Complex Task Research Articles

A - 61 Lifelong Intellectual Developmental Disorder and Autism Spectrum Disorder Diagnosed in Early Adulthood: Implications for a Patient Residing in Frontier America

Abstract Objective Individuals residing in frontier America often lack access to specialized medical care. Obstacles to receiving accurate diagnosis and intervention of neuropsychological conditions in childhood is common and individuals and families may have difficulty in adapting to the implications of such diagnoses later in life. Methods This 18-year-old Caucasian male from frontier Kansas first presented for neurological or neuropsychological workup in adulthood. History was noteworthy for premature birth, late attainment of milestones, respiratory conditions resulting in multiple instances of cardiac arrest, and facial dysmorphism. Education was attended remotely due to risk of respiratory infection. Parents reported several atypical traits, including hyperfixation on automobiles, high pain threshold, and low frustration tolerance resulting in outbursts. Patient was considered socially and academically delayed, which parents attributed to remote school attendance. No support services were in place. Results Neuropsychological evaluation showed FSIQ = 71 and pronounced executive dysfunction. Parental questionnaire evidenced deficits in adaptive functioning, most pronounced in self-direction and social skills. Patient and parents denied significant concerns on behavioral health questionnaire. Paraeducator endorsed concerns in atypicality, attention/learning problems, and functional communication. Conclusions This young man’s presentation and estimated cognitive and functional abilities are consistent with mild intellectual developmental disorder and autism spectrum disorder. On feedback, this information was distressing to parents; capabilities in some complex tasks may have masked his deficits. Although parents were resistant to findings, it is believed these labels will assist in explaining abnormal behaviors, attaining services, and facilitating genetic testing and related medical care.

RegulaTome: a corpus of typed, directed, and signed relations between biomedical entities in the scientific literature.

In the field of biomedical text mining, the ability to extract relations from the literature is crucial for advancing both theoretical research and practical applications. There is a notable shortage of corpora designed to enhance the extraction of multiple types of relations, particularly focusing on proteins and protein-containing entities such as complexes and families, as well as chemicals. In this work, we present RegulaTome, a corpus that overcomes the limitations of several existing biomedical relation extraction (RE) corpora, many of which concentrate on single-type relations at the sentence level. RegulaTome stands out by offering 16 961 relations annotated in >2500 documents, making it the most extensive dataset of its kind to date. This corpus is specifically designed to cover a broader spectrum of >40 relation types beyond those traditionally explored, setting a new benchmark in the complexity and depth of biomedical RE tasks. Our corpus both broadens the scope of detected relations and allows for achieving noteworthy accuracy in RE. A transformer-based model trained on this corpus has demonstrated a promising F1-score (66.6%) for a task of this complexity, underscoring the effectiveness of our approach in accurately identifying and categorizing a wide array of biological relations. This achievement highlights RegulaTome's potential to significantly contribute to the development of more sophisticated, efficient, and accurate RE systems to tackle biomedical tasks. Finally, a run of the trained RE system on all PubMed abstracts and PMC Open Access full-text documents resulted in >18 million relations, extracted from the entire biomedical literature.

Teaching AI to Summarize Like a Human: AReinforcement Learning Experiment

Text summarization is a crucial task in natural language processing (NLP), aiming to distill extensive information into concise and coherent summaries. Traditional summarization methods, including both extractive and abstractive techniques, face challenges in generating summaries that balance brevity and informativeness. This paper explores the application of Reinforce- ment Learning with Human Feedback (RLHF) to address these challenges and enhance the quality of text summarization.We introduce an RLHF-based approach using the FLAN-T5-small model, which integrates human feedback into the reinforcement learning framework to refine summary generation. Our method leverages a dataset from the Hugging Face datasets library, consisting of diverse document-summary pairs. The model is pre-trained on a large corpus and fine-tuned using human feedback, which serves as a reward signal to guide the model towards generating more relevant and coherent summaries.Our experimental results demonstrate that the RLHF-enhanced model significantly outperforms traditional summarization methods. Quantitative evaluations using ROUGE and BLEU metrics reveal substantial improvements in summary quality, with increases of up to 12.5% in ROUGE- 1 and 9.8% in BLEU scores over baseline methods. Qualitative assessments by human evaluators further confirm that the RLHF-based model produces summaries that are more aligned with human expectations in terms of coherence and relevance.This study highlights the potential of RLHF to overcome the limitations of conventional summarization tech- niques, offering a robust framework for generating high-quality summaries across various domains. Future work will explore the scalability of this approach to more complex summarization tasks and the integration of additional feedback mechanisms to further enhance performance.

Relating behaviour of data-aware process models

Data Petri nets (DPNs) have gained traction as a model for data-aware processes, thanks to their ability to balance simplicity with expressiveness, and because they can be automatically discovered from event logs. While model checking techniques for DPNs have been studied, more complex analysis tasks that are highly relevant for BPM are beyond methods known in the literature. We focus here on equivalence and inclusion of process behaviour with respect to language and configuration spaces, optionally taking data into account. Such comparisons are important in the context of key process mining tasks, namely process repair and discovery, and related to conformance checking. To solve these tasks, we propose approaches for bounded DPNs based on constraint graphs, which are faithful abstractions of the reachable state space. Though the considered verification tasks are undecidable in general, we show that our method is a decision procedure DPNs that admit a finite history set. This property guarantees that constraint graphs are finite and computable, and was shown to hold for large classes of DPNs that are mined automatically, and DPNs presented in the literature. The new techniques are implemented in the tool ada, and an evaluation proving feasibility is provided.

Wafer scale and substrate-agnostic growth of MoS2 nanowalls for efficient electrocatalytic hydrogen generation in acidic and alkaline media

Emerging as a promising alternative to expensive platinum-based catalysts for electrocatalytic hydrogen evolution reaction (HER), molybdenum disulfide (MoS2) stands out for its favourable thermodynamic properties. However, the catalytic activity of MoS2 is mostly confined to its edges while the basal plane remains inactive, limiting practical applicability. Fabrication of stable MoS2 structures with enhanced active sites on a given surface area still remains a complex task. Here we introduce a substrate-agnostic, metal-organic chemical vapour deposition (MOCVD) method for large-area 3D dendritic nanostructures of 2D MoS2, termed as “MoS2 nanowalls”. Using scanning and transmission electron microscopy (SEM/TEM), we elucidate the growth mechanism of the MoS2 nanowalls and their branched dendritic structure. Even subjected to extreme pH environments (0 and 14) during the HER, the grown MoS2 nanowalls show remarkable stability even after >170 hours of continuous operation and exhibit excellent catalytic activity with 10 mAcm−2 current density achievable by applying low overpotentials (309±2 mV at pH = 0 and 272±2 mV at pH = 14). The presented large-area growth method for inexpensive MoS2 nanowall based catalyst can pave the way for practical applications of water electrolysis cells operating at low voltages (≤1.5 V).

Universal Ensemble-Embedding Graph Neural Network for Direct Prediction of Optical Spectra from Crystal Structures.

Optical properties in solids, such as refractive index and absorption, hold vast applications ranging from solar panels to sensors, photodetectors, and transparent displays. However, first-principles computation of optical properties from crystal structures is a complex task due to the high convergence criteria and computational cost. Recent progress in machine learning shows promise in predicting material properties, yet predicting optical properties from crystal structures remains challenging due to the lack of efficient atomic embeddings. Here, Graph Neural Network for Optical spectra prediction (GNNOpt) is introduced, an equivariant graph-neural-network architecture featuring universal embedding with automatic optimization. This enables high-quality optical predictions with a dataset of only 944 materials. GNNOpt predicts all optical properties based on the Kramers-Krönig relations, including absorption coefficient, complex dielectric function, complex refractive index, and reflectance. The trained model is applied to screen photovoltaic materials based on spectroscopic limited maximum efficiency and search for quantum materials based on quantum weight. First-principles calculations validate the efficacy of the GNNOpt model, demonstrating excellent agreement in predicting the optical spectra of unseen materials. The discovery of new quantum materials with high predicted quantum weight, such as SiOs, which host exotic quasiparticles with multifold nontrivial topology, demonstrates the potential of GNNOpt in predicting optical properties across a broad range of materials andapplications.

Inequality threat increases laypeople's, but not judges', acceptance of algorithmic decision making in court.

Algorithmic decision making (ADM) takes on increasingly complex tasks in the criminal justice system. Whereas new developments in machine learning could help to improve the quality of judicial decisions, there are legal and ethical concerns that thwart the widespread use of algorithms. Against the backdrop of current efforts to promote the digitization of the German judicial system, this research investigates motivational factors (pragmatic motives, fairness concerns, and self-image-related considerations) that drive or impede the acceptance of ADM in court. We tested two hypotheses: (1) Perceived threat of inequality in legal judgments increases ADM acceptance, and (2) experts (judges) are more skeptical toward technological innovation than novices (general population). We conducted a preregistered experiment with 298 participants from the German general population and 267 judges at regional courts in Bavaria to study how inequality threat (vs. control) relates to ADM acceptance in court, usage intentions, and attitudes. In partial support of the first prediction, inequality threat increased ADM acceptance, effect size d = 0.24, 95% confidence interval (CI) [0.01, 0.47], and usage intentions (d = 0.23, 95% CI [0.00, 0.46]) of laypeople. Unexpectedly, however, this was not the case for experts. Moreover, ADM attitudes remained unaffected by the experimental manipulation in both groups. As predicted, judges held more negative attitudes toward ADM than the general population (d = -0.71, 95% CI [-0.88, -0.54]). Exploratory analysis suggested that generalized attitudes emerged as the strongest predictor of judges' intentions to use ADM in their own court proceedings. These findings elucidate the motivational forces that drive algorithm aversion and acceptance in a criminal justice context and inform the ongoing debate about perceptions of fairness in human-computer interaction. Implications for judicial praxis and the regulation of ADM in the German legal framework are discussed. (PsycInfo Database Record (c) 2024 APA, all rights reserved).

Statistical and machine learning models for predicting spalling in CRCP

Continuously reinforced concrete pavement (CRCP), crucial for the resilience of transportation infrastructure owing to its continuous steel reinforcement, confronts a critical challenge in the form of spalling—a distress phenomenon posing a threat to pavement durability and overall structural integrity. The detachment or breakage of concrete from the surface compromises CRCP’s functionality and raises safety concerns and escalating maintenance costs. To address this pressing issue, our study investigates the multifaceted factors influencing spalling, employing a comprehensive approach that integrates statistical and machine learning techniques for predictive modeling. Descriptive statistics meticulously profile the dataset, emphasizing age, thickness, precipitation, temperature, and traffic parameters. Regression analysis unveils key relationships, emphasizing the significance of age, annual temperature, annual precipitation, maximum humidity, and the initial International Roughness Index (IRI) as influential factors. The correlation matrix heatmap guides feature selection, elucidating intricate interdependencies. Simultaneously, feature importance analysis identifies age, Average Annual Daily Traffic (AADT), and total pavement thickness as crucial contributors to spalling. In machine learning, adopting models, including Gaussian Process Regression and ensemble tree models, is grounded in their diverse capabilities and suitability for the complex task at hand. Their varying predictive accuracies underscore the importance of judicious model selection. This research advances pavement engineering practices by offering nuanced insights into factors influencing spalling in CRCP, refining our understanding of spalling influences. Consequently, the study not only opens avenues for developing improved predictive methodologies but also enhances the durability of CRCP infrastructure, addressing broader implications for informed decision-making in transportation infrastructure management. The selection of Gaussian Process Regression and ensemble tree models stems from their adaptability to capture intricate relationships within the dataset, and their comparative performance provides valuable insights into the diverse predictive capabilities of these models in the context of CRCP spalling.

A - 13 Cognitive Correlates of Technology-Based Instrumental Activities of Daily Living Performance in Older Adults Presenting for Cognitive Evaluations

Abstract Objective Rapid advances in technology have altered how daily activities are performed. Accumulating evidence suggests technology-based instrumental activities of daily living (iADLs) are increasingly common in older adults’ lives, and assessing these skills may improve diagnostic identification of individuals with Alzheimer’s disease and related dementias (ADRD). While iADLs are complex tasks reliant on multiple cognitive processes, prior work suggests memory and executive functioning are the cognitive processes most strongly predictive of iADL performance in ADRD. However, whether similar associations exist with technology-based iADLs has not been established. The goal of the present study was to evaluate the cognitive correlates of technology-based compared to traditional iADL items. Method One hundred seventy-eight individuals (mean age: 75.35 ± 7.30) presenting for evaluation of cognitive status completed tests of memory, executive functioning, language, and processing speed. Care partners completed the Functional Activities Questionnaire (FAQ) and 11 technology-based iADL items. Associations between cognition and traditional and technology-based FAQ items were examined through Pearson’s correlations and multiple regression analyses. Results Cognition was significantly correlated with both traditional and technology-based iADLs (|r’s| > 0.30, p’s.44). In multiple regression models, executive functioning and processing speed but not memory were the strongest predictors of traditional iADL performance (p’s < 0.02), but for independence with technology-based iADLs, executive functioning alone (p < 0.001) emerged. Conclusions Relations between cognition and daily functioning are similar for both traditional and technology-based FAQ items, with executive functioning particularly relevant for both types of tasks. Implications for clinical practice and technology-based interventions are discussed.

Learning human actions from complex manipulation tasks and their transfer to robots in the circular factory

Abstract Process automation is essential to establish an economically viable circular factory in high-wage locations. This involves using autonomous production technologies, such as robots, to disassemble, reprocess, and reassemble used products with unknown conditions into the original or a new generation of products. This is a complex and highly dynamic issue that involves a high degree of uncertainty. To adapt robots to these conditions, learning from humans is necessary. Humans are the most flexible resource in the circular factory and they can adapt their knowledge and skills to new tasks and changing conditions. This paper presents an interdisciplinary research framework for learning human action knowledge from complex manipulation tasks through human observation and demonstration. The acquired knowledge will be described in a machine-executable form and will be transferred to industrial automation execution by robots in a circular factory. There are two primary research objectives. First, we investigate the multi-modal capture of human behavior and the description of human action knowledge. Second, the reproduction and generalization of learned actions, such as disassembly and assembly actions on robots is studied.

Predicting Stock Prices in the Electric Vehicle and Technology Sectors Using Long Short-Term Memory Models

Forecasting stock prices is a complex task because of the volatility and intricate temporal dependencies present in financial markets. Long Short-Term Memory (LSTM) networks, known for their proficiency in handling extended time-series data, offer an effective solution to these challenges. This paper delves into the applications in LSTM networks to forecast stock prices for various companies in the electric vehicle and related sectors, specifically examining data from Tesla, BYD, Panasonic, and NVIDIA. The study assesses the predictive accuracy and performance of these models using RMSE, MSE, MAE, and R metrics. By comparing these metrics across different companies, the paper provides an in-depth analysis of LSTM networks' effectiveness in financial forecasting. The findings indicate that when LSTM networks are optimized through hyperparameter tuning and architectural adjustments, they can accurately identify patterns in stock price data, offering valuable insights for investors and analysts in the electric vehicle, technology, and energy industries.

Optimizing Lung Condition Categorization through a Deep Learning Approach to Chest X-ray Image Analysis

Background: Evaluating chest X-rays is a complex and high-demand task due to the intrinsic challenges associated with diagnosing a wide range of pulmonary conditions. Therefore, advanced methodologies are required to categorize multiple conditions from chest X-ray images accurately. Methods: This study introduces an optimized deep learning approach designed for the multi-label categorization of chest X-ray images, covering a broad spectrum of conditions, including lung opacity, normative pulmonary states, COVID-19, bacterial pneumonia, viral pneumonia, and tuberculosis. An optimized deep learning model based on the modified VGG16 architecture with SE blocks was developed and applied to a large dataset of chest X-ray images. The model was evaluated against state-of-the-art techniques using metrics such as accuracy, F1-score, precision, recall, and area under the curve (AUC). Results: The modified VGG16-SE model demonstrated superior performance across all evaluated metrics. The model achieved an accuracy of 98.49%, an F1-score of 98.23%, a precision of 98.41%, a recall of 98.07% and an AUC of 98.86%. Conclusion: This study provides an effective deep learning approach for categorizing chest X-rays. The model’s high performance across various lung conditions suggests its potential for integration into clinical workflows, enhancing the accuracy and speed of pulmonary disease diagnosis.

A Review of Safe Reinforcement Learning: Methods, Theories and Applications.

Reinforcement Learning (RL) has achieved tremendous success in many complex decision-making tasks. However, safety concerns are raised during deploying RL in real-world applications, leading to a growing demand for safe RL algorithms, such as in autonomous driving and robotics scenarios. While safe control has a long history, the study of safe RL algorithms is still in the early stages. To establish a good foundation for future safe RL research, in this paper, we provide a review of safe RL from the perspectives of methods, theories, and applications. Firstly, we review the progress of safe RL from five dimensions and come up with five crucial problems for safe RL being deployed in real-world applications, coined as "2H3W". Secondly, we analyze the algorithm and theory progress from the perspectives of answering the "2H3W" problems. Particularly, the sample complexity of safe RL algorithms is reviewed and discussed, followed by an introduction to the applications and benchmarks of safe RL algorithms. Finally, we open the discussion of the challenging problems in safe RL, hoping to inspire future research on this thread. To advance the study of safe RL algorithms, we release an open-sourced repository containing major safe RL algorithms at the link.

Label distribution learning for compound facial expression recognition in‐the‐wild: A comparative study

AbstractHuman emotional states encompass both basic and compound facial expressions. However, current works primarily focus on basic expressions, consequently neglecting the broad spectrum of human emotions encountered in practical scenarios. Compound facial expressions involve the simultaneous manifestation of multiple emotions on an individual's face. This phenomenon reflects the complexity and richness of human states, where facial features dynamically convey a combination of feelings. This study embarks on a pioneering exploration of Compound Facial Expression Recognition (CFER), with a distinctive emphasis on leveraging the Label Distribution Learning (LDL) paradigm. This strategic application of LDL aims to address the ambiguity and complexity inherent in compound expressions, marking a significant departure from the dominant Single Label Learning (SLL) and Multi‐Label Learning (MLL) paradigms. Within this framework, we rigorously investigate the potential of LDL for a critical challenge in Facial Expression Recognition (FER): recognizing compound facial expressions in uncontrolled environments. We utilize the recently introduced RAF‐CE dataset, meticulously designed for compound expression assessment. By conducting a comprehensive comparative analysis pitting LDL against conventional SLL and MLL approaches on RAF‐CE, we aim to definitively establish LDL's superiority in handling this complex task. Furthermore, we assess the generalizability of LDL models trained on RAF‐CE by evaluating their performance on the EmotioNet and RAF‐DB Compound datasets. This demonstrates their effectiveness without domain adaptation. To solidify these findings, we conduct a comprehensive comparative analysis of 12 cutting‐edge LDL algorithms on RAF‐CE, S‐BU3DFE, and S‐JAFFE datasets, providing valuable insights into the most effective LDL techniques for FER in‐the‐wild.

Research on the role of LLM in multi-agent systems: A survey

In recent years, the rapid development of large language model (LLM) has demonstrated superior performance in language understanding, text generation, planning, reasoning, and knowledge integration. This has led to the emergence of intelligent agents based on LLM. By leveraging the capabilities of LLM, these agents can effectively make decisions based on given objectives and possess certain learning and adaptation abilities. However, single-agent systems are generally suited to solving relatively simple problems and are limited in handling complex tasks that require coordination. For instance, in fields such as power grid management or traffic control systems, relying solely on a single agent is often insufficient for effective decision-making. In this context, adopting multi-agent systems proves to be more effective: through collaboration among multiple agents, each undertaking specific tasks, complex problems can be efficiently managed through interaction and coordination. This survey will analyze the role of LLM in multi-agent collaboration, discuss and analyze the current research challenges and key issues, and explore potential directions for future development.

Multifunctional Magnetic Muscles for Soft Robotics

Despite recent advancements, artificial muscles have not yet been able to strike the right balance between exceptional mechanical properties and dexterous actuation abilities that are found in biological systems. Here, we present an artificial magnetic muscle that exhibits multiple remarkable mechanical properties and demonstrates comprehensive actuating performance, surpassing those of biological muscles. This artificial muscle utilizes a composite configuration, integrating a phase-change polymer and ferromagnetic particles, enabling active control over mechanical properties and complex actuating motions through remote laser heating and magnetic field manipulation. Consequently, the magnetic composite muscle can dynamically adjust its stiffness as needed, achieving a switching ratio exceeding 2.7 × 10³. This remarkable adaptability facilitates substantial load-bearing capacity, with specific load capacities of up to 1000 and 3690 for tensile and compressive stresses, respectively. Moreover, it demonstrates reversible extension, contraction, bending, and twisting, with stretchability exceeding 800%. We leverage these distinctive attributes to showcase the versatility of this composite muscle as a soft continuum robotic manipulator. It adeptly executes various programmable responses and performs complex tasks while minimizing mechanical vibrations. Furthermore, we demonstrate that this composite muscle excels across multiple mechanical and actuation aspects compared to existing actuators.

An Exploratory Expert-Study for Multi-Type Haptic Feedback for Automotive Virtual Reality Tasks.

Previous research has shown that integrating haptic feedback can improve immersion and realism in automotive VR applications. However, current haptic feedback approaches primarily focus on a single feedback type. This means users must switch between devices to experience haptic stimuli for different feedback types, such as grabbing, collision, or weight simulation. This restriction limits the ability to simulate haptics realistically for complex tasks such as maintenance. To address this issue, we evaluated existing feedback devices based on our requirements analysis to determine which devices are most suitable for simulating these three feedback types. Since no suitable haptic feedback system can simulate all three feedback types simultaneously, we evaluated which devices can be combined. Based on that, we devised a new multi-type haptic feedback system combining three haptic feedback devices. We evaluated the system with different feedback-type combinations through a qualitative expert study involving twelve automotive VR experts. The results showed that combining weight and collision feedback yielded the best and most realistic experience. The study also highlighted technical limitations in current grabbing devices. Our findings provide insights into the effectiveness of haptic device combinations and practical boundaries for automotive virtual reality tasks.

The vision of the circular factory for the perpetual innovative product

Abstract The growing scarcity of global resources demands a transition from linear to circular production patterns. This article presents a novel vision for integrating linear and circular production processes within a flexible and autonomous production system to achieve perpetual product use. The approach aims to preserve the added value of products and to integrate the design of product generations and production systems. Within the circular factory, the following core aspects must be examined: predicting functions of products, managing uncertainty in used products and process sequences, learning human action for complex tasks, implementing changeable, autonomous production systems, and enabling knowledge modeling for the circular factory across domains. Aspired results are a design for circular factory, effective strategies for uncertainty management and autonomous systems adaptation as well as the externalization of operational knowledge. This research is part of the Collaborative Research Center (CRC) 1574, which explores these aspects in detail. For an in-depth understanding of specific components, it is referred to other publications by the CRC 1574.

The Partners of My Partners: Shared Collaborative Experience and Team Performance in Surgical Teams

When teams in organizations are assembled to perform contingent tasks, team members carry with them experiences of prior interaction with partners in different teams. Focal team members share collaborative experiences to the extent that they worked with common external prior partners. Extending current research on team effectiveness, we investigate how shared collaborative experience (SCE) affects team performance. Consistent with the established understanding of team processes as carrying both a teamwork and a taskwork component, we conceptualize SCE as having two distinct dimensions that we call SCE extent and SCE diversity. We posit that high SCE extent increases the ability of teams to refine their teamwork processes, increasing their performance through enhanced coordination and reflexivity. We argue that high SCE diversity hinders the ability of teams to form a shared understanding of task demands, thus undermining team performance. Furthermore, we investigate the contingent effect of task complexity on the relationship between SCE and performance. We argue that the benefits of implicit coordination and the drawbacks of experience diversity decrease as tasks become more complex and require more explicit coordination and wider repertoires of responses. These predictions find support in an analysis of 1343 robot-assisted surgery operations performed by 114 surgeons during a four-year period in a private university hospital. By explicitly recognizing how team members benefit from the network of their shared prior partners, our study contributes to developing a new approach to study the effectiveness of temporary teams in organizations.

WP8.13 - Validating Remote Basic Surgical Skills Certification: A Randomised Controlled Trial

Abstract Aims Simulation based training comprises a fundamental component of modern surgical education, necessitating the development and advancement of affordable, functional, and validated simulators. This study explored the effectiveness of using the Inovus Basic Surgical Skills (BSS) simulator in improving core surgical skills amongst junior trainees. Methods This was a multicentre, RCT between August 2022 – November 2022 involving junior trainees yet to complete a validated basic surgical skills course. All participants completed an initial series of basic surgical tasks, including a range of suturing and knot-tying techniques. Three parameters (objective scoring tool, time taken for task completion, self-assessment questionnaire) were used to evaluate each surgical skill. Subsequently, participants were randomly allocated to the intervention group (BSS simulator) for at home training, or the control group receiving no further training. Results 5 domains were assessed for suturing tasks through respect for tissue, motion and efficiency, visuo-spatial, suture quality and time taken. Knot-tying was assessed by quality and time taken. Independent samples t-test revealed no significant difference between control and intervention groups (p>0.05) despite improvement in task quality and reduced completion time. Self-assessment questionnaires revealed a significant (p<0.05) improvement in confidence performing more complex tasks in intervention groups. Conclusions Study results demonstrate an improvement in both the quality of basic surgical skills and reduced completion time with use of the BSS simulator. Participants report increased confidence post-intervention following use of the BSS simulator. Despite the limitation of a small sample size, these findings underpin the potential benefits of BSS simulator use for surgical education amongst trainees, increasing accessibility to surgical training through an exceptionally realistic remote training environment.