Scanning Tool Research Articles

Indoor mapping accuracy comparison between the apple devices’ LiDAR sensor and terrestrial laser Scanner

ABSTRACT Indoor 3D mapping is crucial for various architectural uses, including transforming physical floor plans and side walls into digital format, offering an interactive visualization of plans, sections, and elevations, and enabling data accessibility for different engineering needs, according to the available scanning tools, 3D interior modelling for surveying indoors usually uses terrestrial laser scanners, as they provide high accuracy but are expensive and take a long time, especially in data processing. Apple recently released smartphones with LiDAR sensors that can create 3D models. This study evaluates the accuracy of these LiDAR sensors, which are nearly cost 2.5% of the terrestrial laser scanners price and produce promising 3D model for indoor mapping. Evaluation is through a comparison between Apple’s iPad Pro 2021 LiDAR sensor and the terrestrial laser scanner Trimble Tx8, a 3D model created of a hall in the university campus using the Laser Scanner Trimble Tx8 and another two-3D models of the same hall using the iPad’s Pro 2021 LiDAR sensor, one of which is dynamic acquisition since the iPad was in motion and the other is static acquisition where the iPad stayed still and scanned the same hall through multiple overlapping scans, Comparisons were conducted depending on lengths, widths, hight and area measurements. In the static acquisition model, linear readings were 1–2 cm off, while area measurements were around 1-meter square accuracy.

IMPACT OF THE WALL-PARALLEL LENGTH SCALE OF ROUGHNESS ON THE DEVELOPMENT OF TURBULENT BOUNDARY LAYERS IN TURBINES

Abstract The detrimental effects of surface roughness on turbulent boundary layers in turbines, and compressors, are well known. Prediction can be problematic, especially for surfaces which are not similar to sand grains. Several publications have proposed additional parameters, slope, skew, etc. to augment a wallnormal measure. Here, we introduce a new roughness parameter, the mean separation, which explicitly measures the average separation between each local minima and its closest local maxima. Scans of turbine blade surfaces have mean separations which are up to six times that of the sand grain surface with the same wallnormal measure. High resolution, < 0.1 μm, area scanning tools and 3-D printing techniques have enabled the development of a “Scan-Scale-Print-Measure” methodology. A surface scan is scaled-up, 3-D printed, applied to a flat-plate and then the turbulent boundary layer is measured. The methodology has enabled parametric studies where the mean separation was increased whilst keeping either the wallnormal or the feature size constant. Both studies demonstrated a surface roughness-loss that was strongly dependent on the mean separation and different to a sand grain correlation. It also showed that for large mean separations the roughness-loss decreased to zero. A study into pits-and-peaks showed that, at the same wallnormal, peaks generated a roughness-loss twice that of pits. An engine representative surface produced only half the roughness-loss that would be attributed to a sand grain surface. The roughness loss was estimated within 5% using the parametric studies.

Towards Automated Accessibility Report Generation for Mobile Apps

Many apps have basic accessibility issues, like missing labels or low contrast. To supplement manual testing, automated tools can help developers and QA testers find basic accessibility issues, but they can be laborious to use or require writing dedicated tests. To motivate our work, we interviewed eight accessibility QA professionals at a large technology company. From these interviews, we synthesized three design goals for accessibility report generation systems. Motivated by these goals, we developed a system to generate whole app accessibility reports by combining varied data collection methods (e.g., app crawling, manual recording) with an existing accessibility scanner. Many such scanners are based on single-screen scanning, and a key problem in whole app accessibility reporting is to effectively de-duplicate and summarize issues collected across an app. To this end, we developed a screen grouping model with 96.9% accuracy (88.8% F1-score) and UI element matching heuristics with 97% accuracy (98.2% F1-score). We combine these technologies in a system to report and summarize unique issues across an app, and enable a unique pixel-based ignore feature to help engineers and testers better manage reported issues across their app’s lifetime. We conducted a user study where 19 accessibility engineers and testers used multiple tools to create lists of prioritized issues in the context of an accessibility audit. Our system helped them create lists they were more satisfied with while addressing key limitations of current accessibility scanning tools.

Harmonizing Norwegian registries onto OMOP common data model: Mapping challenges and opportunities for pregnancy and COVID-19 research

ObjectiveNorwegian health registries covering entire population are used for administration, research, and emergency preparedness. We harmonized these data onto the Observational Medical Outcomes Partnership common data model (OMOP CDM) and enrich real-world data in OMOP format with COVID-19 related data. MethodsData from six registries (2018–2021) covering birth registrations, selected primary and secondary care events, vaccinations, and communicable disease notifications were mapped onto the OMOP CDM v5.3. An Extract-Transform-Load (ETL) pipeline was developed on simulated data using data characterization documents and scanning tools. We ran dashboard quality checks, cohort generations, investigated differences between source and mapped data, and refined the ETL accordingly. ResultsWe mapped 1.5 billion rows of data of 5,673,845 individuals. Among these, there were 804,277 pregnancies, 483,585 mothers together with 792,477 children, and 472,948 fathers. We identified 382,516 positive tests for COVID-19 in 380,794 patients. These figures are consistent with results from source data. In addition to 11 million source codes mapped automatically, we mapped 237 non-standard codes to standard concepts and introduced 38 custom concepts to accommodate pregnancy-related terminologies that were not supported by OMOP CDM vocabularies. A total of 3,700/3,705 (99.8%) checks passed. The 5 failed checks could be explained by the nature of the data and only represent a small number of records. Discussion and conclusionNorwegian registry data were successfully harmonized onto OMOP CDM with high level of concordance and provides valuable source for federated COVID-19 related research. Our mapping experience is highly valuable for data partners with Nordic health registries.

Reverse Engineering Workflows for the Structural Assessment of Historical Buildings

ABSTRACT Supported by advancements in 3D scanning and parametric modeling tools within the architecture, engineering, and construction sectors, reverse engineering processes for cultural heritage (CH) have recently gained popularity. While many studies have focused on simple 3D reconstructions to create virtual environments, a specialized subarea of this research field has targeted more specific digitization objectives, including, among many, structural analysis of building components. This emerging field has not yet been systematically developed due to the intrinsic challenges associated with CH. Within this context, this paper proposes and describes a reverse engineering-based method that utilizes terrestrial laser scanning and visual programming (VP) to analyze displacements and deformations that occurred over time in historical masonry buildings and applied it to the timber trusses, masonry facades, and columns of two selected Italian case study buildings. This method allows for comparing the surveyed condition of these components, considered the “deformed state”, with their ideal configuration, reconstructed using VP algorithms, considered the “original state”. The outcomes of this comparison facilitate the investigation of the components’ structural behavior and support joint considerations to assess the overall condition of the investigated building, providing helpful knowledge for guiding structural improvement interventions.

Are your apps accessible? A GCN-based accessibility checker for low vision users

Context:Accessibility issues (e.g., small size and narrow interval) in mobile applications (apps) lead to obstacles for billions of low vision users in interacting with Graphical User Interfaces (GUIs). Although GUI accessibility scanning tools exist, most of them perform rule-based check relying on complex GUI hierarchies. This might make them detect invisible redundant information, cannot handle small deviations, omit similar components, and is hard to extend. Objective:In this paper, we propose a novel approach, named ALVIN (Accessibility Checker for Low Vision), which represents the GUI as a graph and adopts the Graph Convolutional Neural Networks (GCN) to label inaccessible components. Method:ALVIN removes invisible views to prevent detecting redundancy and uses annotations from low vision users to handle small deviations. Also, the GCN model could consider the relations between GUI components, connecting similar components and reducing the possibility of omission. ALVIN only requires users to annotate the relevant dataset when detecting new kinds of issues. Results:Our experiments on 48 apps demonstrate the effectiveness of ALVIN, with precision of 83.5%, recall of 78.9%, and F1-score of 81.2%, outperforming baseline methods. In RQ2, the usefulness is verified through 20 issues submitted to open-source apps. The RQ3 also illustrates the GCN model is better than other models. Conclusion:To summarize, our proposed approach can effectively detect accessibility issues in GUIs for low vision users, thereby guiding developers in fixing them efficiently.

Developing a classification system for brain tumors using the ResNet152V2 CNN model architecture

According to The American Cancer Society, in 2021 there were 24,530 cases of brain and nervous system tumors. The National Cancer Institute reports that there are approximately 4.4 new cases of brain tumors per 100,000 men and women per year. Brain tumors can be detected using magnetic resonance imaging (MRI), a scanning tool that uses a magnetic field and a computer to record brain images and is able to provide clear visualization of differences in soft tissue such as white matter and gray matter. However, this cannot be done optimally because it still relies on manual analysis, so it cannot classify brain tumor types on larger datasets with the potential for error and a low level of accuracy. To accurately determine the type of brain tumor, a better classification method is needed. The aim of this study is to determine the accuracy of brain tumor calcification using the deep learning model. In this study, the classification of brain tumor types was carried out using the ResNet152V2 convolutional neural network (CNN) model which has a depth of 152 layers. The dataset used in this study was 7,023 MRI images of brain tumors consisting of 1,645 meningiomas, 1,621 gliomas, 1,757 pituitary and 2,000 normal. Research results show an accuracy value of 94.44%, so it can be concluded that the ResNet152V2 model performs well in classifying brain tumor images and can be used as a medium for physicians to more accurately diagnose brain tumor patients more accurately.

Fast, Nondestructive and Precise Biomass Measurements Are Possible Using Lidar-Based Convex Hull and Voxelization Algorithms

Light detection and ranging (lidar) scanning tools are available that can make rapid digital estimations of biomass. Voxelization and convex hull are two algorithms used to calculate the volume of the scanned plant canopy, which is correlated with biomass, often the primary trait of interest. Voxelization splits the scans into regular-sized cubes, or voxels, whereas the convex hull algorithm creates a polygon mesh around the outermost points of the point cloud and calculates the volume within that mesh. In this study, digital estimates of biomass were correlated against hand-harvested biomass for field-grown corn, broom corn, and energy sorghum. Voxelization (r = 0.92) and convex hull (r = 0.95) both correlated well with plant dry biomass. Lidar data were also collected in a large breeding trial with nearly 900 genotypes of energy sorghum. In contrast to the manual harvest studies, digital biomass estimations correlated poorly with yield collected from a forage harvester for both voxel count (r = 0.32) and convex hull volume (r = 0.39). However, further analysis showed that the coefficient of variation (CV, a measure of variability) for harvester-based estimates of biomass was greater than the CV of the voxel and convex-hull-based biomass estimates, indicating that poor correlation was due to harvester imprecision, not digital estimations. Overall, results indicate that the lidar-based digital biomass estimates presented here are comparable or more precise than current approaches.

Revealing tick diversity: Chemical profiling and dynamics in scanning microscopy and molecular phylogenetics.

This study presents a comprehensive investigation into the evolutionary trajectories of Rhipicephalus ticks (Ixodidae) through the interpretation of molecular phylogenetics, elucidating their chromatographic spectrum. The use of advanced chromatographic tools in this study explored the dynamics chemical profiling, providing valuable insights into the evolutionary history and ecological adaptations. Prevalence of Rhipicephalus ticks was 4.5% in sheep and 3.9% in goats. The ITS2 sequence of the Rhipicephalus sanguineus (OK642408) and Rhipicephalus microplus (OK642409) form a distinct clade with sequences from other countries. The 16S rRNA sequences of R. sanguineus (OK560870) clustered with sequences form three lineages, tropical, temperate, and south-eastern. The Cox I gene-identified Rhipicephalus turanicus (OK623472) and R. microplus (OK623463) form separate clades with sequences. The HPLC chromatogram of tick samples reveals a diverse array of identified hydrocarbons, explained the complex chemical composition of their exoskeletons. This analytical approach provides valuable insights into the specific hydrocarbon profiles, allowing for potential applications in species differentiation, ecological studies, and a deeper understanding of the functional roles played by hydrocarbon compounds in tick physiology. The findings revealed the potential of applying molecular phylogenetics tools with chromatography not only to enhance our understanding of tick evolution but also to inform strategies for disease control and management in regions where Rhipicephalus ticks (Ixodidae) are endemic. RESEARCH HIGHLIGHTS: Chemical mapping utilizing advanced chromatographic techniques. Scanning microscopic insights high-resolution scanning tool to observe structural and morphological features of ticks at a molecular level. Molecular phylogeny data elucidate the evolutionary relationships among tick species.

Microservices Security Vulnerability Remediation approach using Veracode and Checkmarx

Abstract — As organizations increasingly adopt microservices architectures for building scalable and resilient applications, ensuring the security of these distributed systems becomes paramount. In this empirical study, we conduct a comprehensive comparative analysis to assess the efficacy of three leading security scanning tools, namely Veracode, Snyk, and Checkmarx, in identifying and remedying security vulnerabilities within microservices applications deployed on the AWS and Azure cloud platforms. The study aims to provide nuanced insights into the performance, usability, and integration capabilities of these tools, offering valuable guidance to organizations striving to fortify their microservices-based infrastructures. By meticulously evaluating scanning capabilities, vulnerability detection accuracy, remediation guidance comprehensiveness, CI/CD pipeline integration proficiency, and overall ease of use, our research sheds light on the relative strengths and weaknesses of each tool in the context of modern cloud-native application security. Through meticulously designed experiments utilizing realistic microservices application scenarios encompassing diverse vulnerability types, including injection attacks, authentication bypasses, and insecure configurations, we present a thorough examination of the tools' capabilities and limitations. The findings from our study contribute to the evolving discourse on microservices security, emphasizing the critical importance of selecting appropriate security scanning solutions tailored to the unique requirements and constraints of cloud-based microservices architectures. By leveraging the insights gleaned from our comparative analysis, organizations can make well-informed decisions regarding tool selection and deployment strategies, thereby bolstering the resilience of their microservices ecosystems against an ever-expanding threat landscape.

Data-Driven Network Anomaly Detection with Cyber Attack and Defense Visualization

The exponential growth in data volumes, combined with the inherent complexity of network algorithms, has drastically affected network security. Data activities are producing voluminous network logs that often mask critical vulnerabilities. Although there are efforts to address these hidden vulnerabilities, the solutions often come at high costs or increased complexities. In contrast, the potential of open-source tools, recognized for their security analysis capabilities, remains under-researched. These tools have the potential for detailed extraction of essential network components, and they strengthen network security. Addressing this gap, our paper proposes a data analytics-driven network anomaly detection model, which is uniquely complemented with a visualization layer, making the dynamics of cyberattacks and their subsequent defenses distinctive in near real-time. Our novel approach, based on network scanning tools and network discovery services, allows us to visualize the network based on how many IP-based networking devices are live, then we implement a data analytics-based intrusion detection system that scrutinizes all network connections. We then initiate mitigation measures, visually distinguishing malicious from benign connections using red and blue hues, respectively. Our experimental evaluation shows an F1 score of 97.9% and a minimal false positive rate of 0.3% in our model, demonstrating a marked improvement over existing research in this domain.

تعزيز الامن السيبراني من خلال اختبار الاختراق الفعال وفحص الثغرات

The number of computer network attacks today is increasing with the sophisticated attack tools and complicated methods hence, building secure systems is required. The demand for regular penetration testing and vulnerability scanning has become an urgent issue. This paper focuses on increase the security of the system resources being tested; Determine the weakness in the popular operating systems; Focus on methodologies and approaches to analyze the system for security that leads to protect the system against external threats. An experimental setup of a virtual penetration testing environment lab is created on a system using virtualization software. By using of the most powerful tools and techniques used today, a successful penetration testing and vulnerability scanning through three phases of processes are implemented. Eventually, the results of this process will help identify potential vulnerabilities in the operating systems and ways to patch them up. Keywords: Cybersecurity, penetration testing, vulnerability scanning tools.

CORRESPONDENCE MATCHING IN 3D MODELS FOR 3D HAND FITTING

Upper limb prosthetic is an area of medical research and development that aims to restore functionality and improve the quality of life of people affected by the loss of one or both upper limbs. The development and implementation of 3D scanning tools and analysis of 3D scanning data requires the use of specialized analysis methods that ensure the achievement of the required indicators. It should take into account the impact of the model resolution on the result. This paper is devoted to the analysis of finding matches between a point cloud of a hand model and another point cloud using Gromov-Wasserstein distance. For analysis, a subset of the MANO dataset was employed, containing a substantial volume of data and serving as a representative sample of the human population. The results obtained indicate the possibility of using this approach in the processing and analysis of three-dimensional data, which serves as one of the stages of designing individualized prostheses.

A dual computational and experimental strategy to enhance TSLP antibody affinity for improved asthma treatment.

Thymic stromal lymphopoietin is a key cytokine involved in the pathogenesis of asthma and other allergic diseases. Targeting TSLP and its signaling pathways is increasingly recognized as an effective strategy for asthma treatment. This study focused on enhancing the affinity of the T6 antibody, which specifically targets TSLP, by integrating computational and experimental methods. The initial affinity of the T6 antibody for TSLP was lower than the benchmark antibody AMG157. To improve this, we utilized alanine scanning, molecular docking, and computational tools including mCSM-PPI2 and GEO-PPI to identify critical amino acid residues for site-directed mutagenesis. Subsequent mutations and experimental validations resulted in an antibody with significantly enhanced blocking capacity against TSLP. Our findings demonstrate the potential of computer-assisted techniques in expediting antibody affinity maturation, thereby reducing both the time and cost of experiments. The integration of computational methods with experimental approaches holds great promise for the development of targeted therapeutic antibodies for TSLP-related diseases.

Exploring the Efficacy and Ethical Implications of Penetration Testing in Network Security

In today's landscape, cybersecurity reigns supreme as cybercrimes surge. Businesses are on high alert, striving to shield themselves from online threats like hacking and data breaches. Various safeguards are in place to brace for such scenarios. However, penetration testing emerges as the ultimate litmus test to gauge the efficacy of these security measures. A penetration tester must meticulously select the appropriate tools, considering factors like budget, time, and scope. This study scrutinizes the effectiveness of scanning tools within a Kali Linux environment. The overarching objective of penetration testing is to ensure networks and systems remain impervious to security threats, preventing unauthorized access to resources. This paper delves into the intricacies of planning and executing a penetration test, targeting project managers or directors contemplating such an endeavor. Undoubtedly, conducting a penetration test is a complex undertaking, and organizations must weigh the suitability of this method for their specific needs.

A framework for automating environmental vulnerability analysis of network services

The primary objective of this paper is to introduce a comprehensive framework designed to automate the assessment of environmental vulnerability status of communication protocols and networked services, within operational contexts. The proposed algorithm leverages the Common Vulnerability Scoring System version 3 (CVSS 3) metrics in conjunction with network security data. The initial step involves the establishment of a network security ontology, which serves to model the environmental attributes associated with the current security posture of communication protocol channels available within an infrastructure. The process commences with the identification and enumeration of all active communication services through a combination of diverse information gathering tools. Subsequently, active network services undergo assessment using a blend of passive scanning and active security analysis tools, which produce the environmental security score. This score can be integrated into vulnerability scoring systems such as CVSS, facilitating the fine-tuning of base CVSS scores, as well as vulnerability mitigation in real-world environments. To validate the proposed framework, we conducted testing across various networks and communication protocols within a controlled environment, thereby offering tangible illustrations for widely-utilized communication protocols.

Validity and reliability of Turkish version of the secondary screening instrument for targeting educational risk questionnaires

ABSTRACT Objective The Secondary Screening Instrument for Targeting Educational Risk Questionnaires (Secondary S.I.F.T.E.R.) is a scanning tool for teachers to evaluate the challenges and educational risks faced by deaf and hard of hearing (DHH) students. In Turkey, there is a need for a screening tool to identify academic risks among DHH students. This study aimed to adapt the Turkish version of the Secondary S.I.F.T.E.R. (Secondary S.I.F.T.E.R.-T) and evaluate its validity and reliability. Materials and Methods The Secondary S.I.F.T.E.R.-T was translated and cross-culturally adapted. Subsequently, the Secondary S.I.F.T.E.R.-T was administered by teachers of 104 DHH students (group 1) and 69 normal-hearing students (group 2) in the 6th to 12th grades to evaluate. Results In the validity analysis of the Secondary S.I.F.T.E.R.-T, the item-total correlation coefficients, content validity ratio, and index were found to be compatible with the literature, and the subscale and total scale scores of the two groups were significantly different. High internal consistency reliability was determined using Cronbach's alpha, split-half Spearman-Brown correlation, and Guttman's coefficients. A scoring grid was developed to identify low-, middle-, and high-performing students. Conclusions The validity and reliability analyzes of the Secondary-S.I.F.T.E.R.-T were found to be acceptable, and scoring grids were determined. Secondary-S.I.F.T.E.R.-T can be used as a screening tool to identify academic risks among DHH students in Turkey.

Improving Immunization Health Care Data Quality using Two-Dimensional Barcoding and Barcode Scanning Practices.

Manual data entry is time-consuming, inefficient, and error prone. In contrast, leveraging two-dimensional (2D) barcodes and barcode scanning tools is a rapid and effective practice for automatically entering vaccine data accurately and completely. CDC pilots documented clinical and public health impacts of 2D barcode scanning practices on data quality and completeness, time savings, workflow efficiencies, and staff experience. Data entry practices and entered records from routine and mass vaccination settings were analyzed. Data quality improvement opportunities were identified. A sample of 50 million emergency use authorization (EUA) coronavirus disease 2019 (COVID-19) vaccine records were analyzed for accuracy and completeness across three data fields: lot number, expiration date, and National Drug Code (NDC). The EUA COVID-19 vaccines lacked a 2D barcode containing these data fields, which necessitated manual data entry at administration. A CDC pilot at clinic compared scanned and manually entered data for routine vaccines across these same data fields. Analysis of 50 million manually entered EUA COVID-19 vaccine administration records indicated significant gaps in data accuracy and completeness across three data fields. Over half of the analyzed EUA vaccine NDCs (53%) and one-third of the expiration dates (35%) had missing or inaccurate data recorded. Pilot data also showed many errors when manually entered. However, when the pilot's routine vaccines were scanned (out of 71,969 records), nearly all entries were complete and accurate across all three data fields (ranging from 99.7% to 99.999% accurate). Vaccine 2D barcode scanning practices increased data accuracy and completeness (up to 99.999% accurate) across data fields assessed. When used consistently, vaccine 2D barcode scanning can resolve issues demonstrated in manually entered data. To realize these benefits, the immunization community should widely use scanning practices. To increase use, CDC developed a Vaccine 2D Barcode National Adoption Strategy and implementation resources.

Rapid risk assessment of plant pathogenic bacteria and protists likely to threaten agriculture, biodiversity and forestry in Zambia

A prioritisation study was conducted to address the lack of adequate information about potential pests likely to be introduced in Zambia and become invasive. The study was conducted by subject matter experts from relevant institutions in and outside Zambia. Although this study focused on major pest categories, this paper only addresses bacteria and Protista. A list of 306 bacterial and 10 Protista species adjudged to affect plants was generated using CABI’s Horizon Scanning Tool. The 316 (total) pest species were refined to focus on pests that affect value chains important to Zambia’s economy. This resulted in a final list of 133 bacteria and eight Protista. Four additional bacteria species considered of phytosanitary interest were added and all 137 bacteria and eight Protista species were subjected to a rapid risk assessment using agreed guidelines. Vectors reported to transmit any of the pathogenic organisms were also subjected to a risk assessment. A proportion of 53% (n = 77 of 145) comprising 73 bacteria and four Protista species were reported as present in Africa. Of these, 42 (57%, n = 73) bacterial species and two (n=4) Protista species were reported in neighbouring countries. Considering a cut-off of 54, the highest scoring pests were 40 bacteria (highest score of 140) and three Protista (highest score of 125). Three actions were suggested for high-scoring pests, a detection surveillance, a pest-initiated pest risk analysis (PRA) or a detection surveillance followed by pest-initiated PRA. A “no action” was suggested where the risk was very low although, for some pathogenic organisms, a “no action” was followed by periodic monitoring. This information will contribute towards proactive prevention and management of biological invasions.

Anticipating emerging medical technologies: The start of an international horizon scanning tool for medical devices

The governance of innovative medical technologies is fraught with uncertainties. Responsible governing bodies prepare for future advances by engaging in anticipatory practices aimed at knowing and acting earlier-on, but little is known about such work. We examine an anticipatory practice of a multinational, mostly European horizon scanning collaboration, drawing on the analytical framework of anticipatory governance and micro-regimes of anticipation. We distinguish four inter-related micro-regimes that emerge from participants’ expectations of a horizon scanning tool. We show how all micro-regimes relate to ideas about anticipatory governance that focus on prediction and reducing uncertainties. Moreover, participants’ expectations about the tool are vested on prior experiences with horizon scanning for pharmaceuticals. We argue that this may affect the governance of medical technologies that do not fit a linear approach, conventional for pharmaceuticals, but develop more iteratively. Finally, we highlight the importance as well as the complexity of engagement between different stakeholder groups. Overall, we conclude a need for anticipatory practices to become more reflexive about the consequences of future visions, the assumptions behind them, and the implications for the actions needed in the present. This would increase the relevance and value of policy tools developed for the governance of emerging medical technologies.