Low Uncertainty Research Articles

Quantifying Uncertainty in Runoff Simulation According to Multiple Evaluation Metrics and Varying Calibration Data Length

In this study, the uncertainty in runoff simulations using hydrological models was quantified based on the selection of five evaluation metrics and calibration data length. The calibration data length was considered to vary from 1 to 11 years, and runoff analysis was performed using a soil and water assessment tool (SWAT). SWAT parameter optimization was then performed using R-SWAT. The results show that the uncertainty was lower when using a calibration data length of five to seven years, with seven years achieving the lowest uncertainty. Runoff simulations using a calibration data length of more than seven years yielded higher uncertainty overall but lower uncertainty for extreme runoff simulations compared to parameters with less than five years of calibration data. Different uncertainty evaluation metrics show different levels of uncertainty, which means it is necessary to consider multiple evaluation metrics rather than relying on any one single metric. Among the evaluation metrics, the Nash–Sutcliffe model efficiency coefficient (NSE) and normalized root-mean-squared error (NRMSE) had large uncertainties at short calibration data lengths, whereas the Kling–Gupta efficiency (KGE) and Percent Bias (Pbias) had large uncertainties at long calibration data lengths.

Riverine nitrate source identification combining δ15N/δ18O-NO3− with Δ17O-NO3− and a nitrification 15N-enrichment factor in a drinking water source region

Dual nitrate isotopes (δ15N/δ18O-NO3−) are an effective tool for tracing nitrate sources in freshwater systems worldwide. However, the initial δ15N/δ18O values of different nitrate sources might be altered by isotopic fractionation during nitrification, thereby limiting the efficiency of source apportionment results. This study integrated hydrochemical parameters, site-specific isotopic compositions of potential nitrate sources, multiple stable isotopes (δD/δ18O-H2O, δ15N/δ18O-NO3− and Δ17O-NO3−), soil incubation experiments assessing the nitrification 15N-enrichment factor (εN), and a Bayesian mixing model (MixSIAR) to reduce/eliminate the influence of 15N/18O-fractionations on nitrate source apportionment. Surface water samples from a typical drinking water source region were collected quarterly (June 2021 to March 2022). Nitrate concentrations ranged from 0.35 to 3.06 mg/L (mean = 0.78 ± 0.46 mg/L), constituting ∼70 % of total nitrogen. A MixSIAR model was developed based on δ15N/δ18O-NO3− values of surface waters and the incorporation of a nitrification εN (−6.9 ± 1.8 ‰). Model source apportionment followed: manure/sewage (46.2 ± 10.7 %) > soil organic nitrogen (32.3 ± 18.5 %) > nitrogen fertilizer (19.7 ± 13.1 %) > atmospheric deposition (1.8 ± 1.6 %). An additional MixSIAR model coupling δ15N/δ18O-NO3− with Δ17O-NO3− and εN was constructed to estimate the potential nitrate source contributions for the June 2021 water samples. Results revealed similar nitrate source contributions (manure/sewage = 43.4 ± 14.1 %, soil organic nitrogen = 29.3 ± 19.4 %, nitrogen fertilizer = 19.8 ± 13.8 %, atmospheric deposition = 7.5 ± 1.6 %) to the original MixSIAR model based on εN and δ15N/δ18O-NO3−. Finally, an uncertainty analysis indicated the MixSIAR model coupling δ15N/δ18O-NO3− with Δ17O-NO3− and εN performed better as it generated lower uncertainties with uncertainty index (UI90) of 0.435 compared with the MixSIAR model based on δ15N/δ18O-NO3− (UI90 = 0.522) and the MixSIAR model based on δ15N/δ18O-NO3− and εN (UI90 = 0.442).

A CMOS Current Sensing Interface With Sub-pA DC Uncertainty

This paper presents a CMOS current sensing interface with a dedicated analysis on dc uncertainty, especially the relationship among averaging, standard deviation and Allan variance. The dependence of the Allan variance on various noise sources is analyzed. The noise leakage mechanisms due to circuit nonidealities that lead to dc uncertainty are presented. A general design strategy toward a low dc uncertainty is drawn in this work. An auto-zeroing (AZ) capacitive transimpedance amplifier (CTIA) is reported for near-zero signal loss and near-perfect noise cancelation to achieve a low dc uncertainty. A prototype design implemented on 180nm CMOS process is presented and the measurement result shows a 73fA dc uncertainty.

An ion trap design for a space-deployable strontium-ion optical clock

Optical atomic clocks demonstrate a better stability and lower systematic uncertainty than the highest performance microwave atomic clocks. However, the best performing optical clocks have a large footprint in a laboratory environment and require specialist skills to maintain continuous operation. Growing and evolving needs across several sectors are increasing the demand for compact robust and portable devices at this capability level. In this paper we discuss the design of a physics package for a compact laser-cooled 88 Sr + optical clock that would, with further development, be suitable for space deployment. We review the design parameters to target a relative frequency uncertainty at the low parts in 10 18 with this system. We then explain the results of finite-element modelling to simulate the response of the ion trap and vacuum chamber to vibration, shock and thermal conditions expected during launch and space deployment. Additionally, an electrostatic model has been developed to investigate the relationship between the ion trap geometrical tolerances and the trapping efficiency. We present the results from these analyses that have led to the design of a more robust prototype ready for experimental testing.

EUPollMap: the European atlas of contemporary pollen distribution maps derived from an integrated Kriging interpolation approach

Abstract. Modern and fossil pollen data are widely used in paleoenvironmental research to characterize past environmental changes in a given location. However, their discrete and discontinuous nature can limit the inferences that can be made from them. Deriving continuous spatial maps of the pollen presence from point-based datasets would enable more robust regional characterization of such past changes. To address this problem, we propose a comprehensive collection of European pollen presence maps including 194 pollen taxa derived from the interpolation of pollen data from the Eurasian Modern Pollen Database (EMPD v2) restricted to the Euro-Mediterranean Basin. To do so, we developed an automatic Kriging-based interpolation workflow to select an optimal geostatistical model describing the spatial variability for each taxon. The output of the interpolation model consists of a series of multivariate predictive maps of Europe at 25 km scale, showing the occurrence probability of pollen taxa, the predicted presence based on diverse probability thresholds, and the interpolation uncertainty for each taxon. Combined visual inspections of the maps and systematic cross-validation tests demonstrated that the ensemble of predictions is reliable even in data-scarce regions, with a relatively low uncertainty, and robust to complex and non-stationary pollen distributions. The maps, freely distributed as GeoTIFF files (https://doi.org/10.5281/zenodo.10015695, Oriani et al., 2023), are proposed as a ready-to-use tool for spatial paleoenvironmental characterization. Since the interpolation model only uses the coordinates of the observation to spatialize the data, the model can also be employed with fossil pollen records (or other presence/absence indicators), thus enabling the spatial characterization of past changes, and possibly their subsequent use for quantitative paleoclimate reconstructions.

Resolution enhanced sea ice concentration: a new algorithm applied to AMSR2 microwave radiometry data

Abstract Passive-microwave sea ice concentration (SIC) algorithms employ different frequencies and polarisations in their operational implementations. Commonly, these algorithms utilise combinations such as 19/37 GHz, yielding reduced measurement uncertainties but at a coarse spatial resolution. Alternatively, these algorithms can solely use 89 GHz, producing a higher spatial resolution but with increased measurement uncertainties. This study evaluates the application of a resolution-enhancing SIC algorithm (reSICCI3LF), initially developed for the coarser Special Sensor Microwave Imager / Sounder, on the Advanced Microwave Scanning Radiometer. By applying reSICCI3LF, we aim to produce a 5 km SIC for 2013–2020 in the Fram Strait and the Barents and Kara Sea region that gains the benefits of both types of algorithms, high spatial resolution and low measurement uncertainty. We present the algorithm tuning, spectral analysis of spatial resolutions, and validation against the Round Robin Data Package of 0% and 100% SIC points and SIC derived from Landsat-8. The findings demonstrate that the reSICCI3LF algorithm produces a SIC field with fine details, achieving a balance between high spatial resolution and lower measurement uncertainties compared to a 89 GHz based SIC. Consequently, this resolution-enhanced SIC technique can potentially initialise higher-resolution coupled ocean and sea ice forecasting systems through data assimilation.

The effect of multimodal care based on Peplau’s interpersonal relationship theory on postoperative recovery in lung cancer surgery: a retrospective analysis

BackgroundLung cancer remains a major global health concern due to its high incidence and mortality rates. With advancements in medical treatments, an increasing number of early-stage lung cancer cases are being detected, making surgical treatment the primary option for such cases. However, this presents challenges to the physical and mental recovery of patients. Peplau known as the “mother of psychiatric associations” has formulated a theory of interpersonal relationships in nursing. Through effective communication between nurses and patients over four periods, she has established a good therapeutic nurse-patient relationship. Therefore, this study aimed to explore the effect of perioperative multimodal nursing based on Peplau’s interpersonal relationship theory on the rehabilitation of patients with surgical lung cancer.MethodsWe retrospectively analyzed 106 patients with non-small cell lung cancer who underwent thoracoscopic lobectomy at our department between June 2021 and April 2022. Patients were categorized into two groups according to the different nursing intervention techniques. The Peplau’s group comprised 53 patients who received targeted nursing interventions, and the control group comprised 53 patients who received conventional nursing care. We observed the patients’ illness uncertainty, quality of life, and clinical symptoms in both groups.ResultsPatients in the Peplau’s group had significantly lower illness uncertainty scores and a significantly higher quality of recovery than those in the control group. However, there were no significant differences in length of post-anesthesia care unit stay, complication rates, and visual analog scores between both groups.ConclusionThe multimodal perioperative nursing based on Peplau’s interpersonal relationship theory not only reduces the illness uncertainty of patients with lung cancer surgery and improves their QoR but also expands the application of this theory in clinical practice, guiding perioperative nursing of patients with lung cancer.ImplicationsThese findings provide practical information for standardized care in a hectic anesthetic care setting.ImpactThe assessed anesthesia nursing model helps reduce uncertainty and promote early recovery in patients with cancer at various stages of their disease, which expands the scope of therapeutic practice and existing theories. It also serves as a guide for care in the anesthesia recovery room.Reporting methodWe adhered to the relevant Equator guidelines and the checklist of items in the case–control study report.Patient or public contributionPatients cooperated with medical staff to complete relevant scales.

Automatedly identify dryland threatened species at large scale by using deep learning

Dryland biodiversity is decreasing at an alarming rate. Advanced intelligent tools are urgently needed to rapidly, automatedly, and precisely detect dryland threatened species on a large scale for biological conservation. Here, we explored the performance of three deep convolutional neural networks (Deeplabv3+, Unet, and Pspnet models) on the intelligent recognition of rare species based on high-resolution (0.3 m) satellite images taken by an unmanned aerial vehicle (UAV). We focused on a threatened species, Populus euphratica, in the Tarim River Basin (China), where there has been a severe population decline in the 1970s and restoration has been carried out since 2000. The testing results showed that Unet outperforms Deeplabv3+ and Pspnet when the training samples are lower, while Deeplabv3+ performs best as the dataset increases. Overall, when training samples are 80, Deeplabv3+ had the best overall performance for Populus euphratica identification, with mean pixel accuracy (MPA) between 87.31 % and 90.2 %, which, on average is 3.74 % and 11.29 % higher than Unet and Pspnet, respectively. Deeplabv3+ can accurately detect the boundaries of Populus euphratica even in areas of dense vegetation, with lower identification uncertainty for each pixel than other models. This study developed a UAV imagery-based identification framework using deep learning with high resolution in large-scale regions. This approach can accurately capture the variation in dryland threatened species, especially those in inaccessible areas, thereby fostering rapid and efficient conservation actions.

Clinical assessment of deep learning-based uncertainty maps in lung cancer segmentation.

Objective. Prior to radiation therapy planning, accurate delineation of gross tumour volume (GTVs) and organs at risk (OARs) is crucial. In the current clinical practice, tumour delineation is performed manually by radiation oncologists, which is time-consuming and prone to large inter-observer variability. With the advent of deep learning (DL) models, automated contouring has become possible, speeding up procedures and assisting clinicians. However, these tools are currently used in the clinic mostly for contouring OARs, since these systems are not reliable yet for contouring GTVs. To improve the reliability of these systems, researchers have started exploring the topic of probabilistic neural networks. However, there is still limited knowledge of the practical implementation of such networks in real clinical settings.Approach. In this work, we developed a 3D probabilistic system that generates DL-based uncertainty maps for lung cancer CT segmentations. We employed the Monte Carlo (MC) dropout technique to generate probabilistic and uncertainty maps, while the model calibration was evaluated by using reliability diagrams. A clinical validation was conducted in collaboration with a radiation oncologist to qualitatively assess the value of the uncertainty estimates. We also proposed two novel metrics, namely mean uncertainty (MU) and relative uncertainty volume (RUV), as potential indicators for clinicians to assess the need for independent visual checks of the DL-based segmentation. Main results. Our study showed that uncertainty mapping effectively identified cases of under or over-contouring. Although the overconfidence of the model, a strong correlation was observed between the clinical opinion and MU metric. Moreover, both MU and RUV revealed high AUC values in discretising between low and high uncertainty cases.Significance. Our study is one of the first attempts to clinically validate uncertainty estimates in DL-based contouring. The two proposed metrics exhibited promising potential as indicators for clinicians to independently assess the quality of tumour delineation.

The Burden of Type 2 Diabetes on the Productivity and Economy in Sub-Saharan Africa: A Life Table Modelling Analysis from a South African Perspective.

The prevalence of type 2 diabetes (T2D) is rapidly increasing in Sub-Saharan Africa (SSA). T2D increases the risk of premature death and reduces quality of life and work productivity. This population life table modelling analysis evaluated the impact of T2D in terms of productivity-adjusted life years (PALYs) on the South African working-age population. Life table modelling was employed to simulate the follow-up of individuals aged 20-65 with T2D in South Africa (SA). Two life table models were developed to simulate health outcomes for a SA cohort with and without diabetes. The difference in the number of deaths, years of life lost (YLL), and PALYs lost between the two cohorts represented the burden of diabetes. Scenarios were simulated in which the proportions of gross domestic productivity (GDP), productivity indices, labour force dropout, and mortality risk trends were adjusted to lower and upper uncertainty bounds. Data were sourced from the International Diabetes Federation, Statistics SA, and both publicly available and published sources. We utilised the World Health Organization (WHO) standard annual discount rate of 3% for YLL and PALYs. In 2019, an estimated 9.5% (7.68% men and 11.37% women) or 3.2 million total working-age people had T2D in SA. Simulated follow-up until retirement predicted 669,427 excess mortality, a loss of 6.2 million years of life (9.3%) and 13 million PALYs (30.6%) in SA. On average, this resulted in 3.1 PALYs lost per person. Based on the GDP per full-time employee in 2019, the PALYs loss equated to US$223 billion, or US$69,875 per person. This study emphasises the significant impact of T2D on society and the economy. Relatively modest T2D prevention and treatment management enhancement could lead to substantial economic benefits in SA.

Determination of thermal conductivity, thermal diffusivity, and specific heat of liquids using a thermoelectric module

Managing heat through working fluids is essential in many applications, as well as the development of new fluids with improved properties. Therefore, the characterization of their thermal properties, which is usually a laborious task, is necessary to design and model new thermal systems. In this study, we show the proof of concept of a new method capable of determining the thermal conductivity, thermal diffusivity, and specific heat capacity of liquids from a single simple measurement, provided their density is known (a property easy to measure). The method is based on the use of a thermoelectric module, which is soldered to a large copper block at one side (heat sink). At the other side, the liquid is added on top of the ceramic external layer of the module. By means of impedance spectroscopy measurements, it is demonstrated for three liquids (water, Luzar, and diethylene glycol) that their thermal properties of can be obtained. In order to do this, a new equivalent circuit was developed to account for the new boundary conditions of the measuring setup. Random and systematic errors were calculated and combined to obtain a total uncertainty <8.6% for the thermal conductivity, <6.3% for the thermal diffusivity, and <6.1% for the specific heat capacity. The reasonably low uncertainties obtained position the new method as a low-cost alternative able to provide the three key thermal properties of liquids from one single measurement and only using a single setup.

P406 A novel active learning-based digital pathology protocol annotation for histologic assessment in Ulcerative Colitis using PICaSSO Histologic Remission Index (PHRI)

Abstract Background Histologic remission (HR) is a critical treatment target in Ulcerative Colitis (UC). Among several scoring systems, the PICaSSO Histologic Remission Index (PHRI) simplifies HR assessment by evaluating the presence of neutrophils in the bowel tissue. Our artificial intelligence (AI) system built upon PHRI showed remarkable accuracy in HR assessment. PHRI assess neutrophils in four different regions of interest, so segmentation of these compartments is crucial to predict PHRI automatically. However, creating labelled histopathological datasets to train fully-supervised segmentation models takes time and effort. Hence, this study explores the impact of an active learning (AL) algorithm on enhancing image segmentation to alleviate the burden of detailed pathologists’ annotation and to standardise protocol annotation. Methods Biopsy samples from an international real-life prospective UC study were digitised into whole slide images (WSI). Initial annotations of superficial epithelium, lumen and epithelium of crypts and lamina propria for 33 WSI were employed to train a U-Net segmentation model at baseline. An AL framework was employed to iteratively select and annotate 15 unannotated images while selecting those with the highest uncertainty. Uncertainty was calculated using Least confidence sampling, Margin Sampling, and Shannon Entropy. The most informative samples, based on the average of the three uncertainty measure, were selected in consecutive batches of 5 images, and pathologists were enlisted in a human-in-the-loop process to refine annotations. Subsequently, the segmentation model was retrained by incorporating the newly refined annotated samples, and its performance was assessed using a fully annotated test set of 19 WSI. Results Following the baseline model training, the model’s segmentation performance assessed by the Dice score and Intersection over Union (IoU) was 0.622 and 0.386, respectively (see Table 1). Applying the AL algorithm with newly annotated images notably improved model performance, especially with 10 images (Dice=0.651, IoU=0.415). However, training the model with an additional 5 newly annotated images, which exhibited lower uncertainty, did not yield further improvement (Dice = 0.651, IoU = 0.415). Thus, the 10 annotated WSI demonstrating more uncertainty resulted crucial for the AL framework. Conclusion This novel AL-based iterative framework exhibits promise in standardising digital tissue annotation by our PHRI-based AI model. It offers a novel approach for both clinical trial and clinical practice, aiming to alleviate the burden of WSI labelling and reduce the bias of annotation, thereby improving histological assessment in UC.

Can the Opening of High-speed Rail Promote Green Innovation Behavior of Enterprises: Evidence from China

This study utilizes the quasi-natural experiment of China's high-speed rail opening and examines the impact on corporate green innovation using green patent data from Shanghai and Shenzhen A-share listed companies between 2007 and 2021.The difference-in-difference model is employed to empirically test the relationship between high-speed rail opening and corporate green innovation, as well as to analyze the underlying mechanisms driving this relationship. The research findings indicate a significant positive association between China's high-speed rail opening and enterprise green innovation, indicating an improvement in green innovation levels following the introduction of high-speed rail. The impact mechanism involves the enhancement of government governance capacity and overall factor productivity within enterprises. Further sub-sample test found that the opening of high-speed rail promoted enterprises' green innovation mainly in enterprises with low environmental uncertainty and enterprises in central and western regions.

Climate change content in Colombian schoolbooks

Education plays a crucial role in addressing the climate crisis, with textbooks serving as influential vehicles for shaping societal perspectives and students’ competencies. However, in Colombia as in many countries worldwide, no prior research has delved into the portrayal of climate change in school textbooks. In this study, we analyze 41 natural science textbooks, examining their treatment of climate change in terms of quantity, related topics, scientific validity (i.e. in line with the state-of-the-art knowledge in climate sciences), uncertainty (i.e. introduction of more substantial doubts or controversies than the ones exposed by climate sciences) and the use of iconography and activities. The results indicate that textbooks inadequately cover the topic, allocating on average less than one page of text to it, with simplistic and sometimes misguided approaches that overlook complexities and contextual variables. Although we observed correct scientific validity and low uncertainty levels in general, 10% of the books have low validity scores and close to 30% contain worrisome uncertainty. The iconography utilized fails to bridge the subject matter with national students’ experiences and activities predominantly focus on rudimentary cognitive skills. In summary, despite certain positive aspects (e.g. low levels of climate denial), Colombian textbooks still require more climate change content with growing complexity, to move beyond their function as mere information sources on climate change and to transform into effective tools for facilitating interaction between knowledge and students’ real-world experiences.

Procedure for automated low uncertainty assessment of empty cavity mode frequencies in Fabry-Pérot cavity based refractometry.

A procedure for automated low uncertainty assessment of empty cavity mode frequencies in Fabry-Pérot cavity based refractometry that does not require access to laser frequency measuring instrumentation is presented. It requires a previously well-characterized system regarding mirror phase shifts, Gouy phase, and mode number, and is based on the fact that the assessed refractivity should not change when mode jumps take place. It is demonstrated that the procedure is capable of assessing mode frequencies with an uncertainty of 30 MHz, which, when assessing pressure of nitrogen, corresponds to an uncertainty of 0.3 mPa.

Global uncertainties and Australian financial markets: Quantile time-frequency connectedness

Contrary to previous research, this study examines the transmission mechanisms between daily trade policy uncertainty (TPU), geopolitical risk (GPR), global financial stress index (FSI) and the three Australian financial markets (i.e., conventional, sustainable, Islamic stock markets) by using the quantile time-frequency connectedness approach. We also utilize the DCC-GARCH-t copula approach to examine the hedging effectiveness of the Australian sustainable and Islamic financial markets against the Australian conventional market's long-term volatility during high and low uncertainty. The time domain QVAR approach show that the Australian sustainable financial market returns receive the highest spillovers of shocks from TPU, FSI and GPR at the lower, medium and higher quantiles. Moreover, in the short term, at the lower quantiles the sustainable financial market receives the highest error variance contributions from FSI, while at higher quantiles, TPU, FSI and GPR transmit higher spillovers of shocks towards the sustainable market. Similarly, in the long term, the sustainable market receives the highest contributions to the error variances from TPU, FSI and GPR (GPR and TPU) at the lower (higher) quantiles. Moreover, at the lower, middle and higher quantiles, a shock in the Islamic (sustainable) market returns causes the highest contribution of error variances in the conventional market, compared with the (sustainable) Islamic market in the short and long run. Finally, the hedging strategy results show that investment in the Islamic financial market is the cheapest hedge against the long-term conventional market risk during high and low uncertainty periods.

Laser-Doppler Anemometry Measurements of Turbulent Swirling Wakes

This study considers the development and evolution of the swirling turbulent wake and their link to the turbulence present in the wake. A wake generator produces swirling wakes with a minimum of artifacts that would complicate the flow. Four swirling wakes have been studied with swirl numbers ranging from 0.19 to 0.37, and the results are compared to those of a nonswirling wake. Two-component laser-Doppler anemometry has been used to measure mean and turbulence quantities in the wake, and the uncertainty of these quantities has been determined. Careful alignment of the laser-Doppler anemometry system and large numbers of independent samples were used to obtain second-order turbulence statistics with low uncertainty. The results indicate that swirl strength impacts wake behavior, and changes in the behavior are linked to modifications in the turbulence. Faster wake width growth and centerline velocity deficit decay have been observed for wakes with higher swirl strength. The results indicate that a minimum swirl level is required to significantly impact wake behavior, and, at some level of swirl, the enhanced wake evolution observed saturates. For the conditions tested, the swirl number that enhances wake diffusion before saturation has been determined to lie between 0.21 and 0.37.

Using fusion effects to decrease uncertainty in distance sampling models when collating data from different surveys

AbstractEstimates of population abundance are required to study the impacts of human activities on populations and assess their conservation status. Despite considerable effort to improve data collection, uncertainty around estimates of cetacean densities can remain large. A fundamental concept underlying distance sampling is the detection function. Here we focus on reducing the uncertainty in the estimation of detection function parameters in analyses combining data sets from multiple surveys, with known effects on the precision of density estimates. We developed detection functions using infinite mixture models that can be applied on data collating multiple species and/or surveys. These models enable automatic clustering by fusing the species and surveys with similar detection functions. We present a simulation analysis of a multisurvey data set in a Bayesian framework where we demonstrated that distance sampling models including fusion effects showed lower uncertainty than classical distance sampling models. We illustrated the benefits of this new model using data of line transect surveys from the Bay of Biscay and Iberian Coast. Future estimates of abundance using conventional distance sampling models on large multispecies surveys or on data sets combining multiple surveys could benefit from this new model to provide more precise density estimates.

Enhanced group decision-making through an intelligent algorithmic approach for multiple-attribute credit evaluation with 2-tuple linguistic neutrosophic sets

With the development of the internet economy, e-commerce has rapidly risen, and a large number of small and micro e-commerce enterprises have emerged. However, these enterprises have low financial information transparency, small scale, and high development uncertainty. Therefore, combining the characteristics of the internet economy, it is of great significance to dynamically evaluate credit risk. This not only helps to enhance the quality and rationality of credit risk evaluation results, but also helps to improve financing efficiency and reduce financing risks. The credit evaluation for small and micro enterprises is a multiple-attribute group decision-making (MAGDM). Recently, the TODIM (an acronym in Portuguese of interactive and multicriteria decision making) and TOPSIS method has been inaugurated to cope with MAGDM issues. The 2-tuple linguistic neutrosophic sets (2TLNSs) are inaugurated as an effective tool for characterizing uncertain information during the credit evaluation for small and micro enterprises. In this paper, the 2-tuple linguistic neutrosophic TODIM-TOPSIS (2TLNN-TODIM-TOPSIS) method is inaugurated to solve the MAGDM under 2TLNSs. Finally, a numerical case study for credit evaluation for small and micro enterprises is inaugurated to confirm the proposed method. The prime contribution of this paper are outlined: (1) The information entropy based on score function and accuracy function are built on the 2TLNSs to obtain weight information; (2) an integrated the 2-tuple linguistic neutrosophic TODIM-TOPSIS (2TLNN-TODIM-TOPSIS) method is established to cope with MAGDM; (3) An illustrative example for credit evaluation for small and micro enterprises has accomplished to illustrate the 2TLNN-TODIM-TOPSIS; (4) some comparative analysis are employed to verify the 2TLNN-TODIM-TOPSIS method.

Estimating angler effort and catch from a winter recreational fishery using a novel Bayesian methodology to integrate multiple sources of creel survey data

The range of survey methodologies for measuring daily activity, catch and harvest (i.e. creel surveys) of recreational anglers, is increasing with the advent of new technologies and improvements in remote sensing. Individual creel survey types frequently give different insights into a fishery due to their unique sources of methodological bias and coverage, which creates a problem for resource managers since markedly different estimates of important fishery metrics can result. We demonstrate a joint estimation approach using a Bayesian statistical framework that can bring together multiple survey types to derive a single estimate for important metrics. This framework is applied to data collected from a relatively large winter fishery and integrates three traditional creel methodologies (i.e. roving, access and aerial counts), each with very different sources of bias, to derive a common estimate of angler effort. Models integrating two survey types are found to be have lower uncertainty in their estimates. Further, reductions in effort for any one survey type is found to be buffered by the joint estimation approach, such that resource managers will likely find benefits in using more than one survey methodology in an integrated fashion to monitor a fishery.