Article
Engineering, Chemical
Kaveh Mollazade, Sven Joerissen, Andreas Nuechter
Summary: In this study, a 3D laser scanning approach was developed to measure internal quality traits of eggs in the food industry. Spectroscopic analysis identified the suitable wavelength region for laser scanning. The issues introduced by the transparency nature of the albumin were addressed through coating with Talcum powder and training a neural network. The results indicated reliable measurement of yolk index and Haugh unit using 3D scanning and 2D shape analysis of the egg parts.
JOURNAL OF FOOD ENGINEERING
(2021)
Article
Remote Sensing
Yuzhou Zhou, Ronggang Huang, Tengping Jiang, Zhen Dong, Bisheng Yang
Summary: Accurate highway alignments and 3D models are crucial for intelligent transportation applications. This paper proposes an effective framework to extract highway alignments and reconstruct highway 3D models, which involves adaptive methods for recognizing pavement points from ALS data, extracting pavement boundaries and lane markings, and minimizing an energy function to extract highway alignments. The method showed high correctness and completeness in alignment extraction and generated 3D models with low root mean square errors.
INTERNATIONAL JOURNAL OF APPLIED EARTH OBSERVATION AND GEOINFORMATION
(2021)
Article
Engineering, Civil
Wen Xiong, Ibrahima Diaw, Yanjie Zhu, Hongwei Zhang, C. S. Cai
Summary: Terrestrial laser scanning technology is widely used in bridge maintenance, but there are challenges when it comes to long-span bridges. To overcome the issue of scan geometry affecting the accuracy of point cloud data, researchers have developed a portable auxiliary reflector. In-field tests conducted on the Ma'anshan Yangtze River Bridge have shown that this method can obtain high-precision point cloud data for long-span bridges and enable further deformation analysis.
JOURNAL OF CIVIL STRUCTURAL HEALTH MONITORING
(2023)
Article
Engineering, Electrical & Electronic
Chen Li, Shuai Chen, Shihao Xu
Summary: This paper proposes a novel electromagnetic biaxial scanning mirror based on 3D printing and laser patterning, which has the advantages of low cost and rapid preparation, and has potential applications in intelligent household, regional monitoring, and industrial cameras.
SENSORS AND ACTUATORS A-PHYSICAL
(2022)
Article
Construction & Building Technology
Xin Fang, Shaojie Jiang, Zongjun Zhang, Ying Wang, Xiaochun Luo
Summary: This paper proposes a method for automating the recognition and localization of cast-in hoist rings of precast concrete components. Point clouds are collected using laser radars placed on the surface of PCCs, and the method follows three steps to implement the automation. The experimental results show that the method achieves desired performance in terms of precision and recall.
AUTOMATION IN CONSTRUCTION
(2021)
Article
Optics
Huanyu Sun, Shiling Wang, Jinxi Bai, Jianpu Zhang, Jin Huang, Xiaoyan Zhou, Dong Liu, Chong Liu
Summary: The paper introduces a method for detecting subsurface damage (SSD) in large polished optics that is difficult to observe, using confocal laser scanning and three-dimensional reconstruction techniques. This method can quickly and accurately display the morphology of SSDs and provide quantitative evaluations of SSDs.
OPTICS AND LASERS IN ENGINEERING
(2021)
Article
Computer Science, Hardware & Architecture
Yuan Liang, Marcin Wozniak
Summary: This study designed a system for virtual reconstruction of architectural spatial structure using laser 3D scanning technology, improving running speed and rendering effect by integrating multiple big data, enhancing user experience.
MOBILE NETWORKS & APPLICATIONS
(2022)
Article
Environmental Sciences
Sukant Chaudhry, David Salido-Monzu, Andreas Wieser
Summary: The study presents a simple model for predicting the resolution capability in a laser scanning point cloud, specifically focusing on the angular direction. It utilizes an elliptical Gaussian beam for quantification and verifies the approximation of RC while considering scanning resolution. The model is accessible and supports assessing the suitability of specific scanners or scanning parameters for different applications.
Article
Energy & Fuels
Chao Gan, Wei-Hua Cao, Kang-Zhi Liu, Min Wu
Summary: A new spatial modeling method for the 3D formation drillability field is proposed in this study, which involves determining formation modes and building random forest models to analyze formation drillability. The method was tested in the Xujiaweizi area and showed effectiveness in spatial 3D formation drillability modeling.
JOURNAL OF PETROLEUM SCIENCE AND ENGINEERING
(2021)
Article
Optics
Liheng Shi, Yue Wang, Ruixue Zhang, Jinxu Zhang, Yuetang Yang, Yang Li, Jiayang Chen, Siyu Zhou, Guanhao Wu
Summary: Coherence scanning interferometer (CSI) enables 3D imaging with nanoscale precision. However, the efficiency of such a system is limited because of the restriction imposed by the acquisition system. Here, we propose a phase compensation method that reduces the interferometric fringe period of femtosecond-laser-based CSI, resulting in larger sampling intervals. Experimental results show that our method can maintain a root-mean-square axial error of below 2 nm at a high scanning speed of 6.44 μm per frame, enabling fast nanoscale profilometry over a wide area.
Article
Optics
Chen Chang, Guohua Wu, Dongyue Yang, Longfei Yin, Bin Luo
Summary: The modified CLEAN algorithm effectively compensates for insufficient samples in the spatial frequency domain, improving the speed and resolution of aperture-synthetic ghost imaging. However, there are still imperfections in the reconstructions, which can be addressed by optimizing the algorithm with a density clustering algorithm.
Article
Chemistry, Analytical
Yuchen Zou, Weiwei Tang, Bin Li
Summary: Spatial segmentation is important in mass spectrometry imaging (MSI) data analysis, as it helps to identify homogeneous/heterogeneous subgroups and provides vital characteristics for biological analysis. This study proposes a segmentation pipeline that utilizes pattern compression by principal component analysis (PCA) for easy and effective segmentation.
Article
Optics
Kejing He, Congying Sui, Tianyu Huang, Rong Dai, Congyi Lyu, Yun-Hui Liu
Summary: A scheme combining stereo matching and laser scanning is proposed for reconstructing the 3D surfaces of transparent objects in this paper. The method utilizes a laser tracking frame to distinguish the laser reflected by the front surface and reconstructs the laser lines from the front surface of the target using stereo vision. Improved matching accuracy and point cloud density are achieved through an internal pixel matching method.
OPTICS AND LASERS IN ENGINEERING
(2022)
Article
Construction & Building Technology
Junzhi Zhang, Jin Huang, Chuanqing Fu, Le Huang, Hailong Ye
Summary: A new experimental method was proposed to generate non-uniformly distributed rust around steel bars in concrete for investigating the characteristics of non-uniform corrosion more accurately. The average and critical cross-sectional area of corroded steel bars were obtained using 3D laser scanning, and the effects of non-uniform corrosion on the degradation of mechanical properties were studied. Comparisons and elaborations were made on corrosion morphology, mechanical properties, and tensile failure patterns between uniformly and non-uniformly corroded steel bars.
CONSTRUCTION AND BUILDING MATERIALS
(2021)
Article
Chemistry, Analytical
Hang Hu, Ruichuan Yin, Hilary M. Brown, Julia Laskin
Summary: This study introduces an unsupervised spatial segmentation approach that combines multivariate clustering and univariate thresholding to effectively segment mass spectrometry imaging (MSI) data, demonstrating its performance and robustness on two tissue sections data sets acquired with different spatial resolutions. The resulting segmentation maps are easy to interpret and project onto the known anatomical regions of the tissue.
ANALYTICAL CHEMISTRY
(2021)
Article
Biodiversity Conservation
Philip E. Hulme, Hazel Mclaren-Swift
Summary: It is crucial to widely disseminate and ensure understanding of the scientific knowledge regarding the socioeconomic and environmental impacts of invasive non-native species. However, research abstracts addressing biological invasions are difficult to read and have become even less readable over time. The readability of abstracts is targeted towards readers with a graduate-level literacy, which is much higher than the average reading level of the general public. Authors from English-speaking countries generate the most complex texts, while abstracts from authors based in non-English-speaking countries have shown an increase in complexity since 2001. Complex abstracts tend to attract more citations, indicating that scientific peers are more influenced by technically challenging abstracts aimed at a highly literate readership. Urgent action is required to address this issue, such as reviewing the readability of published works by authors and editors, implementing structured abstracts, and providing additional plain-language summaries.
BIOLOGICAL INVASIONS
(2022)
Correction
Environmental Sciences
Anthony Ricciardi, Josephine C. Iacarella, David C. Aldridge, Tim M. Blackburn, James T. Carlton, Jane A. Catford, Jaimie T. A. Dick, Philip E. Hulme, Jonathan M. Jeschke, Andrew M. Liebhold, Julie L. Lockwood, Hugh J. MacIsaac, Laura A. Meyerson, Petr Pysek, David M. Richardson, Gregory M. Ruiz, Daniel Simberloff, Montserrat Vila, David A. Wardle
ENVIRONMENTAL REVIEWS
(2022)
Article
Ecology
Sarah Wyse, Philip E. Hulme
Summary: The competition-colonisation trade-off is a key mechanism for maintaining species diversity. This study focuses on the within-species perspective and finds no evidence of a trade-off between competition and colonisation within species.
FUNCTIONAL ECOLOGY
(2022)
Article
Agronomy
Philip E. Hulme
Summary: This study investigated the drivers of herbicide resistance in barley, maize, rice, and wheat crops worldwide using a macroecological approach. The results showed that the global prevalence of herbicide-resistant weeds is likely underestimated, and agricultural intensification and expansion of crop harvested area are the primary drivers of future herbicide-resistant weeds. Developing countries, in particular, may face a higher number of herbicide-resistant weeds due to under-reporting and the growth of their economies. A global strategy for increasing national capability in herbicide resistance research is needed.
PEST MANAGEMENT SCIENCE
(2022)
Article
Agronomy
Philip E. Hulme
Summary: Hierarchical clustering and network analysis were used to classify herbicide MoAs into three distinct groups based on the resistant weed species they have in common, providing insights into the risk of multiple resistance in weeds. The potential for managing resistance by rotating herbicides between rather than within clusters was highlighted, based on crop, weed, and environmental conditions.
PEST MANAGEMENT SCIENCE
(2022)
Article
Biodiversity Conservation
Thomas F. Carlin, Jennifer L. Bufford, Philip E. Hulme, William K. Godsoe
Summary: In this study, climatic niche shifts of three weed species were compared between their native range and three introduced ranges. The presence and direction of climatic niche shifts were found to be inconsistent between the introduced ranges for each species. However, niche shifts within an introduced range were qualitatively similar among species.
BIOLOGICAL INVASIONS
(2023)
Review
Agriculture, Multidisciplinary
Christopher E. Buddenhagen, Graeme Bourdot, Mike Cripps, Nigel Bell, Paul Champion, Mike Dodd, Han Eerens, Hossein Ghanizadeh, Andrew Griffiths, Kerry Harrington, Peter Heenan, Philip E. Hulme, Trevor James, John Kean, Shona Lamoureaux, Joe Neal, Zachary Ngow, Irena Obadovic, Sofia Orre-Gordon, Helen Percy, Phil Rolston, Katherine Tozer, Ben Wynne-Jones, Sue Zydenbos
Summary: Pastures play a significant role in global agriculture, but weed-related productivity losses are a major concern. Addressing issues such as reduced access to herbicides, rethinking weed management, and minimizing environmental impacts are crucial for sustainable pasture management. Interdisciplinary research is needed to tackle biosecurity and weed management challenges while maintaining productivity.
NEW ZEALAND JOURNAL OF AGRICULTURAL RESEARCH
(2023)
Article
Ecology
Giovanny Perez, Montserrat Vila, Belinda Gallardo
Summary: Invasive alien species pose a significant threat to biodiversity conservation, disrupting ecosystem services and compromising human well-being. This study evaluated the potential impacts of four invasive plant species on three ecosystem services in Europe under current and future climate change scenarios. The results show that food provisioning, soil erosion regulation, and maintenance of biological diversity are the most affected services. Furthermore, the study found that Western Europe and the British Isles are the regions most affected by future impacts, while the Mediterranean region may experience lower impacts due to northwards expansion of invaders.
ECOSYSTEM SERVICES
(2022)
Article
Plant Sciences
Alvaro Bayon, Oscar Godoy, Montserrat Vila
Summary: Urban parks and gardens host a variety of non-native woody plant species, some of which are invasive and may cause negative impacts on the environment and society, with competition and habitat modification being the most common potential impacts.
ANALES DEL JARDIN BOTANICO DE MADRID
(2022)
Book Review
Biodiversity Conservation
Montserrat Vila
BIOLOGICAL INVASIONS
(2023)
Article
Biodiversity Conservation
Sima Sohrabi, Montserrat Vila, Eskandar Zand, Javid Gherekhloo, Saeid Hassanpour-bourkheili
Summary: Like other developing countries, Iran is at risk of alien plant invasion due to its rich native biodiversity and lack of regulation on importing new plants. This study examines the characteristics, distribution, and potential impacts of 52 alien plants introduced to Iran over the past 30 years. The findings highlight the need for specific management programs to mitigate the introduction and spread of alien plants and reduce their impacts on biodiversity, ecosystem services, and human livelihoods. Climate change's effects on new invasions and the expansion of current invaders should also be a priority for further research.
BIOLOGICAL INVASIONS
(2023)
Article
Plant Sciences
Javier Galan Diaz, Montserrat Vila, Ingrid M. Parker, Enrique G. de la Riva
Summary: The study aims to understand the role of exotic species in plant communities and investigates the relative importance of plant traits, environmental factors, and invasion status in biological invasions. The results highlight the importance of niche differences between native and colonizer species as a community assembly mechanism, which is strongly influenced by habitat filtering. The study emphasizes the context-dependent nature of trait comparisons and the need for a regional perspective in interpreting community assembly processes.
JOURNAL OF ECOLOGY
(2023)
Article
Biodiversity Conservation
Ruben Bernardo-Madrid, Pablo Gonzalez-Moreno, Belinda Gallardo, Sven Bacher, Montserrat Vila
Summary: This study quantified and compared the consistency of protocol question scores in impact assessments of 60 terrestrial, freshwater and marine organisms, revealing that the majority of assessments showed high consistency, with some showing low consistency. Consistency was related to impact types and protocols used, suggesting room for improvement in repeatability.
Article
Biodiversity Conservation
Ana Montero-Castano, Marcelo A. Aizen, Pablo Gonzalez-Moreno, Laura Cavallero, Montserrat Vila, Carolina L. Morales
Summary: Upon arrival to a new area, alien species have to overcome a series of barriers to survive, reproduce, and spread along the invasion continuum. Failing to understand the role of different barriers and factors across the invasion stages limit our ability to predict invasion dynamics. In this study, we investigate how the European plant Cytisus scoparius overcomes survival and reproductive barriers in Nahuel Huapi National Park, Argentina, by evaluating the influence of climatic and landscape factors, species traits, and their interaction with patch cover, plant height, and pollinator visitation rates.
BIOLOGICAL INVASIONS
(2023)
Article
Agronomy
Philip E. Hulme
Summary: The variation in the number of herbicide-resistant weed species worldwide is related to differences in agricultural intensification, such as per capita GDP, cropland area, and herbicide inputs. The number of resistant weed species is influenced by the time since resistance was first observed, and the problem is expected to worsen over time in many countries. Integrated weed management strategies should be implemented proactively to reduce the risk of herbicide-resistant weeds.
FIELD CROPS RESEARCH
(2023)
Article
Construction & Building Technology
Jia Liang, Qipeng Zhang, Xingyu Gu
Summary: A lightweight PCSNet-based segmentation model is developed to address the issues of insufficient performance in feature extraction and boundary loss information. The introduction of generalized Dice loss improves prediction performance, and the visualization of class activation mapping enhances model interpretability.
AUTOMATION IN CONSTRUCTION
(2024)
Article
Construction & Building Technology
Gilsu Jeong, Minhyuk Jung, Seongeun Park, Moonseo Park, Changbum Ryan Ahn
Summary: This study introduces a contextual audio-visual approach to recognize multi-equipment activities in tunnel construction sites, improving monitoring effectiveness. Tested against real-world operation data, the model achieved remarkable results, emphasizing the potential of contextual multimodal models in enhancing operational efficiency in complex construction sites.
AUTOMATION IN CONSTRUCTION
(2024)
Article
Construction & Building Technology
Jin Wang, Zhigao Zeng, Pradip Kumar Sharma, Osama Alfarraj, Amr Tolba, Jianming Zhang, Lei Wang
Summary: This study presents a dual-path network for pavement crack segmentation, combining Convolutional Neural Network (CNN) and transformer. A lightweight CNN encoder is used for local feature extraction, while a novel transformer encoder integrates high-low frequency attention mechanism and efficient feedforward network for global feature extraction. Additionally, a complementary fusion module is introduced to aggregate intermediate features extracted from both encoders. Evaluation on three datasets confirms the superior performance of the proposed network.
AUTOMATION IN CONSTRUCTION
(2024)
Article
Construction & Building Technology
Pierre Gilibert, Romain Mesnil, Olivier Baverel
Summary: This paper introduces a flexible method for crafting 2D assemblies adaptable to various geometric assumptions in the realm of sustainable construction. By utilizing digital fabrication technologies and optimization approaches, precise control over demountable buildings can be achieved, improving mechanical performance and sustainability.
AUTOMATION IN CONSTRUCTION
(2024)
Review
Construction & Building Technology
Jorge Loy-Benitez, Myung Kyu Song, Yo-Hyun Choi, Je-Kyum Lee, Sean Seungwon Lee
Summary: This paper discusses the advancement of tunnel boring machines (TBM) through the application of artificial intelligence. It highlights the significance of AI-based management subsystems for automatic TBM operations and presents recent contributions in this field. The paper evaluates modeling, monitoring, and control subsystems and suggests research paths for integrating existing management subsystems into TBM automation.
AUTOMATION IN CONSTRUCTION
(2024)
Review
Construction & Building Technology
Alireza Shamshiri, Kyeong Rok Ryu, June Young Park
Summary: This paper reviews the application of text mining and natural language processing in the construction field, highlighting the need for automation and minimizing manual tasks. The study identifies potential research opportunities in strengthening overlooked construction aspects, coupling diverse data formats, and leveraging pre-trained language models and reinforcement learning.
AUTOMATION IN CONSTRUCTION
(2024)
Article
Construction & Building Technology
Zhengyi Chen, Hao Wang, Keyu Chen, Changhao Song, Xiao Zhang, Boyu Wang, Jack C. P. Cheng
Summary: This study proposes an improved coverage path planning system that leverages building information modeling and robotic configurations to optimize coverage performance in indoor environments. Experimental validation shows the effectiveness and applicability of the system. Future research will focus on further enhancing coverage ratio and optimizing computation time.
AUTOMATION IN CONSTRUCTION
(2024)
Review
Construction & Building Technology
Yonglin Fu, Junjie Chen, Weisheng Lu
Summary: This study presents a review of human-robot collaboration (HRC) in modular construction manufacturing (MCM), focusing on tasks, human roles, and interaction levels. The review found that HRC solutions are applicable to various MCM tasks, with a primary focus on timber component production. It also revealed the diverse collaborative roles humans can play and the varying levels of interaction with robots.
AUTOMATION IN CONSTRUCTION
(2024)
Article
Construction & Building Technology
Qiong Liu, Shengbo Cheng, Chang Sun, Kailun Chen, Wengui Li, Vivian W. Y. Tam
Summary: This paper presents an approach to enhance the path-following capability of concrete printing by integrating steel cables into the printed mortar strips, and validates the feasibility and effectiveness of this approach through experiments.
AUTOMATION IN CONSTRUCTION
(2024)
Article
Construction & Building Technology
Honghu Chu, Lu Deng, Huaqing Yuan, Lizhi Long, Jingjing Guo
Summary: The study proposes a method called Cascade CATransUNet for high-resolution crack image segmentation. This method combines the coordinate attention mechanism and self-cascaded design to accurately segment cracks. Through a customized feature extraction architecture and an optimized boundary loss function, the proposed method achieves impressive segmentation performance on HR images and demonstrates its practicality in UAV crack detection tasks.
AUTOMATION IN CONSTRUCTION
(2024)
Article
Construction & Building Technology
Daniel Lamas, Andres Justo, Mario Soilan, Belen Riveiro
Summary: This paper introduces a new method for creating synthetic point clouds of truss bridges and demonstrates the effectiveness of a deep learning approach for semantic and instance segmentation of these point clouds. The proposed methodology has significant implications for the development of automated inspection and monitoring systems for truss bridges.
AUTOMATION IN CONSTRUCTION
(2024)
Article
Construction & Building Technology
Kahyun Jeon, Ghang Lee, Seongmin Yang, Yonghan Kim, Seungah Suh
Summary: This study proposes two enhanced unsupervised text classification methods for domain-specific non-English text. The results of the tests show that these methods achieve excellent performance on Korean building defect complaints, outperforming state-of-the-art zero-shot and few-shot text classification methods, with minimal data preparation effort and computing resources.
AUTOMATION IN CONSTRUCTION
(2024)
Article
Construction & Building Technology
Yoonhwa Jung, Julia Hockenmaier, Mani Golparvar-Fard
Summary: This study introduces a transformer-based natural language processing model, UNIfORMATBRIDGE, that automatically labels activities in a project schedule with Uniformat classification. Experimental results show that the model performs well in matching unstructured schedule data to Uniformat classifications. Additionally, the study highlights the importance of this method in developing new techniques.
AUTOMATION IN CONSTRUCTION
(2024)
Article
Construction & Building Technology
De-Graft Joe Opoku, Srinath Perera, Robert Osei-Kyei, Maria Rashidi, Keivan Bamdad, Tosin Famakinwa
Summary: This paper introduces a digital twin technology combining Building Information Modelling and the Internet of Things for the construction industry, aiming to optimize building conditions. The technology is implemented in a university library, successfully achieving real-time data capture and visual representation of internal conditions.
AUTOMATION IN CONSTRUCTION
(2024)
Article
Construction & Building Technology
Zaolin Pan, Yantao Yu
Summary: The construction industry faces safety and workforce shortages globally, and worker-robot collaboration is seen as a solution. However, robots face challenges in recognizing worker intentions in construction. This study tackles these challenges by proposing a fusion method and investigating the best granularity for recognizing worker intentions. The results show that the proposed method can recognize multi-granular worker intentions effectively, contributing to seamless worker-robot collaboration in construction.
AUTOMATION IN CONSTRUCTION
(2024)
