Vision-based pavement marking detection and condition assessment : a case study

Pavement markings constitute an effective way of conveying regulations and guidance to drivers. They constitute the most fundamental way to communicate with road users, thus, greatly contributing to ensuring safety and order on roads. However, due to the increasingly extensive traffic demand, pavement markings are subject to a series of deterioration issues (e.g., wear and tear). Markings in poor condition typically manifest as being blurred or even missing in certain places. The need for proper maintenance strategies on roadway markings, such as repainting, can only be determined based on a comprehensive understanding of their as-is worn condition. Given the fact that an efficient, automated and accurate approach to collect such condition information is lacking in practice, this study proposes a vision-based framework for pavement marking detection and condition assessment. A hybrid feature detector and a threshold-based method were used for line marking identification and classification. For each identified line marking, its worn/blurred severity level was then quantified in terms of worn percentage at a pixel level. The damage estimation results were compared to manual measurements for evaluation, indicating that the proposed method is capable of providing indicative knowledge about the as-is condition of pavement markings. This paper demonstrates the promising potential of computer vision in the infrastructure sector, in terms of implementing a wider range of managerial operations for roadway management

GujiBERT and GujiGPT: Construction of Intelligent Information Processing Foundation Language Models for Ancient Texts

In the context of the rapid development of large language models, we have meticulously trained and introduced the GujiBERT and GujiGPT language models, which are foundational models specifically designed for intelligent information processing of ancient texts. These models have been trained on an extensive dataset that encompasses both simplified and traditional Chinese characters, allowing them to effectively handle various natural language processing tasks related to ancient books, including but not limited to automatic sentence segmentation, punctuation, word segmentation, part-of-speech tagging, entity recognition, and automatic translation. Notably, these models have exhibited exceptional performance across a range of validation tasks using publicly available datasets. Our research findings highlight the efficacy of employing self-supervised methods to further train the models using classical text corpora, thus enhancing their capability to tackle downstream tasks. Moreover, it is worth emphasizing that the choice of font, the scale of the corpus, and the initial model selection all exert significant influence over the ultimate experimental outcomes. To cater to the diverse text processing preferences of researchers in digital humanities and linguistics, we have developed three distinct categories comprising a total of nine model variations. We believe that by sharing these foundational language models specialized in the domain of ancient texts, we can facilitate the intelligent processing and scholarly exploration of ancient literary works and, consequently, contribute to the global dissemination of China's rich and esteemed traditional culture in this new era.Comment: 22pages,0 figur

Generation of Embryonic Origin-Specific Vascular Smooth Muscle Cells from Human Induced Pluripotent Stem Cells

Vascular smooth muscle cells (VSMCs), a highly mosaic tissue, arise from multiple distinct embryonic origins and populate different regions of our vascular network with defined boundaries. Accumulating evidence has revealed that the heterogeneity of VSMC origins contributes to region-specific vascular diseases such as atherosclerosis and aortic aneurysm. These findings highlight the necessity of taking into account lineage-dependent responses of VSMCs to common vascular risk factors when studying vascular diseases. This chapter describes a reproducible, stepwise protocol for the generation of isogenic VSMC subtypes originated from proepicardium, second heart field, cardiac neural crest, and ventral somite using human induced pluripotent stem cells. By leveraging this robust induction protocol, patient-derived VSMC subtypes of desired embryonic origins can be generated for disease modeling as well as drug screening and development for vasculopathies with regional susceptibility

Generation of Quiescent Cardiac Fibroblasts Derived from Human Induced Pluripotent Stem Cells.

Myocardial fibrosis is a hallmark of cardiac remodeling, which can progressively lead to heart failure, a leading cause of death worldwide. The effector cells of fibrosis in the heart are cardiac fibroblasts (CFs). There is currently no effective therapeutic strategy clinically available to specifically attenuate maladaptive responses of CFs. Large-scale applications such as high-throughput drug screening are difficult due to the limited availability of human primary CFs, thus limiting the development of future treatments. Here, we describe a robust induction protocol that can be used to generate a scalable, consistent, genetically defined source of quiescent CFs from human induced pluripotent stem cells for cardiac fibrosis modeling, drug discovery, and tissue engineering

coherent depth test scheme in freepipe

ACM SIGGRAPHThis paper presents a rasterization rendering pipeline namely FreePipe. The system builds a bridge between the traditional graphics pipelines and the general purpose computing architecture CUDA by taking advantages of both sides. The core of FreePipe is a z-buffer based rasterizer entirely implemented in CUDA. The graphics pipeline can be easily mapped to the CUDA programming model with each stage fully open to the programmers. Within this flexible architecture, we propose a coherent depth test scheme for concurrent capture of both depth and color attributes using the atomicCAS operation in CUDA. The scheme can be easily extended to multiple level for efficient rendering of order independent transparency, which has significant performance improvement over the state-of-the-art methods based on traditional graphics pipelines. Copyright © 2010 by the Association for Computing Machinery, Inc

Ontological knowledge base for concrete bridge rehabilitation project management

Concrete bridges are critical infrastructures, which require effective rehabilitation to maintain a good condition. Bridge rehabilitation projects have complex constraints and multiple participants. Constraint management is critical for such projects. Integrating and searching for relevant information is a key step for constraint management to timely remove constraints. However, accessing project information still relies on manual searching, which can delay information flow in constraint management and hinder constraint removal. Thus, this study introduces the concrete bridge rehabilitation project management ontology (CBRPMO) to improve information integration and constraint management. The CBRPMO was created by comprehensively collecting domain knowledge of bridge rehabilitation and following standard procedures. Reasoning rules were combined with an application programming interface (i.e. OWL API) to enable functions not supported in traditional ontologies (e. g. temporal computation and dynamic updating). As such, the CBRPMO can effectively handle dynamic information in ongoing projects. The CBRPMO was validated in a case study. The results show that the CBRPMO can: 1) integrate project information of constraints, tasks and procedures, participants, and relations between these project entities; 2) support various management functions based on dynamic project information, including evaluating project progress, constraint removal, and participants’ performance. The CBRPMO contributes to the industry by: 1) extending the application of ontologies in the bridge sector to cover the rehabilitation stage; 2) enhancing functions of conventional ontologies; and 3) reducing information searching time compared to manual searching, which improves constraint management approaches by automating the information searching step

Developing a hybrid approach to extract constraints related information for constraint management

Construction projects face various constraints (e.g., materials and equipment). Constraint management approaches such as advanced working packaging (AWP) can remove constraints and ensure smooth work. However, due to inefficient information extraction, the prerequisite of AWP, i.e., identifying and modelling constraints, are performed manually. Efforts that integrate constraint information into project knowledge bases are also limited. This paper proposes a hybrid approach to automatically extract and integrate constraint information from texts. The approach combines a deep learning model with pre-defined rules. The model extracts constraint entities whereas rules created based on domain knowledge are used to establish relations between these entities. Extracted information is encoded into the original ontologies. The approach can extract both entities and relations with over 90% accuracy. The original ontologies can be successfully enriched and support semantic queries. The approach improves AWP by partially automating constraint identification and modelling as well as ontology development for information integration

Optimizing Faulting Prediction for Rigid Pavements Using a Hybrid SHAP-TPE-CatBoost Model

Faulting refers to the common and significant distress in Jointed Plain Concrete Pavement (JPCP), which has an adverse impact on the pavement roughness. Nevertheless, the existing fault prediction models continue to heavily rely on conventional linear regression techniques or basic machine learning approaches, which leaves room for improvement in training efficiency and interpretability. To enhance training efficiency and accuracy, this study developed five novel faulting prediction models. These models are based on five basic machine learning algorithms: Random Forest (RF), Additive Boosting (AdaBoost), Gradient Boosting Decision Tree (GBDT), Light Gradient Boosting Machine (LightGBM), and Categorical Boost (CatBoost), combined with the tree-structured Parzen estimator (TPE). The five models are TPE-RF, TPE-AdaBoost, TPE-GBDT, TPE-LightGBM, and TPE-CatBoost. In addition to selecting the best-performing model, this study incorporated the Shapley Additive Explanation (SHAP) technique and developed TPE-SHAP-CatBoost to improve the interpretability of the model’s predictions. The process involved extracting historical data on pavement performance, including 17 variables, from the Long-Term Pavement Performance (LTPP) database for 160 instances of observation. Firstly, the Boruta method was used to identify the final set of input variables. Secondly, the TPE technique, which is a Bayesian optimization method, was applied to automatically select the optimal hyperparameters for the base models. Finally, SHAP was used to provide both global and local explanations of the model’s outputs. The results indicate that the TPE-CatBoost model achieves the highest accuracy with an R2 value of 0.906. Furthermore, the TPE-SHAP-CatBoost model identified the primary factors influencing faulting by incorporating SHAP and provided explanations of the model’s results at both the global and local levels. These research findings highlight the ability of the proposed model to accurately predict faulting, providing precise and interpretable guidance for pavement maintenance while reducing workload for pavement engineers in data collection and management