Expert Users Research Articles

Arbitrageurs in the Bitcoin ecosystem: Evidence from user-level trading patterns in the Mt. Gox exchange platform

We mine the leaked history of trades on Mt. Gox, the dominant Bitcoin exchange from 2011 to early 2014, in order to detect the triangular arbitrage conducted on the platform. To this end, we exploit user identifiers per trade to identify and describe the individual trading patterns of 440 arbitrageurs. Moreover, we introduce proxies for expertise and document that the expert users' distribution of profits first-order stochastically dominates that of non-expert users. Most importantly, by including user fixed effects, we show that expert users make profits on arbitrage by reacting quickly to plausible exogenous variations on the official exchange rates. A small number of expert arbitrageurs are able to conduct the vast majority of the arbitrage actions and systematically yield higher profits: our results provide empirical evidence that arbitrageurs are few and sophisticated users, characterized by the ability to incorporate information and to quickly react to exogenous shocks within short time scale intervals.

Pengaruh Efektivitas Sistem Informasi Akuntansi, Kesesuaian Tugas Dan Keahlian Pemakai Terhadap Kinerja Karyawan

This study aims to determine the effect of accounting information systems on employee performance, to determine the effect of task suitability on employee performance, and to determine the effect of user expertise on employee performance. The sample in this study were all company employees at Astra Motor Denpasar Branch as many as 50 respondents. This research was conducted at Astra Motor Denpasar Branch. Data collection is done through a questionnaire. The analysis technique used is multiple linear regression. The results showed that there was a positive and significant influence between the effectiveness of the accounting information system on the performance of PT. Astra International Tbk-Honda (Astra Motor Denpasar Branch). There is a positive and significant influence between Task Conformity on Performance at PT. Astra International Tbk-Honda (Astra Motor Denpasar Branch). There is a positive and significant influence between User Expertise on Performance at PT. Astra International Tbk-Honda (Astra Motor Denpasar Branch).

Evaluating the usability of a co-designed power assisted exercise graphical user interface for people with stroke

BackgroundDigital advancement of power assisted exercise equipment will advance exercise prescription for people with stroke (PwS). This article reports on the remote usability evaluation of a co-designed graphical user interface (GUI) and denotes an example of how video-conference software can increase reach to participants in the testing of rehabilitation technologies. The aim of this study was to evaluate the usability of two sequential versions of the GUI.MethodsWe adopted a mixed methods approach. Ten professional user (PU) (2M/8F) and 10 expert user (EU) participants (2M/8F) were recruited. Data collection included a usability observation, a ‘think aloud’ walk through, task completion, task duration and user satisfaction as indicated by the Post Study System Usability Questionnaire (PSSUQ). Identification of usability issues informed the design of version 2 which included an additional submenu. Descriptive analysis was conducted upon usability issues and number of occurrences detected on both versions of the GUI. Inferential analysis enabled comparison of task duration and PSSUQ data between the PU and EU groups.ResultsAnalysis of the ‘think aloud’ walkthrough data enabled identification of 22 usability issues on version 1 from a total of 100 usability occurrences. Task completion for all tasks was 100%. Eight usability issues were directly addressed in the development of version 2. Two recurrent and 24 new usability issues were detected in version 2 with a total of 86 usability occurrences. Paired two tailed T-tests on task duration data indicated a significant decrease amongst the EU group for task 1.1 on version 2 (P = 0.03). The mean PSSUQ scores for version 1 was 1.44 (EU group) and 1.63 (PU group) compared with 1.40 (EU group) and 1.41 (PU group) for version 2.ConclusionsThe usability evaluation enabled identification of usability issues on version 1 of the GUI which were effectively addressed on the iteration of version 2. Testing of version 2 identified usability issues within the new submenu. Application of multiple usability evaluation methods was effective in identifying and addressing usability issues in the GUI to improve the experience of PAE for PwS. The use of video-conference software to conduct synchronous, remote usability testing is an effective alternative to face to face testing methods.

Artificial Intelligence in Evaluation of Permanent Impairment: New Operational Frontiers.

Artificial intelligence (AI) and machine learning (ML) span multiple disciplines, including the medico-legal sciences, also with reference to the concept of disease and disability. In this context, the International Classification of Diseases, Injuries, and Causes of Death (ICD) is a standard for the classification of diseases and related problems developed by the World Health Organization (WHO), and it represents a valid tool for statistical and epidemiological studies. Indeed, the International Classification of Functioning, Disability, and Health (ICF) is outlined as a classification that aims to describe the state of health of people in relation to their existential spheres (social, family, work). This paper lays the foundations for proposing an operating model for the use of AI in the assessment of impairments with the aim of making the information system as homogeneous as possible, starting from the main coding systems of the reference pathologies and functional damages. Providing a scientific basis for the understanding and study of health, as well as establishing a common language for the assessment of disability in its various meanings through AI systems, will allow for the improvement and standardization of communication between the various expert users.

A novel mobile health application to support cancer surveillance needs of patients and families with cancer predisposition syndromes.

At least 5%-10% of malignancies occur secondary to an underlying cancer predisposition syndrome (CPS). For these families, cancer surveillance is recommended with the goal of identifying malignancy earlier, in a presumably more curable form. Surveillance protocols, including imaging studies, bloodwork, and procedures, can be complex and differ based on age, gender, and syndrome, which adversely affect adherence. Mobile health (mHealth) applications (apps) have been utilized in oncology and could help to facilitate adherence to cancer surveillance protocols. Applying a user-centered mobile app design approach, patients with a CPS and/or primary caregivers were interviewed to identify current methods for care management and barriers to compliance with recommended surveillance protocols. Broad themes from these interviews informed the design of the mobile app, HomeTown, which was subsequently evaluated by usability experts. The design was then converted into software code in phases, evaluated by patients and caregivers in an iterative fashion. User population growth and app usage data were assessed. Common themes identified included general distress surrounding surveillance protocol scheduling and results, difficulty remembering medical history, assembling a care team, and seeking resources for self-education. These themes were translated into specific functional app features, including push reminders, syndrome-specific surveillance recommendations, ability to annotate visits and results, storage of medical histories, and links to reliable educational resources. Families with CPS demonstrate a desire for mHealth tools to facilitate adherence to cancer surveillance protocols, reduce related distress, relay medical information, and provide educational resources. HomeTown may be a useful tool for engaging this patient population.

Sharing the knowledge: exploring cultural heritage through an ontology-based platform

AbstractThe exponential growth of data has made ontology modeling an up-and-coming research area for knowledge representation. One of the domains of interest is represented by Cultural Heritage, in which modeling of environments, such as archaeological parks, could lead to their protection and enhancement. In this scenario, the valuable dataset on the Naples Urban Archaeological Park (PAUN) is of particular interest. Due to its peculiarity, the database could benefit from innovative techniques for retrieving the information, representing a large part of the information contained within the DatabencArt platform, which collects data on Campania's cultural heritage. This paper aims to introduce an ontology-based approach to improve data retrieval, which could help expert users in the field (archaeologists, art historians, geologists, etc.). Scholars need to be able to easily compare the information. To this end, a semantic search is able to transform a vast amount of data into linked concepts. We mapped all concepts according to the ArCo ontology and equipped the DatabencArt platform with a semantic query layer, making the information accessible through ArCo ontology's properties. To evaluate the proposed approach, we designed an experimental campaign involving expert users to assess the results obtained in the system's queries through Sparql queries. The obtained results are promising.

Penegakan Diagnosa Keperawatan pada Pasien Gastroesofagus Reflux Disease dengan Aplikasi Sistem Pakar Forward Chaining

This study aimed to see the use of a computer-based expert system with the forward channeling method for detecting Gastroesophageal Reflux Disease and its application in nursing. The research method used in writing this literature review is based on 25 pieces of literature from 2018-2022. This expert system is run with PHP and MYSQL programs. The results of this study are that the Forward Chaining Expert System program will detect gastroesophageal reflux disease based on the symptoms inputted into the system and display an intervention plan that can be carried out like an expert. Still, this system cannot yet display 100% appropriate disease diagnosis because it requires a series of other tests that have not been prepared. In conclusion, with this expert system technology, health workers and users are increasingly assisted and shorten the time needed for disease diagnosis and treatment.
 
 Keywords: Expert System, Forward Chaining, Gastroesophageal Reflux Disease

Design and Website Design for Vivree Photography

Vivree Photography is a wedding photography service domiciled in Surabaya with a target market of millennials aged 22 - 30 years. Vivree Photography has been established since 2019 and has been running a photography business that focuses on wedding photography and has met many consumers. After evaluating this professional impression, the website is felt to be very important because it is an image and also to be trusted in the field of business that is involved by looking at other competitors. The website as a professional image also enables competitors to create marketing to increase customer engagement. This research uses a literature study in the form of a literature review using national and international journals and books that discuss websites for business, UI / UX on websites and customer engagement. In addition, the literature review method is also carried out using quantitative methods with expert users who are competent in technology and photography, extreme users who have used the services of Vivree Photography, and a discussion group forum by 15 people aged 22-30 years to review the results of websites that have been published. create and provide further suggestions for the Vivree Photograph website. Therefore, from the results of the research, it can be concluded that website design and UI / UX design which has a monochrome minimalist design style and has a warm tone and emphasis on writing and features on the website are very important for a business website, especially a service business in order to improve marketing and also customers. engagement Vivree Photography.
 
 Keywords: Professional, Design, Website, Promotional Media, Consumer

A METHOD FOR ESTIMATING THE ACCURACY OF THE TIE POINTS CLOUD: THE CASE STUDY OF PALAZZO DE FALCO

Abstract. In the context of cultural heritage documentation and conservation, an accurate 3D model is essential to properly describe an artifact of historical relevance and provide the expert user with verified metric data. In this paper we propose a workflow aimed at estimating errors in the image orientation steps, which is useful for improving the accuracy of the sparse cloud (composed of Tie Points). Some parameters provided as input to the processing software, often overlooked by the user in favour of the default ones, are analysed. Specifically, tests are carried out using the commercial software package Agisoft Metashape and involve the analysis of three parameters: (i) the accuracy of measuring the Ground Control Points (GCPs) provided by the user; (ii) the accuracy of the Tie Points (TPs) computed by the software; and (iii) the accuracy of collimating the GCPs on the image. The accuracy of the GCPs is estimated by implementing the formulas of error propagation in MATLAB, considering the three concurring sources of error: the coordinate measurement with the Total Station (TS), the coordinate transformation from the local system to the UTM/ETRF00 System, and the GNSS measurement for estimating the transformation parameters. The transformation parameters are calculated using the Bursa Wolf method. The collimation accuracy of GCPs on the image is estimated by the reprojection error of each single GCPs on the image plane. The accuracy of the GCPs will be estimated by the standard error per unit weight (SEUW), accurate GCPs being expected to have a SEUW value close to unity. SEUW is also the overall indicator for estimating the accuracy of the TPS cloud because it is influenced, in different ways, by all three parameters analysed. Test Area is a historic building located in the town of Fisciano, a few kilometres from the city of Salerno, known as Palazzo De Falco. The results highlight how these parameters, if not properly considered, can significantly affect the final 3D model.

Penerapan Augmented Reality sebagai Media Pembelajaran Perubahan Wujud Benda untuk Sekolah Dasar Berbasis Android

The COVID 19 outbreak has had a tremendous impact. In Indonesia in the education sector, face-to-face learning in the classroom is replaced by online learning by utilizing internet applications and technology. With such sudden changes in learning methods, UNICEF Indonesia and the Ministry of Religious Affairs of the Republic of Indonesia conducted a survey on the level of development and satisfaction in learning, and the results in elementary school learning are very many children who find it difficult to learn and understand the material, especially materials that require time, practice and reasoning, as well as material Changes in The Form of Objects in the subjects of Natural Sciences Elementary School Third grade. With such case studies, the authors provide solutions by providing more interesting and easy-to-understand learning methods, namely using the concept of Augmented Reality. This android-based Perwuda CPG app combines the concept of markers in the form of picture cards with Augmented Reality apps. Markers contained on the picture card will be captured by the camera from the smartphone device and then processed to bring up 3D objects and some 3D objects that have been animated from changes in the shape of objects on the layer in realtime. The results of this Perwuda CPG trial on several types of smartphones show that all functions in the Application are running well. Then for user satisfaction researchers use heuristic evaluation method with 10 heuristics to three expert usability evaluators. There are 30 questions for each evaluator, in evaluating the application evaluators find problems, but the problem is found by some evaluators only, where the first evaluator finds 17 problems, the second evaluator finds 6 problems, the third evaluator finds 12 problems. The results of each evaluator in the number and in average so as to produce the highest severity rating for CPG application problems that is in visibility of system status with an average number of values - average value of 1.22, and severity rating is the lowest consistency and standards with an average number - average value of 0.22

Aplikasi Perhitungan Penyusutan Inventaris Barang menggunakan Decreasing Charge Method Berbasis Website

The Fajar Sejahtera Indonesia Foundation is a non-governmental social organization that has assets in four districts in the city of Medan. However, every asset in the form of goods experiences a decrease in value each year. Calculating depreciation for purchasing planning is still difficult as it is done manually on paper. The research objective is to create an asset depreciation financing application using the decreasing charge method based on a website. The programming is done using PHP, MySQL, XAMPP, and Visual Studio Code, employing the research and development method, including potential and problem identification, data collection, product design, design validation, design revision, product testing, product revision, usage testing, final product revision, and mass production. However, this research is limited to the product testing phase. Data collection techniques involve interviews, observations, and literature reviews. The system is tested using a validation test divided into three parts: expert validation, field testing, and user testing. Our findings resulted in a web-based application for calculating inventory depreciation. The expert validation test yielded a score of 3.8, indicating high validity; the field testing resulted in a score of 4.0, indicating high validity; and the user testing with five participants achieved a score of 83.2%, indicating high effectiveness.

Fully Automated 3D echocardiographic algorithms: accurate and time saving - the answer for 3D in routine clinical practice?

Abstract Funding Acknowledgements Type of funding sources: None. Introduction 3D left ventricular ejection fraction (LVEF) quantification methods are more accurate and reproducible than 2D echocardiography, however, conventional 3D is time consuming and requires extensive user expertise, thus hindering its routine implementation in busy echocardiography laboratories and its use by inexperienced physicians. HeartModel A.I. (HM) is a simple, fast, recently validated 3D automated analysis software that detects LV endocardial surfaces and calculates LVEF. The aim of this work is to evaluate the performance of HM with experienced and inexperienced physicians, its time saving potential and to assess whether this software can be a better alternative to 2D measurements in routine echocardiography. Methods Prospective analysis of 30 nonconsecutive patients referred for transthoracic echocardiogram in a university hospital echocardiography lab, from 1st February 2021 to 31st March 2021. 2D biplane LVEF was measured by an experienced and inexperienced physician (less than 250 echocardiograms performed), then the same physicians used the automated analysis software to assess LVEF (blinded for each other results). The time to make the measurements was registered. Comparisons of agreement between LVEF measurements (experienced versus inexperienced physicians) included linear regression with Pearson correlation coefficients and Bland-Altman analyses to assess the bias and limits of agreement (defined as 2SD around the mean). Results A total of 30 patients were included, mean age of 68.6 ± 20.1 years and 60% male. HM showed significantly lower acquisition times in both inexperienced (72±17s versus 173± 44s, P<0.01) and experienced (56±12s versus 126±29s, P<0.01) physicians. The difference in time of acquisition between 2D and HM was approximately 101s for inexperienced users and around 70s for experienced users. Regarding LVEF assessment, HM acquisitions compared to 2D measurements showed stronger correlations between experienced and inexperienced physicians (r= 0,98, P<0,01 versus r= 0,92, P<0,01) with minimal bias (−0,5 versus −0,6) and stronger agreement (HM limits of agreement: ± 5,8% versus 2D limits of agreement: ± 12,5%) Conclusion 3D LVEF assessment by HM significantly reduced acquisition times and exhibited higher interobserver agreement than 2D Simpson’s biplane method. These results suggest that automated 3D algorithms, such as HM, may play a key role in implementing 3D measurements in routine practice in busy echocardiography laboratories and allow the use of 3D echocardiography at early stages of physicians training.

IMPLEMENTASI APLIKASI SISTEM PAKAR MENGGUNAKAN METODE FORWARD CHAINING

The Expert System is a solution that helps parents diagnose possible hyperactivity in their children from an early age. This expert system is designed for several stages including system requirements analysis, design, coding and testing. This expert system is represented in the form of rules and its reasoning method is forward chaining, namely H. concludes hyperactivity from several symptoms. The result of the system is a kind of hyperactivity from diagnosis and solution to diagnosis. In operation, this system has two user rights, namely expert users and ordinary users. Experts in this system are child development experts. This system can process disease information, symptoms and context. The result of this system is the result of a diagnosis of hyperactivity based on the presence of symptoms.
 Hyperactivity Disorder is a pattern of behavior in someone who doesn't want to relax. Hyperactive children are children with attention deficit disorder. Hyperactivity or Attention Deficit Hyperactivity Disorder (ADHD) or also known as Hyperkinetic Disorder. Hyperactivity is a combination of attention problems, hyperactivity and impulsive behavior. The diagnosis of hyperactivity is usually made by a doctor or child development specialist. In fact, parents can also diagnose early the possibility of hyperactivity in children by observing their child's behavior in everyday life, especially through the way they communicate and interact socially. with peers and children's imagination.

Simulating soil salinity dynamics, cotton yield and evapotranspiration under drip irrigation by ensemble machine learning.

Cotton is widely used in textile, decoration, and industry, but it is also threatened by soil salinization. Drip irrigation plays an important role in improving water and fertilization utilization efficiency and ensuring crop production in arid areas. Accurate prediction of soil salinity and crop evapotranspiration under drip irrigation is essential to guide water management practices in arid and saline areas. However, traditional hydrological models such as Hydrus require more variety of input parameters and user expertise, which limits its application in practice, and machine learning (ML) provides a potential alternative. Based on a global dataset collected from 134 pieces of literature, we proposed a method to comprehensively simulate soil salinity, evapotranspiration (ET) and cotton yield. Results showed that it was recommended to predict soil salinity, crop evapotranspiration and cotton yield based on soil data (bulk density), meteorological factors, irrigation data and other data. Among them, meteorological factors include annual average temperature, total precipitation, year. Irrigation data include salinity in irrigation water, soil matric potential and irrigation water volume, while other data include soil depth, distance from dripper, days after sowing (for EC and soil salinity), fertilization rate (for yield and ET). The accuracy of the model has reached a satisfactory level, R2 in 0.78-0.99. The performance of stacking ensemble ML was better than that of a single model, i.e., gradient boosting decision tree (GBDT); random forest (RF); extreme gradient boosting regression (XGBR), with R2 increased by 0.02%-19.31%. In all input combinations, other data have a greater impact on the model accuracy, while the RMSE of the S1 scenario (input without meteorological factors) without meteorological data has little difference, which is -34.22%~19.20% higher than that of full input. Given the wide application of drip irrigation in cotton, we recommend the application of ensemble ML to predict soil salinity and crop evapotranspiration, thus serving as the basis for adjusting the irrigation schedule.

Created, Intended, Articulated and Projected: Four Perceptions of Purpose Around the Archival Document for Expert Users

This article presents a new model of four perceptions of purpose relating to the archival document in archival organisations. It explains created purpose (it is what it is), intended purpose (it is what the creating or host organisation attests it as), articulated purpose (it is what the archival organisation presents it as) and projected purpose (it is what the user signifies it as) and outlines why an understanding of these may be useful for expert users in research. The article also references and supports discourse covering the conceptualisation and critical reflection of users and their interaction with the archival document.

Using an expert survey and user feedback to construct PRECHECK: A checklist to evaluate preprints on COVID-19 and beyond

Background: The quality of COVID-19 preprints should be considered with great care, as their contents can influence public policy. Efforts to improve preprint quality have mostly focused on introducing quick peer review, but surprisingly little has been done to calibrate the public’s evaluation of preprints and their contents. The PRECHECK project aimed to generate a tool to teach and guide scientifically literate non-experts to critically evaluate preprints, on COVID-19 and beyond. Methods: To create a checklist, we applied a four-step procedure consisting of an initial internal review, an external review by a pool of experts (methodologists, meta-researchers/experts on preprints, journal editors, and science journalists), a final internal review, and an implementation stage. For the external review step, experts rated the relevance of each element of the checklist on five-point Likert scales, and provided written feedback. After each internal review round, we applied the checklist on a set of high-quality preprints from an online list of milestone research works on COVID-19 and low-quality preprints, which were eventually retracted, to verify whether the checklist can discriminate between the two categories. Results: At the external review step, 26 of the 54 contacted experts responded. The final checklist contained four elements (Research question, study type, transparency and integrity, and limitations), with ‘superficial’ and ‘deep’ levels for evaluation. When using both levels of evaluation, the checklist was effective at discriminating high- from low-quality preprints. Its usability was confirmed in workshops with our target audience: Bachelors students in Psychology and Medicine, and science journalists. Conclusions: We created a simple, easy-to-use tool for helping scientifically literate non-experts navigate preprints with a critical mind. We believe that our checklist has great potential to help guide decisions about the quality of preprints on COVID-19 in our target audience and that this extends beyond COVID-19.

Human-centred feasibility restoration in practice

Decision systems for solving real-world combinatorial problems must be able to report infeasibility in such a way that users can understand the reasons behind it, and determine how to modify the problem to restore feasibility. Current methods mainly focus on reporting one or more subsets of the problem constraints that cause infeasibility. Methods that also show users how to restore feasibility tend to be less flexible and/or problem-dependent. We describe a problem-independent approach to feasibility restoration that combines existing techniques from the literature in novel ways to yield meaningful, useful, practical, and flexible user support. We evaluated the resulting framework on three real-world applications and conducted a qualitative expert user study with participants from different application domains.

Automatic Multiple Articulator Segmentation in Dynamic Speech MRI Using a Protocol Adaptive Stacked Transfer Learning U-NET Model.

Dynamic magnetic resonance imaging has emerged as a powerful modality for investigating upper-airway function during speech production. Analyzing the changes in the vocal tract airspace, including the position of soft-tissue articulators (e.g., the tongue and velum), enhances our understanding of speech production. The advent of various fast speech MRI protocols based on sparse sampling and constrained reconstruction has led to the creation of dynamic speech MRI datasets on the order of 80-100 image frames/second. In this paper, we propose a stacked transfer learning U-NET model to segment the deforming vocal tract in 2D mid-sagittal slices of dynamic speech MRI. Our approach leverages (a) low- and mid-level features and (b) high-level features. The low- and mid-level features are derived from models pre-trained on labeled open-source brain tumor MR and lung CT datasets, and an in-house airway labeled dataset. The high-level features are derived from labeled protocol-specific MR images. The applicability of our approach to segmenting dynamic datasets is demonstrated in data acquired from three fast speech MRI protocols: Protocol 1: 3 T-based radial acquisition scheme coupled with a non-linear temporal regularizer, where speakers were producing French speech tokens; Protocol 2: 1.5 T-based uniform density spiral acquisition scheme coupled with a temporal finite difference (FD) sparsity regularization, where speakers were producing fluent speech tokens in English, and Protocol 3: 3 T-based variable density spiral acquisition scheme coupled with manifold regularization, where speakers were producing various speech tokens from the International Phonetic Alphabetic (IPA). Segments from our approach were compared to those from an expert human user (a vocologist), and the conventional U-NET model without transfer learning. Segmentations from a second expert human user (a radiologist) were used as ground truth. Evaluations were performed using the quantitative DICE similarity metric, the Hausdorff distance metric, and segmentation count metric. This approach was successfully adapted to different speech MRI protocols with only a handful of protocol-specific images (e.g., of the order of 20 images), and provided accurate segmentations similar to those of an expert human.

An autonomous X-ray image acquisition and interpretation system for assisting percutaneous pelvic fracture fixation.

Percutaneous fracture fixation involves multiple X-ray acquisitions to determine adequate tool trajectories in bony anatomy. In order to reduce time spent adjusting the X-ray imager's gantry, avoid excess acquisitions, and anticipate inadequate trajectories before penetrating bone, we propose an autonomous system for intra-operative feedback that combines robotic X-ray imaging and machine learning for automated image acquisition and interpretation, respectively. Our approach reconstructs an appropriate trajectory in a two-image sequence, where the optimal second viewpoint is determined based on analysis of the first image. A deep neural network is responsible for detecting the tool and corridor, here a K-wire and the superior pubic ramus, respectively, in these radiographs. The reconstructed corridor and K-wire pose are compared to determine likelihood of cortical breach, and both are visualized for the clinician in a mixed reality environment that is spatially registered to the patient and delivered by an optical see-through head-mounted display. We assess the upper bounds on system performance through in silico evaluation across 11 CTs with fractures present, in which the corridor and K-wire are adequately reconstructed. In post hoc analysis of radiographs across 3 cadaveric specimens, our system determines the appropriate trajectory to within 2.8 ± 1.3mm and 2.7 ± 1.8[Formula: see text]. An expert user study with an anthropomorphic phantom demonstrates how our autonomous, integrated system requires fewer images and lower movement to guide and confirm adequate placement compared to current clinical practice. Code and data are available.

In-vial dried urine spot collection and processing for quantitative analyses.

Analysis of dried urine spots (DUSs) is becoming an emerging technique in clinical, toxicological, and forensic chemistry due to the fully non-invasive collection, facile transportation, and simple storage of DUS samples. Correct DUS collection and elution is of the utmost importance because inadequate DUS sampling/processing may have direct consequences on quantitative DUS analyses and these aspects were, for the first time, comprehensively investigated in this contribution. Various groups of endogenous and exogenous species were selected as model analytes and their concentrations were monitored in DUSs collected on standard cellulose-based sampling cards. Strong chromatographic effects were observed for most analytes having a crucial impact on their distribution within the DUSs during sampling. Concentrations of target analytes were up to 3.75-fold higher in the central DUS sub-punch in comparison to the liquid urine. Consequently, substantially reduced concentrations of these analytes were determined in peripheral DUS sub-punches demonstrating that sub-punching, often applied to dried material spots, is not acceptable for quantitative DUS analyses. Hence, a simple, rapid, and user-friendly procedure was suggested, which employed an in-vial collection of a known urine volume on a pre-punched sampling disc (using a low-cost micropipette designed for patient-centric clinical sampling) and in-vial processing of the whole DUS. Excellent accuracy (0.20%) and precision (0.89%) of liquid transfers were achieved by the micropipette, which was also applied to remote DUS collection by laic and expert users. The resulting DUS eluates were analysed by capillary electrophoresis (CE) for the determination of endogenous urine species. The CE results demonstrated no significant differences between the two user groups, elution efficiencies of 88-100% (in comparison to the liquid urine), and precision better than 5.5%.