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Abstract In this paper, the feasibility of Structural Health Monitoring (SHM) employing a novel Fibonacy Sequence (FS)-based Optimization Algorithms (OAs) and up-to-date computing techniques is investigated for a large-scale railway bridge. During recent decades, numerous metaheuristic intelligent OAs have been proposed and immediately gained a lot of momentum. However, the major concern is how to employ OAs to deal with real-world problems, especially the SHM of large-scale structures. In addition to the requirement of high accuracy, a high computational cost is putting up a major barrier to the real application of OAs. Therefore, this article aims at addressing these two aforementioned issues. First, we propose employing the optimal ability of the golden ratio formulated by the well-known FS to remedy the shortcomings and improve the accuracy of OAs, specifically, a recently proposed new algorithm, namely Salp Swarm Algorithm (SSA). On the other hand, to deal with the high computational cost problems of OAs, we propose employing an up-to-date computing technique, termed superscalar processor to conduct a series of iterations in parallel. Moreover, in this work, the vectorization technique is also applied to reduce the size of the data. The obtained results show that the proposed approach is highly potential to apply for SHM of real large-scale structures.

Abstract Artificial neural network (ANN) has been commonly used to deal with many problems. However, since this algorithm applies backpropagation algorithms based on gradient descent (GD) technique to look for the best solution, the network may face major risks of being entrapped in local minima. To overcome those drawbacks of ANN, in this work, we propose a novel ANN working parallel with metaheuristic algorithms (MAs) to train the network. The core idea is that first, (1) GD is applied to increase the convergence speed. (2) If the network is stuck in local minima, the capacity of the global search technique of MAs is employed. (3) After escaping from local minima, the GD technique is applied again. This process is applied until the target is achieved. Additionally, to increase the efficiency of the global search capacity, a hybrid of particle swarm optimization and genetic algorithm (PSOGA) is employed. The effectiveness of ANNPSOGA is assessed using both numerical models and measurement. The results demonstrate that ANNPSOGA provides higher accuracy than traditional ANN, PSO, and other hybrid ANNs (even a higher level of noise is employed) and also considerably decreases calculational cost compared with PSO.

Intro: Dengue epidemics can rapidly increase demand in healthcare services across many endemic settings. However, there remains a lack of tools which can rapidly inform patient management and can be used at the point of care. Digital clinical decision-support systems (CDSSs) allow for efficient organisation of care as well as improve the quality of patient management. It is important that these tools are designed for the end-user and with the healthcare setting in mind to increase adoption and usability. Methods: We adopted a ground-up human-centred design approach to design a digital CDSS system for dengue management in Vietnam (D-CASS). A multidisciplinary team of data scientists, clinicians and social scientists were involved in a series of activities designed to map clinical processes, essential tasks and decision-making priorities which were crucial in the management of dengue at our hospital setting. Frontend development was undertaken by a specialist UX designer with regular feedback from the research team and clinical end-users. Findings: Based on our previous work, the desired features for the CDSS identified were: i) patient organisation, ii) availability of guidelines and calculators with easy access, iii) display of results and iv) inference models for dengue diagnosis on admission AUCROC 0.86 (IQR, 0.84—0.86) and further risk stratification for hospitalised patients based on possible complications AUCROC 0.83 (CI, 0.76—0.85). A web-based reactive framework suitable for display on computers and tablets was produced. Priority was placed on usability and modularity so that the system can be repurposed. Conclusion: Dengue Clinical Decision Support Application (D-CASS) is a bespoke and rapidly scalable CDSS produced following clinical pathways, clinician's needs, and usability in mind. Further work will focus on prospective evaluation and iterative improvement of the CDSS including (i) end-user testing and (ii) prospective model performance. If successful, the CDSS will be implemented and deployed to evaluate its clinical utility.

Recent integration of LiDAR into smartphones opens up a whole new world of possibilities for 3D indoor/outdoor mapping. Although these new systems offer an unprecedent opportunity for the democratization in the use of scanning technology, data quality is lower than data captured from high-end LiDAR sensors. This paper is focused on discussing the capability of recent Apple smart devices for applications related with 3D mapping of indoor and outdoor environments. Indoor scenes are evaluated from a reconstruction perspective, and three geometric aspects (local precision, global correctness, and surface coverage) are considered using data captured in two adjacent rooms. Outdoor environments are analysed from a mobility point of view, and elements defining the physical accessibility in building entrances are considered for evaluation.

Topography is a fundamental input to hydrologic models critical for generating realistic streamflow networks as well as infiltration and groundwater flow. Although there exist several national topographic datasets for the United States, they may not be compatible with gridded models that require hydrologically consistent digital elevation models (DEMs). Here, we present a national topographic dataset developed to support gridded hydrologic simulations at 1 km and 250 m spatial resolution over the contiguous United States. The workflow is described step by step in two parts: (a) DEM processing using a Priority Flood algorithm to ensure hydrologically consistent drainage networks and (b) slope calculation and smoothing to improve drainage performance. The accuracy of the derived stream network is evaluated by comparing the derived drainage area to drainage areas reported by the national stream gage network. The slope smoothing steps are evaluated using the runoff simulations with an integrated hydrologic model. Our DEM product started from the National Water Model DEM to ensure our final datasets will be as consistent as possible with this existing national framework. Our analysis shows that the additional processing we provide improves the consistency of simulated drainage areas and the runoff simulations that simulate gridded overland flow (as opposed to a network routing scheme). The workflow uses an open-source R package, and all output datasets and processing scripts are available and fully documented. All of the output datasets and scripts for processing are published through CyVerse at 250 m and 1 km resolution. The DOI link for the dataset is https://doi.org/10.25739/e1ps-qy48 (Zhang and Condon, 2020).

This multi-case study investigated self-regulation of preservice science teachers' (PSTs), focusing on their ability to learn from their own teaching experiences and to regulate teaching practices to pose higher-level cognitive questions. The participants were three PSTs enrolled in a certification program for teaching secondary science, representing low, intermediate, and high self-regulated learning (SRL). Data were collected from classroom materials, semi-structured interviews on planning classroom questions, classroom observations, classroom audio recordings, and semi-structured interviews on enacting questions. The findings revealed that while each case exhibited unique characteristics, all PSTs demonstrated improvements in their SRL practices and questioning. The analysis highlighted PSTs' SRL practices in planning and enacting classroom questions, suggesting ways to enhance PSTs' questioning skills through the integration of SRL into teacher preparation. Furthermore, the findings underscored the importance of university supervisors and cooperating teachers tailoring their coaching to PSTs based on their initial SRL skills and utilizing well-defined models of SRL development for guidance.

Automated 3D reconstruction of indoor environments from point clouds has been a topic of intensive research in recent years. Different methods developed for the generation of 3D indoor models have achieved promising results on different case studies. However, a comprehensive evaluation and comparison of the performance of these methods has not been available. This paper presents the preliminary results of the ISPRS benchmark on indoor modelling, an initiative of Working Group IV/5 to benchmark the performance of indoor modelling methods using a public dataset and a comprehensive evaluation framework. The performances of the different methods are compared through geometric quality evaluation of the reconstructed models in terms of completeness, correctness, and accuracy of wall elements. The results show that the reconstruction methods generally achieve high completeness but lower correctness for the reconstructed models while accuracies range from 0.5 cm to 6.7 cm.

Building Information Models (BIMs) are of paramount importance in lifecycle management of buildings as they enable collaboration among various stakeholders at different phases of a construction project, from planning to maintenance and operation. However, there is usually inconsistency between the as-is condition of the building and its existing BIM, because BIMs are generally not updated to reflect changes in the environment. Monitoring the changes during a building’s lifecycle and keeping the BIM up-to-date is useful for a variety of applications. Yet this process often involves manual surveying inspections, which are very time-consuming, error-prone, and laborious. In this paper, we present an automated approach for building change detection through a comparison between the BIM and a point cloud of the building indoor environment. The approach is based on point classification and surface coverage to identify discrepancies between the BIM and the point cloud. Experiments on a synthetic dataset and an ISPRS Benchmark dataset show the potential of the proposed approach not only for change detection and identifying discrepancies, but also for locating the removed and new structures of the building in comparison with the BIM. The results are useful for updating the BIM to represent the as-is condition of the building and for temporal analysis of changes during a building’s lifecycle.

Existing indoor mapping systems have limitations in terms of time efficiency and flexibility in complex environments. While backpack and handheld systems are more flexible and can be used for mapping multi-storey buildings, in some application scenarios, e.g. emergency response, a light-weight indoor mapping eyewear or head-mounted system has practical advantages. In this paper, we investigate the spatial mapping capability of Microsoft Hololens mixed reality eyewear for 3D mapping of large indoor environments. We provide a geometric evaluation of 3D mesh data captured by the Hololens in terms of local precision, coverage, and global correctness in comparison with terrestrial laser scanner data and a reference 3D model. The results indicate the high efficiency and flexibility of Hololens for rapid mapping of relatively large indoor environments with high completeness and centimetre level accuracy.

Automated reconstruction of 3D interior models has recently been a topic of intensive research due to its wide range of applications in Architecture, Engineering, and Construction. However, generation of the 3D models from LiDAR data and/or RGB-D data is challenged by not only the complexity of building geometries, but also the presence of clutters and the inevitable defects of the input data. In this paper, we propose a stochastic approach for automatic reconstruction of 3D models of interior spaces from point clouds, which is applicable to both Manhattan and non-Manhattan world buildings. The building interior is first partitioned into a set of 3D shapes as an arrangement of permanent structures. An optimization process is then applied to search for the most probable model as the optimal configuration of the 3D shapes using the reversible jump Markov Chain Monte Carlo (rjMCMC) sampling with the Metropolis-Hastings algorithm. This optimization is not based only on the input data, but also takes into account the intermediate stages of the model during the modelling process. Consequently, it enhances the robustness of the proposed approach to inaccuracy and incompleteness of the point cloud. The feasibility of the proposed approach is evaluated on a synthetic and an ISPRS benchmark dataset.

Despite recent progress in the development of methods for automated reconstruction of indoor models, a comparative performance evaluation of these methods is not available due to the lack of publicly available benchmark datasets and a common evaluation framework. The ISPRS Benchmark on Indoor Modelling is an effort to enable comparison and benchmarking of indoor modelling methods by providing a benchmark dataset and a comprehensive evaluation framework. In this paper, we propose a framework for the evaluation of indoor modelling methods, and discuss various quality aspects of the reconstruction methods as well as the reconstructed models. We discuss the challenges in quantitative quality evaluation of indoor models through comparison with a reference model, and propose suitable measures and methods for comparing an automatically reconstructed indoor model with a reference.

3D models of indoor environments are essential for many application domains such as navigation guidance, emergency management and a range of indoor location-based services. The principal components defined in different BIM standards contain not only building elements, such as floors, walls and doors, but also navigable spaces and their topological relations, which are essential for path planning and navigation. We present an approach to automatically reconstruct topological relations between navigable spaces from point clouds. Three types of topological relations, namely containment, adjacency and connectivity of the spaces are modelled. The results of initial experiments demonstrate the potential of the method in supporting indoor navigation.

Prior research has explored how international students' interactions with domestic students and with other international students predict their self-reported short-term outcomes, particularly in the context of the environmental challenges that international students may encounter. The present study builds upon this earlier work by using institutional data from dining hall meal swipe cards to measure interpersonal relationships and examine how these meal-indices from the first semester predict international students' subsequent credits earned, grades, retention, and graduation. Within a sample of 1,634 first-year international students, the results indicate that social networks with international students and with domestic students both predict postsecondary success outcomes; the findings are even significant when based on dining hall data measured at the end of students' first week of classes. The patterns vary somewhat across outcomes: meal-indices with both groups of students consistently predict international students' chances of graduation, relationships with international students are more consistently related to retention, and relationships with domestic students are more often associated with college GPA and credits earned.

The aim of this study is to identify factors associated with Vietnamese-Australian parents' language use and attitudes towards home language maintenance. Vietnamese-Australian parents (n = 151) with children aged under 18 completed a survey regarding demographic factors and factors conceptualised by Spolsky's language policy theory: language practices, language ideologies, and language management. Bivariate analyses and multiple regression models were conducted to explore associations between parents' language use and their attitudes towards home language maintenance and associated factors. "Parents' language use with their child" was significantly associated with their language practices (parents' language use in social situations). "Parents' language use in social situations" was significantly associated with language practices (parents' Vietnamese and English proficiency, parents' language use with their child), language management (frequency of attendance at community events), and one demographic factor (age). "Parents' attitudes towards home language maintenance" was significantly associated with language ideology factors (perceptions of cultural identity, belief in the importance of English language maintenance, belief that home language strengthens relative bonds and widens career options), and one demographic factor (income). The results can be used to assist families with Vietnamese heritage to maintain their home language by informing targeted approaches to supporting language maintenance at the community and family level.

The use of data to explain natural phenomena has been a core feature of science education, and science educators continue to call for an increased emphasis on teaching data practices. This mixed methods design-based research study adds to the growing body of research on data practices in science by explaining the learning trends of science teachers involved in a three-year collaborative professional development (PD) using computational thinking (CT) and self-regulated learning (SRL) as a means to support teachers in implementing data practices. The PD resulted in long-term high-quality teacher learning outcomes for all three elements of the PD (data practices, CT, and SRL) despite the upheaval of teaching platforms due to the COVID-19 pandemic. Since the teachers were involved in design-based research, their professional learning focused on collaboratively creating an electronic, interactive notebook with lessons for use in science classrooms across the United States. Creating a common product as an outcome of the PD may have served as motivation for teachers to learn about and implement more data practices, CT, and SRL, which suggests that design-based research is another valuable design for teacher PD.

Sentinel-5 (S5) and its precursor (S5P) are future European satellite missions aiming at global monitoring of methane (CH4) column-average dry air mole fractions (XCH4). The spectrometers to be deployed onboard the satellites record spectra of sunlight backscattered from the Earth's surface and atmosphere. In particular, they exploit CH4 absorption in the shortwave infrared spectral range around 1.65 μm (S5 only) and 2.35 μm (both S5 and S5P) wavelength. Given an accuracy goal of better than 2 % for XCH4 to be delivered on regional scales, assessment and reduction of potential sources of systematic error such as spectroscopic uncertainties is crucial. Here, we investigate how spectroscopic errors propagate into retrieval errors on the global scale. To this end, absorption spectra of a ground-based Fourier transform spectrometer (FTS) operating at very high spectral resolution serve as estimate for the quality of the spectroscopic parameters. Feeding the FTS fitting residuals as a perturbation into a global ensemble of simulated S5- and S5P-like spectra at relatively low spectral resolution, XCH4 retrieval errors exceed 0.6 % in large parts of the world and show systematic correlations on regional scales, calling for improved spectroscopic parameters.

Pulmonary artery hypertension (PAH) is a disorder of elevated resistance in the pulmonary arterial vessels, reflected by elevation of measured pulmonary artery pressure (PAP), and presenting with breathlessness and, if untreated, progressing to right heart failure and death. The heightened prevalence of PAH in populations with underlying systemic autoimmune conditions, particularly scleroderma and its variants, is well recognised, consistent with the proposed autoimmune contribution to PAH pathogenesis, along with disordered thrombotic, inflammatory, and mitogenic factors. Rheumatoid arthritis (RA) is one of a group of systemic autoimmune conditions featuring inflammatory symmetrical erosive polyarthropathy as its hallmark. This study explored the prevalence of PAH in a population of unselected individuals with RA, using exercise echocardiography (EchoCG). The high prevalence of EchoCG-derived elevation of PAP (EDEPP) in this population (14%) suggests that, like other autoimmune conditions, RA may be a risk factor for PAH. Patients with RA may therefore represent another population for whom PAH screening with noninvasive tools such as EchoCG may be justified.

The Greenhouse gases Observing SATellite (GOSAT) was launched on 23 January 2009 to monitor the global distributions of carbon dioxide and methane from space. It has operated continuously since then. Here, we describe a retrieval algorithm for column abundances of these gases from the short-wavelength infrared spectra obtained by the Thermal And Near infrared Sensor for carbon Observation-Fourier Transform Spectrometer (TANSO-FTS). The algorithm consists of three steps. First, cloud-free observational scenes are selected by several cloud-detection methods. Then, column abundances of carbon dioxide and methane are retrieved based on the optimal estimation method. Finally, the retrieval quality is examined to exclude low-quality and/or aerosol-contaminated results. Most of the retrieval random errors come from instrumental noise. The interferences due to auxiliary parameters retrieved simultaneously with gas abundances are small. The evaluated precisions of the retrieved column abundances for single observations are less than 1% in most cases. The interhemispherical differences and temporal variation patterns of the retrieved column abundances show features similar to those of an atmospheric transport model.

Vibration-based structural health monitoring (SHM) for long-span bridges has become a dominant research topic in recent years. The Nam O Railway Bridge is a large-scale steel truss bridge located on the unique main rail track from the north to the south of Vietnam. An extensive vibration measurement campaign and model updating are extremely necessary to build a reliable model for health condition assessment and operational safety management of the bridge. The experimental measurements are carried out under ambient vibrations using piezoelectric sensors, and a finite element (FE) model is created in MATLAB to represent the physical behavior of the structure. By model updating, the discrepancies between the experimental and the numerical results are minimized. For the success of the model updating, the efficiency of the optimization algorithm is essential. Particle swarm optimization (PSO) algorithm and genetic algorithm (GA) are employed to update the unknown model parameters. The result shows that PSO not only provides a better accuracy between the numerical model and measurements, but also reduces the computational cost compared to GA. This study focuses on the stiffness conditions of typical joints of truss structures. According to the results, the assumption of semi-rigid joints (using rotational springs) can most accurately represent the dynamic characteristics of the truss bridge considered.

We present the first study of the influence of line mixing among CO2 lines on the remote sensing retrieval of atmospheric carbon dioxide. This is done in the bands near 1.6 and 2.1 μm which will be used by the Greenhouse Gases Observatory Satellite (GOSAT) instrument and eventual successors of the Orbiting Carbon Observatory (OCO). A purely theoretical analysis is first made, based on simulations of atmospheric spectra. It shows that line mixing cannot be neglected since disregarding this process induces significant errors in the calculated absorption coefficients, leading to systematic structures in the spectral fit residuals and airmass-dependent biases in the retrieved CO2 amounts. These theoretical predictions are then confirmed by using atmospheric solar-absorption spectra measured by a ground-based Fourier transform spectrometer. It is first shown that including line mixing in the forward model used for the inversion leads to a very significant reduction of the residuals in the 2.1 μm region. Secondly, the inclusion of line mixing reduces the dependence of the retrieved CO2 on the airmass and greatly improves the consistency between values obtained independently from spectra in the 1.6 and 2.1 μm bands. These results open promising prospects for various ground-based and space-borne experiments monitoring the carbon dioxide atmospheric amounts.

An efficient synthesis of the exorbitantly priced Lambert-Eaton myasthenic syndrome drug amifampridine was developed and applied in the second-semester undergraduate organic chemistry laboratory. The two-step synthesis entails a nucleophilic aromatic substitution reaction of commercial 4-chloro-3-nitropyridine with methanolic ammonia to afford 4-amino-3-nitropyridine. Subsequent palladium-catalyzed nitro reduction provides amifampridine in high yield and purity. Amifampridine can alternatively be prepared by advanced undergraduate students in four linear steps beginning with 4-hydroxypyridine. Nitration of 4-hydroxypyridineto 4-hydroxy-3-nitropyridine and subsequent treatment with phosphoryl chloride provides the intermediate 4-chloro-3-nitropyridine, which is then subjected to the S[subscript N]Ar and reduction reactions to cleanly afford amifampridine without the need for column chromatography.