuracy.The method was evaluated on two public datasets.F1 reaches 0.99 on IoT-23 dataset and 1.0 on Kaggle dataset, both of which have good performance.The experimental results show that the proposed model can effectively identify the attack traffic from the massive traffic, better prevent the attack of hackers on the application, protect the IoT devices, and thus protect the related users.…”
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Detecting 3D Salinity Anomalies from Soil Sampling Points: A Case Study of the Yellow River Delta, China by Zhoushun Han, Xin Fu, Jianing Yu, Hengcai Zhang

“…To overcome this problem, this study proposes a 3D Soil-Salinity Anomaly Structure Extraction (3D-SSAS) methodology to discover soil-salinity anomalies and step forward in revealing the irregular 3D structure of soil-anomaly salinity areas from limited sampling points. …”
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Reusable thiophene-based fluorescent sensor for detection of toxic Au³ ⁺ in real samples: Integrated spectroscopic and computational insight by Hasher Irshad, Katrine Qvortrup

“…Therefore, real samples were also analyzed for the trace detection of Au3+ and ultra-fast, reversible and quantitative detection of Au3+ was achieved.…”
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Development of a TaqMan-based quantitative real-time PCR for the detection of Porcine circovirus type 3 by Zhiqiang Hu, Zhiqiang Hu, Ranran Lai, Wei Xu, Ran Guan, Ran Guan, Mingxiang Li, Mingxiang Li, Zhimin Zhang, Zhimin Zhang, Guiying Hao, Guiying Hao, Guangwen Yan, Guangwen Yan

Subjects: “…porcine circovirus type 3…”
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