Performance Assessment Research Articles

A Probabilistic-Based Resource Flow Method for the Seismic Performance Assessment of Multi-State Systems

ABSTRACT Most engineering systems, such as power supply and medical systems, are complex systems that are usually vulnerable during severe earthquakes. Some components in these systems can perform in multiple damage states, resulting in the partial or total capacity loss of the system. However, the quantification of the performance of a complex multi-state system is difficult. This article proposes a probability-based method called the resource flow method, which expresses a complex system by a logic diagram, defines the operation of the system by the resource delivery of the connected components, and calculates the component functionality by logical symbols.

Accounting for relative age effect in sports performance assessment: Developing age-adjusted performance corridors using mixed models

ABSTRACT This study developed a novel statistical approach to account for relative age effect (RAE) on physical performance in young populations. Data from 1715 elite youth French rugby union players (age: mean = 15.9 yrs, sd = 1.2 ; height: mean = 177 cm, sd = 9 ; mass: mean = 79 kg, sd = 16 ) were analysed using their 50 m sprint times. Linear mixed models were employed to characterise the relationship between age and physical performance, incorporating log transformations to address non-linearity and additional parameters to refine model accuracy. Confidence intervals around predictions established performance corridors for each age, while estimated individual effects enabled the calculation of personalised progression curves and corridors. The findings provided novel insights into accounting for RAE and underscored the utility of advanced statistical methods in performance assessment. Specifically, the approach addressed non-linearity in progression with respect to both age and performance level, introduced a new performance indicator – the corridors – that integrates chronological and relative age, and provided a personalised framework for tracking athletes, progression over time.

From 'Sovereign Self-Proclaimed Experts' to 'Impressionable Sceptics'-Developing a Patient Typology to Distinguish Patients' Interactions With Healthcare: A Qualitative Study in Germany.

Person-centredness in health systems puts patients and their preferences at the centre of healthcare. However, there is not an 'one size fits all' approach as patients are heterogenous and have varying interactions with and perceptions of healthcare, and assessments of the health system performance. This study aims to explore these patient differences by (1) identifying core attributes of patients that shape their general approach to and interactions with healthcare and (2) deriving specific patient types based on these core attributes. The qualitative study included content analysis of semi-structured, problem-oriented interviews with 27 participants selected with the aim of maximum variation and heterogeneity (e.g., regarding age, gender, health status, place of living) from the Berlin-Brandenburg region of Germany. Based on the interviews with the participants, three core patient attributes were found that shape interactions with healthcare: (1) taking care of health and illness, (2) the self-assigned patient role, and (3) the patient-assigned healthcare provider role. Seven patient types were identified across (opposing) manifestations of these core attributes, ranging from 'sovereign self-proclaimed experts' (focussing-autonomous-fulfiller) to 'impressionable sceptics' (ignoring-heteronomous-seller). Consideration of the identified patient types and their different ways of engaging with healthcare providers and their varying perceptions of the health system can help to develop strategies to promote person-centredness in health systems. Furthermore, this typology can inform providers about the diverse ways in which patients may perceive healthcare interactions, and it can be useful for the training of future physicians and other healthcare professionals.

Survival analysis using machine learning in transplantation: a practical introduction

BackgroundSurvival analysis is a critical tool in transplantation studies. The integration of machine learning techniques, particularly the Random Survival Forest (RSF) model, offers potential enhancements to predictive modeling and decision-making. This study aims to provide an introduction to the application of the RSF model in survival analysis in kidney transplantation alongside a practical guide to develop and evaluate predictive algorithms.MethodsWe employed a RSF model to analyze a simulated dataset of kidney transplant recipients. The data were split into training, validation, and test sets using split sample (70%-30%) and cross-validation (5-folds) techniques to evaluate model performance. Hyperparameter tuning strategies were employed to select the best model. The concordance index (C-index) and Integrated Brier Score (IBS) were used for internal validation. Additionally, time-dependent AUC, F1 score, accuracy, and precision were evaluated to provide a comprehensive assessment of the model's predictive performance. Finally, a Cox Proportional Hazards model was fitted to compare the results of the main metrics between both models. All analyses were supported by step-by-step code to ensure reproducibility.Findings.The RSF model obtained a C-index of 0.774, an IBS of 0.090. The F1 score was of 0.945, accuracy was 89.67 and precision was 90.99%. The time-dependent ROC analysis produced an AUC of 0.709, indicating a moderate predictive performance. Lastly, the analysis shows that the three most important variables are donor age, BMI, and recipient age.ConclusionsThis study demonstrates the robustness and potential of the RSF model in kidney transplant analysis, achieving strong validation metrics and highlighting its advantages in managing complex, censored data, while emphasizing the need for further exploration of hybrid models and clinical integration.

Development and performance assessment of a novel scroll compressor-based oxygen generator integrated ventilator

Current ventilators rely on wall outlets or cylinders for oxygen supply, which limits their continuous use in the field or emergencies. In this study, we proposed a ventilator prototype that can achieve stand-alone oxygenated respiratory support, by designing and integrating a high-performance oxygen generator, and optimizing the control strategies of the whole system. Based on the designed oil-free scroll compressor and pressure swing adsorption (PSA) system, we first realized a mobile high-flow oxygen generator, which achieved an output flow greater than 17 L/min with an oxygen concentration of 93% ± 3%. The ventilator was also designed to synchronize with the respiratory state, to optimize the trigger performance for the pressure support of early inspiration, and reduce the gas supply in the late inspiratory phase to avoid pressure overshoot in the early expiratory phase. The respiratory synchronization of the integrated ventilator was estimated by the recorded chest movement of the subjects. Satisfactory respiratory synchronization was realized with an inspiratory trigger delay (ITD) time of less than 200 ms and sound respiratory waveform tracking. By regulating the PSA strategy, the oxygen generation and utilization efficiencies could be further improved. Ultimately, under the setting of inspiratory positive airway pressure (IPAP) at 10 cmH2O, and expiratory positive airway pressure (EPAP) at 4 cmH2O, we achieved non-invasive ventilation with a maximum oxygen concentration of 58% ± 1.75%. In conclusion, the proposed oxygen generator integrated ventilator could provide reliable oxygenated respiratory support in emergencies, such as on-site first aid, patient transport, and military field environments.

A comprehensive review on the treatment of pharmaceutically active compounds using moving bed biofilm reactor: A systematic meta-analysis coupled with meta-neural approach.

A comprehensive review on the treatment of pharmaceutically active compounds using moving bed biofilm reactor: A systematic meta-analysis coupled with meta-neural approach.

A comparative assessment of geotechnical performance, cost and carbon footprint of expansive soil treated with cement, lime and fly ash

ABSTRACT Chemical stabilisation enhances strength and reduces the swell characteristic of expansive soils, and cement, lime and fly ash (FA) have been used as stabilisers. Currently, there is a scarcity of studies addressing the mix optimisation of expansive soil stabilised with cement, lime, and FA, considering factors such as strength, swell characteristics, cost, and emissions. Thus, the objective of this research is to develop an optimal mix for stabilising expansive soil using cement, lime, and FA, based on these parameters. The samples were stabilised with 2%–12% cement, 1%–6% lime and 5%–30% FA, and testing including unconfined compressive strength (UCS), swell pressure and California bearing ratio (CBR) were conducted. Utility analysis was undertaken by using Multi-Attribute Utility Theory (MAUT) incorporating UCS, cost/UCS, swell pressure, swell percent, and emissions as key parameters. The findings revealed that UCS of the cement stabilised samples increases with the cement content, while the optimum lime and FA contents based on UCS were 3% and 15% respectively. The swell pressure values of cement, lime and FA stabilised soils reduced by 11.6%–35.9%, 11.6%–22.8% and 45.0%–65.6%, respectively. Overall utility analysis revealed that 15% FA stabilised mix is the optimum mix in terms of strength, swell, cost, and emission.

Blood lactate accumulation during maximal cycling sprints and its relationship to sprint performance characteristics.

Blood lactate accumulation (ΔBLC) during maximal short-term exercise is a crucial indicator of peak glycolytic activation in anaerobic performance assessment. However, the relationship between ΔBLC and sprint performance remains inconsistent, potentially due to variations in testing protocols and the use of absolute rather than relative performance metrics. This study investigated the relationship between ΔBLC and cycling sprint performance, hypothesizing normalization to body weight is essential for accurate metabolic performance evaluation. Twenty-two trained male athletes performed a 10-s maximal isokinetic cycling sprint on an ergometer. Power output and cadence were continuously recorded to calculate peak power (Ppeak), time to peak power (tPpeak), mean power (Pmean), and power increase during the lactic phase (maxΔP, ΔP). Capillary blood samples were collected pre-exercise and up to 12min post-exercise to determine pre-exercise (BLCpre) and maximal post-exercise blood lactate concentration (BLCmax). ΔBLC was calculated as BLCmax-BLCpre. Statistical analysis included Pearson correlations and stepwise multiple regression. ΔBLC exhibited significant correlations with body-weight-normalized maxΔP (r = 0.78, p < 0.001), Pmean (r = 0.70, p < 0.001), and Ppeak (r = 0.65, p < 0.01). In contrast, no significant correlations were observed with absolute metrics (p > 0.05). Stepwise regression analysis identified adjusted maxΔP and Pmean as the strongest predictors of ΔBLC (adjusted R2 = 0.648, p < 0.001). Relative, body-weight-adjusted metrics, particularly maxΔP and Pmean, are strongly associated with ΔBLC. The use of these relative metrics may enhance the precision of anaerobic performance assessment, facilitate more effective training monitoring, and improve talent identification processes in sports requiring high-intensity efforts.

Improved cancer detection through feature selection using the binary Al Biruni Earth radius algorithm

With the advancement of medical technology, a large amount of complex data on cancers is produced for diagnosing and treating cancers. However, not all this data is useful, as many features are redundant or irrelevant, which can reduce the accuracy of machine learning models. Metaheuristic algorithms have been employed to select features to address this issue. Although the efficacy of these algorithms has been demonstrated, challenges related to scalability and efficiency persist when handling large medical datasets. In this study, a binary version of the Advanced Al-Biruni Earth Radius (bABER) algorithm is proposed for the intelligent removal of unnecessary data and identifying the most essential features for cancer detection. Unlike traditional methods that rely on a single approach, bABER is evaluated using seven medical datasets and compared with eight widely used binary metaheuristic algorithms, including bSC, bPSO, bWAO, bGWO, bMVO, bSBO, bFA, and bGA. Statistical tests such as ANOVA and the Wilcoxon signed-rank test are conducted to ensure a thorough performance assessment. The results indicate that the bABER algorithm significantly outperforms other methods, making it a valuable tool for improving cancer diagnosis. By refining feature selection, this approach enhances existing machine learning models, leading to more accurate and reliable medical predictions. This study contributes to improved data-driven decision-making in healthcare, bringing the field closer to faster and more precise cancer detection.

Using performance assessments instead of high-stakes tests: A promising strategy for a better future

Using performance assessments instead of high-stakes tests: A promising strategy for a better future

Predictive Performance Assessment in Simulation Training using Machine Learning

Predictive Performance Assessment in Simulation Training using Machine Learning

Thermal Performance and Cost Assessment Analysis of a Double-Pass V-Trough Solar Air Heater

Solar air heating systems offer an effective alternative for reducing greenhouse gas emissions at a profitable cost. This work details the design, construction, and experimental evaluation of a novel double-pass V-trough solar air heater with semicircular receivers, which was built with low-cost materials readily available in the Mexican market. Thermal performance tests were conducted in accordance with the ANSI-ASHRAE 93-2010 standard. The results indicated a peak collector efficiency of 0.4461 and total heat losses of 8.8793 W/(m2 °C), with an air mass flow rate of 0.0174 kg/s. The instantaneous thermal efficiency varied between 0.2603 and 0.5633 with different air flow rates and an inlet air temperature close to the ambient temperature. The outlet air temperature reached 70 °C, making it suitable for dehydrating fruits or vegetables at competitive operating costs. Additionally, a second-law analysis was carried out, and the exergy efficiency was between 0.0037 and 0.0407. Finally, a Levelized Cost of Energy analysis was performed, and the result was USD 0.079/kWh, which was 31% lower than that of a conventional electric air heater system.

SAFENET-2 – fracture evolution in crystalline rocks (from lab to in situ scale)

Abstract. The DECOVALEX Task SAFENET is dedicated to advancing the understanding of fracture nucleation and evolution processes in crystalline rocks, with applications in nuclear waste management and geothermal reservoir engineering. Further improvements to fracture mechanics models are required in two distinct areas. Firstly, there is a need to enhance numerical methods for fracture mechanics under varying thermo-hydro-mechanical (THM) conditions. Secondly, there is a requirement to develop applied tools for performance and safety assessment in the context of nuclear waste management, as well as for reservoir optimisation in geothermal applications. Building on the achievements of SAFENET, which concentrated on benchmarking fracture models and experimental laboratory analyses, SAFENET-2 is dedicated to extending and validating models from the laboratory to the field scale. This paper gives a detailed description of the SAFENET-2 experimental programme work plan and modelling exercises. The experiments will be carried out at the rock mechanics laboratories of the University of Edinburgh and Chongqing University. For field data, the STIMTEC experiment at the Reiche Zeche teaching and research mine (Technische Universität Bergakademie Freiberg) is used. The individual steps of the Task are described in detail in this paper. As a result of SAFENET, the benchmark suite will be made available as interactive exercises via a web portal, thus promoting the concept of open science. The paper is a tool for teams to organise their work efficiently and is also an overview and insight for the community.

Enhancing flood prediction through remote sensing, machine learning, and Google Earth Engine

Floods are the most common natural hazard, causing major economic losses and severely affecting people’s lives. Therefore, accurately identifying vulnerable areas is crucial for saving lives and resources, particularly in regions with restricted access and insufficient data. The aim of this study was to automate the identification of flood-prone areas within a data-scarce, mountainous watershed using remote sensing (RS) and machine learning (ML) models. In this study, we integrate the Normalized Difference Flood Index (NDFI), using Google Earth Engine to generate flood inventory, which is considered a crucial step in flood susceptibility mapping. Seventeen determining factors, namely, elevation, slope, aspect, curvature, the Stream Power Index (SPI), the Topographic Wetness Index (TWI), the Topographic Ruggedness Index (TRI), the Topographic Position Index (TPI), distance from roads, distance from rivers, stream density, rainfall, lithology, the Normalized Difference Vegetation Index (NDVI), land use, length slope (LS) factor, and the Convergence Index were used to map the flood vulnerability. This study aimed to assess the predictive performance of gradient boosting, AdaBoost, and random forest. The model performance was evaluated using the area under the curve (AUC). The performance assessment results showed that random forest (RF) achieved the highest accuracy (1), followed by random forest and gradient boosting ensemble (RF-GB) (0.96), gradient boosting (GB) (0.95), and AdaBoost (AdaB) (0.83). Additionally, in this research study, we employed the Shapely Additive Explanations (SHAP) method, to explain machine learning model predictions and determine the most contributing factor in each model. This study introduces a novel approach to generate flood inventory, providing significant insights into flood susceptibility mapping, and offering potential pathways for future research and practical applications. Overall, the research emphasizes the need to integrate urban planning with emergency preparedness to build safer and more resilient communities.

Interpretable machine learning models for prolonged Emergency Department wait time prediction

ObjectiveProlonged Emergency Department (ED) wait times lead to diminished healthcare quality. Utilizing machine learning (ML) to predict patient wait times could aid in ED operational management. Our aim is to perform a comprehensive analysis of ML models for ED wait time prediction, identify key feature importance and associations with prolonged wait times, and interpret prediction model clinical relevance among ED patients.MethodsThis is a single-centered retrospective study. We included ED patients assigned an Emergency Severity Index (ESI) level of 3 at triage. Patient wait times were categorized as <30 minutes and ≥30 minutes (prolonged wait time). We employed five ML algorithms - cross-validation logistic regression (CVLR), random forest (RF), extreme gradient boosting (XGBoost), artificial neural network (ANN), and support vector machine (SVM) - for predicting patient prolonged wait times. Performance assessment utilized accuracy, recall, precision, F1 score, false positive rate (FPR), and false negative rate (FNR). Furthermore, using XGBoost as an example, model key features and partial dependency plots (PDP) of these key features were illustrated. Shapley additive explanations (SHAP) were employed to interpret model outputs. Additionally, a top key feature interaction analysis was conducted.ResultsAmong total 177,665 patients, nearly half of them (48.20%, 85,632) experienced prolonged ED wait times. Though all five ML models exhibited similar performance, minimizing FNR is associated with the most clinical relevance for wait time predictions. The top features influencing patient wait times and gaining the top ranked interactions were ED crowding condition and patient mode of arrival.ConclusionsNearly half of the patients experienced prolonged wait times in the ED. ML models demonstrated acceptable performance, particularly in minimizing FNR when predicting ED wait times. The prediction of prolonged wait times was influenced by multiple interacting factors. Proper application of ML models to clinical practice requires interpreting their predictions of prolonged wait times in the context of clinical significance.

Low creatinine to cystatin C ratio is associated with lower muscle volumes and poorer gait speeds in the longitudinal Integrated Women's Health Program cohort.

Little is known about the longitudinal associations between creatinine-cystatin C ratios (CCR) with muscle volume and function during the menopausal transition. We investigated the longitudinal relationship of baseline CCR, with muscle volumes measured by magnetic resonance imaging (MRI), and objectively measured muscle strength and physical performance after 6.6-year follow-up. Participants from the Integrated Women's Health Programme (IWHP) cohort (n = 891, baseline mean age 56.2 ± 6.0) who attended both baseline and follow-up visits underwent objectively measured muscle strength and physical performance assessments and MRI. Creatinine to cystatin C ratio was calculated as (creatinine [mg/dL] / cystatin C [mg/L]) and low CCR were those in the lowest tertile (CCR < 8.16). Multivariable regression analyses were used to determine the associations of baseline CCR with muscle volumes and function 6.6 years later. Baseline low CCR was associated with lower MRI-measured muscle volumes and poorer physical function 6.6 years later. Compared to high CCR group, mean fat-free thigh muscle volume of the low CCR group was 0.350 L lower (95% CI, 0.183-0.518) after adjustment for covariates. Similarly, the low CCR group was associated with 0.029 m/s slower (95% CI, 0.006-0.053) slower mean usual gait and 0.049 m/s slower (95% CI, 0.020-0.078) mean narrow gait speeds. CCR was not associated with handgrip strength and repeated chair stands and one-leg stand tests. Low CCR at baseline was associated with lower fat-free muscle volumes and poorer gait speeds 6.6 years later. The potential of CCR as a predictive biomarker for adverse events related to sarcopenia in midlife women merits further investigation.

MALDI-TOF MS experience with the identification of complex microorganisms using two devices in a National Reference Laboratory.

This work detailed a matrix-assisted laser desorption/ionization time-of-flight (MALDI-TOF) experience with the identification of a large set of complex microorganisms using two devices in the settings of a National Reference Laboratory. In Argentina, we coordinate the national network of mass spectrometry (MS), an extended work group formed in year 2015 that includes all public and private laboratories that have incorporated the technology as a diagnostic tool through the different commercial platforms available on the market. At this time, as a National Reference Laboratory, we conducted this comprehensive assessment of MALDI-TOF MS performance for the identification of diverse bacteria, specifically targeting pathogens of significant public health concern. During investigation, we identified certain limitations inherent to the methodology and the results obtained have considerable impact that must be transferred to the users of MS platforms in clinical laboratories all over the world.

Financial turmoil in English professional rugby: a holistic performance assessment of English rugby union clubs (2003–2022)

Financial turmoil in English professional rugby: a holistic performance assessment of English rugby union clubs (2003–2022)

A comprehensive assessment of thermal performance of 3D printed concrete lattice walls

A comprehensive assessment of thermal performance of 3D printed concrete lattice walls

Artificial Intelligence and Augmented Reality: Revolutionizing E-Learning for the Education of Tomorrow

E-Learning, supported by emerging technologies such as Artificial Intelligence (AI) and Augmented Reality (AR), is reinventing the entire education sector by offering more immersive and personalized learning experiences. Despite their promising potential, their integration remains limited, particularly in terms of personalizing learning paths and optimizing educational interactions. This article demonstrates, through a study based on multiple case studies and an assessment of student performance before and after the implementation of these technologies in E-Learning platforms, the benefits that emerge, including a significant increase in engagement and knowledge retention, as well as a positive impact on learning trajectories, with greater motivation and improved performance. The implementation of AI and AR has made learning paths more individualized, enhancing both motivation and performance, and strengthening learning and educational interactions. In these developments, AI and AR appear to be essential tools in shaping the future of education, introducing innovative teaching methods that are more interactive and learner-centered.