Performance Assessment Research Articles

Meta-analysis of [18F]FDG-PET/CT in pulmonary sarcoidosis.

18F-Fluorodeoxyglucose (FDG) PET/CT is emerging as a tool in the diagnosis and evaluation of pulmonary sarcoidosis, however, there is limited consensus regarding its diagnostic performance and prognostic value. A meta-analysis was conducted with PubMed, Science Direct, MEDLINE, Scopus, and CENTRAL databases searched up to and including September 2023. 1355 studies were screened, with seventeen (n = 708 patients) suitable based on their assessment of the diagnostic performance or prognostic value of FDG-PET/CT. Study quality was assessed using the QUADAS-2 tool. Forest plots of pooled sensitivity and specificity were generated to assess diagnostic performance. Pooled changes in SUVmax were correlated with changes in pulmonary function tests (PFT). FDG-PET/CT in diagnosing suspected pulmonary sarcoidosis (six studies, n = 400) had a pooled sensitivity of 0.971 (95%CI 0.909-1.000, p = < 0.001) and specificity of 0.873 (95%CI 0.845-0.920)(one study, n = 169). Eleven studies for prognostic analysis (n = 308) indicated a pooled reduction in pulmonary SUVmax of 4.538 (95%CI 5.653-3.453, p = < 0.001) post-treatment. PFTs displayed improvement post-treatment with a percentage increase in predicted forced vital capacity (FVC) and diffusion capacity of the lung for carbon monoxide (DLCO) of 7.346% (95%CI 2.257-12.436, p = 0.005) and 3.464% (95%CI -0.205-7.132, p = 0.064), respectively. Reduction in SUVmax correlated significantly with FVC (r = 0.644, p < 0.001) and DLCO (r = 0.582, p < 0.001) improvement. In cases of suspected pulmonary sarcoidosis, FDG-PET/CT demonstrated good diagnostic performance and correlated with functional health scores. FDG-PET/CT may help to guide immunosuppression in cases of complex sarcoidosis or where treatment rationalisation is needed. FDG-PET/CT has demonstrated a high diagnostic performance in the evaluation of suspected pulmonary sarcoidosis with radiologically assessed disease activity correlating strongly with clinically derived pulmonary function tests. In diagnosing pulmonary sarcoidosis, FDG-PET/CT had a sensitivity and specificity of 0.971 and 0.873, respectively. Disease activity, as determined by SUVmax, reduced following treatment in all the included studies. Reduction in SUVmax correlated with an improvement in functional vital capacity, Diffusion Capacity of the Lungs for Carbon Monoxide, and subjective health scoring systems.

Task reliability index for operator performance and failure probability assessment in control room simulators

Main control room simulators are an important information source for improving the empirical basis of human reliability analysis (HRA). Both quantitative and qualitative observations provide valuable insight on the operators’ ability to successfully perform their tasks. However, relevant portions of this evidence are not used in typical approaches for estimation of human error probabilities (HEP), which are based on failure counts. With small sample sizes and the operator performance levels representative for nuclear power plants, very little information enters the probability estimates. This paper presents a novel approach to formalize the collection and use of reliability-relevant evidence from simulators. The methodology treats task reliability with the plant-centred orientation characteristic of the systems or Probabilistic Safety Assessment (PSA) perspective as well as a human-centred view of task performance based in human factors engineering. These are combined to form a Task reliability index (TRI) able to distinguish finer performance variations and consider indications of potential failures. Bayesian updating of HEP distributions is suggested by translating the TRI data into pseudo-failures and trials, preserving the convenient coupling of Beta-Binomial distributions in the Bayesian analysis. The approach is demonstrated using data from the International HRA Empirical Study and is shown to produce results that are comparable to those obtained in the empirical study, which were based on comprehensive expert analyses.

Exploring the relationship between caffeine metabolism-related CYP1A2 rs762551 polymorphism and team sport athlete status and training adaptations

BackgroundThis study aimed to achieve a dual objective: to compare the frequencies of CYP1A2 rs762551 genotypes between team sport athletes and a control group, and to determine the association between the rs762551 polymorphism and changes in physical performance after a six-week training program among elite basketball players.MethodsThe study encompassed an analysis of 504 individuals, comprising 320 athletes and 184 controls. For the Turkish cohort, DNA was isolated using the buccal swab method, and genotyping was conducted using the KASP technique. Performance assessments included the Yo-Yo IR2 and 30 m sprint tests. For Russian participants, DNA samples were extracted from peripheral blood, a commercial kit was used for DNA extraction, and genotyping of the rs762551 polymorphism was conducted using DNA microarray.ResultNotably, a statistically significant linear decline in the prevalence of the CC genotype was observed with ascending levels of athletic achievement within team sports (sub-elite: 18.0%, elite: 8.2%, highly elite: 0%; p = 0.001). Additionally, the CA genotype was the most prevalent genotype in the highly elite group compared to controls (80.0% vs. 45.1%, p = 0.048). Furthermore, statistically significant improvements in Yo-Yo IR2 performance were noted exclusively among basketball players harboring the CA genotype (p = 0.048).ConclusionsThe study’s findings indicate that the rs762551 CC genotype is a disadvantage in elite team sports, whereas the CA genotype provides an advantage in basketball performance.

Performance assessment of a new serological diagnostic test for COVID-19 with candidate peptides from spike and nucleocapsid viral proteins.

Numerous commercial tests for the serological diagnosis of COVID-19 have been produced in recent years. However, it is important to note that these tests exhibit significant variability in their sensitivity, specificity, and accuracy of results. Therefore, the objective of this study was to utilize bioinformatics tools to map SARS-CoV-2 peptides, with the goal of developing a new serological diagnostic test for COVID-19. Two peptides from the S protein and one from the N protein were selected and characterized in silico, chemically synthesized, and used as a serological diagnostic tool to detect IgM, IgG, and IgA anti-SARS-CoV-2 antibodies through the ELISA technique, confirmed as positive and negative samples by RT-qPCR or serology by ELISA. The results showed a sensitivity, specificity, Positive Predictive Value and Negative Predictive Value of 100% (p < 00001, 95% CI) for the proposed test. Although preliminary, this study brings proof-of-concept results that are consistent with the high-performance rates of the ELISA test when compared to other well-established methods for diagnosing COVID-19.

Panoramas, Keys to Unlock Complexity in Digital Humanities and Data Humanism Methodological Analysis, Performance Assessment and Cataloguing of 31 Real Cases

In the interdisciplinary landscape shaped by Digital Humanities (DH) and Data Humanism (DHu), panoramas represent immersive narratives and interactive environments that simplify access to complex, interdisciplinary content, serving as ‘sensory forms of knowledge’. However, the lack of shared definitions hinders their integration into cultural sectors, where professionals often struggle with the rapid adoption of new technologies. Therefore, this study highlights 31 panorama case studies as examples of their triggering effect and as a foundation for broader collaborative theoretical work aimed at developing standardised definitions.

Self-Gated Radial Free-Breathing Liver MR Elastography: Assessment of Technical Performance in Children at 3 T.

Conventional liver magnetic resonance elastography (MRE) requires breath-holding (BH) to avoid motion artifacts, which is challenging for children. While radial free-breathing (FB)-MRE is an alternative for quantifying liver stiffness (LS), previous methods had limitations of long scan times, acquiring two slices in 5 minutes, and not resolving motion during reconstruction. To reduce FB-MRE scan time to 4 minutes for four slices and to investigate the impact of self-gated (SG) motion compensation on FB-MRE LS quantification in terms of agreement, intrasession repeatability, and technical quality compared to conventional BH-MRE. Prospective. Twenty-six children without fibrosis (median age: 12.9 years, 15 females). 3 T; Cartesian gradient-echo (GRE) BH-MRE, research application radial GRE FB-MRE. Participants were scanned twice to measure repeatability, without moving the table or changing the participants' position. LS was measured in areas of the liver with numerical confidence ≥90%. Technical quality was examined using measurable liver area (%). Agreement of LS between BH-MRE and FB-MRE was evaluated using Bland-Altman analysis for SG acceptance rates of 40%, 60%, 80%, and 100%. LS repeatability was assessed using within-subject coefficient of variation (wCV). The differences in LS and measurable liver area were examined using Kruskal-Wallis and Wilcoxon signed-rank tests. P < 0.05 was considered significant. FB-MRE with 60% SG achieved the closest agreement with BH-MRE (mean difference 0.00 kPa). The LS ranged from 1.70 to 1.83 kPa with no significant differences between BH-MRE and FB-MRE with varying SG rates (P = 0.52). All tested methods produced repeatable LS with wCV from 4.4% to 6.5%. The median measurable liver area was smaller for FB-MRE (32%-45%) than that for BH-MRE (91%-93%) (P < 0.05). FB-MRE with 60% SG can quantify LS with close agreement and comparable repeatability with respect to BH-MRE in children. 2 TECHNICAL EFFICACY: Stage 1.

Climate classification systems for validating Earth system models

Abstract Climate Classification Systems (CCSs) were developed in the 19th century to explain the global distribution of plants and animals, but were given a new life in the Earth physics community in the 21st century as a means to validate Earth System Models (ESMs). The purpose of this paper is to critically review the evolution of this approach to validation and to discuss the current status. It is shown that CCSs can not only measure model performance, but also help identify and deconstruct systematic model biases. CCSs provide a rational and comprehensive zonation of the global environment using quantitative estimates of physical variables such as precipitation, temperature, and humidity. They offer several advantages over single variables due to the high sensitivity of the resulting classes to small changes in input variables and dimensionality reduction. CCSs encompass model behavior on both energy and water balances, allowing for a more comprehensive assessment of ESM performance.

Crystal Structure Prediction and Performance Assessment of Hydrogen Storage Materials: Insights from Computational Materials Science

Hydrogen storage materials play a pivotal role in the development of a sustainable hydrogen economy. However, the discovery and optimization of high-performance storage materials remain a significant challenge due to the complex interplay of structural, thermodynamic and kinetic factors. Computational materials science has emerged as a powerful tool to accelerate the design and development of novel hydrogen storage materials by providing atomic-level insights into the storage mechanisms and guiding experimental efforts. In this comprehensive review, we discuss the recent advances in crystal structure prediction and performance assessment of hydrogen storage materials from a computational perspective. We highlight the applications of state-of-the-art computational methods, including density functional theory (DFT), molecular dynamics (MD) simulations, and machine learning (ML) techniques, in screening, evaluating, and optimizing storage materials. Special emphasis is placed on the prediction of stable crystal structures, assessment of thermodynamic and kinetic properties, and high-throughput screening of material space. Furthermore, we discuss the importance of multiscale modeling approaches that bridge different length and time scales, providing a holistic understanding of the storage processes. The synergistic integration of computational and experimental studies is also highlighted, with a focus on experimental validation and collaborative material discovery. Finally, we present an outlook on the future directions of computationally driven materials design for hydrogen storage applications, discussing the challenges, opportunities, and strategies for accelerating the development of high-performance storage materials. This review aims to provide a comprehensive and up-to-date account of the field, stimulating further research efforts to leverage computational methods to unlock the full potential of hydrogen storage materials.

Gaussian process regression driven rapid life-cycle based seismic fragility and risk assessment of laminated rubber bearings supported highway bridges subjected to multiple uncertainty sources

Structural attributes, corrosion of steel rebars in reinforced concrete (RC) columns, thermal oxidative aging of inherent rubber in laminated rubber bearings (LRBs), replacement of LRBs, and the time-dependent seismic hazard level at the bridge site are well recognized as the main factors that affect the accurate assessment of the seismic performance of RC bridges implemented with LRBs. However, the probabilistic seismic demand model (PSDM) and probabilistic structural capacity model (PSCM) of deteriorated bridge components may not exhibit homoscedastic log-linearity. And the expansion of seismic fragility surfaces and seismic risk curves in the temporal dimension requires a considerable computational cost on the nonlinear analysis, which regretfully has not been well addressed in current studies. To fill in this gap, this study proposes a life-cycle based seismic fragility and risk assessment framework for LRBs supported RC bridges considering deterioration mechanisms of bridge components during their life cycles demonstrated by a three-span LRBs supported highway bridge considering multiple uncertainties such as the replacement interval for LRBs. In this framework, Gaussian process regression (GPR) is employed to effectively construct the PSDM and PSCM of deteriorated bridge components, and then the time-dependent seismic fragility surfaces of bridge components and bridge system are established. Life-cycle based seismic risk analysis is conducted to quantify the coupled time-dependent effects of deterioration and site seismic hazard on a newly built bridge during its design service life and a deteriorated bridge within its remaining service life, respectively. Results indicate that GPR can successfully capture the nonlinearity and heteroscedasticity characteristics of PSDM and PSCM, leading to a significant reduced number of nonlinear analysis cases to generate the time-dependent seismic fragility surfaces and seismic risk curves. Consequently, the proposed framework using GPR can significantly improve the accuracy and efficiency of life-cycle based seismic performance assessment when uncertainties of structural attributes, deterioration progress and seismic hazard are taken into consideration.

Exploratory analysis of injury severity under different levels of driving automation (SAE Levels 2 and 4) using multi-source data

Vehicles equipped with automated driving capabilities have shown potential to improve safety and operations. Advanced driver assistance systems (ADAS) and automated driving systems (ADS) have been widely developed to support vehicular automation. Although the studies on the injury severity outcomes that involve automated vehicles are ongoing, there is limited research investigating the difference between injury severity outcomes for the ADAS and ADS equipped vehicles. To ensure a comprehensive analysis, a multi-source dataset that includes 1,001 ADAS crashes (SAE Level 2 vehicles) and 548 ADS crashes (SAE Level 4 vehicles) is used. Two random parameters multinomial logit models with heterogeneity in the means of random parameters are considered to gain a better understanding of the variables impacting the crash injury severity outcomes for the ADAS (SAE Level 2) and ADS (SAE Level 4) vehicles. It was found that while 67 percent of crashes involving the ADAS equipped vehicles in the dataset took place on a highway, 94 percent of crashes involving ADS took place in more urban settings. The model estimation results also reveal that the weather indicator, driver type indicator, differences in the system sophistication that are captured by both manufacture year and high/low mileage as well as rear and front contact indicators all play a role in the crash injury severity outcomes. The results offer an exploratory assessment of safety performance of the ADAS and ADS equipped vehicles using the real-world data and can be used by the manufacturers and other stakeholders to dictate the direction of their deployment and usage.

Sustainability evaluation and life cycle assessment of concretes including pozzolanic by-products and alkali-activated binders

The construction industry's top priorities for achieving a sustainable future include minimizing the use of non-renewable resources, managing waste generation, and reducing related gas emissions. This fact emphasizes the need for using natural materials such as pozzolan as a substitute or supplemental component in the production of Portland cement concrete. This study presents a review of evaluating the sustainability performance and life cycle assessment of pozzolan-based concrete compared with ordinary Portland cement concrete using the preference selection index approach and evaluating CO2 gas emissions during material life cycle stages. Furthermore, the durability and environmental effects of using pozzolan in concrete were comprehensively investigated. The advantages of using Pozzolans-based concrete in the development of sustainability aspects have been precisely predicted through the application of the PSI method, which depends on several criteria based on the properties of different cement types. It was discovered that substituting pozzolanic material for a portion of Portland cement when producing concrete significantly reduces the environmental effects (i.e., energy use and carbon dioxide emissions).

Analysis of Sharia Banking Performance Assessment with a Balanced Scorecard Perspective in the South Tangerang City Region

This research examines whether Sharia banks in South Tangerang City can achieve good performance if measured using the Balanced Score Card (BSC) approach from the perspective of finance, customers, internal business processes, and learning and growth. This field research observes population behaviour or events in a particular area. This research is descriptive and analytical. The research results show that Bank Syariah ABC UIN Syarif Hidayatullah Branch and Bank Syariah XYZ Ciputat Branch show adequate financial health, viewed from a financial perspective showing an increase in ROI of 3.14%, while Bank Syariah in 2022. Viewed from a customer perspective, the number of savings customers at Bank Syariah ABC has decreased by 1.25%. Meanwhile, from an internal business process perspective, Bank Syariah XYZ is not considered good enough because the response time is quite long when responding to customers. Viewed from a growth and learning perspective, the performance of these two Sharia banks is in good condition.

On the Potential of Biomass-Fueled Externally Fired Micro-Gas Turbines in the Energy Transition: Off-Design Performance Analysis

Abstract The potential of replacing the use of natural gas with biomass gasification syngas through an externally fired micro-gas turbine (EFMGTs) is the main scope of this study. This includes the performance assessment at various off-design and ambient conditions compared to a reference natural-gas-fired Micro-Gas Turbine. The penetration of biomass use in the decentralized combined heat and power (CHP) sector can reduce fossil fuel dependency and contribute to the achievement of the net-zero emissions target. For this purpose, an analytical externally fired thermodynamic model is incorporated and validated with an artificial neural network (ANN) based on a natural-gas-fired micro-gas turbine unit. An operating strategy is proposed to ensure the system's safe operation under any fuel input conditions. The performance between the investigated cases is compared using an exergetic analysis. The main loss contributors that determine each case's performance are the exit losses. The substantial decrease of the latter results in high externally fired part-load efficiency, maximizing 110% of design-point efficiency. System performance has a linear dependency on ambient conditions. The increased flexibility introduced by the proposed operating strategy case facilitates the transition from natural gas to biomass, especially for higher heat-to-power ratio demands. The analysis highlights that the current externally fired configuration lags behind in high electrical demands (&amp;gt;90 kWel). However, this deficiency is diminished in cold ambient temperatures (&amp;lt;0 °C), indicating that the proposed technology is very opportune for these climatic conditions.

Assessment of Operational Performance of Unsignalized Intersection using Microsimulation: A Case Study of Intersection at Pepsicola, Kathmandu

Assessment of Operational Performance of Unsignalized Intersection using Microsimulation: A Case Study of Intersection at Pepsicola, Kathmandu

Ballistic impact wave and bulge profile propagation characteristics and blunt injury assessment of UHMWPE laminate composite

Amid escalating localized conflicts worldwide, the elucidation of the mechanisms and quantification blunt trauma the emergency services and military personnel confronted becomes imperatively. However, the understanding of bullet-induced bulge formation on the laminate's back face, impact wave propagation, and consequent trauma assessment methods remains incomplete. This study applies 3-dimension digital image correlation (3D-DIC) method to reveals these phenomena by utilizing a lead-core bullet traveling at 334.93 m/s to impact an ultra-high molecular weight polyethylene (UHMWPE) laminate. The results show that the initial transverse propagation velocity of bulge profile is 24.79% smaller than the theoretical calculation, which indicates the reduction caused by cohesive matrix. The transverse wave demonstrates a double exponential attenuation pattern, which could be decomposed into two single exponential attenuation curves. The bulge profile shows hyperbolic pattern, which reveals the stability of the bulge shape and the temporal evolution pattern. Increasing the laminate-body gap leads to a decline in Blunt Criterion (BC) and the Abbreviated Injury Scale (AIS) values reduce from 2 to 0 correspondingly. The numerical model confirms the compression wave velocity as 1447.77 ± 123.10 m/s in the thickness direction and 9542.86 ± 3087.75 m/s in the fiber direction. These findings reveals and quantified the propagation patterns of back bulge and compressive wave, and thus could provide data-driven insights to improve body armor performance and blunt trauma assessment for individual soldiers.

Predicting Structural Temperature of Ancient City Walls: A Case Study Using Ambient Temperature and Deep Learning

ABSTRACT Accurate prediction of the structural temperature field is required for the performance assessment of engineering structures and for maintaining structural safety. However, there are complex time-lag effects between ambient temperature and structural temperature. It is difficult to accurately describe and predict the structural temperature using traditional linear and nonlinear fitting methods. Hence, this study uses the advantages of gated recurrent unit (GRU) neural networks in dealing with complex nonlinear mapping issues, further considering solar radiation and developing a structural temperature prediction framework using ambient temperature and the deep learning model. The framework is applied in structural temperature prediction for an ancient city wall in China. The solar altitude angle at the location of the city wall is calculated, and the input and output structures of the deep learning neural network model are optimized. In the long-term prediction of the structural temperature of the city wall, the root mean square error (RMSE) of the GRU model is less than 0.6°C, which solves the problem of low accuracy caused by time-lag effects.

Medical School Regulatory Compliance Burdens for the Physician-Scientist.

Medical school research faculty is increasingly required to complete more comprehensive and time consuming compliance steps for regulatory oversight. These relate to animal studies, information technology, biosafety, and human resources. For physician-scientists, the additional role in clinical care adds to these research areas with regulatory compliance in patient care and ever-growing web trainings. The sum of all these compliance regimes is a considerable time and cost burden, diminished research performance, and disengagement of faculty from colleagues, collaborations, and institutions. Many research and clinical compliance processes were put in place, often using legacy systems, in well-meaning attempts to address straightforward regulations in humane animal care, safe use of biological agents, and medical care delivery. However, their accumulation and negative impact on faculty performance demand time, energy, and resources that impact academic productivity. There are solutions to a relentlessly increasing regulatory load for research faculty, which involve vertical integration, convergence, and performance assessment in medical school and health system compliance regimes. ANN NEUROL 2024.

Validity of task-specific metrics for assessment in perineal proctectomy.

Perineal proctectomy is a complex procedure that requires advanced skills. Currently, there are no simulators for training in this procedure. As part of our objective of developing a virtual reality simulator, our goal was to develop and validate task-specific metrics for the assessment of performance for this procedure. We conducted a three-phase study to establish task-specific metrics, obtain expert consensus on the appropriateness of the developed metrics, and establish the discriminant validity of the developed metrics. In phase I, we utilized hierarchical task analysis to formulate the metrics. In phase II, a survey involving expert colorectal surgeons determined the significance of the developed metrics. Phase III was aimed at establishing the discriminant validity for novices (PGY1-3) and experts (PGY4-5 and faculty). They performed a perineal proctectomy on a rectal prolapse model. Video recordings were independently assessed by two raters using global ratings and task-specific metrics for the procedure. Total scores for both metrics were computed and analyzed using the Kruskal-Wallis test. A Mann-Whitney U test with Benjamini-Hochberg correction was used to evaluate between-group differences. Spearman's rank correlation coefficient was computed to assess the correlation between global and task-specific scores. In phase II, a total of 23 colorectal surgeons were recruited and consensus was obtained on all the task-specific metrics. In phase III, participants (n = 22) included novices (n = 15) and experts (n = 7). There was a strong positive correlation between the global and task-specific scores (rs = 0.86; P < 0.001). Significant between-group differences were detected for both global (χ2 = 15.38; P < 0.001; df = 2) and task-specific (χ2 = 11.38; P = 0.003; df = 2) scores. Using a biotissue rectal prolapse model, this study documented high IRR and significant discriminant validity evidence in support of video-based assessment using task-specific metrics.

Spinel-structured catalyst formed by Fe, Cu, and Co composites for efficient toluene oxidation

Due to its distinctive structure and properties, MFe2O4 has been extensively investigated in catalytic oxidation. In this study, we synthesized a ternary metal composite Fe-based spinel catalyst using the sol-gel method for toluene oxidation. The catalytic performance assessments showed that the catalytic performance of Cu0.7Co0.3Fe2O4 (T90 = 262 °C, 100,000mL·g-1·h-1) was superior to that of CuFe2O4 (T90 = 270 °C) and CoFe2O4 (T90 = 310 °C), compared to binary metal spinel catalysts. The characterization results indicate that the strong interactions among Cu, Co, and Fe in the ternary metal composite structure increased the content of oxygen vacancy at lower temperatures, thereby increasing the content of surface reactive oxygen species and improving catalytic performance. This study highlights the potential application of Fe-based spinel structures to develop more efficient spinel catalysts.

A new sol-gel fluorescent sensor to track carbonyl compounds

Carbonyl compounds are ubiquitous quality trackers that provide information about food product degradation as well as air and water pollution levels. In addition, they are used as biomarkers for medical diagnoses. With more user-friendly sensors, their fast detection and easy quantification are highly relevant. The synthesis, characterization, and performance assessment of a new sensor based on aniline fluorescence to monitor carbonyls in real time is reported. A cost-effective synthesis using a straightforward sol-gel process led to the construction of a nontoxic silica-based material with high porosity, which can be used with almost no sample preparation. The material exhibits a rapid (< 1 min) fluorescence decrease upon interaction with carbonyl groups. The limit of detection is as low as ca. 5 × 10−4 mol·L−1 for hexanal, while fluorescence extinction occurs at much higher concentrations (5 × 10−1·mol L−1), which enables the sensor to be used with a very broad range of detection. Real-time monitoring is possible since the fluorescence loss correlates with the concentration of carbonyl moieties. The performance was validated in simulating as well as in real media, making this sensor suitable for use in a wide range of applications.