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Building information modelling (BIM) has been adopted in the construction industry. The success of BIM implementation relies on the accurate building information stored in BIM models. However, building information in BIM models can be inaccurate, out-of-date, or missing in real-world projects. 3D laser scanning has been leveraged to capture the accurate as-is conditions of buildings and create as-is BIM models of buildings; this is known as the scan-to-BIM process. Although industry practitioners and researchers have implemented and studied the scan-to-BIM process, there is no framework that systematically defines and discusses the key steps and considerations in the process. This study proposes an application-oriented framework for scan-to-BIM, which describes the four major steps of a scan-to-BIM process and their relationships. The framework is oriented towards the specific BIM application to be implemented using the created as-is BIM, and includes four steps: (1) identification of information requirements, (2) determination of required scan data quality, (3) scan data acquisition, and (4) as-is BIM reconstruction. Two illustrative examples are provided to demonstrate the feasibility of the proposed scan-to-BIM framework. Furthermore, future research directions within the scan-to-BIM framework are suggested.

Background Primary care providers (PCPs) are typically the first to screen and evaluate patients for neurocognitive disorders (NCDs), including mild cognitive impairment and dementia. However, data on PCP attitudes and evaluation and management practices are sparse. Our objective was to quantify perspectives and behaviors of PCPs and neurologists with respect to NCD evaluation and management. Methods A cross-sectional survey with 150 PCPs and 50 neurologists in the United States who evaluated more than 10 patients over age 55 per month. The 51-item survey assessed clinical practice characteristics, and confidence, perceived barriers, and typical practices when diagnosing and managing patients with NCDs. Results PCPs and neurologists reported similar confidence and approaches to general medical care and laboratory testing. Though over half of PCPs performed cognitive screening or referred patients for cognitive testing in over 50% of their patients, only 20% reported high confidence in interpreting results of cognitive tests. PCPs were more likely to order CT scans than MRIs, and only 14% of PCPs reported high confidence interpreting brain imaging findings, compared to 70% of specialists. Only 21% of PCPs were highly confident that they correctly recognized when a patient had an NCD, and only 13% were highly confident in making a specific NCD diagnosis (compared to 72 and 44% for neurologists, both p  < 0.001). A quarter of all providers identified lack of familiarity with diagnostic criteria for NCD syndromes as a barrier to clinical practice. Conclusions This study demonstrates how PCPs approach diagnosis and management of patients with NCDs, and identified areas for improvement in regards to cognitive testing and neuroimaging. This study also identified all providers’ lack of familiarity with published diagnostic criteria for NCD syndromes. These findings may inform the development of new policies and interventions to help providers improve the efficacy of their decision processes and deliver better quality care to patients with NCDs.

A drastic change in communication is happening with digitization. Technological advancements will escalate its pace further. The human health care systems have improved with technology, remodeling the traditional way of treatments. There has been a peak increase in the rate of telehealth and e-health care services during the coronavirus disease 2019 (COVID-19) pandemic. These implications make reversible data hiding (RDH) a hot topic in research, especially for medical image transmission. Recovering the transmitted medical image (MI) at the receiver side is challenging, as an incorrect MI can lead to the wrong diagnosis. Hence, in this paper, we propose a MSB prediction error-based RDH scheme in an encrypted image with high embedding capacity, which recovers the original image with a peak signal-to-noise ratio (PSNR) of ∞ dB and structural similarity index (SSIM) value of 1. We scan the MI from the first pixel on the top left corner using the snake scan approach in dual modes: i) performing a rightward direction scan and ii) performing a downward direction scan to identify the best optimal embedding rate for an image. Banking upon the prediction error strategy, multiple MSBs are utilized for embedding the encrypted PHR data. The experimental studies on test images project a high embedding rate with more than 3 bpp for 16-bit high-quality DICOM images and more than 1 bpp for most natural images. The outcomes are much more promising compared to other similar state-of-the-art RDH methods.

To systematically evaluate and compare the diagnostic accuracy of CT perfusion (CTP), non-enhanced computed tomography (NCCT) and computed tomography angiography (CTA) in detecting acute ischemic stroke. We searched seven databases and screened the reference lists of the included studies. The risk of bias in the study quality was assessed using QUADASII. We produced paired forest plots in RevMan to show the variation of the sensitivity and specificity estimates together with their 95% CI. We used a hierarchical summary ROC model to summarize the sensitivity and specificity of CTP in detecting ischemic stroke. We identified 27 studies with a total of 2168 patients. The pooled sensitivity of CTP for acute ischemic stroke was 82% (95% CI 75-88%), and the specificity was 96% (95% CI 89-99%). CTP was more sensitive than NCCT and had a similar accuracy with CTA. There were no statistically significant differences in the sensitivity and specificity between patients who underwent CTP within 6 hours of symptom onset and beyond 6 hours after symptom onset. No adverse events were reported in the included studies. CTP is more accurate than NCCT and has similar accuracy to CTA in detecting acute ischemic stroke. However, the evidence is not strong. There is potential benefit of using CTP to select stroke patients for treatment, but more high-quality evidence is needed to confirm this result.

Dragon’s Blood (DB) serves as a precious Chinese medicine facilitating blood circulation and stasis dispersion. Daemonorops draco (D. draco; Qi-Lin-Jie) and Dracaena cochinchinensis (D. cochinchinenesis; Long-Xue-Jie) are two reputable plant sources for preparing DB. This work was designed to comprehensively characterize and compare the metabolome differences between D. draco and D. cochinchinenesis, by integrating liquid chromatography/mass spectrometry and untargeted metabolomics analysis. Offline two-dimensional liquid chromatography/ion mobility-quadrupole time-of-flight mass spectrometry (2D-LC/IM-QTOF-MS), by utilizing a powerful hybrid scan approach, was elaborated for multicomponent characterization. Configuration of an XBridge Amide column and an HSS T3 column in offline mode exhibited high orthogonality (A0 0.80) in separating the complex components in DB. Particularly, the hybrid high-definition MSE-high definition data-dependent acquisition (HDMSE-HDDDA) in both positive and negative ion modes was applied for data acquisition. Streamlined intelligent data processing facilitated by the UNIFI™ (Waters) bioinformatics platform and searching against an in-house chemical library (recording 223 known compounds) enabled efficient structural elucidation. We could characterize 285 components, including 143 from D. draco and 174 from D. cochinchinensis. Holistic comparison of the metabolomes among 21 batches of DB samples by the untargeted metabolomics workflows unveiled 43 significantly differential components. Separately, four and three components were considered as the marker compounds for identifying D. draco and D. cochinchinenesis, respectively. Conclusively, the chemical composition and metabolomic differences of two DB resources were investigated by a dimension-enhanced analytical approach, with the results being beneficial to quality control and the differentiated clinical application of DB.

Rates of diabetes-related Emergency Department (ED) visits in Florida increased by 54% between 2011 and 2016. However, little information is available on geographic disparities of ED visit rates and how these disparities changed over time in Florida and yet this information is important for guiding resource allocation for diabetes control programs. Therefore, the objectives of this study were to (a) investigate geographic disparities and temporal changes in non-gestational diabetes-related ED visit rates in Florida and (b) identify predictors of geographic disparities in non-gestational diabetes-related ED visit rates. The ED data for the period between 2016 and 2019 were obtained from the Florida Agency for Healthcare Administration. Records of non-gestational diabetes-related ED visits were extracted using the International Classification of Diseases (ICD)-10 codes. Monthly non-gestational diabetes-related ED visit rates were computed and temporal changes were investigated using the Cochran-Armitage trend test. County-level non-gestational diabetes-related ED visit rates per 100,000 person-years were calculated and their geographic distributions were visualized using choropleth maps. Clusters of counties with high non-gestational diabetes-related ED visit rates were identified using Kulldorff's circular and Tango's flexible spatial scan statistics. Predictors of non-gestational diabetes-related ED visit rates were investigated using negative binomial model. The geographic distributions of significant ( ≤ 0.05) high-rate clusters and predictors of ED visit rates were displayed on maps. There was a significant ( < 0.001) increase in non-gestational diabetes-related ED visit rates from 266 visits per 100,000 person-months in January 2016 to 332 visits per 100,000 person-months in December 2019. Clusters of high non-gestational diabetes-related ED visit rates were identified in the northern and south-central parts of Florida. Counties with high percentages of non-Hispanic Black, current smokers, uninsured, and populations with diabetes had significantly higher non-gestational diabetes-related ED visit rates, while counties with high percentages of married populations had significantly lower ED visit rates. The study findings confirm geographic disparities of non-gestational diabetes-related ED visit rates in Florida with high-rate areas observed in the rural northern and south-central parts of the state. Specific attention is required to address disparities in counties with high diabetes prevalence, high percentages of non-Hispanic Black, and uninsured populations. These findings are useful for guiding public health efforts geared at reducing disparities and improving diabetes outcomes in Florida.

The scarcity and difficulty in acquiring Side-scan sonar target images limit the application of deep learning algorithms in Side-scan sonar target detection. At present, there are few amplification methods for Side-scan sonar images, and the amplification image quality is not ideal, which is not suitable for the characteristics of Side-scan sonar images. Addressing the current shortage of sample augmentation methods for Side-scan sonar, this paper proposes a method for augmenting single underwater target images using the CBL-sinGAN network. Firstly, considering the low resolution and monochromatic nature of Side-scan sonar images while balancing training efficiency and image diversity, a sinGAN network is introduced and designed as an eight-layer pyramid structure. Secondly, the Convolutional Block Attention Module (CBAM) is integrated into the network generator to enhance target learning in images while reducing information diffusion. Finally, an L1 loss function is introduced in the network discriminator to ensure training stability and improve the realism of generated images. Experimental results show that the accuracy of shipwreck target detection increased by 4.9% after training with the Side-scan sonar sample dataset augmented by the proposed network. This method effectively retains the style of the images while achieving diversity augmentation of small-sample underwater target images, providing a new approach to improving the construction of underwater target detection models.

A healthy diet is important to prevent lifestyle diseases. Food labels have been proposed as a policy tool to improve the healthiness of food choices, as they provide information about nutritional content and health attributes which may otherwise have been unknown to the consumer. This study investigates the effect of food labels with different formats on dietary quality by using home-scan panel data and difference-in-difference methods to compare the change in dietary quality over time for households that start to use food labels with households that do not use labels. I find that the use of front-of-pack (FOP) nutritional labels increases overall dietary quality, which is driven by reduced intake of added sugar and increased intake of fiber. The use of back-of-pack (BOP) nutritional tables does not influence dietary quality. There is no additional benefit to overall dietary quality by using both labels. However, the results indicate that there could be a benefit of using both labels on certain nutrients. The results imply that additional policies are needed to supplement food labels in order to improve dietary quality.

With climate change and urban development, water systems are changing faster than ever. Currently, the ecological status of water systems is still judged based on single point measurements, without taking into account the spatial and temporal variability of water quality and ecology. There is a need for better and more dynamic monitoring methods and technologies. Aquatic drones are becoming accessible and intuitive tools that may have an important role in water management. This paper describes the outcomes, field experiences and feedback gathered from the use of underwater drones equipped with sensors and video cameras in various pilot applications in The Netherlands, in collaboration with local water managers. It was observed that, in many situations, the use of underwater drones allows one to obtain information that would be costly and even impossible to obtain with other methods and provides a unique combination of three-dimensional data and underwater footage/images. From data collected with drones, it was possible to map different areas with contrasting vegetation, to establish connections between fauna/flora species and local water quality conditions, or to observe variations of water quality parameters with water depth. This study identifies opportunities for the application of this technology, discusses their limitations and obstacles, and proposes recommendation guidelines for new technical designs.

Urban vitality is the life force of a city. In this paper, starting from three subsystems of population, economy, and function, the comprehensive index system for measuring urban vitality was constructed respectively from three scales: grid, prefecture-level administrative region, and urban agglomeration. GIS spatial analysis methods were used to measure the urban vitality index and analyze the spatial distribution pattern. Then, the MGWR was used to reveal the main factors affecting the difference in urban vitality and analyze the influence mechanism of urban vitality. Accordingly, countermeasures and suggestions for creating vibrancy were put forward. The result shows the following: At the grid scale, urban vitality presents a spatial distribution pattern of “large dispersion, small agglomeration”, which has significant differentiation characteristics of city scale and hierarchy. At the administrative region scale, the overall vitality of cities at the prefecture level and above in China is not high, and the spatial differences are large. The spatial scan identified 28 vigorous cities with high potential, belonging to 6 vigorous clusters. On the scale of urban agglomeration, according to the degree of vitality, there are three gradients. The spatial difference in urban vitality was affected by the internal characteristics and external environment.