Performance Comparison Of Methods Research Articles

Comparative Efficiency and Performance of Glass Design Methods

Comparative Efficiency and Performance of Glass Design Methods

Comparative Performance of COVID-19 Test Methods in Healthcare Workers during the Omicron Wave.

The COVID-19 pandemic presents unique requirements for accessible, reliable testing, and many testing platforms and sampling techniques have been developed over the course of the pandemic. Not all test methods have been systematically compared to each other or a common gold standard, and the performance of tests developed in the early epidemic have not been consistently re-evaluated in the context of new variants. We conducted a repeated measures study with adult healthcare workers presenting for SARS-CoV-2 testing. Participants were tested using seven testing modalities. Test sensitivity was compared using any positive PCR test as the gold standard. A total of 325 individuals participated in the study. PCR tests were the most sensitive (saliva PCR 0.957 ± 0.048, nasopharyngeal PCR 0.877 ± 0.075, oropharyngeal PCR 0.849 ± 0.082). Standard nasal rapid antigen tests were less sensitive but roughly equivalent (BinaxNOW 0.613 ± 0.110, iHealth 0.627 ± 0.109). Oropharyngeal rapid antigen tests were the least sensitive (BinaxNOW 0.400 ± 0.111, iHealth brands 0.311 ± 0.105). PCR remains the most sensitive testing modality for the diagnosis of COVID-19 and saliva PCR is significantly more sensitive than oropharyngeal PCR and equivalent to nasopharyngeal PCR. Nasal AgRDTs are less sensitive than PCR but have benefits in convenience and accessibility. Saliva-based PCR testing is a viable alternative to traditional swab-based PCR testing for the diagnosis of COVID-19.

Fast Rail Fastener Screw Detection for Vision-Based Fastener Screw Maintenance Robot Using Deep Learning

Fastener screws are critical components of rail fasteners. For the fastener screw maintenance robot, an image-based fast fastener screw detection method is urgently needed. In this paper, we propose a light-weight model named FSS-YOLO based on YOLOv5n for rail fastener screw detection. The C3Fast module is presented to replace the C3 module in the backbone and neck to reduce Params and FLOPs. Then, the SIoU loss is introduced to enhance the convergence speed and recognition accuracy. Finally, for the enhancement of the screw detail feature fusion, the shuffle attention (SA) is incorporated into the bottom-up process in the neck part. Experiment results concerning CIoU and DIoU for loss, MobileNetv3 and GhostNet for light-weight improvement, simple attention mechanism (SimAM), and squeeze-and-excitation (SE) attention for the attention module, and YOLO series methods for performance comparison are listed, demonstrating that the proposed FSS-YOLO significantly improves the performance, with higher accuracy and lower computation cost. It is demonstrated that the FSS-YOLO is 7.3% faster than the baseline model in FPS, 17.4% and 19.5% lower in Params and FLOPs, respectively, and the P, mAP@50, Recall, and F1 scores are increased by 10.6% and 6.4, 13.4%, and 12.2%, respectively.

Comparative performance of biofire pneumonia panel and standard culture-based methods for diagnosing pneumonia in critically ill patients: Impact on antibiotic stewardship

IntroductionLower respiratory tract infections (LRTIs) are a common cause of morbidity and mortality worldwide. Accurate identification of the pathogens causing LRTIs is crucial for ensuring of diagnostic and antibiotic stewardship. The Biofire Pneumonia Panel (BFPP) is a molecular diagnostic test that allows rapid detection of various bacterial and viral pathogens. In this study, we compared the performance of BFPP with standard culture methods for the detection of pathogens. Materials and methodsRespiratory samples from 70 patient with suspected LRTIs were tested using both BFPP and standard culture methods. The distribution of isolated bacterial pathogens was analyzed, and the sensitivity and specificity of BFPP were calculated. Additionally, the performance of BFPP in detecting antimicrobial resistance genes was evaluated. The results were compared with those obtained from VITEK-2 antimicrobial susceptibility testing and culture-based methods. ResultsAmong the suspected LRTI cases, BFPP identified a single pathogen in 32.8% of cases and multiple pathogens in 40% of cases. The standard culture method detected a single pathogen in 47.1% of cases. BFPP showed a sensitivity of 93.9% and a specificity of 45.9% for the total sample. The performance of BFPP in detecting antimicrobial resistance genes varied for different pathogens with overall sensitivity of 40.1% and specificity of 95.9%. ConclusionThe Biofire Pneumonia Panel (BFPP) demonstrated high sensitivity for several bacterial pathogens, indicating its potential as a rapid diagnostic tool. However, its performance varied for different microorganisms, and it had limitations in detecting certain pathogens and antimicrobial resistance genes for which still required more further studies to explore different resistance gene mechanism that can be incorporated in this panel in future. The BFPP can complement standard culture methods as a rapid tool in the diagnosis of LRTIs.

Comparative Performance of Different Fertilizer Recommendation Methods on Growth and Yield of Rice (Oryza sativa) in Old Brahmaputra Floodplain Soils

Improving rice productivity is the challenge for the farmer due to rapid soil health deterioration in intensive agricultural system. Therefore, the present study was conducted to evaluate comparative performance of different nutrient management practices in rice production in Bangladesh. The experiment was laid out in a randomized complete block design with three replications. There were six treatments namely, T1 (Control-no fertilizer), T2 (Farmer’s Practice), T3 (Fertilizer Recommendation Guide-2018), T4 (BAU Soil Testing Kit), T5 (Soil Test Basis) and T6 (Rice Crop Manager). The results revealed that all the treatments showed better performances over control (T1). Treatment T4 (BAU Soil Testing Kit) produced the highest value of plant height (90.18 cm), panicle length (24.90), filled grains panicle-1 (122.60) and 1000-grain weight (26.67g) while T5 (Soil Test Basis) produced maximum effective tillers hill-1 (12.80). The highest grain yield (6.29 t ha-1) and straw yield (7.73 t ha-1) were recorded in treatment T4 (BAU Soil Testing Kit). Treatment T4 (BAU Soil Testing Kit) also showed the highest grain yield increase (61.49%) and straw yield increase (90.49%) over control. The highest total N, P and K uptake (108.84 kg ha-1, 24.54 kg ha-1 and 128.17 kg ha-1) were obtained in the treatment T4 (BAU Soil Testing Kit). Control treatment T1 (No fertilizers) showed minimum performances in all the cases compared to other treatments. The overall result demonstrated that the application of fertilizers following BAU Soil Testing Kit method of fertilizer application system is a better option for rice production.

DNN-Based Relative Localization Technique for Real-Time Positioning of Moving Unmanned Swarm Robots

The unmanned swarm robot system, which enables multiple robots to collaborate and perform a variety of tasks, is extensively researched for its potential applications. Accurate determination of the location of swarm robots during operation is of paramount importance, and various positioning algorithms are employed to achieve this. Specifically, in situations where global positioning system (GPS) signals are unavailable, fixed anchor nodes with known location information can be utilized for localization. However, in scenarios where fixed anchor nodes are not present, and the robots operate in a swarm, applying this technology poses challenges, necessitating a localization technique that relies solely on distance information between the robots. This paper proposes a deep neural network (DNN) technique that utilizes only the distance information between moving nodes to predict the real-time relative coordinates of each node. It is assumed that the distances between nodes are updated sequentially and periodically according to a predetermined measurement cycle. A grid-based localization technique is used as the existing method for performance comparison. Computer simulation results demonstrate that the proposed DNN-based relative Localization technique exhibits superior localization performance compared to the existing Grid-based method. Furthermore, the proposed technique shows similar performance regardless of the distance measurement cycle, indicating that it is not significantly affected by the cycle. Therefore, applying the proposed relative Localization algorithm to swarm robots could enable real-time and accurate relative positioning, facilitating precise location tracking of the swarm.

Comparative Performance of Learning Methods In Stock Price Prediction Case Study: MNC Corporation

Shares are a popular business investment, the development of information technology now allows everyone to buy and sell shares easily online, investment players, both retail and corporate, are trying to make predictions. The purpose of this study is to find out comparative performance of learning methods in stock price prediction. There are currently many research papers discussing stock predictions. using machine learning / deep learning / neural networks, in this research the author will compare several superior methods found in the latest paper findings, including CNN, RNN LSTM, MLP, GRU and their variants. From the 16 result relationships and patterns that occur in each variable and each variable is proven to show its respective role with its own weight, in general we will summarize the conclusions in chapter V below, but in each analysis there are secondary conclusions that we can get in detail. The variable that has the most significant effect on RMSE is variable B (repeatable data) compared to other variables because it has a difference in polarity that is so far between yes and no. The configuration of input timestep (history)=7 days and output timetep (prediction)=1 day is best for the average model in general.

Deep Learning for Physical Damage Detection in Buildings: A Comparison of Transfer Learning Methods

The detection of physical damage in buildings is a critical task in ensuring the safety and integrity of structures. In this study, the effectiveness of deep learning methods for detecting physical damage in buildings, specifically focusing on cracks, defects, moisture, and undamaged classes was investigated. Transfer learning methods, including VGG16, GoogLeNet, and ResNet50, were used to classify a dataset of 7200 images. The dataset was split into training, validation, and testing sets, and the performance of the models was evaluated by using metrics such as accuracy, precision, recall, and F1-score. Results show that all three models achieved high accuracy on the test set, with VGG16 and ResNet50 outperforming GoogLeNet. Additionally, precision, recall, and F1-score metrics indicate strong performance across all classes, with VGG16 and ResNet50 achieving particularly high scores. It is demonstrated the effectiveness of deep learning methods for physical damage detection in buildings and provides insights into the comparative performance of transfer learning methods.

Comparative Performance of High-Throughput Methods for Protein pKa Predictions.

The medically relevant field of protein-based therapeutics has triggered a demand for protein engineering in different pH environments of biological relevance. In silico engineering workflows typically employ high-throughput screening campaigns that require evaluating large sets of protein residues and point mutations by fast yet accurate computational algorithms. While several high-throughput pKa prediction methods exist, their accuracies are unclear due to the lack of a current comprehensive benchmarking. Here, seven fast, efficient, and accessible approaches including PROPKA3, DeepKa, PKAI, PKAI+, DelPhiPKa, MCCE2, and H++ were systematically tested on a nonredundant subset of 408 measured protein residue pKa shifts from the pKa database (PKAD). While no method outperformed the null hypotheses with confidence, as illustrated by statistical bootstrapping, DeepKa, PKAI+, PROPKA3, and H++ had utility. More specifically, DeepKa consistently performed well in tests across multiple and individual amino acid residue types, as reflected by lower errors, higher correlations, and improved classifications. Arithmetic averaging of the best empirical predictors into simple consensuses improved overall transferability and accuracy up to a root-mean-square error of 0.76 pKa units and a correlation coefficient (R2) of 0.45 to experimental pKa shifts. This analysis should provide a basis for further methodological developments and guide future applications, which require embedding of computationally inexpensive pKa prediction methods, such as the optimization of antibodies for pH-dependent antigen binding.

Comparative performance of microbiological methods for the detection of tuberculous meningitis pathogens in cerebrospinal fluid

The aim of this study was to comprehensively evaluate metagenomic next-generation sequencing (mNGS), Acid-fast bacillus stain (AFB), MGIT960 culture, polymerase-chain-reaction (PCR), and Xpert MTB/RIF in the diagnosis of tuberculous meningitis (TBM). A cohort of 280 patients who presented with suspected TBM (ie, headache or altered mental status with clinical signs of meningism) were analyzed. The sensitivities of the 5 assays for the diagnosis of TBM ranged from 10.0% to 70.0%. The AFB had the lowest sensitivity of 10.0% (0.5-45.9), while mNGS and PCR had the highest sensitivity, both at 70.0% (35.4-91.9). mNGS demonstrated a distinct advantage in identifying a wider array of pathogens, including viruses, in CSF samples. PCR was a cost-effective option with excellent sensitivity and specificity. However, no single method was statistically significantly better than any other in the diagnosis of TBM. New diagnostic techniques are urgently needed for the independent, rapid and accurate detection of Mycobacterium tuberculosis to guide the diagnosis of TBM.

Kinematic analysis of three redundant parallel mechanisms for fracture reduction surgery

This paper mainly deals with the kinematic analysis and performance comparisons of three redundant parallel mechanisms for fracture reduction surgery. First, two novel kinematically redundant parallel mechanisms for surgical robots are proposed based on the Gough–Stewart parallel platform. The kinematics of the three mechanisms are established, and their workspaces are analyzed. The analysis result shows that the workspace volume of the redundant mechanism can be improved by 71.6%. Second, considering the stroke of redundant actuators, the effects of the slider strokes on the position and orientation workspaces are obtained. The multi-objective optimization method is used to optimize the geometric parameters of the three mechanisms. Third, the singularities of the three redundant parallel mechanisms are compared based on the dimensionally homogeneous Jacobian matrix. Finally, experiments are conducted to verify the effectiveness of the performance comparison methods in this paper.

Comparative performance of regionalization methods for model parameterization in ungauged Himalayan watersheds

Study regionThe study region is 23 different watersheds across Nepal. Study focusThis study aims at assessing the strengths and weaknesses of widely used regionalization methods for simulating daily hydrograph and flow duration curve in a comparatively large sample of 23 medium to small-sized watersheds across Nepal. We employed a deductive approach based on extractable watershed properties to test the performance of four regionalization methods: principal component regression (PCR), random forests (RF) under regression-based methods, spatial proximity (SP), and physical similarity (PS) under donor-based methods in a leave-one-out cross-validation (LOOCV) setting. New hydrological insights for the regionThe GR4J rainfall-runoff model coupled with Cemaneige snow module (GR4J-CN) could provide good simulation for majority of the watersheds with median NSE of 0.76 and 0.74 for calibration and validation periods respectively. Model simulation using parameter values predicted from different regionalization methods showed satisfactory results in majority of the watersheds for daily hydrograph simulation. While there wasn’t a single method that performed well in all of the watersheds, the physical similarity methods was found to be the most robust. Visual comparison of errors in flow duration curve (FDC) also indicated physical similarity method as a better approach in ungauged watersheds of Nepal. Further experiment using multiple donors using the output averaging option was found to increase the performance of donor-based methods while the parameter averaging option resulted in a drop in performance. The study provides a comprehensive assessment of regionalization methods and advocates the use of hydrological model regionalization as a promising tool for streamflow prediction in ungauged Himalayan watersheds.

Critical evaluation of assessor difference correction approaches in sensory analysis

In sensory data analysis, assessor-dependent scaling effects may hinder the analysis of product differences. Romano et al. (2008) compared several approaches to reduce scaling differences between assessors by their ability to maximise the product effect F-values in a mixed ANOVA analysis. Their study on a sensory dataset of 14 cheese samples assessed by twelve assessors on a continuous scale showed that some of these approaches apparently improved the F-value of the product effect. However, this direct comparison is only legitimate if these F-values originate from the same null distribution. To obtain the null distributions of the different correction methods, we employed a permutation approach on the same cheese dataset also used by Romano et al. (2008) and a random noise simulation approach. Based on the empirically obtained null distributions, we calculated the corrected product effect significance to directly compare the performance of the preprocessing methods.Our results show that the null distributions of some preprocessing methods do not correspond to the expected F-distribution. In particular for the ten Berge method, the null distribution is shifted towards higher F-values. Therefore, an observed increase of the product effect F-value, as compared to the F-value on raw data, does not necessarily lead to increased product effect significance. If p-values are calculated based on such inflated F-values, significance may thus be overestimated. In contrast, calculation of p-values directly from the empirical null distributions obtained by permutation provides a common ground to properly compare method performance. Moreover, we show that differences in reproducibility between assessors, as they exist in real-world sensory datasets, may lead to overestimation of product effect significance by the mixed assessor model (MAM).

Selection of tube inserts in heat exchanger retrofit applications for increased energy recovery

To improve the thermal performance of conventional heat exchangers, the use of tube inserts to increase heat transfer is a suitable option. The number of new geometries that keep appearing in the open literature makes it necessary to develop practical comparison methods for their selection in real life applications. Most performance comparison methods provide an indication of how the heat transfer increases in relation to friction. This is useful but still the decision is qualitative since the whole picture in terms of the actual size and pressure drop cannot be anticipated as in the case of retrofit of existing heat exchangers. This paper introduces an alternative approach for the comparison of tube inserts for use in retrofit applications. The methodology is confirmed by comparing the performance predictions using the thermodynamic quantity of irreversibility. The comparison results are graphically displayed and allow to quickly identify the most suitable promoter from the thermal and hydraulic points of view. The results show that among the different turbulence promoters analysed in this work, the best performance is obtained from the perforated twisted tape that provides heat load increments between 4.2% and 6.4% with pressure drops 3.4 times larger than the plain tube.

External Quality Assurance programs for processing methods provide evidence on impact of preanalytical variables

An annual External Quality Assurance (EQA) program has been provided to processing laboratories over the last ten years, allowing them to assess the performance of their processing methods, such as nucleic acid extractions or peripheral blood mononuclear cell (PBMC) isolation and cryopreservation. The objective of this study was to perform a global analysis on almost 1000 EQA scheme/participant data in order to assess (i) the impact of critical preanalytical factors on quantitative or qualitative attributes of different types of specimens and (ii) laboratory performance pattern over time. Statistical analysis was performed within each EQA scheme based on categorized preanalytical data provided by the participants and on centralized measurements of relevant quality attributes of the produced specimens (z-scores): DNA, cell-free (cf)DNA or RNA extraction from blood, DNA or RNA extraction from formalin fixed tissue, DNA or RNA extraction from frozen tissue, DNA extraction from saliva or stool, viable PBMC isolation and cryopreservation. The most critical preanalytical factors in nucleic acid extraction schemes were the nucleic acid extraction method and kit, the elution buffer, the enzymes used during extraction, the input material quantity and the storage temperature. Several indications of laboratory performance improvement over time could be seen. The conclusions are that EQA for processing methods provides unique evidence-based insights into the impact of preanalytical factors and the comparative performance of different processing methods and kits, while supporting laboratories in validating their processing methods.

Graph convolutional networks-based method for estimating design loads of complex buildings in the preliminary design stage

Data-driven methods have shown powerful capabilities in estimating design cooling, heating and electricity loads of general buildings during the preliminary design stage. However, it is still quite challenging for them to handle complex buildings. Since conventional methods cannot describe the complex structures properly in a mathematical way. To address this challenge, this study proposes a graph convolutional networks-based method. The method includes three steps, i.e., graph representation, graphical model learning, and graphical model interpretation. Graph representation is designed to divide complex buildings into several basic blocks and utilizes graphs to represent the relationships among blocks. A graph convolutional network model, a state-of-art graphical model, is then trained to learn the energy load patterns of each basic block using the graphs. After that, class activation mapping, an effective interpretation algorithm for graph convolutional networks, is applied to quantify the contribution of each building feature to the model output. Operational data of 800 simulated complex buildings with significantly different structures are generated using SketchUp and Openstudio to verify the performance of the proposed method. Four representative data-driven models (artificial neural networks, random forest, support vector regression and gradient boosting tree) and one fast physical principle-based estimation method are selected as baseline methods for performance comparison with the proposed method. Results show that the proposed method has the highest accuracy. According to the results of class activation mapping, it is also demonstrated that the knowledge learned using a graph convolutional network is consistent with the domain knowledge.

A Novel Method for Baroreflex Sensitivity Estimation Using Modulated Gaussian Filter.

The evaluation of baroreflex sensitivity (BRS) has proven to be critical for medical applications. The use of α indices by spectral methods has been the most popular approach to BRS estimation. Recently, an algorithm termed Gaussian average filtering decomposition (GAFD) has been proposed to serve the same purpose. GAFD adopts a three-layer tree structure similar to wavelet decomposition but is only constructed by Gaussian windows in different cutoff frequency. Its computation is more efficient than that of conventional spectral methods, and there is no need to specify any parameter. This research presents a novel approach, referred to as modulated Gaussian filter (modGauss) for BRS estimation. It has a more simplified structure than GAFD using only two bandpass filters of dedicated passbands, so that the three-level structure in GAFD is avoided. This strategy makes modGauss more efficient than GAFD in computation, while the advantages of GAFD are preserved. Both GAFD and modGauss are conducted extensively in the time domain, yet can achieve similar results to conventional spectral methods. In computational simulations, the EuroBavar dataset was used to assess the performance of the novel algorithm. The BRS values were calculated by four other methods (three spectral approaches and GAFD) for performance comparison. From a comparison using the Wilcoxon rank sum test, it was found that there was no statistically significant dissimilarity; instead, very good agreement using the intraclass correlation coefficient (ICC) was observed. The modGauss algorithm was also found to be the fastest in computation time and suitable for the long-term estimation of BRS. The novel algorithm, as described in this report, can be applied in medical equipment for real-time estimation of BRS in clinical settings.

Addressing delayed case reporting in infectious disease forecast modeling.

Infectious disease forecasting is of great interest to the public health community and policymakers, since forecasts can provide insight into disease dynamics in the near future and inform interventions. Due to delays in case reporting, however, forecasting models may often underestimate the current and future disease burden.In this paper, we propose a general framework for addressing reporting delay in disease forecasting efforts with the goal of improving forecasts. We propose strategies for leveraging either historical data on case reporting or external internet-based data to estimate the amount of reporting error. We then describe several approaches for adapting general forecasting pipelines to account for under- or over-reporting of cases. We apply these methods to address reporting delay in data on dengue fever cases in Puerto Rico from 1990 to 2009 and to reports of influenza-like illness (ILI) in the United States between 2010 and 2019. Through a simulation study, we compare method performance and evaluate robustness to assumption violations. Our results show that forecasting accuracy and prediction coverage almost always increase when correction methods are implemented to address reporting delay. Some of these methods required knowledge about the reporting error or high quality external data, which may not always be available. Provided alternatives include excluding recently-reported data and performing sensitivity analysis. This work provides intuition and guidance for handling delay in disease case reporting and may serve as a useful resource to inform practical infectious disease forecasting efforts.

Diagnostic Performance of Antigen Rapid Diagnostic Tests, Chest Computed Tomography, and Lung Point-of-Care-Ultrasonography for SARS-CoV-2 Compared with RT-PCR Testing: A Systematic Review and Network Meta-Analysis.

(1) Background: The comparative performance of various diagnostic methods for severe acute respiratory syndrome coronavirus-2 (SARS-CoV-2) infection remains unclear. This study aimed to investigate the comparison of the 3 index test performances of rapid antigen diagnostic tests (RDTs), chest computed tomography (CT), and lung point-of-care-ultrasonography (US) with reverse transcription-polymerase chain reaction (RT-PCR), the reference standard, to provide more evidence-based data on the appropriate use of these index tests. (2) Methods: We retrieved data from electronic literature searches of PubMed, Cochrane Library, and EMBASE from 1 January 2020, to 1 April 2021. Diagnostic performance was examined using bivariate random-effects diagnostic test accuracy (DTA) and Bayesian network meta-analysis (NMA) models. (3) Results: Of the 3992 studies identified in our search, 118 including 69,445 participants met our selection criteria. Among these, 69 RDT, 38 CT, and 15 US studies in the pairwise meta-analysis were included for DTA with NMA. CT and US had high sensitivity of 0.852 (95% credible interval (CrI), 0.791–0.914) and 0.879 (95% CrI, 0.784–0.973), respectively. RDT had high specificity, 0.978 (95% CrI, 0.960–0.996). In accuracy assessment, RDT and CT had a relatively higher than US. However, there was no significant difference in accuracy between the 3 index tests. (4) Conclusions: This meta-analysis suggests that, compared with the reference standard RT-PCR, the 3 index tests (RDTs, chest CT, and lung US) had similar and complementary performances for diagnosis of SARS-CoV-2 infection. To manage and control COVID-19 effectively, future large-scale prospective studies could be used to obtain an optimal timely diagnostic process that identifies the condition of the patient accurately.

Performance Comparison of Three Data-Poor Methods With Various Types of Data on Assessing Southern Atlantic Albacore Fishery

In the world, more than 80% of the fisheries by numbers and about half of the catches have not been formally analyzed and evaluated due to limited data. It has led to the fast growth of data-poor evaluation methods. There have been various studies carried out on the comparative performance of data-poor and data-moderate methods in evaluating fishery exploitation status. However, most studies to date have focused on coastal fish stocks with simple data sources. It is important to pay attention to high sea fisheries because they are exploited by multiple countries, fishing gears and data may be divrsified and inconsistent. Furthermore, a comparison of the performance of catch-based, length-based, and abundance-based methods to estimate fishery status is needed. This study is the first attempt to apply catch-based, length-based, and abundance-based data-poor methods to stock assessment for an oceanic tuna fishery and to compare the performance with a data-moderate model. Results showed that the three data-poor methods with various types of data did not produce an entirely consistent stock status of the southern Atlantic albacore (Thunnus alalunga) fishery in 2005, as the estimated B2005/BMSY ranged from 0.688 to 1.3 and F2005/FMSY ranged from 0.708 to 1.6. The Monte Carlo Catch maximum sustainable yield model (CMSY) produced a similar time series of B/BMSY and F/FMSY and stock status (recovering) to the Bayesian state-space Schaefer model (BSM). The abundance-based method (AMSY) gave the most conservative condition (overfished) of this fishery. Sensitivity analysis showed the results of the length-based Bayesian biomass estimation method (LBB) are sensitive to Linf settings, and the results with higher Linf were similar to those of other models. However, the LBB results with setting Linf at lower levels produced more optimistic conditions (healthy). Our results highlight that attention should be paid to the settings of model parameter priors and different trends implied in various types of data when using these data-poor methods.