Comparative Performance Assessment Research Articles

Comparative assessment of the diagnostic performances of particle-based multianalyte technology and commercial ELISA for antiphospholipid autoantibody testing

Background: Diagnosing Antiphospholipid Syndrome (APS) relies heavily on laboratory findings, particularly the detection of specific antibodies like lupus anticoagulant (LA), IgG and/or IgM anti-cardiolipin (aCL), and IgG and/or IgM anti-β2 glycoprotein 1 (aB2GP1). Although ELISA is widely used in the US for this purpose, standardization between different assay methodologies remains challenging, leading to significant variability across laboratories. Particle-based multi-analyte technology (PMAT) offers a streamlined one-step detection for all six antiphospholipid (aPL) autoantibodies, covering aCL and aB2GP1 of IgA, IgG, and IgM isotypes. Methods: In this study involving 224 subjects, including 34 clinically diagnosed with APS, alongside 160 non-APS patients and 30 healthy donors, PMAT’s performance was evaluated against commercial ELISA in detecting aPL antibodies. Results: At the manufacturer’s suggested cutoff, PMAT exhibited sensitivity comparable to ELISA, albeit with a low to moderate decrease in specificity for certain antibodies. With anti-CL IgM alone, PMAT displayed a 17.7% decrease in sensitivity, accompanied by a corresponding 31.1% increase in specificity compared to ELISA. However, applying a stricter cutoff (88–90% specificity), IgA and IgM antibodies yielded 5.9–17.6% higher sensitivities with PMAT, and IgG antibodies displayed similar sensitivity. Conclusions: In this study cohort, PMAT demonstrated higher or comparable sensitivity to that of commercial ELISA for all six aPL antibodies at a specificity cutoff near 90%. Notably, PMAT demonstrated superior sensitivity and specificity overall in detecting IgA aCL and aB2GP1 antibodies. This study highlights the potential of automated PMAT for detecting aPL antibodies in APS evaluation.

Comparative assessment of growth performance, heat resistance and carcass traits in four poultry genotypes reared in hot-humid tropical environment.

This study investigated the impact of heat stress on growth and carcass traits in four poultry genotypes-Giriraja, Country chicken, Naked Neck and Kadaknath reared in a hot and humid tropical environment. Birds from all genotypes had ad libitum access to feed and water while being challenged with consistently high environmental temperatures in the experimental shed. Daily diurnal meteorological data were recorded inside and outside the shed. The study specifically examined growth variables and carcass characteristics. Significant differences (p < 0.01) were observed in body weight and average daily gain at various intervals. Notably, feed intake showed significant differences (p < 0.01) across weeks, indicating interactions between genotypes and time intervals. The feed conversion ratio (FCR) varied significantly (p < 0.01), with the highest FCR recorded in the Kadaknath breed. Livability percentages were similar across groups, except for Giriraja, which had significantly lower livability (p < 0.01). Carcass traits, including dressing, wings, feathers and giblet percentages, showed significant differences among genotypes (p < 0.01). Hepatic mRNA expression of growth-related genes revealed numerical variations, with Naked Neck displaying the highest (p < 0.05) fold change in IGF-1 expression compared to other genotypes. The study recognized in the Naked Neck genotype to possess higher resilience in maintaining homoeostasis and uncompromised growth under heat stress, providing valuable insights for sustainable poultry farming in challenging environmental conditions.

Comparative assessment of psychometric performance on the adjusting amounts versus the 21-item Monetary Choice Delay Discounting tasks among young adult substance users.

Delay discounting (DD) assessments offer a wide variety of procedures to suit specific clinical and research needs. This study compared the reliability and validity of two DD tasks: (a) an adjusting amounts task presented on a computer (AAC) and (b) the 21-item Monetary Choice Task, which was administered online (MCT). Participants were 1,573 Spanish young-adults reporting past-month substance use. Measures included quantity and severity of drug use (i.e., cigarette smoking, cannabis, alcohol) and two DD assessments (i.e., AAC, MCT). Reliability was assessed using both the classical test and item response theory. Correlations and linear regressions examined the validity of both DD tasks in relation to substance use. The MCT showed higher internal consistency than the AAC (α = .941 vs. α = .748). AAC precision was adequate for moderate levels of discounting (θ values between -2 and +2), but the MCT showed superior reliability at low, moderate, and high levels of discounting (θ values between -1 and +1.5). Both DD tasks showed more significant correlations for alcohol-related measures (|rs| ranged between .053 and .093) compared to cigarettes and cannabis. The incremental validity of DD tasks in relation to nicotine dependence (AUClogd: β = -.664, 95% CI [-1.256, -.071]) and alcohol problems (AUClogd: β = -3.098, 95% CI [-5.209, -.988]) was only supported for the AAC. The MCT was more reliable than the AAC for measuring impulsive choice in young adult substance users. Nevertheless, the AAC may serve as a valid marker of nicotine dependence and alcohol problems. (PsycInfo Database Record (c) 2024 APA, all rights reserved).

Comparative assessment of emissions, performance, and economics parameters for a dual–fuel diesel generator operating with rice bran biodiesel and hydrogen

The first step to achieving an energy transition is partially substituting fossil fuels with other more environmentally friendly alternatives, such as hydrogen gas. The current research aims to evaluate the influence of hydrogen in a diesel generator fueled with rice bran biodiesel. The above encourages the use of hydrogen and biodiesel production from residual raw material. For the development of the research, a diesel engine bench was used, which operated in five load conditions: 20 %, 40 %, 60 %, 80 %, and 100 %, and was fed with three fuels: −100 %, RB-10 %, and RB-10 % + H2(30 %). The results show that the mixture RB-10 % + H2(30 %) causes a 3.14 % reduction in BSFC and a 3.26 % increase in energy conversion efficiency. In addition, it is observed that a 9.90 %, 12.57 %, and 10.99 % decrease in HC, CO, and smoke opacity emissions compared to pure diesel. On the other hand, the mixture RB-10 % + H2(30 %) reduces by 4.44 %, 5.07 %, and 7.06 % the environmental, social, and ecological impact due to CO2, HC, and CO emissions, as well as a 3.93 % reduction in engine operating cost compared to RB-10 % biodiesel. In general, hydrogen injection is a promising alternative to promote the use of rice bran biodiesel due to its increased performance characteristics and reduced pollutant emissions without the need to modify the engine.

Governing accelerating Universe via newly reconstructed Hubble parameter by employing empirical data simulations

A new parametrization of the Hubble parameter is proposed to explore the issue of the cosmological landscape. The constraints on model parameters are derived through the Markov Chain Monte Carlo (MCMC) method by employing a comprehensive union of datasets such as 34 data points from cosmic chronometers (CC), 42 points from baryonic acoustic oscillations (BAO), a recently updated set of 1701 Pantheon+ (P22) data points derived from Type Ia supernovae (SNeIa), and 162 data points from gamma-ray bursts (GRBs). Furthermore, the models are compared by using the Akaike Information Criterion (AIC) and Bayesian Information Criterion (BIC), so that a comparative assessment of model performance can be available. Additionally, we compare the Dainotti relation via Gaussian likelihood analysis versus new likelihoods and Calibration of the Dainotti relation through a model-independent method. The kinematic behavior of the models is also investigated by encompassing the transition from deceleration to acceleration and the evolution of the jerk parameter. From the analysis of the parametric models, it is strongly indicated that the Universe is currently undergoing an accelerated phase with diagnostics of the model validating the quintessence phase.

Comparative performance assessment of pilot irrigation schemes in Uganda

Irrigation schemes across sub-Saharan Africa are constructed with the intention of increasing agricultural production to increase food security, reduce poverty and improve economic growth. However, most of these schemes are not performing as expected. This study therefore, diagnosed performance gaps in the pilot irrigation schemes of Mubuku and Doho in Uganda and analysed sustainable improvement options. Data was collected through systematic review of literature and scheme data, direct measurements at the schemes, field surveys, inspections, and key informant interviews. For each scheme, data for climate, irrigation, flow measurements, crop yields and farm gate prices were collected. Comparative indicators of agricultural output, water supply, financial and physical sustainability were used to assess scheme performance using standard approaches by International Water Management Institute (IWMI). The findings showed that the schemes were not performing optimally with crop yields being far below the attainable potential. The major contributing factors to the low performance were low water use efficiency and low agricultural output. Poor flow control, poor water distribution, and poor on-farm water application contributed to low water use efficiency. Low agricultural production was attributed to poor crop yields resulting from poor agronomic practices, poor irrigation scheduling and low produce prices. The financial self-sufficiency indicator pointed to farmers’ inability to operate and maintain irrigation schemes effectively. Improving the irrigation schemes performance requires a multidisciplinary approach targeting the improvement water use efficiency and agricultural output.

Comparative seismic performance evaluation of friction, self-centering and hybrid self-centering dampers

This study explores comparative seismic performance of a shape memory alloy (SMA)-only device, a friction damper, and a hybrid self-centering device. The self-centering device considered in this study, named as superelastic friction dampers (SFDs), is a hybrid damper operating on a mechanism where SMA cables and frictional components are arranged in parallel. A detailed description of the device and an experimental characterization of mechanical behavior are presented first. Then, an 8-story archetype steel frame building is designed and modeled with each seismic protection device (SMA-only, hybrid self-centering, and friction only). To enable comparative performance assessment, each device is designed to have similar initial stiffness and the same maximum force at design displacement. Nonlinear time history analyses are conducted using a total of 44 ground motion records. The results are evaluated to compare performance of each frame design at design-basis and maximum considered earthquake hazard levels. Next, a risk-based seismic hazard demand analysis framework that involves comprehensive incremental dynamic analyses is employed for further assessing the performance of each system. The experimental results validate the parallel working mechanism of SMA cables and frictional components in the SFD, with 90% of the damper deformations recovered. Comparative analysis of seismic responses demonstrates the excellent performance of the SFDs in reducing interstory drift ratio, residual interstory drift ratio, and absolute acceleration responses. Furthermore, fragility analysis and risk-based seismic hazard assessment further reveal that the SFDs provide a well-balanced combination of energy dissipation capability and self-centering ability, thereby enhancing the seismic resilience of structures.

Meta-learners for few-shot weakly-supervised medical image segmentation

Most uses of Meta-Learning in visual recognition are very often applied to image classification, with a relative lack of work in other tasks such as segmentation and detection. We propose a new generic Meta-Learning framework for few-shot weakly supervised segmentation in medical imaging domains. The proposed approach includes a meta-training phase that uses a meta-dataset. It is deployed on an out-of-distribution few-shot target task, where a single highly generalizable model, trained via a selective supervised loss function, is used as a predictor. The model can be trained in several distinct ways, such as second-order optimization, metric learning, and late fusion. Some relevant improvements of existing methods that are part of the proposed approach are presented. We conduct a comparative analysis of meta-learners from distinct paradigms adapted to few-shot image segmentation in different sparsely annotated radiological tasks. The imaging modalities include 2D chest, mammographic, and dental X-rays, as well as 2D slices of volumetric tomography and resonance images. Our experiments consider in total 9 meta-learners, 4 backbones, and multiple target organ segmentation tasks. We explore small-data scenarios in radiology with varying weak annotation styles and densities. Our analysis shows that metric-based meta-learning approaches achieve better segmentation results in tasks with smaller domain shifts compared to the meta-training datasets, while some gradient- and fusion-based meta-learners are more generalizable to larger domain shifts. Guidelines learned from the comparative performance assessment of the analyzed methods are summarized to support those readers interested in the field.

Comparative Performance Assessment of Novel Fluorescence Immunoassay POCTs for Measuring Circulating Levels of Vitamin-D.

Vitamin D (Vit D) is a fat-soluble molecule acting like a hormone, and it is involved in several biological mechanisms such as gene expression, calcium homeostasis, bone metabolism, immune modulation, viral protection, and neuromuscular functions. Vit D deficiency can lead to chronic hypocalcemia, hyperparathyroidism, and many other pathological conditions; in this context, low and very low levels of 25-hydroxy-vitamin D (25-OH-D) were found to be associated with an increased risk of COVID-19 infection and the likelihood of many severe diseases. For all these reasons, it is important to quantify and monitor 25-OH-D levels to ensure that the serum/blood concentrations are not clinically suboptimal. Serum concentration of 25-OH-D is currently the main indicator of Vit D status, and it is currently performed by different assays, but the most common quantitation techniques involve immunometric methods or chromatography. Nevertheless, other quantitation techniques and instruments are now emerging, such as AFIAS-1® and AFIAS-10® (Boditech and Menarini) based on the immunofluorescence analyzer, that guarantee an automated system with cartridges able to give quick and reliable results as a point-of-care test (POCT). This work aims to compare AFIAS-1® and AFIAS-10® (Boditech and Menarini) Vit D quantitation with Ultra High-Performance Liquid Chromatography coupled with tandem mass spectrometry that currently represents the gold standard technique for Vit D quantitation. The analyses were performed in parallel on 56 samples and in different conditions (from fresh and frozen plasma) to assess the reliability of the results. Any statistically significant differences in methods, the fixed error, and the error proportional to concentration were reported. Results obtained in all conditions showed a good correlation between both AFIAS® instruments and LC-MS/MS, and we can affirm that AFIAS-1® and AFIAS-10® are reliable instruments for measuring 25-OH-D with accuracy and in a fast manner.

Comparative Performance Assessment of Physical-Based and Data-Driven Machine-Learning Models for Simulating Streamflow: A Case Study in Three Catchments across the US

Comparative Performance Assessment of Physical-Based and Data-Driven Machine-Learning Models for Simulating Streamflow: A Case Study in Three Catchments across the US

A Qualitative and Comparative Performance Assessment of Logically Centralized SDN Controllers via Mininet Emulator

An alternative networking approach called Software Defined Networking (SDN) enables dynamic, programmatically efficient network construction, hence enhancing network performance. It splits a traditional network into a centralized control plane and a configurable data plane. Because the core component overseeing every data plane action is the controller in the control plane, which may contain one or more controllers and is thought of as the brains of the SDN network, controller functionality and performance are crucial to achieve optimal performances. There is much controller research available in the existing literature. Nevertheless, no qualitative comparison study of OpenFlow-enabled distributed but logically centralized controllers exists. This paper includes a quantitative investigation of the performance of several distributed but logically centralized SDN controllers in custom network scenarios using Mininet, as well as a thorough qualitative comparison of them. More precisely, we give a qualitative evaluation of their attributes and classify and categorize 13 distributed but logically centralized SDN controllers according to their capabilities. Additionally, we offer a comprehensive SDN emulation tool, called Mininet-based SDN controller performance assessment, in this study. Using six performance metrics—bandwidth, round-trip time, delay, jitter, packet loss, and throughput—this work also assesses five distributed but logically centralized controllers within two custom network scenarios (uniform and non-uniform host distribution). Our analysis reveals that the Ryu controller outperforms the OpenDayLight controller in terms of latency, packet loss, and round-trip time, while the OpenDayLight controller performs well in terms of throughput, bandwidth, and jitter. Throughout the entire experiment, the HyperFlow and ONOS controllers performed worst in all performance metrics. Finally, we discuss detailed research findings on performance. These experimental results provide decision-making guidelines when selecting a controller.

Analysing the Nexus between the Financial Sector and Economic Growth in Nigeria: A Comparative Investigation using, BVAR, Linear Regression (OLS), and PPML Models

Aims: This study aims to analyze the complex relationship between the financial sector and economic growth in Nigeria. The study aims to provide comprehensive insights into this nexus by employing a comparative investigation using three distinct models: Linear Regression, Poisson Pseudo Maximum Likelihood (PPML), and Bayesian Vector Autoregression (BVAR).&#x0D; Methodology: The study then applied three different models, with a specific focus on the BVAR(2) model, supported by various diagnostic tests and stability assessments. The inclusion of Linear regression analysis and Poisson Pseudo Maximum Likelihood Estimator (PPML) enhances the depth of the study, providing nuanced insights into the impact of specific financial sector variables on economic growth.&#x0D; Results: The BVAR (2) model emerges as the optimal choice, demonstrating its reliability in capturing dynamic interactions and offering a powerful tool for policymakers. Specific results, such as the significant negative impact of D(CPS) in the regression analysis and the high R-squared in PPML, provide actionable insights into areas requiring policy interventions and underscore the substantial contribution of the financial sector to economic growth.&#x0D; Conclusion: The comparative assessment of model performances, favoring the BVAR model, guides future research and policy considerations, providing a reliable framework for further investigations. The study's insights are positioned as valuable for policymakers seeking to enhance economic growth through strategic interventions in the financial sector. Overall, the abstract succinctly encapsulates the aims, methodology, results, and concluding implications of the study on the nexus between the financial sector and economic growth in Nigeria.

Artificial intelligence at sentencing: when do algorithms perform well enough to replace humans?

AbstractArtificial intelligence is currently supplanting the work of humans in many societal contexts. The purpose of this article is to consider the question of when algorithmic tools should be regarded as performing sufficiently well to replace human judgements and decision-making at sentencing. More precisely, the question as to which are the ethically plausible criteria for the comparative performance assessments of algorithms and humans is considered with regard to both risk assessment algorithms that are designed to provide predictions of recidivism and sentencing algorithms designed to determine sentences in individual criminal cases. It is argued, first, that the prima facie most obvious assessment criteria do not stand up to ethical scrutiny. Second, that ethically plausible criteria presuppose ethical theory on penal distribution which currently has not been sufficiently developed. And third, that the current lack of assessment criteria has comprehensive implications regarding when algorithmic tools should be implemented in criminal justice practice.

Creep-Fatigue Life Evaluation for Grade 91 Steels with Various Origins and Service Histories

Grade 91 steel is widely used in the boilers and piping of thermal power plants. There has been significant research interest in understanding the variations in creep characteristics among different heats of this steel for effective plant management. In recent years, thermal power plants have been subjected to frequent load changes and startup/shutdown to adjust power supply and demand and stabilize frequencies. These operational shifts have raised concerns regarding the potential for creep-fatigue damage in high-temperature components. Therefore, this research focuses on creep-fatigue properties of Grade 91 steel and their predictability. Tensile, creep, strain-controlled fatigue, and strain-controlled creep-fatigue tests were performed on six Grade 91 steels with different heats and/or histories, and the characteristics in each test were compared. As a result, the variations in creep-fatigue life among the materials were found to be correlated with the difference in creep characteristics and stress level during stress relaxation. Furthermore, the study involved a comparative assessment of the predictive performance of creep-fatigue life using five different approaches: time fraction, classical ductility exhaustion, modified ductility exhaustion, energy-based, and hybrid approaches. Among these approaches, the hybrid approach, based on inelastic strain energy density at fracture formulated as a function of inelastic strain rate, exhibited the most accurate predictive performance.

An application of artificial neural network (ANN) for comparative performance assessment of solar chimney (SC) plant for green energy production

This study aims to optimize the power generation of a conventional Manzanares solar chimney (SC) plant through strategic modifications to the collector inlet height, chimney diameter, and chimney divergence. Employing a finite volume-based solver for numerical analysis, we systematically scrutinize influential geometric parameters, including collector height (hi = 1.85 to 0.1 m), chimney inlet diameter (dch = 10.16 to 55.88 m), and chimney outlet diameter (do = 10.16 to 30.48 m). Our findings demonstrate that reducing the collector inlet height consistently leads to increased power output. The optimal collector inlet height of hi = 0.2 m results in a significant power increase from 51 to 117.42 kW (~ 2.3 times) without additional installation costs, accompanied by an efficiency of 0.25%. Conversely, enlarging the chimney diameter decreases the chimney base velocity and suction pressure. However, as turbine-driven power generation rises, the flow becomes stagnant beyond a chimney diameter of 45.72 m. At this point, power generation reaches 209 kW, nearly four times greater than the Manzanares plant, with an efficiency of 0.44%. Nevertheless, the cost of expanding the chimney diameter is substantial. Furthermore, the impact of chimney divergence is evident, with power generation, collector efficiency, overall efficiency, and collector inlet velocity all peaking at an outer chimney diameter of 15.24 m (corresponding to an area ratio of 2.25). At this configuration, power generation increases to 75.91 kW, approximately 1.5 times more than the initial design. Remarkably, at a low collector inlet height of 0.2 m, combining it with a chimney diameter of 4.5 times the chimney inlet diameter (4.5dch) results in an impressive power output of 635.02 kW, signifying a substantial 12.45-fold increase. To model the performance under these diverse conditions, an artificial neural network (ANN) is effectively utilized.

Fatal crashes and rare events logistic regression: an exploratory empirical study.

Fatal road accidents are statistically rare, posing challenges for accurate estimation through the classic logit model (LM). This study seeks to validate the efficacy of a rare events logistic model (RELM) in enhancing the precision of fatal crash estimations. Both LM and RELM were employed to examine the relationship between pertinent risk factors and the incidence of fatal crashes. Crash-injury datasets sourced from Hillsborough County, Florida served as the empirical basis for evaluating the performance metrics of both LM and RELM. The analysis revealed that RELM yielded more accurate predictions of fatal crashes compared to LM. Receiver operating characteristic (ROC) curves were constructed, and the area under the curve (AUC) for each model was computed to offer a comparative performance assessment. The empirical evidence notably favored RELM over LM as substantiated by superior AUC values. The study offers empirical validation that RELM is demonstrably more proficient in predicting fatal crashes than the LM, thereby recommending its application for nuanced traffic safety analytics.

Feasibility of carbon-capturing in building heating systems: A life cycle thinking-based approach

Buildings are responsible for 40 % of the global energy consumption, thus leading to significant climate change and other environmental impacts. Adopting carbon capturing, storage, and utilization technologies (CCSU) can be a solution for reducing greenhouse gas (GHG) emissions from natural gas building heating systems. However, the current knowledge base lacks definitive knowledge on the possibilities and impacts of building level CCSU. The goal of this study is to investigate the feasibility of implementing CCSU technologies in natural gas building heating systems. A comparative performance assessment framework was developed to evaluate the environmental and economic performance of building-level CCSU technologies over their life cycle. These performances were then compared against alternative GHG emissions mitigation methods used for building heating systems. The results obtained by a case study indicated that adopting CCSU technologies in residential buildings is not economically and environmentally competitive against electricity-based heating systems when the regional electricity is generated using renewable energy sources and nuclear energy. However, there is a potential for the building-level CCSU in regions that depend on fossil fuels to generate electricity. The research outcomes will further develop and improve building-level carbon capturing technologies and will provide confidence to stakeholders to invest.

Comparative performance assessment of nano-composite cathode of LMSO-BSCMF for low temperature solid oxide fuel cell applications

Comparative performance assessment of nano-composite cathode of LMSO-BSCMF for low temperature solid oxide fuel cell applications

Comparative assessment of agroforestry performances between pea and barley intercropping with olive tree under Mediterranean conditions

Comparative assessment of agroforestry performances between pea and barley intercropping with olive tree under Mediterranean conditions

Comparative Performance Assessment of Sizing of Electric Motor through Analytical Approach for Electric Vehicle Application

For battery electric vehicle (BEV), electric motor is the only power source. The motor drive system in electric vehicle has many challenges like cost, weight, efficiency, detail torque speed characteristics, motor power rating. Accurate motor power rating prediction is very much necessary to fulfil the performance requirement of electric vehicle. Selection of oversized motor for Electric vehicle application results into overprice motor, more energy consumption and decline in vehicle range. Due to this there will expansion of battery size and increases the overall cost of vehicle. On the other hand, selection of undersized motor leads to poor vehicle performance that limits the drivability of vehicle. This paper gives detailed calculations of motor rating of electric motor. New approach is developed to predict the correct power rating of motor without affecting the desired vehicle performance. According to the requirement, accurate prediction of power rating of motor has been taken for cost efficient and upgradation the performance of motor. The work presented in this paper adopts a method of vehicle performance assessment on the basis of comparison with four different methods with new developed method. Traction motor is major &amp; most expensive component of EV. Selection of motor play’s crucial role in the development of electric vehicle. Undersized and oversized motor results in rise in cost of motor which ultimately increases the cost of vehicle. But by using appropriate motor this cost been reduced up to 50-60% with performance enhancement.