Level Of Expertise Research Articles

PIT-EASY survey: validation of the European Pituitary Pathology Group proposal for reporting pituitary neuroendocrine tumors.

The aim of this multicenter prospective survey called PIT-EASY was to assess the relevance of the European Pituitary Pathology Group (EPPG) diagnostic tools for pituitary neuroendocrine tumors (PitNETs) to improve the quality of their histological diagnosis. Each center performed at least 30 histological cases of PitNETs using the EPPG tools and assessed their value using a scorecard with 10 questions. For each center, the histological cases were carried out by pathologists with varying levels of expertise in pituitary pathology defined as junior, intermediate, and expert. Two hundred and ninety histological cases were collected from six French and Italian centers. The three EPPG tools were validated and regarded as helpful for a more accurate and time-efficient diagnosis. The usefulness of level 2 and level 3 of the "EPPG's multi-step approach for immunohistochemistry" including pituitary transcription factors (PIT1, TPIT, and SF1) and chromogranin, SSTRs, and P53 respectively was higher in "other non-functioning" (silent plurihormonal PIT1, silent corticotroph, and null cell): 88% vs 32%, p < 10-6 and 42% vs 14%, p = 0.002, respectively. The diagnostic algorithm proved more useful for junior pathologists (p = 0.0001) and those with intermediate experience. PIT-EASY survey confirmed the importance of a standardized approach to PitNETs for an accurate and reproducible diagnosis and served as validation of the EPPG proposal. The tool appeared to be of practical value to junior participants and staff with intermediate experience for safe routine diagnostic reporting.

Efficiency Divide: Comparative Analysis of Human &amp; Neural Network Algorithm Development

The paper delves into a comparative analysis between human and artificial intelligence (AI) capabilities in algorithm development, with a specific focus on the challenges presented in the "Advent of Code." The research thoroughly investigates the performance of Generative Pre-trained Transformers (GPTs), such as ChatGPT and Bard, in solving intricate algorithmic problems and benchmarks these results against those achieved by human participants. A sizeable portion of the study is dedicated to understanding the nuances of prompt engineering in AI and how it affects the problem-solving process, alongside exploring the choice of programming languages used by both AI and humans. The methodology of the research is extensive, involving the participation of both AI models and human subjects, who vary in their levels of programming expertise. This approach allows for a comprehensive evaluation of the correctness and efficiency of solutions, along with the time taken to resolve the given problems. The results from this study reveal intriguing insights. While AI models like GPTs demonstrate an impressive speed in problem resolution, they often fall short in accuracy when compared to human problem-solvers, particularly in tasks demanding deeper contextual understanding and creative reasoning. Furthermore, the study delves into the impact of time constraints on the effectiveness of problem-solving strategies employed by both AI and humans. It finds that under strict time constraints, AI models can quickly generate solutions, but these solutions may lack the depth and accuracy found in those devised by human participants. This aspect of the research highlights the trade-off between speed and precision in AI-driven problem solving. The research extends its implications beyond mere performance comparison. It suggests the potential for a synergistic approach where the computational efficiency and rapid problem-solving abilities of AI can be effectively combined with the nuanced understanding and creative problem-solving skills inherent in humans. This hybrid approach could redefine the future landscape of programming and algorithm development. The study not only provides a critical analysis of the capabilities of AI in the realm of algorithmic problem-solving but also paves the way for future exploration into the collaborative dynamics of human-AI interaction in programming. It highlights the evolving role of AI in programming and underscores the importance of balancing AI’s computational prowess with human creativity and adaptability in solving complex, real-world problems.

Comparing Relative Value Units for Intramedullary Limb Lengthening Procedures to Common Pediatric Orthopaedic Surgeries to Determine Adequate Compensation.

Reimbursement for surgical procedures is determined by a computation of the relative value unit (RVU) associated with CPT codes. It is based on the amount of work required to provide a service, resources available, and level of expertise involved. Given the evolution of changes in the limb lengthening field, we wanted to evaluate whether the RVU values were comparable across different orthopaedic subspecialties. Consequently, this study compares the work relative value unit (wRVU) totals of 3 common pediatric orthopaedic surgeries-arthroscopic ACL reconstruction, spinal fusion for adolescent idiopathic scoliosis, and antegrade femoral intramedullary limb lengthening. This was an IRB-approved, multicenter, retrospective chart review. Charts of subjects who had ACL reconstructions, including meniscal repairs; spinal fusion surgeries for adolescent idiopathic scoliosis (7 to 12 levels), including Ponte osteotomies, and femoral antegrade internal limb lengthening procedures, each completed by fellowship-trained orthopaedic surgeons were reviewed. Comparisons were carried out between several parameters, including mean duration per procedure, number of CPT codes billed per procedure, number of postoperative visits in the 90-day global period, and the wRVU for each procedure. Fifty charts (25 per center) per procedure were reviewed. The wRVU per hour was lowest in the antegrade femur lengthening group ( P < 0.0001). The number of postoperative visits in the 90-day global postsurgery period was significantly higher in the antegrade femur lengthening group ( P < 0.0001). Intramedullary limb lengthening also had the least number of CPT codes billed. RVUs per time are statistically lowest in the limb lengthening group and highest in the scoliosis group. The limb lengthening patient also requires significantly more visits and time in the postoperative period compared with the other groups. These extra visits during the global period do not add any RVU value to the lengthening surgeon and occupy clinic spots that could be filled with new patients. Based on these data, a review of the RVU values assigned to the limb lengthening codes may be necessary. Level III-retrospective comparison study.

Nucleus drop following phacoemulsification surgery: Incidence, risk factors and clinical outcomes.

To determine the incidence, risk factors and clinical outcomes in patients with nucleus drop (ND) following phacoemulsification surgery at a tertiary care ophthalmic institute. Medical records of patients with ND during phacoemulsification between January'2019 and December'2022 were reviewed retrospectively. Incidence of ND was calculated overall, and according to surgical expertise levels and surgical steps. Associated pre- and intra-operative risk factors were assessed. Clinical outcome parameters [best-corrected visual acuity (BCVA) and intraocular pressure (IOP)] were compared with respect to timing of PPV (immediate vs early vs delayed), location (sulcus vs scleral fixated-IOL) and timing of IOL placement (primary vs secondary). Among 93,760 phacoemulsifications, ND was noted in 130 patients (0.1%). Significant direct and associated pre-operative risk factors were advanced cataract, glaucoma, posterior polar cataract, and diabetes mellitus. Junior medical consultants had 40 ND (mostly in non-complex cases), whereas senior medical consultants had 90 ND (mostly in complex cases). ND most frequently occurred during emulsification of nuclear fragments. Sulcus placement of IOL during primary surgery yielded significantly better visual outcome compared to secondary procedures, although timing of PPV had insignificant effect. 77.1% had BCVA of 6/12 or better at final follow-up. Timing of IOL implantation, PPV, and type of IOL did not have any significant effect on IOP. Though the incidence of ND is low, it is a dreaded complication. Accurate pre-operative evaluation, risk stratification, distribution of cases among surgeons based on their surgical expertise, and immediate or early management of ND yields better anatomical and functional outcomes.

Behavioral and cortical dynamics underlying superior accuracy in short-distance passes

Improved pass accuracy is a prominent determinant of success in football. It demands an effective interaction of complex behavioral and cortical dynamics. Exploring differences in the ability to sustain an accurate pass behavior in a stable setting and the associated cortical dynamics at different expertise levels may provide an insight into skilled strategies contributing to superior accuracy in football. The aim of this study is to compare trial-to-trial variability of pass biomechanics and the corresponding cortical dynamics during short-distance passes between novices and experienced football players. Thirty participants (15 novices, 15 football players) performed 90 short-distance passes. The intertrial variability of pass biomechanics (foot acceleration, range of hip flexion, knee flexion and foot rotation) was assessed by means of multiscale entropy. The task-related cortical dynamics were analyzed via source-derived event-related spectral perturbations. Experienced players demonstrated higher accuracy and overall lower entropy values across multiple time scales which was significant for hip flexion. The electroencephalography data revealed group differences in parieto-occipital alpha desynchronization and frontal theta synchronization in successive phases of passes. The current findings suggest that experienced football players may show a skilled ability to recruit and retain pass biomechanics promoting higher accuracy, whereas novices may show an explorative behavior with higher spatial variability. This difference may be associated with distinctive visuospatial and attentional strategies acquired with expertise in football. Our study provides an insight into expertise-specific behavioral and cortical dynamics of superior accuracy in football and a basis for its prospective investigation in enriched contexts.

A deep blur learning-based motion artifact reduction algorithm for dental cone-beam computed tomography images

We propose a motion artifact correction algorithm (DMBL) for reducing motion artifacts in reconstructed dental cone-beam computed tomography (CBCT) images based on deep blur learning. A blur encoder was used to extract motion-related degradation features to model the degradation process caused by motion, and the obtained motion degradation features were imported in the artifact correction module for artifact removal. The artifact correction module adopts a joint learning framework for image blur removal and image blur simulation for treatment of spatially varying and random motion patterns. Comparative experiments were conducted to verify the effectiveness of the proposed method using both simulated motion data sets and clinical data sets. The experimental results with the simulated dataset showed that compared with the existing methods, the PSNR of the proposed method increased by 2.88%, the SSIM increased by 0.89%, and the RMSE decreased by 10.58%. The results with the clinical dataset showed that the proposed method achieved the highest expert level with a subjective image quality score of 4.417 (in a 5-point scale), significantly higher than those of the comparison methods. The proposed DMBL algorithm with a deep blur joint learning network structure can effectively reduce motion artifacts in dental CBCT images and achieve high-quality image restoration.

Enhancing Electronic Design Automation Tools with an ML-Based Information Retrieval System

Over the past fifty years, Electronic Design Automation (EDA) tools have played a crucial role in the semiconductor industry, assisting in the design, simulation, and manufacturing of integrated circuits (ICs). However, the sophisticated nature of these tools often demands extensive expertise, which can be a barrier for many users. Mastery of these tools necessitates specialized knowledge and skills, including comprehension of complex algorithms, design methodologies, and tool-specific workflows. To address this challenge, this paper introduces a machine learning (ML) based information retrieval system designed to enhance the usability of EDA tools. The objective of this system is to simplify user interactions and make EDA tools more accessible to designers, regardless of their expertise level. The main idea of this ML-driven system is to provide a chatbot-like interface that facilitates efficient, context-aware searches and offers interactive, step-by-step guidance on using various tool functionalities. By integrating natural language processing and machine learning techniques, the system can understand user queries, extract relevant information from the tool&amp;apos;s documentation, and provide context-specific guidance. This approach helps to mitigate the steep learning curve associated with advanced EDA applications and enhances tool accessibility. Consequently, it promotes a more intuitive interaction with sophisticated EDA software, thus fostering enhanced usability of complex tools in the semiconductor industry. This work exemplifies the transformative potential of integrating machine learning with conversational user interfaces in making sophisticated software applications more user-friendly.

Board gender diversity and cyber security disclosure in the Indonesian banking industry: a two-tier governance context

PurposeThis study aims to determine the effect of board gender diversity on cyber security disclosure (CSD) in the banking sector of Indonesia as a developing country that adheres to a two-tier system.Design/methodology/approachThis study uses a panel data of 47 banks listed on the Indonesia Stock Exchange from 2014 to 2021. The board gender diversity is measured by three proxies, the proportion of women on the board, BLAU Index value and the critical mass of women. The authors used generalized method of moments estimation to eliminate the simultaneous equation bias.FindingsThe results show that the women board of commissioners increases CSD, and the women of board of directors/top management team were significantly negative for CSD.Research limitations/implicationsFirst, this research was only conducted in the banking sector. The results cannot be generalized to non-financial companies. Second, there is no measurement of the quality of the board from the level of education, experience, expertise and other characteristics of diversity such as age, nationality and religion.Practical implicationsThe study has revealed the need for the government’s role in providing oversight of the presence of women on the board so that banks fully comply with Indonesia Financial Services Authority regulations. Banks should also actively launch policies regarding the presence of women on the board to give a positive effect to stakeholders that women play an important role in decision making. Banks must also adjust the composition of female commissioners with a threshold of two people to maximize their function as supervisors.Originality/valueThis is the first research conducted on the banking sector in Indonesia as a developing country that adheres to a two-tier system. The results of this study provide evidence that patriarchal culture is still dominant in Indonesia.

Phronesis in climate change and sustainability education and practice: Importance of the immeasurable goodness of a professional

AbstractSustainability transformation calls for actors and agents of all types from all organisations and subsectors of society. Thus, sustainability‐themed and focused education has become a staple of higher education institutions, and the professionals and expertise produced—through learning of all sorts—have become an integral part of various organisations and their attempts to enact sustainability. A whole range of research and associated educational programmes have been founded, and various frameworks, for example those at the intersection of higher education and sustainability, and professionalism and climate change, have been study and development foci. However, few attempts at qualitative reflection and assessment have been made in this important field with its vital hypothesis that higher level of education, expertise, and professionalism will lead to higher societal sustainability impact by those educated actors. In this article, I propose that the Aristotelian concept of phronesis—as contextual and practical wisdom—can offer a framing for both reasoning about and assessing the qualitative side of being a climate change and sustainability professional. The conditions of being a sustainability professional, and the essence of phronesis thereof, are analysed from thematic in‐depth interviews with high‐level professionals, representing different organisations from various societal subsectors.

From Audience to Evaluator: When Visibility into Prior Evaluations Leads to Convergence or Divergence in Subsequent Evaluations Among Professionals

Collective evaluation processes, which offer individuals an opportunity to assess quality, have transcended mainstream sectors (e.g., books, restaurants) to permeate professional contexts from within and across organizations to the gig economy. This paper introduces a theoretical framework to understand how evaluators’ visibility into prior evaluations influences the subsequent evaluation process: the likelihood of evaluating at all and the value of the evaluations that end up being submitted. Central to this discussion are the conditions under which evaluations converge—are more similar to prior evaluations—or diverge—are less similar—as well as the mechanisms driving observed outcomes. Using a quasinatural experiment on a platform where investment professionals submit and evaluate investment recommendations, I compare evaluations that are made with and without the possibility of prior ratings influencing the subsequent evaluation process. I find that when prior ratings are visible, convergence occurs. The visibility of prior evaluations decreases the likelihood that a subsequent evaluation occurs by about 50%, and subsequent evaluations become 54%–63% closer to the visible rating. Further analysis suggests that peer deference is a dominant mechanism driving convergence, and only professionals with specialized expertise resist peer deference. Notably, there is no evidence that initial ratings are related to long-term performance. Thus, in this context, convergence distorts the available quality signal for a recommendation. These findings underscore how the structure of evaluation processes can perpetuate initial stratification, even among professionals with baseline levels of expertise. Supplemental Material: The online appendix is available at https://doi.org/10.1287/orsc.2017.11285 .

Multi-objective Evolutionary Neural Architecture Search for Recurrent Neural Networks

Artificial neural network (NN) architecture design is a nontrivial and time-consuming task that often requires a high level of human expertise. Neural architecture search (NAS) serves to automate the design of NN architectures and has proven to be successful in automatically finding NN architectures that outperform those manually designed by human experts. NN architecture performance can be quantified based on multiple objectives, which include model accuracy and some NN architecture complexity objectives, among others. The majority of modern NAS methods that consider multiple objectives for NN architecture performance evaluation are concerned with automated feed forward NN architecture design, which leaves multi-objective automated recurrent neural network (RNN) architecture design unexplored. RNNs are important for modeling sequential datasets, and prominent within the natural language processing domain. It is often the case in real world implementations of machine learning and NNs that a reasonable trade-off is accepted for marginally reduced model accuracy in favour of lower computational resources demanded by the model. This paper proposes a multi-objective evolutionary algorithm-based RNN architecture search method. The proposed method relies on approximate network morphisms for RNN architecture complexity optimisation during evolution. The results show that the proposed method is capable of finding novel RNN architectures with comparable performance to state-of-the-art manually designed RNN architectures, but with reduced computational demand.

Optimizing cost of quality in commercial projects using fuzzy expert system

ABSTRACT Previous studies on the Cost of Quality (COQ) have focused on direct costs, employing prevention, appraisal, and failure costs (PAF). However, the advent of ISO 9001–2015] edition underscores the importance of the risk approach in quality management, necessitating the inclusion of project risks in COQ calculation. This paper introduces a prototype Fuzzy Expert System (FES) model tailored for determining optimal COQ in commercial projects while accounting for project risks. Using the means of the Prevention - Appraisal - Failure model and utilizing data gathered from prior research and validated through official questionnaires and interviews, this study assigns weights to quality risks and allocates them to respective COQ components. Risks such as inadequate planning, coordination issues, construction errors, and delays are identified as influential factors affecting COQ. Our findings suggest that COQ in commercial projects typically amounts to approximately five percent of the project budget. Validation of the prototype Fuzzy Expert System achieved through application to five actual cases (including two for system familiarization and three for validation), demonstrates an accuracy rate of 95%. The prototype Fuzzy Expert System emerges as a valuable tool for COQ evaluation, serving as a reliable checkpoint for individuals with varying levels of expertise in the field.

Visual analysis of fitness landscapes in architectural design optimization

In architectural design optimization, fitness landscapes are used to visualize design space parameters in relation to one or more objective functions for which they are being optimized. In our design study with domain experts, we developed a visual analytics framework for exploring and analyzing fitness landscapes spanning data, projection, and visualization layers. Within the data layer, we employ two surrogate models and three sampling strategies to efficiently generate a wide array of landscapes. On the projection layer, we use star coordinates and UMAP as two alternative methods for obtaining a 2D embedding of the design space. Our interactive user interface can visualize fitness landscapes as a continuous density map or a discrete glyph-based map. We investigate the influence of surrogate models and sampling strategies on the resulting fitness landscapes in a parameter study. Additionally, we present findings from a user study (N = 12), revealing how experts’ preferences regarding projection methods and visual representations may be influenced by their level of expertise, characteristics of the techniques, and the specific task at hand. Furthermore, we demonstrate the usability and usefulness of our framework by a case study from the architecture domain, involving one domain expert.

Site Management Strategy in Highway Project Management

Highway project management involves overseeing the on-site construction of a highway project, taking into account the specific circumstances and conditions of the site. This type of management requires a high level of expertise and is characterized by its dynamic and systematic approach. Effective on-site management can ensure the quality, cost-saving, smooth progress, and safety of highway construction. However, there are still some problems in the implementation of site management in some highway projects, which seriously affect the improvement of site management. This paper analyzes the characteristics and existing problems of highway engineering management, and puts forward the effective strategies of site management, hoping that this study will help to improve highway engineering site management.

Point-of-care testing: state-of-the art and perspectives.

Point-of-care testing (POCT) is becoming an increasingly popular way to perform laboratory tests closer to the patient. This option has several recognized advantages, such as accessibility, portability, speed, convenience, ease of use, ever-growing test panels, lower cumulative healthcare costs when used within appropriate clinical pathways, better patient empowerment and engagement, and reduction of certain pre-analytical errors, especially those related to specimen transportation. On the other hand, POCT also poses some limitations and risks, namely the risk of lower accuracy and reliability compared to traditional laboratory tests, quality control and connectivity issues, high dependence on operators (with varying levels of expertise or training), challenges related to patient data management, higher costs per individual test, regulatory and compliance issues such as the need for appropriate validation prior to clinical use (especially for rapid diagnostic tests; RDTs), as well as additional preanalytical sources of error that may remain undetected in this type of testing, which is usually based on whole blood samples (i.e.,presence of interfering substances, clotting, hemolysis, etc.). There is no doubt that POCT is a breakthrough innovation in laboratory medicine, but the discussion on its appropriate use requires further debate and initiatives. This collective opinion paper, composed of abstracts of the lectures presented at the two-day expert meeting "Point-Of-Care-Testing: State of the Art and Perspective" (Venice, April 4-5, 2024), aims to provide a thoughtful overview of the state-of-the-art in POCT, its current applications, advantages and potential limitations, as well as some interesting reflections on the future perspectives of this particular field of laboratory medicine.

Usability Testing of a Web Tool for Dissemination and Implementation Science Models

Dissemination and Implementation science is dedicated to increasing the speed of evidence-based research translated into practice as guided by one or multiple D&I theories, models, and frameworks. The Dissemination and Implementation Models in Health Research and Practice web tool guides users on how to plan, select, combine, adapt, use, and assess theories, models, and frameworks. This paper describes usability testing to update the web tool. Iterative user testing was conducted with implementation science research and clinical participants to facilitate updates and optimize the functionality of the tool. A multi-step protocol involved quantitative and qualitative data collection including a survey, interviews, and a usability testing session. Data from the pre-testing surveys were summarized as frequencies. Data from the usability testing sessions were analyzed using a hybrid adapted deductive rapid matrix qualitative analysis. Data from the interviews were analyzed by deductive a priori coding. Fifteen interviewees represented different research and clinical groups and levels of expertise utilizing D&I TMFs. Participants were purposively selected to represent a range of disciplines and D&I expertise, all invited via one-time email. The 847 total interview comments were reduced by similarity to 259 comments, and 142 were feasible changes fitting the priorities of the web tool. Changes to content, format, and functionality are described in this paper. The iterative usability testing elicited improvements to the web tool including adding more examples, definitions, visuals, and tutorials and simplifying the written content. The web tool remains flexible for additions concerning health equity, de-implementation, and other issues.

Sustainable Management of Manufacturing Processes: A Literature Review

Industrial economic activity is one of the primary causes of environmental degradation and a source of issues related to social inequality. Nevertheless, research has shown that the application of the sustainable entrepreneurship model can significantly improve environmental and social conditions while maintaining continuous and sustainable economic growth. Despite that, statistics show relatively low engagement in sustainable entrepreneurship, potentially due to a lack of expertise among entrepreneurs. Due to the low engagement, available sustainability performance metrics are not a relevant measurement tool. Thus, the purpose of this study is to identify a set of criteria that evaluate expertise levels in sustainable entrepreneurship within the manufacturing industry. Through the application of monographic and secondary data analysis methods, the authors carried out a literature review. The findings indicated that sustainable management is a key component to the triple-bottom-line (TBL) framework that is used as a conceptual basis for sustainable entrepreneurship. Additionally, the authors identified 19 criteria of sustainable entrepreneurship within four dimensions of sustainability (environmental, social, economic, and management). Despite the limitations, with a more refined literature review, the criteria could be applied as a comprehensive resource to estimate expertise in sustainable entrepreneurship in future studies on small and medium enterprises (SMEs).

Simple Determination of Affinity Constants of Antibodies by Competitive Immunoassays.

The affinity constant, also known as the equilibrium constant, binding constant, equilibrium association constant, or the reciprocal value, the equilibrium dissociation constant (Kd), can be considered as one of the most important characteristics for any antibody-antigen pair. Many methods based on different technologies have been proposed and used to determine this value. However, since a very large number of publications and commercial datasheets do not include this information, significant obstacles in performing such measurements seem to exist. In other cases where such data are reported, the results have often proved to be unreliable. This situation may indicate that most of the technologies available today require a high level of expertise and effort that does not seem to be available in many laboratories. In this paper, we present a simple approach based on standard immunoassay technology that is easy and quick to perform. It relies on the effect that the molar IC50 approaches the Kd value in the case of infinitely small concentrations of the reagent concentrations. A two-dimensional dilution of the reagents leads to an asymptotic convergence to Kd. The approach has some similarity to the well-known checkerboard titration used for the optimization of immunoassays. A well-known antibody against the FLAG peptide, clone M2, was used as a model system and the results were compared with other methods. This approach could be used in any case where a competitive assay is available or can be developed. The determination of an affinity constant should belong to the crucial parameters in any quality control of antibody-related products and assays and should be mandatory in papers using immunochemical protocols.

Website for Online Election System

The goal of this article is to present the design and execution of an online voting system that will improve the election process's accessibility, security, and efficiency. The integrity and confidentiality of votes cast are guaranteed by the proposed system, which makes use of secure web technologies and contemporary cryptography techniques. Real-time result tabulation, end-to-end encryption, and user authentication are important features. Regardless of their level of technical expertise, voters may easily traverse the platform thanks to the user-friendly design of the system. Strong safeguards are also put in place to guard against fraud and guarantee voters' confidentiality, answering worries about the possibility of cyberattacks and illegal access. The online voting system has undergone extensive testing and review, and the results show that it significantly outperforms traditional voting systems in terms of voter participation rates, convenience, and accuracy.

HORA 3D: Personalized Flood Risk Visualization as an Interactive Web Service

AbstractWe propose an interactive web‐based application to inform the general public about personal flood risks. Flooding is the natural hazard affecting most people worldwide. Protection against flooding is not limited to mitigation measures, but also includes communicating its risks to affected individuals to raise awareness and preparedness for its adverse effects. Until now, this is mostly done with static and indiscriminate 2D maps of the water depth. These flood hazard maps can be difficult to interpret and the user has to derive a personal flood risk based on prior knowledge. In addition to the hazard, the flood risk has to consider the exposure of the own house and premises to high water depths and flow velocities as well as the vulnerability of particular parts. Our application is centered around an interactive personalized visualization to raise awareness of these risk factors for an object of interest. We carefully extract and show only the relevant information from large precomputed flood simulation and geospatial data to keep the visualization simple and comprehensible. To achieve this goal, we extend various existing approaches and combine them with new real‐time visualization and interaction techniques in 3D. A new view‐dependent focus+context design guides user attention and supports an intuitive interpretation of the visualization to perform predefined exploration tasks. HORA 3D enables users to individually inform themselves about their flood risks. We evaluated the user experience through a broad online survey with 87 participants of different levels of expertise, who rated the helpfulness of the application with 4.7 out of 5 on average.