Building Information Modeling (BIM) provides an integrated 3D environment to manage large-scale engineering projects. The Architecture, Engineering and Construction (AEC) industry explores 4D visualizations over these datasets for virtual construction planning. However, existing solutions lack adequate visual mechanisms to inspect the underlying schedule and make inconsistencies readily apparent. The goal of this paper is to apply best practices of information visualization to improve 4D analysis of construction plans. We first present a review of previous work that identifies common use cases and limitations. We then consulted with AEC professionals to specify the main design requirements for such applications. These guided the development of CasCADe, a novel 4D visualization system where task sequencing and spatio-temporal simultaneity are immediately apparent. This unique framework enables the combination of diverse analytical features to create an information-rich analysis environment. We also describe how engineering collaborators used CasCADe to review the real-world construction plans of an Oil & Gas process plant. The system made evident schedule uncertainties, identified work-space conflicts and helped analyze other constructability issues. The results and contributions of this paper suggest new avenues for future research in information visualization for the AEC industry.
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http://dx.doi.org/10.1109/TVCG.2017.2745105 | DOI Listing |
Cytotherapy
December 2024
Barcia Novel Therapies, Lexington, Massachusetts, USA. Electronic address:
Macrophage-based cell therapies represent a cutting-edge frontier in immunotherapy, offering distinct advantages over conventional approaches like CAR-T. This review explores the potential of macrophages to orchestrate both innate and adaptive immune responses, enhancing the body's ability to combat diseases locally and systemically. Dubbed a "Smart Cell Therapy," macrophages can initiate and coordinate complex immunological cascades, leveraging multiple immune system components while also performing effector functions.
View Article and Find Full Text PDFSensors (Basel)
December 2024
School of Information and Electronic Engineering, Zhejiang University of Science and Technology, Hangzhou 310023, China.
Deep unfolding networks (DUNs) have attracted growing attention in compressed sensing (CS) due to their good interpretability and high performance. However, many DUNs often improve the reconstruction effect at the price of a large number of parameters and have the problem of feature information loss during iteration. This paper proposes a novel adaptive memory-augmented unfolding network for compressed sensing (AMAUN-CS).
View Article and Find Full Text PDFSensors (Basel)
December 2024
College of Engineering, Huaqiao University, Quanzhou 362021, China.
Grasping objects of irregular shapes and various sizes remains a key challenge in the field of robotic grasping. This paper proposes a novel RGB-D data-based grasping pose prediction network, termed Cascaded Feature Fusion Grasping Network (CFFGN), designed for high-efficiency, lightweight, and rapid grasping pose estimation. The network employs innovative structural designs, including depth-wise separable convolutions to reduce parameters and enhance computational efficiency; convolutional block attention modules to augment the model's ability to focus on key features; multi-scale dilated convolution to expand the receptive field and capture multi-scale information; and bidirectional feature pyramid modules to achieve effective fusion and information flow of features at different levels.
View Article and Find Full Text PDFSensors (Basel)
December 2024
Department of Computer Science, University of Bristol, Bristol BS8 1QU, UK.
Detecting anomalies in distributed systems through log analysis remains challenging due to the complex temporal dependencies between log events, the diverse manifestation of system states, and the intricate causal relationships across distributed components. This paper introduces a TLAN (Temporal Logical Attention Network), a novel deep learning framework that integrates temporal sequence modeling with logical dependency analysis for robust anomaly detection in distributed system logs. Our approach makes three key contributions: (1) a temporal logical attention mechanism that explicitly models both time-series patterns and logical dependencies between log events across distributed components, (2) a multi-scale feature extraction module that captures system behaviors at different temporal granularities while preserving causal relationships, and (3) an adaptive threshold strategy that dynamically adjusts detection sensitivity based on system load and component interactions.
View Article and Find Full Text PDFMicromachines (Basel)
November 2024
Department of Metabolic Health Research, Netherlands Organisation for Applied Scientific Research (TNO), 2333 BE Leiden, The Netherlands.
Background: To accurately measure permeability of compounds in the intestine, there is a need for preclinical in vitro models that accurately represent the specificity, integrity and complexity of the human small intestinal barrier. Intestine-on-chip systems hold considerable promise as testing platforms, but several characteristics still require optimization and further development.
Methods: An established intestine-on-chip model for tissue explants was adopted for intestinal cell monolayer culture.
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