The reliability of assistance systems modulates the sense of control and acceptability of human operators.

Individuals are increasingly required to interact with complex and autonomous technologies, which often has a significant impact on the control they experience over their actions and choices. A better characterization of the factors responsible for modulating the control experience of human operators is therefore a major challenge to improve the quality of human-system interactions. Using a decision-making task performed in interaction with an automated system, we investigated the influence of two key properties of automated systems, their reliability and explicability, on participants' sense of agency (SoA), as well as the perceived acceptability of system choices. The results show an increase in SoA associated with the most explicable system. Importantly, the increase in system explicability influenced participants' ability to regulate the control resources they engaged in the current decision. In particular, we observed that participants' SoA varied with system reliability in the "explained" condition, whereas no variation was observed in the "non-explained" condition. Finally, we found that system reliability had a direct impact on system acceptability, such that the most reliable systems were also considered the most acceptable systems. These results highlight the importance of studying agency in human-computer interaction in order to define more acceptable automation technologies.

Smart Home Automation-Based Hand Gesture Recognition Using Feature Fusion and Recurrent Neural Network.

Gestures have been used for nonverbal communication for a long time, but human-computer interaction (HCI) via gestures is becoming more common in the modern era. To obtain a greater recognition rate, the traditional interface comprises various devices, such as gloves, physical controllers, and markers. This study provides a new markerless technique for obtaining gestures without the need for any barriers or pricey hardware. In this paper, dynamic gestures are first converted into frames. The noise is removed, and intensity is adjusted for feature extraction. The hand gesture is first detected through the images, and the skeleton is computed through mathematical computations. From the skeleton, the features are extracted; these features include joint color cloud, neural gas, and directional active model. After that, the features are optimized, and a selective feature set is passed through the classifier recurrent neural network (RNN) to obtain the classification results with higher accuracy. The proposed model is experimentally assessed and trained over three datasets: HaGRI, Egogesture, and Jester. The experimental results for the three datasets provided improved results based on classification, and the proposed system achieved an accuracy of 92.57% over HaGRI, 91.86% over Egogesture, and 91.57% over the Jester dataset, respectively. Also, to check the model liability, the proposed method was tested on the WLASL dataset, attaining 90.43% accuracy. This paper also includes a comparison with other-state-of-the art methods to compare our model with the standard methods of recognition. Our model presented a higher accuracy rate with a markerless approach to save money and time for classifying the gestures for better interaction.

Digital process control of multi-step assays on centrifugal platforms using high-low-high rotational-pulse triggered valving.

Due to their capability for comprehensive sample-to-answer automation, the interest in centrifugal microfluidic systems has greatly increased in industry and academia over the last quarter century. The main applications of these "Lab-on-a-Disc" (LoaD) platforms are in decentralised bioanalytical point-of-use / point-of-care testing. Due to the unidirectional and omnipresent nature of the centrifugal force, advanced flow control is key to coordinate multi-step / multi-reagent assay formats on the LoaD. Formerly, flow control was often achieved by capillary burst valves which require gradual increments of the spin speed of the system-innate spindle motor. Recent advanced introduced a flow control scheme called 'rotational pulse actuated valves'. In these valves the sequence of valve actuation is determined by the architecture of the disc while actuation is triggered by freely programmable upward spike (i.e. Low-High-Low (LHL)) in the rotational frequency. This paradigm shift from conventional 'analogue' burst valves to 'digital' pulsing significantly increases the number of sequential while also improving the overall robustness of flow control. In this work, we expand on these LHL valves by introducing High-Low-High (HLH) pulse-actuated (PA) valving which are actuated by 'downward' spike in the disc spin-rate. These HLH valves are particularly useful for high spin-rate operations such as centrifugation of blood. We introduce two different HLH architectures and then combine the most promising with LHL valves to implement the time-dependent liquid handling protocol underlying a common liver function test panel.

Full-electric microfluidic platform to capture, analyze and selectively release single cells.

Current single-cell technologies require large and expensive equipment, limiting their use to specialized labs. In this paper, we present for the first time a microfluidic device which demonstrates a combined method for full-electric cell capturing, analyzing, and selectively releasing with single-cell resolution. All functionalities are experimentally demonstrated on Saccharomyces cerevisiae. Our microfluidic platform consists of traps centered around a pair of individually accessible coplanar electrodes, positioned under a microfluidic channel. Using this device, we validate our novel Two-Voltage method for trapping single cells by positive dielectrophoresis (pDEP). Cells are attracted to the trap when a high voltage (VH) is applied. A low voltage (VL) holds the already trapped cell in place without attracting additional cells, allowing full control over the number of trapped cells. After trapping, the cells are analyzed by broadband electrochemical impedance spectroscopy. These measurements allow the detection of single cells and the extraction of cell parameters. Additionally, these measurements show a strong correlation between average phase change and cell size, enabling the use of our system for size measurements in biological applications. Finally, our device allows selectively releasing trapped cells by turning off the pDEP signal in their trap. The experimental results show the techniques potential as a full-electric single-cell analysis tool with potential for miniaturization and automation which opens new avenues towards small-scale, high throughput single-cell analysis and sorting lab-on-CMOS devices.

BlockAuth: A blockchain-based framework for secure vehicle authentication and authorization.

Intelligent Transport System (ITS) offers inter-vehicle communication, safe driving, road condition updates, and intelligent traffic management. This research intends to propose a novel decentralized "BlockAuth" architecture for vehicles, authentication, and authorization, traveling across the border. It is required because the existing architects rely on a single Trusted Authority (TA) for issuing certifications, which can jeopardize privacy and system integrity. Similarly, the centralized TA, if failed, can cause the whole system to collapse. Furthermore, a unique "Proof of Authenticity and Integrity" process is proposed, redirecting drivers/vehicles to their home country for authentication, ensuring the security of their credentials. Implemented with Hyperledger Fabric, BlockAuth ensures secure vehicle authentication and authorization with minimal computational overhead, under 2%. Furthermore, it opens up global access, enforces the principles of separation of duty and least privilege, and reinforces resilience via decentralization and automation.

CD44-SNA1 integrated cytopathology for delineation of high grade dysplastic and neoplastic oral lesions.

The high prevalence of oral potentially-malignant disorders exhibits diverse severity and risk of malignant transformation, which mandates a Point-of-Care diagnostic tool. Low patient compliance for biopsies underscores the need for minimally-invasive diagnosis. Oral cytology, an apt method, is not clinically applicable due to a lack of definitive diagnostic criteria and subjective interpretation. The primary objective of this study was to identify and evaluate the efficacy of biomarkers for cytology-based delineation of high-risk oral lesions. A comprehensive systematic review and meta-analysis of biomarkers recognized a panel of markers (n: 10) delineating dysplastic oral lesions. In this observational cross sectional study, immunohistochemical validation (n: 131) identified a four-marker panel, CD44, Cyclin D1, SNA-1, and MAA, with the best sensitivity (>75%; AUC>0.75) in delineating benign, hyperplasia, and mild-dysplasia (Low Risk Lesions; LRL) from moderate-severe dysplasia (High Grade Dysplasia: HGD) along with cancer. Independent validation by cytology (n: 133) showed that expression of SNA-1 and CD44 significantly delineate HGD and cancer with high sensitivity (>83%). Multiplex validation in another cohort (n: 138), integrated with a machine learning model incorporating clinical parameters, further improved the sensitivity and specificity (>88%). Additionally, image automation with SNA-1 profiled data set also provided a high sensitivity (sensitivity: 86%). In the present study, cytology with a two-marker panel, detecting aberrant glycosylation and a glycoprotein, provided efficient risk stratification of oral lesions. Our study indicated that use of a two-biomarker panel (CD44/SNA-1) integrated with clinical parameters or SNA-1 with automated image analysis (Sensitivity >85%) or multiplexed two-marker panel analysis (Sensitivity: >90%) provided efficient risk stratification of oral lesions, indicating the significance of biomarker-integrated cytopathology in the development of a Point-of-care assay.

The Impact of the Coronavirus Disease 2019 Pandemic on the Clinical Environment.

The Nuclear Medicine Technology Certification Board performed an impact survey on the coronavirus disease 2019 pandemic to better assess the current state of nuclear medicine practice within the United States, as well as the perceptions and experiences of technologists working during the pandemic. Methods: A web-based automation platform was used to create, collect, and analyze the survey data. Results: The survey revealed many department protocol variations during the pandemic, a decrease in patient volume, and several other concerns and issues. Experiences regarding staffing and wage changes were varied. Conclusion: This research showed significant inconsistencies in practice and stresses to nuclear medicine technology during the pandemic, as well as concerns for the workforce pipeline. NMTCB decided to delay the JTA process and conduct additional research regarding the workforce.

The impact of non-driving related tasks on the development of driver sleepiness and takeover performances in prolonged automated driving.

INTRODUCTION: Vehicle automation is thought to improve road safety since numerous accidents are caused by human error. However, the lack of active involvement and monotonous driving environments due to automation may contribute to drivers' passive fatigue and sleepiness. Previous research indicated that non-driving related tasks (NDRTs) were beneficial in maintaining drivers' arousal levels but detrimental to takeover performance. METHOD: A 3·2 mixed design (between subjects: driving condition; within subjects: takeover orders) simulator experiment was conducted to explore the development of driver sleepiness in prolonged automated driving context and the effect of NDRTs on driver sleepiness development, and to further evaluate the impact of driver sleepiness and NDRTs on takeover performance. Sixty-three participants were randomly assigned to three driving conditions, each lasting 60 min: automated driving while performing driving environment monitoring task; visual NDRTs task; and visual NDRTs with scheduled driving environment monitoring task. Two hazardous events occurring at about the 5th and 55th min needed to be handled during the respective driving. RESULTS: Drivers performing monitoring tasks had a faster development of driver sleepiness than drivers in the other two conditions in terms of both subjective and objective indicators. Takeover performance of drivers performing monitoring task were undermined due to driver sleepiness in terms of braking and steering reaction times, the time between saccade latency and braking or steering reaction times, and so forth. Additionally, NDRTs impaired the drivers' takeover ability in terms of saccade latency, max braking pedal input, max steering velocity, minimum time to collision, and so forth. This study shows that NDRTs with scheduled road environment monitoring task improve takeover performance during prolonged automated driving by helping to maintain driver alertness. PRACTICAL APPLICATIONS: Findings from this work provide some technical assistance in the development of driver sleepiness monitoring systems for conditionally automated vehicles.

Emerging technology in agriculture: Opportunities and considerations for occupational safety and health researchers.

INTRODUCTION: A variety of factors are driving the development of robotics and automation in the agriculture industry including the nature of work, workforce shortages, and a variety of economic, climatic, technologic, political, and social factors. While some new robotics and automated machines are available commercially, most are still being developed. This provides occupational safety and health researchers an unprecedented opportunity to mitigate risks and benefit the health and safety of agriculture workers. METHOD: The NIOSH Office of Agriculture Safety and Health (OASH) is working to better understand how the advancements in automation and robotics is affecting workers. OASH is coordinating with the NIOSH Center of Occupational Robotics Research (CORR) to help to increase the understanding of human/machine interactions; improve the ability to identify injuries and fatalities involving automation/ robotics; and provide guidance on working safely with automation/ robotics. OASH also joined a small team of academics and industry to organize the SAfety For Emerging Robotics and Autonomous aGriculture or (SAFER AG) Workshop to identify gaps in knowledge and research needs that connect to issues related to risks and regulations/standards, occupational safety research, and impacts on workforce and society. This workshop was sponsored by USDA NIFA. PRACTICAL APPLICATIONS: Occupational safety and health experts need to engage and collaborate with developers of technology. It is also increasingly important for occupational safety and health researchers and practitioners to not only become familiar with existing manufacturing safety standards, but also the lengthy standards development process. Joining consensus standards groups to help shape new standards for emerging technologies may help to mitigate adverse worker impacts. NIOSH's Office of Agriculture Safety and Health will continue to identify research gaps, support new research projects, education, outreach efforts and the development of best practices with our partners.

Feasibility and Clinical Acceptability of Automation-Assisted 3D Conformal Radiotherapy Planning for Patients With Cervical Cancer in a Resource-Constrained Setting.

PURPOSE: The Ocean Road Cancer Institute (ORCI) in Tanzania began offering 3D conformal radiation therapy (3DCRT) in 2018. Steep learning curves, high patient volume, and a limited workforce resulted in long radiation therapy (RT) planning workflows. We aimed to establish the feasibility of implementing an automation-assisted cervical cancer 3DCRT planning system. MATERIALS AND METHODS: We performed chart abstractions on 30 patients with cervical cancer treated with 3DCRT at ORCI. The Radiation Planning Assistant (RPA) generated a new automated set of contours and plans on the basis of anonymized computed tomography images. Each were assessed for edit time requirements, dose-volume safety metrics, and clinical acceptability by two ORCI physician investigators. Dice similarity coefficient (DSC) agreement analysis was conducted between original and new contour sets. RESULTS: The average time to manually develop treatment plans was 7 days. Applying RPA, automated same-day contours and plans were developed for 29 of 30 patients (97%). Of the 29 evaluable contours, all were approved with <2 minutes of edit time. Agreement between clinical and RPA contours was highest for the rectum (median DSC, 0.72) and bladder (DSC, 0.90). Agreement was lower with the primary tumor clinical target volume (CTVp; DSC, 0.69) and elective nodal clinical target volume (CTVn; DSC, 0.63). All RPA plans were approved with <4 minutes of edit time. RPA target coverage was excellent, covering the CTVp with median V45 Gy 100% and CTVn with median V45 Gy 99.9%. CONCLUSION: Automation-assisted 3DCRT contouring yielded high levels of agreement for normal structures. The RPA met all planning safety metrics and sustained high levels of clinical acceptability with minimal edit times. This tool offers the potential to significantly decrease RT planning timelines while maintaining high-quality RT delivery in resource-constrained settings.

A systematic review on the influence factors, measurement, and effect of driver workload.

Driver workload (DWL) is an important factor that needs to be considered in the study of traffic safety. The research focus on DWL has undergone certain shifts with the rapid development of scientific and technological advancements in the field of transportation in recent years. This study aims to grasp the state of research on DWL by both bibliometric analysis and individual critical literature review. The knowledge structure and development trend are described using bibliometric analysis. The knowledge mapping method is applied to mine the available literature in depth. It is discovered that one of the current research focus on DWL has shifted towards investigating its application in the field of autonomous driving. Subjective questionnaires and experimental tests (including both simulation technology and field study) are the main approaches to analyze DWL. An individual critical literature review of the influencing factors, measurement, and performance of DWL is provided. Research findings have shown that DWL was highly impacted by both intrinsic (e.g., age, temperament, driving experience) and external factors (e.g., vehicles, roads, tasks, environments). Scholars are actively exploring the combined effects of various factors and the level of vehicle automation on DWL. In addition to assess DWL by using subjective measures or physiological parameter measures separately, studies have started to improve classification accuracy by combining multiple measurement methods. Safety thresholds of DWL are not sufficiently studied due to the various interference items corresponding to different scenarios, but it is expected to quantify the DWL and find the threshold by establishing assessment models considering these intrinsic and external multiple-factors simultaneously. Driver or vehicle performance indicators are controversial to measure DWL directly, but they were suitable to reflect the impact of DWL in different driving conditions.

High-Throughput Optogenetics Experiments in Yeast Using the Automated Platform Lustro.

Optogenetics offers precise control over cellular behavior by utilizing genetically encoded light-sensitive proteins. However, optimizing these systems to achieve the desired functionality often requires multiple design-build-test cycles, which can be time-consuming and labor-intensive. To address this challenge, we have developed Lustro, a platform that combines light stimulation with laboratory automation, enabling efficient high-throughput screening and characterization of optogenetic systems. Lustro utilizes an automation workstation equipped with an illumination device, a shaking device, and a plate reader. By employing a robotic arm, Lustro automates the movement of a microwell plate between these devices, allowing for the stimulation of optogenetic strains and the measurement of their response. This protocol provides a step-by-step guide on using Lustro to characterize optogenetic systems for gene expression control in the budding yeast Saccharomyces cerevisiae. The protocol covers the setup of Lustro's components, including the integration of the illumination device with the automation workstation. It also provides detailed instructions for programming the illumination device, plate reader, and robot, ensuring smooth operation and data acquisition throughout the experimental process.

Establishing gaze markers of perceptual load during multi-target visual search.

Highly-automated technologies are increasingly incorporated into existing systems, for instance in advanced car models. Although highly automated modes permit non-driving activities (e.g. internet browsing), drivers are expected to reassume control upon a 'take over' signal from the automation. To assess a person's readiness for takeover, non-invasive eye tracking can indicate their attentive state based on properties of their gaze. Perceptual load is a well-established determinant of attention and perception, however, the effects of perceptual load on a person's ability to respond to a takeover signal and the related gaze indicators are not yet known. Here we examined how load-induced attentional state affects detection of a takeover-signal proxy, as well as the gaze properties that change with attentional state, in an ongoing task with no overt behaviour beyond eye movements (responding by lingering the gaze). Participants performed a multi-target visual search of either low perceptual load (shape targets) or high perceptual load (targets were two separate conjunctions of colour and shape), while also detecting occasional auditory tones (the proxy takeover signal). Across two experiments, we found that high perceptual load was associated with poorer search performance, slower detection of cross-modal stimuli, and longer fixation durations, while saccade amplitude did not consistently change with load. Using machine learning, we were able to predict the load condition from fixation duration alone. These results suggest monitoring fixation duration may be useful in the design of systems to track users' attentional states and predict impaired user responses to stimuli outside of the focus of attention.

Ion-Exchange Chromatography: Basic Principles and Application.

Ion-exchange chromatography (IEC) is a fractionation technique that allows for the separation of ionizable molecules on the basis of differences in their electrostatic properties. Its large sample-handling capacity, broad applicability (particularly to proteins and enzymes), moderate cost, powerful resolving ability, ability to perform simultaneous quantitation, and ease of scale-up and automation have led to it becoming one of the most versatile and widely used of all liquid chromatography (LC) techniques. In this chapter, we review the basic principles of IEC, as well as the broader criteria for selecting IEC conditions. By way of further illustration, we outline basic laboratory protocols to partially purify a soluble serine peptidase from bovine whole brain tissue, covering crude tissue extract preparation through to partial purification of the target enzyme using a form of IEC, namely, anion-exchange chromatography. Protocols for assaying total protein and enzyme activity in both pre- and post-IEC fractions are also described.

Real-Time Monocular Skeleton-Based Hand Gesture Recognition Using 3D-Jointsformer.

Automatic hand gesture recognition in video sequences has widespread applications, ranging from home automation to sign language interpretation and clinical operations. The primary challenge lies in achieving real-time recognition while managing temporal dependencies that can impact performance. Existing methods employ 3D convolutional or Transformer-based architectures with hand skeleton estimation, but both have limitations. To address these challenges, a hybrid approach that combines 3D Convolutional Neural Networks (3D-CNNs) and Transformers is proposed. The method involves using a 3D-CNN to compute high-level semantic skeleton embeddings, capturing local spatial and temporal characteristics of hand gestures. A Transformer network with a self-attention mechanism is then employed to efficiently capture long-range temporal dependencies in the skeleton sequence. Evaluation of the Briareo and Multimodal Hand Gesture datasets resulted in accuracy scores of 95.49% and 97.25%, respectively. Notably, this approach achieves real-time performance using a standard CPU, distinguishing it from methods that require specialized GPUs. The hybrid approach's real-time efficiency and high accuracy demonstrate its superiority over existing state-of-the-art methods. In summary, the hybrid 3D-CNN and Transformer approach effectively addresses real-time recognition challenges and efficient handling of temporal dependencies, outperforming existing methods in both accuracy and speed.

Blind Modalities for Human Activity Recognition.

Human Activity Recognition (HAR) has attracted considerable interest due to its ability to facilitate automation in various application areas, including but not limited to smart homes, active assisted living, and security. At present, optical modalities such as RGB, depth, and thermal imaging are prevalent in the field due to the effectiveness of deep learning algorithms like Convolutional Neural Networks (CNNs) and the abundance of publicly available image data. However, unconventional modalities such as radar, WiFi, seismic and environmental sensors are emerging as potential alternatives due to their capacity for contactless long-range sensing in spatially constrained environments and preservation of visual privacy. This work gives an overview of the HAR modalities landscape and discusses works that apply these emerging modalities in new and unconventional ways to inform researchers and practitioners about challenges and opportunities in the field of HAR.

Exposure to automation explains religious declines.

The global decline of religiosity represents one of the most significant societal shifts in recent history. After millennia of near-universal religious identification, the world is experiencing a regionally uneven trend toward secularization. We propose an explanation of this decline, which claims that automation-the development of robots and artificial intelligence (AI)-can partly explain modern religious declines. We build four unique datasets composed of more than 3 million individuals which show that robotics and AI exposure is linked to 21st-century religious declines across nations, metropolitan regions, and individual people. Key results hold controlling for other technological developments (e.g., electricity grid access and telecommunications development), socioeconomic indicators (e.g., wealth, residential mobility, and demographics), and factors implicated in previous theories of religious decline (e.g., individual choice norms). An experiment also supports our hypotheses. Our findings partly explain contemporary trends in religious decline and foreshadow where religiosity may wane in the future.

Application of Intelligent Medical Sensing Technology.

With the popularization of intelligent sensing and the improvement of modern medical technology, intelligent medical sensing technology has emerged as the times require. This technology combines basic disciplines such as physics, mathematics, and materials with modern technologies such as semiconductors, integrated circuits, and artificial intelligence, and has become one of the most promising in the medical field. The core of intelligent medical sensor technology is to make existing medical sensors intelligent, portable, and wearable with full consideration of ergonomics and sensor power consumption issues in order to conform to the current trends in cloud medicine, personalized medicine, and health monitoring. With the development of automation and intelligence in measurement and control systems, it is required that sensors have high accuracy, reliability, and stability, as well as certain data processing capabilities, self-checking, self-calibration, and self-compensation, while traditional medical sensors cannot meet such requirements. In addition, to manufacture high-performance sensors, it is also difficult to improve the material process alone, and it is necessary to combine computer technology with sensor technology to make up for its performance shortcomings. Intelligent medical sensing technology combines medical sensors with microprocessors to produce powerful intelligent medical sensors. Based on the original sensor functions, intelligent medical sensors also have functions such as self-compensation, self-calibration, self-diagnosis, numerical processing, two-way communication, information storage, and digital output. This review focuses on the application of intelligent medical sensing technology in biomedical sensing detection from three aspects: physical sensor, chemical sensor, and biosensor.

The Cilialyzer - A freely available open-source software for the analysis of mucociliary activity in respiratory cells.

BACKGROUND AND OBJECTIVE: Primary ciliary dyskinesia (PCD) is a rare genetic disorder causing a defective ciliary structure, which predominantly leads to an impaired mucociliary clearance and associated airway disease. As there is currently no single diagnostic gold standard test, PCD is diagnosed by a combination of several methods comprising genetic testing and the examination of the ciliary structure and function. Among the approved diagnostic methods, only high-speed video microscopy (HSVM) allows to directly observe the ciliary motion and therefore, to directly assess ciliary function. In the present work, we present our recently developed freely available open-source software - termed "Cilialyzer", which has been specifically designed to support and facilitate the analysis of the mucociliary activity in respiratory epithelial cells captured by high-speed video microscopy. METHODS: In its current state, the Cilialyzer software enables clinical PCD analysts to load, preprocess and replay recorded image sequences as well as videos with a feature-rich replaying module facilitating the commonly performed qualitative visual assessment of ciliary function (including the assessment of the ciliary beat pattern). The image processing methods made accessible through an intuitive user interface allow clinical specialists to comfortably compute the ciliary beating frequency (CBF), the activity map and the "frequency correlation length" - an observable getting newly introduced. Furthermore, the Cilialyzer contains a simple-to-use particle tracking interface to determine the mucociliary transport speed. RESULTS: Cilialyzer is fully written in the Python programming language and freely available under the terms of the MIT license. The proper functioning of the computational analysis methods constituting the Cilialyzer software is demonstrated by using simulated and representative sample data from clinical practice. Additionally, the software was used to analyze high-speed videos showing samples obtained from healthy controls and genetically confirmed PCD cases (DNAI1 and DNAH11 mutations) to show its clinical applicability. CONCLUSIONS: Cilialyzer serves as a useful clinical tool for PCD analysts and provides new quantitative information awaiting to be clinically evaluated using cohorts of PCD. As Cilialyzer is freely available under the terms of a permissive open-source license, it serves as a ground frame for further development of computational methods aiming at the quantification and automation of the analysis of mucociliary activity captured by HSVM.

libSBOLj3: a graph-based library for design and data exchange in synthetic biology.

SUMMARY: The Synthetic Biology Open Language version 3 data standard provides a graph-based approach to exchange information about biological designs. The new data model has major updates and offers several features for software tools. Here, we present libSBOLj3 to facilitate data exchange and provide interoperability between computer-aided design and automation tools using this standard. The library adopts a graph-based approach. Tool developers can extend these graphs with application-specific information and use detailed validation reports to identify errors and interoperability issues and apply best practice rules. AVAILABILITY AND IMPLEMENTATION: The libSBOLj3 library is implemented in Java and can be downloaded or used as a Maven dependency. The open-source project, code examples and documentation about accessing and using the library are available via GitHub at https://github.com/SynBioDex/libSBOLj3.