Unified Reference Research Articles

Laboratory support for seromonitoring of anti-pertussis post-vaccination immunity

Monitoring of humoral immunity in 3–4 year-old anti-pertussis vaccinated children is one of the approaches for pertussis epidemiological surveillance. It’s a predictive indicator for comprehensive assessment of morbidity in the region. The agglutination reaction (RA) used earlier has been displaced with enzyme immunoassay (ELISA). So, two questions arose: regarding i) a relevance of using ELISA in whole-cell vaccinated individuals, and ii) correctness of large-scale data interpretation without applying a unified IgG reference value. Study aims were to assess humoral anti-pertussis population immunity by ELISA, to determine features of humoral protection in vaccinated subjects after using various vaccine types, to identify age features, to make recommendations for evaluating seromonitoring data. Blood serum samples from anti-pertussis vaccinated children in Samara region (n = 1729) were examined in 2016–2020; children were divided into 4 age groups ( 3, 3–4, 5–7, ≥ 7.1 years) and into 3 type vaccines groups: cellular, acellular and combined. We used two ELISA kits, which are designed to assess post-vaccination immunity, according to the manufacturer’s instructions, quantifying IgG againstB. pertussis, which differ in units and reference values. Statistical analysis was performed using StatTech program v.2.6.2. The largest and smallest percentage of seronegative children was in the cellular-vaccinated and acellular-vaccinated group, respectively. Seronegative percentage in the “indicator group” reaches 55.2% being significantly higher than acceptable 10.0%-level, and differs from the literature data (30–40%), which were obtained mainly with RA. This may suggest about an incorrect interpretation of post-vaccination immunity data evaluation strictly according to manufacturers’ reference criteria, especially for whole-cell vaccinated subjects. A method for calculating the seronegative percentage is proposed that relies on the prognostic negative data level as a correction factor. The required values were obtained during comparison studies of random serum samples from 3–4-year-old children (n = 70) using two ELISA kits (“RIDASCREEN®Bordetella”, “SeroPertussis™ IgG”) and intra-laboratory comparison panel. It should be given preference for sets with a greater prognostic negative data level, if population seronegative percentage is assessed.

New insights into the enteric methane production based on the archaeal genome map of ruminant gastrointestinal tract

IntroductionAs one of the important components of ruminant gastrointestinal tract (GIT) microbiome, archaea are involved in many biological processes, especially methanogenesis. However, the diversity and individual metabolic characteristics of archaea in this habitat remain largely understudied, partly due to the lack of a unified reference genome catalog. ObjectivesThis study aimed to construct a unified genome map for taxonomic and functional exploration of ruminant GIT archaea in the future. MethodsWe collected archaeal genomes from public sources and new data of this study. We performed phylogenetic analysis, functional genomics analyses, prophages identification based on the genomes. Using collected genomes as a reference, we conducted metagenomic and metatranscriptomic analyses on rumen fluid samples from 18 dairy cows with different methane (CH4) production. ResultsWe constructed the ruminant GIT archaeal genomes (RGAG) by compiling 405 strain-level (160 species) non-redundant archaeal genomes from more than 10 ruminant species. The functional heterogeneity and methanogenic structure within RGAG was investigated. RGAG possessed 1,124 (99.5 %) unknown microbial biosynthetic gene clusters. A survey of RGAG-borne prophages identified 63 prophages with 122 host-beneficial genes and 18 auxiliary metabolic genes (AMGs). The pipeline for both metagenomics and metatranscriptomics generated in the study revealed the roles of archaeal genomes under-assessed in general analyses of muti-omics. The highly expressed genus Methanosphaera was negatively correlated with CH4 production. ConclusionA unified genome map of ruminant GIT archaea is constructed in the study. Functional genomics indicates that the multifaceted functions of RGAG remains undiscovered. Multi-omics analyses reveals the advantages of metatranscriptomics over metagenomics in studying rumen archaeal communities. Differences in rumen archaeal community structure among cattle with different CH4 production may reflect the balance between rumen hydrogen production and methanogenesis. Our work provides new understanding of archaeal functions in the ruminant GIT and potential targets for future CH4 reduction.

A Review of Friction in Low-Stress Mechanics of Fibrous Flexible Materials.

The structure of a fabric is a highly complex assembly of fibres, which have order and regularity as well as disorder and randomness. The complexity of the structure poses challenges in defining its mechanical behaviour, particularly at low stress, which is typical to end uses. The coexistence of multiple deformations and the high degree of nonlinearity of the fabric due to fibre friction make its stress-strain relationship complicated. This article reviews the literature on friction related to the low-stress mechanics of fabrics, and it establishes its range and regularity to help with finding a unified reference model, in which although the physical meanings of fabric tensile, shear, and bending vary, they follow consistent mathematical regularities. So, invariably, their disorder and randomness needed in defining them can be obtained from fabric measurement data. It defines the scope and patterns of friction to facilitate the development of a unified reference model. It argues that although the physical interpretations of fabric tensile, shear, and bending characteristics may differ, they adhere to consistent mathematical regularities within this model, and hence extracting disorder and randomness from fabric measurement data may be achievable. This paper concludes with a number of recommendations, postulating that hysteresis caused by friction between fibres in a fabric is an important component of mechanical information, and it coexists with its purely elastic component, but it cannot be obtained directly by measurement. Seeking means to effectively decompose the friction hysteresis and pure elastic components contained in fabric mechanics measurement data will provide an accurate characterization of fabric mechanical properties and hence an accurate modelling and simulation of its behaviour, and will impact many traditional and industrial textile end uses.

A database of high-pressure crystal structures from hydrogen to lanthanum

This paper introduces the HEX (High-pressure Elemental Xstals) database, a complete database of the ground-state crystal structures of the first 57 elements of the periodic table, from H to La, at 0, 100, 200 and 300 GPa. HEX aims to provide a unified reference for high-pressure research, by compiling all available experimental information on elements at high pressure, and complementing it with the results of accurate evolutionary crystal structure prediction runs based on Density Functional Theory. Besides offering a much-needed reference, our work also serves as a benchmark of the accuracy of current ab-initio methods for crystal structure prediction. We find that, in 98% of the cases in which experimental information is available, ab-initio crystal structure prediction yields structures which either coincide or are degenerate in enthalpy to within 300 K with experimental ones. The main manuscript contains synthetic tables and figures, while the Crystallographic Information File (cif) for all structures can be downloaded from the related figshare online repository.

Benefit of classical leveling for geoid-based vertical reference frames

Classically, vertical reference frames were realized as national or continent-wide networks of geopotential differences derived from geodetic leveling, i.e., from the combination of spirit leveling and gravimetry. Those networks are affected by systematic errors in leveling, leading to tilts in the order of decimeter to meter in larger networks. Today, there opens the possibility to establish a worldwide unified vertical reference frame based on a conventional (quasi)geoid model. Such a frame would be accessible through GNSS measurements, i.e., physical heights would be derived by the method of GNSS-leveling. The question arises, whether existing geodetic leveling data are abolished completely for the realization of vertical reference frames, are used for validation purposes only, or whether existing or future geodetic leveling data can still be of use for the realization of vertical reference frames. The question is mainly driven by the high quality of leveled potential differences over short distances. In the following we investigate two approaches for the combination of geopotential numbers from GNSS-leveling and potential differences from geodetic leveling. In the first approach, both data sets are combined in a common network adjustment leading to potential values at the benchmarks of the leveling network. In the second approach, potential differences from geodetic leveling are used as observable for regional gravity field modeling. This leads to a grid of geoid heights based on classical observables like gravity anomalies and now also on leveled potential differences. Based on synthetic data and a realistic stochastic model, we show that incorporating leveled potential differences improves the quality of a continent-wide network of GNSS-heights (approach 1) by about 40% and that formal and empirical errors of a regional geoid model (approach 2) are reduced by about 20% at leveling benchmarks. While these numbers strongly depend on the chosen stochastic model, the results show the benefit of using leveled potential differences for the realization of a modern geoid-based reference frame. Independent of the specific numbers of the improvement, an additional benefit is the consistency (within the error bounds of each observation type) of leveling data with vertical coordinates from GNSS and a conventional geoid model. Even though we focus on geodetic leveling, the methods proposed are independent of the specific technique used to observe potential (or equivalently height) differences and can thus be applied also to other techniques like chronometric or hydrodynamic leveling.

Drag in Vegetation Canopy: Considering Sheltering and Blockage Effects

AbstractVegetation plays a crucial role in river hydrodynamic processes, and the accurate prediction of canopy drag force is essential for effective river management and ecosystem protection. The interactions within the vegetation canopies must be quantified to understand their impact on drag force. Through a series of flume experiments, we conducted an investigation into the canopy interaction mechanism of rigid emergent aquatic vegetation, particularly focusing on the blockage and sheltering effects. Our experimental design includes various combinations of lateral and longitudinal spacing, as well as special single‐row and single‐column arrangements. This allowed us to provide a more precise understanding of how lateral and longitudinal spacing affect the blockage and sheltering effects. Furthermore, we introduced a unified reference velocity that combines two effects, based on which we have established a widely applicable drag model that can predict drag under various density conditions. Lastly, we proposed a critical characteristic value for quantifying drag. This value is instrumental in revealing the ultimate performance of drag under different spacing arrangements. The findings provide a reliable approach for predicting drag in rigid emergent vegetation canopies, significantly enhancing our understanding of vegetation's influence on hydrodynamic processes and offering a practical tool for river management and ecosystem protection.

Dynamic Validation of Calibration Accuracy and Structural Robustness of a Multi-Sensor Mobile Robot.

For mobile robots, the high-precision integrated calibration and structural robustness of multi-sensor systems are important prerequisites for ensuring healthy operations in the later stage. Currently, there is no well-established validation method for the calibration accuracy and structural robustness of multi-sensor systems, especially for dynamic traveling situations. This paper presents a novel validation method for the calibration accuracy and structural robustness of a multi-sensor mobile robot. The method employs a ground-object-air cooperation mechanism, termed the "ground surface simulation field (GSSF)-mobile robot -photoelectric transmitter station (PTS)". Firstly, a static high-precision GSSF is established with the true north datum as a unified reference. Secondly, a rotatable synchronous tracking system (PTS) is assembled to conduct real-time pose measurements for a mobile vehicle. The relationship between each sensor and the vehicle body is utilized to measure the dynamic pose of each sensor. Finally, the calibration accuracy and structural robustness of the sensors are dynamically evaluated. In this context, epipolar line alignment is employed to assess the accuracy of the evaluation of relative orientation calibration of binocular cameras. Point cloud projection and superposition are utilized to realize the evaluation of absolute calibration accuracy and structural robustness of individual sensors, including the navigation camera (Navcam), hazard avoidance camera (Hazcam), multispectral camera, time-of-flight depth camera (TOF), and light detection and ranging (LiDAR), with respect to the vehicle body. The experimental results demonstrate that the proposed method offers a reliable means of dynamic validation for the testing phase of a mobile robot.

Secured intelligent transportation with privacy retention through blockchain framework

The Secured Independent Intelligent Transport System (SIITS) is poised to revolutionize traditional transport management systems, leveraging autonomous vehicles (AVs) connected through an open-channel Internet to link Traffic Command Centers (TCCs), Road Side Units (RSUs), and AVs within the SIITS framework. However, this reliance on the Internet exposes users to various security risks, safety vulnerabilities, and other challenges that impede the progress of SIITS applications. In this method, ensuring robust security management and trustworthiness is paramount for the long-term adoption of this innovative trend. While previous efforts have focused on integrating security methods from multiple disciplines into a unified reference design, this article presents a reference architecture primarily centered around ITS safety. Additionally, the article introduces a proposed framework for enhancing ITS safety, addressing the confidence issues. To further address these challenges, the article offers categorization of goods, Big Data methods and services, and validates the utility of ITS business analytics for corporate applications through a groundbreaking multi-tier ITS security architecture.

Abstract 7432: Improving FAIRness of computational approaches for cancer: The computational resources for cancer research portal

Abstract The Computational Resources for Cancer Research website is a public portal supported by NCI that democratizes access to available computational resources for the cancer research community. In supporting an emerging FAIR (Findable, Accessible, Interoperable, Reusable) data science ecosystem inclusive of software, models and data, the goals of the portal are to serve cancer data scientists by increasing understanding, access, and adoption of computational resources for cancer research, and to foster new collaborative research projects. Initially populated with resources developed in NCI supported collaborations with the US Department of Energy, the portal is serving as a unified reference and resource for computational resources developed broadly by the cancer community. These collaborative projects include CANDLE (the Cancer Distributed Learning Environment), IMPROVE (Innovative Methods and data for PRedictive Oncology model Validation and Evaluation), ADMIRRAL (AI Driven Multi-scale Investigation of Ras-Raf Activation Lifecycle), MOSSAIC (Model Outcomes using Surveillance Data and Scalable AI for Cancer) and ATOM (Accelerating Therapeutics for Opportunities in Medicine). The resources include software, models, and datasets as well as descriptive use cases and references to tutorials associated with many of the described resources. The portal also integrates content from the Predictive Oncology Model and Data Clearinghouse (modac.cancer.gov) which can be accessed through REST APIs. Ongoing development of the portal is driven by community insights and contributions including input from an interdisciplinary advisory committee, working groups, early users, and preliminary feedback received at AACR 2023. The portal will also support community contributions of computational resources. In addition, the portal highlights computational approaches in emerging and challenging areas, such as digital twins for cancer. The portal will enable the cancer research community to learn about new computational research methods through use cases and educational material, access cancer related software, datasets, and AI models, engage with peers and cross-disciplinary researchers at different career levels through an interactive forum, explore emerging areas, such as computational imaging, community-based standards for data management, predictive radiation oncology, and digital twin technologies, learn about in industry and community events, conferences, and workshops, and create new collaborative research projects. The presentation will deliver insights into key resources offered through the Computational Resources for Cancer Research Portal, how the portal will help the cancer research community identify and adopt computational resources of interest, and how researchers can join and participate in this growing community. Citation Format: Eric Stahlberg, Lynn Borkon, Ruth Frost, Sara Jones, Amar Khalsa, Lauren Lewis, Emily Greenspan, Sunita Menon. Improving FAIRness of computational approaches for cancer: The computational resources for cancer research portal [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2024; Part 1 (Regular Abstracts); 2024 Apr 5-10; San Diego, CA. Philadelphia (PA): AACR; Cancer Res 2024;84(6_Suppl):Abstract nr 7432.

Template-Guided Hierarchical Multi-View Registration Framework of Unordered Bridge Terrestrial Laser Scanning Data.

The registration of bridge point cloud data (PCD) is an important preprocessing step for tasks such as bridge modeling, deformation detection, and bridge health monitoring. However, most existing research on bridge PCD registration only focused on pairwise registration, and payed insufficient attention to multi-view registration. In addition, to recover the overlaps of unordered multiple scans and obtain the merging order, extensive pairwise matching and the creation of a fully connected graph of all scans are often required, resulting in low efficiency. To address these issues, this paper proposes a marker-free template-guided method to align multiple unordered bridge PCD to a global coordinate system. Firstly, by aligning each scan to a given registration template, the overlaps between all the scans are recovered. Secondly, a fully connected graph is created based on the overlaps and scanning locations, and then a graph-partition algorithm is utilized to construct the scan-blocks. Then, the coarse-to-fine registration is performed within each scan-block, and the transformation matrix of coarse registration is obtained using an intelligent optimization algorithm. Finally, global block-to-block registration is performed to align all scans to a unified coordinate reference system. We tested our framework on different bridge point cloud datasets, including a suspension bridge and a continuous rigid frame bridge, to evaluate its accuracy. Experimental results demonstrate that our method has high accuracy.

A Novel Framework for Image Matching and Stitching for Moving Car Inspection under Illumination Challenges.

Vehicle exterior inspection is a critical operation for identifying defects and ensuring the overall safety and integrity of vehicles. Visual-based inspection of moving objects, such as vehicles within dynamic environments abounding with reflections, presents significant challenges, especially when time and accuracy are of paramount importance. Conventional exterior inspections of vehicles require substantial labor, which is both costly and prone to errors. Recent advancements in deep learning have reduced labor work by enabling the use of segmentation algorithms for defect detection and description based on simple RGB camera acquisitions. Nonetheless, these processes struggle with issues of image orientation leading to difficulties in accurately differentiating between detected defects. This results in numerous false positives and additional labor effort. Estimating image poses enables precise localization of vehicle damages within a unified 3D reference system, following initial detections in the 2D imagery. A primary challenge in this field is the extraction of distinctive features and the establishment of accurate correspondences between them, a task that typical image matching techniques struggle to address for highly reflective moving objects. In this study, we introduce an innovative end-to-end pipeline tailored for efficient image matching and stitching, specifically addressing the challenges posed by moving objects in static uncalibrated camera setups. Extracting features from moving objects with strong reflections presents significant difficulties, beyond the capabilities of current image matching algorithms. To tackle this, we introduce a novel filtering scheme that can be applied to every image matching process, provided that the input features are sufficient. A critical aspect of this module involves the exclusion of points located in the background, effectively distinguishing them from points that pertain to the vehicle itself. This is essential for accurate feature extraction and subsequent analysis. Finally, we generate a high-quality image mosaic by employing a series of sequential stereo-rectified pairs.

A deep learning approach to recognizing fine-grained expressway location reference from unstructured texts in Chinese

Complex nested and discontinuous location references are common in unstructured text. Extracting them is essential for accurate location information retrieval and spatial inference. However, traditional methods struggle with these references due to annotation system and model architecture limitations. In this study, we introduce a deep learning approach to uniformly recognize flat, nested, and discontinuous location references, motivated by recognizing fine-grained expressway location references. The approach uses a pre-trained language model to generate semantic sentence representations and a distance and direction-aware Transformer for contextual encoding. Then, it recognizes location references by modeling the adjacency and boundary relations between word pairs. We evaluated the approach on seven benchmark datasets and compared it with state-of-the-art methods. The results show that the approach achieves higher accuracy with faster inference, validating our modeling paradigm and architecture. The ablation study further confirms the effectiveness of submodules in architecture. These findings can provide valuable insights for developing advanced unified location reference recognition methods. Moreover, the detailed labeled dataset for location references can facilitate the evaluation and comparison of unified recognition methods and systems.

An efficient method for measuring and identifying geometric and dynamic errors in dual five-axis machine tools

The dual five-axis machining machine is state-of-the-art equipment designed for producing thin-walled aerospace parts like skins and panels. It comprises two five-axis machine tools with mirrored synchronous motions: one bi-rotary machine for milling and the other machine for follow-up support. The high absolute accuracy of the five-axis machine tools themselves and the high synchronous motion accuracy between the two machines are both crucial to ensure machining accuracy. This paper proposes an efficient method to measure and identify geometric and dynamic errors of dual five-axis machine tools. Using the R-test combined with three measurement trajectories to measure the absolute motion error of two five-axis machine tools and the synchronous motion error between the two machines. The designed trajectories include the main rotary axis linkage conditions and can fully reflect the primary motion forms of the dual five-axis machine tool. The main position-independent geometric errors and rotational axis linkage dynamic errors are identified by converting quasi-static and dynamic measurement data from three different trajectories into a unified reference coordinate system. The entire process requires only one instrument installation, effectively avoiding time-consuming repetitive installations and calibration processes while reducing the impact of installation errors on results. The effectiveness of the proposed method for measuring and identifying the geometric and dynamic errors is analyzed combined with Monte Carlo simulation. Experiments were carried out on a dual five-axis mirror milling system (MMS) with a 5000 mm X-stroke. Both simulations and experiments confirmed that the proposed method is efficient in quickly identifying key error sources of the MMS.

The current situation of Russia’s GNSS continuous operating reference station network and thinking on future development

The GNSS (Global Navigation Satellite Systems) continuous operating reference station network not only provides a reliable technical foundation and data support for achieving high-precision navigation and positioning services, but also can be integrated with other Earth observation systems (such as laser ranging, gravity measurement, etc.) to improve the accuracy and stability of Earth observation data. It is used to establish regional and global coordinate frameworks and provide data to support scientific research such as seismology, geo-tectonics, and oceanography. This article first systematically introduces the construction, development history and current situation of the Russian GNSS continuous operating reference station network, and then carefully analyzes the construction, operation, and application status and existing problems of the Russian reference station network. Finally, after careful consideration, it discusses the problems and challenges faced in the planning of reference station network construction, equipment localization, and national unified reference station network construction, and provides some suggestions and ideas.

Единая система отсчета геометрических характеристик размерных элементов деталей. Часть II. Геометрические характеристики цилиндрических элементов деталей

The unified reference system for the geometric characteristics of the part is the Cartesian rectangular coordinate system, which is materialized by sets of design datums that limit the part to six degrees of freedom: three linear and three angular. ISO standards for geometric tolerances do not use coordinate systems. In this context, research in the field of increasing the accuracy of parts by introducing linear and angular coordinates of part elements is relevant. The paper presents the second part of the article Unified Reference System for Geometric Characteristics of Dimensional Elements of Parts, which focuses on the geometric characteristics of cylindrical elements of parts. It is shown that the datums of cylindrical elements are the axes of cylinders of maximum material capable of restricting four, two and one degrees of freedom for the part depending on the functional purpose of the element. The accuracy of coordinating dimensions should be specified by symmetrical tolerances for linear and angular coordinates of elements.

Non-contact method of thickness measurement for thin-walled rotary shell parts based on chromatic confocal sensor

In this paper, a non-contact method of thickness measurement for thin-walled rotary shell parts based on chromatic confocal sensor is presented. To obtain thickness, a flip method is applied to acquire surface profiles from both sides of the workpiece. The decentration and tilt errors of the workpiece are measured by a centering system consisting of a laser interferometer and a collimated tube. By establishing a unified reference coordinate system, the external and internal surface profiles are reconstructed and the thickness is calculated. A measurement experiment for a sapphire spherical shell workpiece was conducted on a four-axis measurement system and the result is 2.99341 mm. A wavelength-tuning interferometer and a dynamic interferometer were utilized to measure the sapphire spherical shell and an aluminum concave sphere and the results show in good agreement.

Quality evaluation index development for nutritional management in patients with nasopharyngeal carcinoma during peri-radiotherapy

ObjectivesIt is necessary to construct an evaluation index for patients with nasopharyngeal carcinoma during peri-radiotherapy to provide a reference for the evaluation of the quality of nutritional management of patients with nasopharyngeal carcinoma during peri-radiotherapy. The aim of this study was to construct a set of scientific, comprehensive, and feasible indicators for evaluating the quality of nutrition management in patients with nasopharyngeal carcinoma during peri-radiotherapy to provide a unified reference basis for objective nutritional evaluation of these patients during the peri-radiotherapy period and to provide insights to the clinical treatment and care of these patients. MethodsA multidisciplinary research team was set up from December 2021 to April 2022. We took the three-dimensional quality structure model as the theoretical framework; based on the literature review, the first draft of the nutrition management quality evaluation index for patients with nasopharyngeal carcinoma during peri-radiotherapy was formed by a semi-structured interview. The Delphi correspondence method was used to survey 18 experts from 12 cities in China. The multidimensional analytical hierarchy process was used to determine the evaluation index and weight of nutrition management quality of patients with nasopharyngeal carcinoma during peri-radiotherapy. ResultsThe effective questionnaire recovery rates of the two rounds of letters were 90.005% and 100%, respectively, and the expert authority coefficients were 0.906 and 0.918, respectively. The Kendall harmony coefficients of the two rounds of letters were 0.271 to 0.313 and 0.309 to 0.349, respectively. The nutrition management quality evaluation index of patients with nasopharyngeal carcinoma during peri-radiotherapy was constructed and included 3 first-level indexes, 10 second-level indexes, and 71 third-level indexes. ConclusionThe evaluation index of the nutrition management quality of patients with nasopharyngeal carcinoma during peri-radiotherapy is scientific and reliable, and it may have a certain guiding significance for nurses to evaluate the quality of nutrition management of these patients during this period.

Hybrid learning-oriented approaches for predicting Covid-19 time series data: A comparative analytical study

Using medical science alongside time series data analysis can be given a strong tool to develop efficient decision support systems in Corona pandemic. In this regard, many hybrid learning-oriented (HL) approaches have been presented, which rely on modeling the linear and non-linear components of the time series. However, there is a lack of comprehensive study of such approaches to achieve a macro vision of Covid-19 data prediction models in an unified reference. We conducted a comparative analytical study on (HL) approaches for predicting Covid-19 data. The main scope of current study is the investigate of such approaches. The original contribution of the paper is to present a reference-point and roadmap for future studies, which is provided in three forms. First, we experimentally evaluated the efficiency of all learning-based combinations on types of Covid-19 data in a similar context. Second, we tried to provide a guidance for choosing a more proper hybrid through valid empirical and statistical evaluations. Third, we presented an efficient and generalizable approach called HL-ALL (Hybrid Learning ARIMA LSTM LSTM). Evaluation results show high potential of HL-ALL in dealing Covid-19 data when prediction.

Real‐time mosaic of multiple fisheye surveillance videos based on geo‐registration and rectification

AbstractA distributed fisheye video surveillance system (DFVSS) can monitor a wide area without blind spots, but it is often affected by the viewpoint discontinuity and space inconsistency of multiple videos in the area. This paper proposes a novel real‐time fisheye video mosaic algorithm for wide‐area surveillance. First, by extending the line photogrammetry theory under central projection to spherical projection, a fisheye video geo‐registration model is established and estimated using orthogonal parallel lines on the ground, so that all videos of DFVSS are in the unified reference system to eliminate the space inconsistency between them. Second, by combining the photogrammetry orthorectification technique with thin‐plate spline transformation, a fisheye video rectification model is established to eliminate serious distortion in geo‐registered fisheye videos and align them accurately. Third, the viewport‐dependent video selection strategy and video look‐up table computation technique are adopted to create a high‐resolution panorama from input fisheye videos in real time. A parking lot of about 0.4 km2 monitored by eight fisheye cameras was selected as the test area. The experimental result shows the line re‐projection error in fisheye videos is about 0.5 pixels, and the overall efficiency, including panorama creation and mapping to the ground as texture, is not <30 fps. It indicates that the proposed algorithm can achieve a good balance between the limitation of video transmission bandwidth and the smooth observation requirement of computer equipment for the panorama, which is of great value for the construction and application of DFVSS.

Data processing, analysis, and evaluation methods for co-design of coreless filament-wound building systems

Abstract The linear design workflow for structural systems, involving a multitude of iterative loops and specialists, obstructs disruptive innovations. During design iterations, vast amounts of data in different reference systems, origins, and significance are generated. This data is often not directly comparable or is not collected at all, which implies a great unused potential for advancements in the process. In this paper, a novel workflow to process and analyse the data sets in a unified reference frame is proposed. From this, differently sophisticated iteration loops can be derived. The developed methods are presented within a case study using coreless filament winding as an exemplary fabrication process within an architectural context. This additive manufacturing process, using fiber-reinforced plastics, exhibits great potential for efficient structures when its intrinsic parameter variations can be minimized. The presented method aims to make data sets comparable by identifying the steps each data set needs to undergo (acquisition, pre-processing, mapping, post-processing, analysis, and evaluation). These processes are imperative to provide the means to find domain interrelations, which in the future can provide quantitative results that will help to inform the design process, making it more reliable, and allowing for the reduction of safety factors. The results of the case study demonstrate the data set processes, proving the necessity of these methods for the comprehensive inter-domain data comparison.