Objective Performance Evaluation Research Articles

Return of showjumping horses to sporting activity after colic surgery.

The return to performance after colic surgery is crucial for competition horses. While studies have investigated racehorse performance following colic surgery by analysing racing participation and earnings, this approach does not apply to showjumping horses, leaving a gap in the literature regarding their objective performance evaluation. To assess the short- and long-term survival and return to performance in showjumping horses after colic surgery. Retrospective case series. Medical records of horses with acute colic requiring surgical treatment were analysed, and data for showjumping horses (Group 1) were retrieved. Telephone follow-ups were conducted and national competition databases were used to collect pre- and postoperative showjumping competition entries for Group 1 and for randomly selected horses (Group 2) participating in the same competitions as a comparison group. Of 253 horses undergoing colic surgery, 96 were recorded as showjumpers. The median long-term survival was 2.73 (0.01-6.14) years. Among these horses, 59 were competing at the time of surgery, and of these, 46 (78%) returned to competition and 41 (89.1%) competed at the same or higher level, while 5 (10.9%) competed at a lower level. At a 2-year follow-up, 63.6% of the showjumping horses that underwent colic surgery were alive. No significant differences were observed in the level of competition and career length between horses, which underwent colic surgery, and the randomly selected comparison group. Small sample size and a single-centre design. Showjumping horses can make a successful return to competition after colic surgery, with the majority performing at the same or higher level as before the procedure.

A Bibliometric Analysis of Online Assessment Platforms in English as a Foreign Language (EFL) Classrooms

This study presents a bibliometric analysis of research on online assessment platforms in English as a Foreign Language (EFL) classrooms, with a focus on Indonesian educational contexts from 2018 to 2022. The analysis highlights the growing recognition of digital tools in enhancing student assessment and learning outcomes. The findings reveal a significant increase in the number of publications on online assessment tools, reflecting the shift toward technology-driven education, especially during the COVID-19 pandemic. Quantitative research methods are predominantly used, emphasizing the objective evaluation of student performance through standardized tests, such as the Cornell Critical Thinking Test (CCTT). However, a noticeable gap exists in qualitative research, which could provide deeper insights into the contextual factors affecting the effectiveness of these tools. Additionally, the study identifies concerns regarding the transparency of data collection instruments, particularly their validity and reliability. The study concludes that while quantitative approaches dominate the field, there is a need for more balanced research methodologies and rigorous validation of assessment tools to ensure credible and comprehensive insights into the role of online assessments in EFL education.

A multiband terahertz metamaterial sensor evaluated using a multiresponse variable fusion framework for the precise detection of trace fluoroquinolone antibiotics

A specific structural multiband terahertz (THz) metamaterial sensor (TMS) was designed herein to improve the sensitivity of detecting multiple trace fluoroquinolone (FQ) antibiotics in water, and a multiple resonance response competition and selection fusion (MRR-CSF) framework was proposed for accurate FQ analysis. The TMS based on three-concentric C4-symmetrical split-ring (TC4) structures generated five bands at 0.797, 0.900, 1.097, 1.198, and 1.777 THz. The multiband resonance signals for three types of FQs, including enrofloxacin (ENR), pefloxacin (PEF), and nadifloxacin (NAD), were detected by the TMS, which increased as the antibiotic concentrations increased (5–140 ng/L). The proposed method can solve the problems of low accuracy and poor robustness of conventional single-response regression by combining selected and fused TMS-effective resonance response variables with three multivariate regression strategies. The calculated comprehensive metrics also provide an objective evaluation of multiband TMS performance. The designed TMS combined with the proposed MRR-CSF framework showed 21 %–27 %, 13 %–17 %, and 12 %–13 % improvements in the detection accuracy for ENR, PEF, and NAD, respectively, according to the univariate regression results. The limit of detection for the ENR, PEF, and NAD antibiotic solutions was achieved at 5 ng/L, and the reliable range of recoveries (100 % ± 25 %), average recoveries (>90 %), and relative standard deviation values (<5 %) were obtained for the concentration prediction of newly prepared FQs. This study demonstrates the promising prospect of applying the multiband TMS combined with the MRR-CSF framework for accurate biochemical sensing and detection.

Application of Deep Convolutional Generative Adversarial Network to Identification of Bridge Structural Damage

In recent years, the increase in the number of vehicles on the road has impacted the service life of bridges, and thus, their performance. In China, bridge inspections must be performed and documented at least once per year using labor-intensive and subjective approaches. In this study, a computationally efficient damage identification methodology for bridges was developed based on a deep convolutional generative adversarial network (DCGAN). To predict the damage to bridges, the vibration data of the structural damage are extracted from sensor measurements on the bridges and applied to the DCGAN model. The proposed damage identification method was validated on a simple supported beam bridge and a continuous girder bridge. The effects of sample size and noise on the identification accuracy were also evaluated. The performance of the DCGAN in the damage identification process is robust and reliable. This method for structural health monitoring offers a more objective evaluation of the overall performance and condition of a bridge, thus enabling bridge owners to efficiently allocate finite maintenance resources.

IQAGPT: computed tomography image quality assessment with vision-language and ChatGPT models

Large language models (LLMs), such as ChatGPT, have demonstrated impressive capabilities in various tasks and attracted increasing interest as a natural language interface across many domains. Recently, large vision-language models (VLMs) that learn rich vision–language correlation from image–text pairs, like BLIP-2 and GPT-4, have been intensively investigated. However, despite these developments, the application of LLMs and VLMs in image quality assessment (IQA), particularly in medical imaging, remains unexplored. This is valuable for objective performance evaluation and potential supplement or even replacement of radiologists’ opinions. To this end, this study introduces IQAGPT, an innovative computed tomography (CT) IQA system that integrates image-quality captioning VLM with ChatGPT to generate quality scores and textual reports. First, a CT-IQA dataset comprising 1,000 CT slices with diverse quality levels is professionally annotated and compiled for training and evaluation. To better leverage the capabilities of LLMs, the annotated quality scores are converted into semantically rich text descriptions using a prompt template. Second, the image-quality captioning VLM is fine-tuned on the CT-IQA dataset to generate quality descriptions. The captioning model fuses image and text features through cross-modal attention. Third, based on the quality descriptions, users verbally request ChatGPT to rate image-quality scores or produce radiological quality reports. Results demonstrate the feasibility of assessing image quality using LLMs. The proposed IQAGPT outperformed GPT-4 and CLIP-IQA, as well as multitask classification and regression models that solely rely on images.

The Influence of Lecturer Performance Evaluation on Improving the Quality of Learning at STKIP Harapan Bima

This research examines the influence of lecturer performance evaluation on the quality of learning at STKIP Harapan Bima using quantitative and qualitative approaches. Data was collected through questionnaires from 200 students and in-depth interviews with lecturers and students. The research results show that 75% of students are satisfied with lecturers' performance, with an average satisfaction score of 4.2 out of 5. The quality of learning has increased, shown by 60% of students getting an A or B grade and 70% of students feeling an increase in the quality of learning after routine lecturer performance evaluations are carried out. Statistical analysis with Pearson correlation shows a significant positive correlation between lecturer performance evaluation and learning quality (r = 0.65, p &lt; 0.01), with lecturer performance evaluation explaining 42% of the variability in learning quality (R² = 0.42). The most influential factor is the availability of lecturers for consultation. Perceptions of lecturers and students show that objective and constructive performance evaluations can increase lecturer motivation and, ultimately, the quality of learning. Research recommendations include implementing comprehensive and routine performance evaluations, using evaluation results for lecturer professional development, and further studies on specific factors and more extensive evaluation methods.

Football teaching and training based on video surveillance using deep learning.

The objective performance evaluation of an athlete is essential to allow detailed research into elite sports. The automatic identification and classification of football teaching and training exercises overcome the shortcomings of manual analytical approaches. Video monitoring is vital in detecting human conduct acts and preventing or reducing inappropriate actions in time. The video's digital material is classified by relevance depending on those individual actions. The research goal is to systematically use the data from an inertial measurement unit (IMU) and data from computer vision analysis for the deep Learning of football teaching motion recognition (DL-FTMR). There has been a search for many libraries. The studies included have examined and analyzed training through profound model construction learning methods. Investigations show the ability to distinguish the efficiency of qualified and less qualified officers for sport-specific video-based decision-making assessments. Video-based research is an effective way of assessing decision-making due to the potential to present changing in-game decision-making scenarios more environmentally friendly than static picture printing. The data showed that the filtering accuracy of responses is improved without losing response time. This observation indicates that practicing with a video monitoring system offers a play view close to that seen in a game scenario. It can be an essential way to improve the perception of selection precision. This study discusses publicly accessible training datasets for Human Activity Recognition (HAR) and presents a dataset that combines various components. The study also used the UT-Interaction dataset to identify complex events. Thus, the experimental results of DL-FTMR give a performance ratio of 94.5%, behavior processing ratio of 92.4%, athletes energy level ratio of 92.5%, interaction ratio of 91.8%, prediction ratio of 92.5%, sensitivity ratio of 93.7%, and the precision ratio of 94.86% compared to the optimized convolutional neural network (OCNN), Gaussian Mixture Model (GMM), you only look once (YOLO), Human Activity Recognition- state-of-the-art methodologies (HAR-SAM). This finding proves that exercising a video monitoring system that provides a play view similar to that seen in a game scenario can be a valuable technique to increase selection accuracy perception.

Enhancing educational evaluation through predictive student assessment modeling

This study evaluates several machine learning models used in predicting student performance. The data utilized in this study was collected from 253 undergraduate students participating in five classes within one of three courses offered by VnCodelab, an interactive learning management system, to provide insights into student performance. Leveraging the data-rich environment of the interactive learning management system proposed earlier, this study focuses on training a predictive model that forecasts student grades based on the comprehensive data collected during the teaching process. The proposed model capitalizes on the data obtained from students' engagement patterns, time spent on exercises, and progress tracking across learning activities. This study compared five different base classifiers— Random Forest (RF), Logistic Regression (LR), Support Vector Machine (SVM), Naïve Bayes (NB), and k-nearest Neighbor (k-NN), and an ensemble learning method Stacking Classifier —utilizing a dataset comprising 13 features. The research assesses the model's accuracy, reliability, and implications, contributing to the evolution of educational evaluation by introducing predictive assessment as a transformative tool. The results indicate that the Stacking Classifier accurately predicts students' grade ranges, surpassing individual base classification models by effectively combining their predictive capabilities. Integrating data-driven forecasting into the educational ecosystem can transform teaching methodologies and foster an informed, engaged, and empowered learning environment. This approach cultivates a proactive learning community by empowering students with real-time academic progress forecasts. Educators benefit from data-informed insights that facilitate more effective and objective performance evaluation.

AIxSuture: vision-based assessment of open suturing skills

PurposeEfficient and precise surgical skills are essential in ensuring positive patient outcomes. By continuously providing real-time, data driven, and objective evaluation of surgical performance, automated skill assessment has the potential to greatly improve surgical skill training. Whereas machine learning-based surgical skill assessment is gaining traction for minimally invasive techniques, this cannot be said for open surgery skills. Open surgery generally has more degrees of freedom when compared to minimally invasive surgery, making it more difficult to interpret. In this paper, we present novel approaches for skill assessment for open surgery skills.MethodsWe analyzed a novel video dataset for open suturing training. We provide a detailed analysis of the dataset and define evaluation guidelines, using state of the art deep learning models. Furthermore, we present novel benchmarking results for surgical skill assessment in open suturing. The models are trained to classify a video into three skill levels based on the global rating score. To obtain initial results for video-based surgical skill classification, we benchmarked a temporal segment network with both an I3D and a Video Swin backbone on this dataset.ResultsThe dataset is composed of 314 videos of approximately five minutes each. Model benchmarking results are an accuracy and F1 score of up to 75 and 72%, respectively. This is similar to the performance achieved by the individual raters, regarding inter-rater agreement and rater variability. We present the first end-to-end trained approach for skill assessment for open surgery training.ConclusionWe provide a thorough analysis of a new dataset as well as novel benchmarking results for surgical skill assessment. This opens the doors to new advances in skill assessment by enabling video-based skill assessment for classic surgical techniques with the potential to improve the surgical outcome of patients.

Development of performance and learning rate evaluation models in robot-assisted surgery using electroencephalography and eye-tracking

The existing performance evaluation methods in robot-assisted surgery (RAS) are mainly subjective, costly, and affected by shortcomings such as the inconsistency of results and dependency on the raters’ opinions. The aim of this study was to develop models for an objective evaluation of performance and rate of learning RAS skills while practicing surgical simulator tasks. The electroencephalogram (EEG) and eye-tracking data were recorded from 26 subjects while performing Tubes, Suture Sponge, and Dots and Needles tasks. Performance scores were generated by the simulator program. The functional brain networks were extracted using EEG data and coherence analysis. Then these networks, along with community detection analysis, facilitated the extraction of average search information and average temporal flexibility features at 21 Brodmann areas (BA) and four band frequencies. Twelve eye-tracking features were extracted and used to develop linear random intercept models for performance evaluation and multivariate linear regression models for the evaluation of the learning rate. Results showed that subject-wise standardization of features improved the R2 of the models. Average pupil diameter and rate of saccade were associated with performance in the Tubes task (multivariate analysis; p-value = 0.01 and p-value = 0.04, respectively). Entropy of pupil diameter was associated with performance in Dots and Needles task (multivariate analysis; p-value = 0.01). Average temporal flexibility and search information in several BAs and band frequencies were associated with performance and rate of learning. The models may be used to objectify performance and learning rate evaluation in RAS once validated with a broader sample size and tasks.

Rapid detection of non-normal teeth on dental X-ray images using improved Mask R-CNN with attention mechanism.

Dental health has been getting increased attention. Timely detection of non-normal teeth (caries, residual root, retainer, teeth filling, etc.) is of great importance for people's health, well-being, and quality of life. This work proposes a rapid detection of non-normal teeth based on improved Mask R-CNN, aiming to achieve comprehensive screening of non-normal teeth on dental X-ray images. An improved Mask R-CNN based on attention mechanism was used to develop a non-normal teeth detection method trained on a high-quality annotated dataset, which can segment the whole mask of each non-normal tooth on the dental X-ray image immediately. The average precision (AP) of the proposed non-normal teeth detection was 0.795 with an intersection-over-union of 0.5 and max detections (maxDets) of 32, which was higher than that of the typical Mask R-CNN method (AP = 0.750). In addition, validation experiments showed that the evaluation metrics (AP, recall, precision-recall (P-R) curve) of the proposed method were superior to those of the Mask R-CNN method. Furthermore, the experimental results indicated that proposed method exhibited a high sensitivity (95.65%) in detecting secondary caries. The proposed method took about 0.12s to segment non-normal teeth on one dental X-ray image using the laptop (8G memory, NVIDIA RTX 3060 graphics processing unit), which was much faster than conventional manual methods. The proposed method enhances the accuracy and efficiency of abnormal tooth diagnosis for practitioners, while also facilitating early detection and treatment of dental caries to substantially lower patient costs. Additionally, it can enable rapid and objective evaluation of student performance in dental examinations.

Evaluation of Emission Reduction Performance of Power Enterprises Based on Least Squares Support Vector Machine

In response to the increasingly severe environmental pollution problem at present, many traditional power companies are facing the promotion of environmental protection work. The promotion of Energy Conservation and Emission Reduction (ECER) work needs sufficient theory and data support. Therefore, this study proposes a performance evaluation model for ECER in power enterprises based on the improved lion swarm algorithm optimized Least Squares Support Vector Machine (LSSVM). The evaluation index system has been reconstructed according to the current environment and era characteristics. The LSSVM has been used to evaluate the ECER work of power enterprises, and the training efficiency of Lion Swarm Optimization (LSO) algorithm has been solved using chaos theory. An improved LSO algorithm is utilized to improve the parameter selection problem of the LSSVM model, to achieve a scientific and objective evaluation of the ECER performance of power enterprises. The research findings denote that the algorithm put forward in this study has better training efficiency, higher accuracy and stability, as well as better response time. It also exhibits minimal errors in simulation experiments. In summary, the ECER performance evaluation model proposed by this research institute can effectively output correct scoring results, providing data support for the promotion of environmental protection work.

A Comparative Evaluation of Optical See-Through Augmented Reality in Surgical Guidance.

During traditional surgeries, planning and instrument guidance is displayed on an external screen. Recent developments of augmented reality (AR) techniques can overcome obstacles including hand-eye discoordination and heavy mental load. Among these AR technologies, optical see-through (OST) schemes with stereoscopic displays can provide depth perception and retain the physical scene for safety considerations. However, limitations still exist in certain AR systems and the influence of these factors on surgical performance is yet to explore. To this end, experiments of multi-scale surgical tasks were carried out to compare head-mounted display (HMD) AR and autostereoscopic image overlay (AIO) AR, concerning objective performance and subjective evaluation. To solely analyze effects brought by display techniques, the tracking system in each included display system was identical and similar tracking accuracy was proved by a preliminary experiment. Focus and context rendering was utilized to enhance in-situ visualization for surgical guidance. Latency values of all display systems were assessed and a delay experiment proved the latency differences had no significant impact on user performance. Results of multi-scale surgical tasks showed that HMD outperformed in detailed operations probably due to stable resolution along the depth axis, while AIO had better performance in larger-scale operations for better depth perception. This article helps point out the critical limitations of current OST AR techniques and potentially promotes the progress of AR applications in surgical guidance.

수처리용 섬유여과기 성능평가인자 도출 및 평가방법개발에 관한 연구

This study investigated the performance evaluation factors for the fiber filtration process in water treatment and researched the performance evaluation items and methods for assessing these factors. South Korea's water supply and sewage policies focus on modernizing civil structures and networks and introducing advanced treatment facilities. Reflecting these policies, the Waterworks Act describes the minimum requirements that water supply facilities must meet to ensure a consistent quality (water quality) of drinking water. Therefore, the Waterworks Act has introduced usage standards for water treatment processes and materials for water supply to manage the quality of drinking water. Firstly, the Ministry of Environment has issued water supply facility standards, which explain the minimum standards for water supply facilities. Secondly, there are hygiene and safety standards (suitability certification) and group standard certification. Standards and certifications are established to evaluate materials and products suitable for water supply facilities. Thus, two criteria are required to apply them to water supply facilities.&#x0D; In Republic of Korea, a new concept of water treatment process has been developed that can replace traditional water treatment processes. This involves a fiber filtration process that contracts and relaxes fiber strands, removing turbidity and suspended solids. There is a movement to adopt this process. Such fiber filters can provide better treatment efficiency, use smaller sites, and have faster processing speeds. However, the use of different evaluation methods by manufacturers has prevented consumers from accurately understanding the performance and raised doubts about the reliability of the technology.&#x0D; The result of this study is the development of a standardized performance evaluation method, and the derivation of performance evaluation factors for fiber filters. Through standardized performance evaluation factors and methods, an objective performance evaluation of the fiber filtration process can be conducted.

Development of an Automated and Scalable Virtual Assistant to Aid in PPE Adherence: A Study with Implications for Applications within Anesthesiology.

Virtual assistants (VAs) are conversational agents that are able to provide cognitive aid. We developed a VA device for donning and doffing personal protective equipment (PPE) procedures and compared it to live human coaching to explore the feasibility of using VAs in the anesthesiology setting. An automated, scalable, voice-enabled VA was built using the Amazon Alexa device and Alexa Skills application. The device utilized voice-recognition technology to allow a touch-free interactive user experience. Audio and video step-by-step instructions for proper donning and doffing of PPE were programmed and displayed on an Echo Show device. The effectiveness of VA in aiding adherence to PPE protocols was compared to traditional human coaching in a randomized, controlled, single-blinded crossover design. 70 anesthesiologists, anesthesia assistants, respiratory therapists, and operating room nurses performed both donning and doffing procedures, once under step-by-step VA instructional guidance and once with human coaching. Performance was assessed using objective performance evaluation donning and doffing checklists. More participants in the VA group correctly performed the step of "Wash hands for 20 seconds" during both donning and doffing tests. Fewer participants in the VA group correctly performed the steps of "Put cap on and ensure covers hair and ears" and "Tie gown on back and around neck". The mean doffing total score was higher in the VA group; however, the donning score was similar in both groups. Our study demonstrates that it is feasible to use commercially available technology to create a voice-enabled VA that provides effective step-by-step instructions to healthcare professionals.

Augmented reality for surgical skills training, update on the topic

Augmented reality (AR) combines digital information with physical reality, allowing users to interact with virtual data in their real environment. In medical education, this technology is attractive because it allows access to information without taking your eyes off the surgical field. The integration of surgical simulators in resident training offers flexible practice without direct supervision, with benefits such as objective performance evaluation, practice of unusual procedures, and the development of non-technical skills. In addition, simulators are useful for teaching new techniques to experts. In summary, AR and simulators offer valuable opportunities to improve surgical training. The objective of this review is to update the current state of augmented reality in surgical training. The use of augmented reality as an assessment tool in surgical training presents interesting perspectives that deserve consideration. AR can provide objective performance metrics by measuring the technical competency of surgical trainees, whether in a simulated operating environment or in real-world situations. This offers the opportunity to address subjective variability and potential bias in current assessment methods, which often rely on supervisor observation and rating. However, despite these advantages, there has not yet been a comprehensive review to evaluate the use of AR in surgical training. The cost-benefit and implications for data management have not yet been addressed.

The Rating of Basketball Players' Competitive Performance Based on RBF-EVA Method

Basketball, as an offensive and defensive game centered around high altitude, has become an international mass competitive sport. Traditional methods cannot comprehensively evaluate the future potential of players, nor can they simply add up individual competitive abilities to judge the overall competitive performance of a team. To address these issues, this article proposes a video-based RBF neural network competitive scoring method, which analyzes players' past sports behavior, captures every subtle difference in their abilities, and achieves objective evaluation of players' competitive performance. Through comparative experiments, the accuracy of the test results is improved by about 5% compared to conventional RBF methods. This indicates that the improved RBF neural network designed in this article has significantly better prediction performance than traditional convolutional neural networks. This study provides a new method for evaluating the competitive performance of basketball players and has important guiding significance for basketball training and skill enhancement.

RLaMs-Dehazing: Optimized Depth Map Improvement Single Colour Image Dehazing

Visibility Degradation is a classical problem owing to the presence of Atmospheric Particulate Matter (APM). There are different image dehazing algorithms. Any one method cannot be relied upon as each haze condition is unique. An innovative algorithm has been proposed inverting the image formation atmospheric scattering model [2, 32]. The model has been improvised by one key factor. This is Regularized Lagrangian multiplier (RLaM) based Depth Map (DM) refinement. The algorithm has low time complexity which intrigues real-time efficient applications. Different state-of-the-art visibility algorithms have been studied and their subjective and objective performance evaluations have been evaluated. Extensive investigation shows remarkable improvement with the proposed algorithm. This method is equally applicable to different atmospheric conditions. Time complexity experimented with execution time and Big (O) for real-time effectiveness. Extensive experiment results show the potential of the proposed algorithm independent of the influence of atmospheric conditions and capturing devices adaptive to computer vision applications. Time complexity and quality output trade-off achieved with the removal of ringing artifacts efficiently.

DEA에 의한 전통시장의 효율성측정 개선에 관한 연구

In order to increase the effectiveness of traditional market policies, in which enormous funds are invested, it can be said that objective and transparent performance evaluation of traditional markets takes precedence. This study proposed two improvements to the existing DEA method in order to improve efficiency studies on traditional markets. In other words, by using a two-step approach using bootstrap, we first calculated the bias-corrected efficiency and examined how external environmental factors affect the efficiency of the traditional market. First, in order to improve the current DEA efficiency index, which is overestimated based on a finite number of samples, we tried to obtain a bias-corrected efficiency index through bootstrapping. This improvement has caused significant changes in the efficiency rankings among existing traditional markets. Second, truncated regression analysis was conducted by bootstrapping to explain the differences in efficiency between traditional markets that inevitably occur depending on the external environment such as location of traditional markets. As a result, it was found that the existence of large retail stores had a negative effect on the efficiency of traditional markets, while the existence of public facilities and transportation facilities had a positive effect on efficiency. If such an external environment becomes an important cause for the difference in efficiency between traditional markets, regardless of internal management efforts in traditional markets, it can be said that appropriate adjustments and modifications are required in performance evaluation.

A108Using a high-fidelity part-task model as a replacement for animal models to enhance emergency medicine thoracotomy training

A local course has been delivered since 2018 to provide training in rare, time-critical, life-saving procedures that emergency medicine trainees may need to perform, including emergency lateral thoracotomy. Previously pig thoraxes have been used to provide thoracotomy training in this course. In 2022 a part-task model was purchased for thoracotomy training to replace this. We compare the use of this to animal models in our provision of simulation teaching of this skill. Animal models are useful due to availability, low cost, and provision of hands-on experience for learners. However, there are associated ethical, religious, psychological limitations with using animals for educational purposes. They may less accurately represent human anatomy and pathology. Their timely acquisition can pose a challenge with defrosting requirements and variable condition which can impact on the quality of training. There are infection risks due to fluid leakage, associated unpleasant odour and animal products require incineration for disposal. The high-fidelity part-task mannequin can anatomically and physiologically replicate the thoracotomy experience to provide realistic simulation training. The mannequin is equipped with realistic external human features and internal organ anatomy, including a beating heat and blood, to provide an immersive training experience. It is reusable with repair, which could be cost-effective long term and more environmentally-friendly. This model enables thoracotomy skill practice in a safe, controlled environment without infection risk. Another advantage is the ability to provide standardized training, better allowing for objective evaluation of performance which can be challenging with animal models. This model ( The immersive lateral thoracotomy model we are using, which is fully repairable to be used multiple times for authentic learner experience There are disadvantages to use of this model for the department, but the overall learning experience and sustainability is felt to be superior to animal models for training in emergency thoracotomy. Authors confirm that all relevant ethical standards for research conduct and dissemination have been met. The submitting author confirms that relevant ethical approval was granted, if applicable.