Uncertainty In Values Research Articles

An Uncertainty-Guided Deep Learning Method Facilitates Rapid Screening of CYP3A4 Inhibitors.

Cytochrome P450 3A4 (CYP3A4), a prominent member of the P450 enzyme superfamily, plays a crucial role in metabolizing various xenobiotics, including over 50% of clinically significant drugs. Evaluating CYP3A4 inhibition before drug approval is essential to avoiding potentially harmful pharmacokinetic drug-drug interactions (DDIs) and adverse drug reactions (ADRs). Despite the development of several CYP inhibitor prediction models, the primary approach for screening CYP inhibitors still relies on experimental methods. This might stem from the limitations of existing models, which only provide deterministic classification outcomes instead of precise inhibition intensity (e.g., IC50) and often suffer from inadequate prediction reliability. To address this challenge, we propose an uncertainty-guided regression model to accurately predict the IC50 values of anti-CYP3A4 activities. First, a comprehensive data set of CYP3A4 inhibitors was compiled, consisting of 27,045 compounds with classification labels, including 4395 compounds with explicit IC50 values. Second, by integrating the predictions of the classification model trained on a larger data set and introducing an evidential uncertainty method to rank prediction confidence, we obtained a high-precision and reliable regression model. Finally, we use the evidential uncertainty values as a trustworthy indicator to perform a virtual screening of an in-house compound set. The in vitro experiment results revealed that this new indicator significantly improved the hit ratio and reduced false positives among the top-ranked compounds. Specifically, among the top 20 compounds ranked with uncertainty, 15 compounds were identified as novel CYP3A4 inhibitors, and three of them exhibited activities less than 1 μM. In summary, our findings highlight the effectiveness of incorporating uncertainty in compound screening, providing a promising strategy for drug discovery and development.

Nexus of the CSR disclosures and corporate financial performance: evidence from Asian countries climate policy uncertainty.

Financial performance is a critical aspect of a company's overall health and sustainability. It directly influences investor decisions, stock market performance, credit ratings, and the company's ability to access capital. Corporate financial performance is influenced by multitude of facts, both internal and external such as disclosure of the information, and social and environmental factors. On the ground of the facts, we aimed to investigate non-financial firms that belong to Asian economies affected by climate policy uncertainty and corporate social responsibility disclosures in terms of their financial performance. To conduct quantitative study analysis, we used the two effective statistical tools such as two-stage regression method and generalized method of movement (GMM). Our results show that corporate high value of social responsibility disclosure and climate policy uncertainty has significant negative impact on return on asset (ROA) of the listed organizations of China, Pakistan, and India. Moreover, CSR disclosure attributes higher values such as social (SC) and governance score (GOV), and climate policy uncertainty (CPU) has significant negative relationship with return on equity (ROE) and earning per share (EPS) respectively, while a higher value of ESG total score and the environmental (ENV) score has a significant positive impact on ROE and EPS. Additionally, the research concludes that climate policy uncertainty is a key factor that motivates CSR disclosure practices, which ultimately improves corporate financial performance. Moreover, we concluded from our finding that the climate policy uncertainty creates ambiguity surrounding government regulations, international agreements, or market mechanisms that affect financial performance. Moreover, environmental disclosure information that has the large part in total ESG scores attract the investors around the globe which leads to rise in the financial performance, while the other attributes of the CSR disclosure decrease performance. This study advocated the great implications for researchers, investors, the government, and regulatory authorities. Policy makers can make the policy about the CSR disclosure for creating the good image of the organization to attract investors around the globe.

Comparison of Pressure Sensor in Flow Analyzer Design for Peep Measurement on Ventilators

The PEEP parameter is one of the parameters that need to be considered in mechanical ventilation because inappropriate parameter values can increase the risk of death for patients. Monitoring of these parameters can be done using a flow analyzer. The purpose of this study is to find out which pressure sensor is stable and has high accuracy. This study compares MPX2010DP and MPX5010DP pressure sensors where the readings of the two sensors will be compared with the pressure read by DPM (Digital Pressure Meter). The pressure read is the pressure obtained from the lung test with 11 pressure reading points and carried out as many as 5 experiments. The results of this study found that the smallest percentage of error was found in the MPX2010DP sensor at a pressure of 5 cmH2O rising pressure and 20 cmH2O rising pressure with an error percentage of 0.00%, while the largest percentage of error was found in the MPX2010DP sensor at a pressure of 5 cmH2O down pressure with an error percentage of 5.16%. The largest standard deviation value is 0.52 found on the MPX5010DP sensor at a pressure of 20 cmH2O rising pressure. The greatest uncertainty value is 0.23 found in the MPX5010DP sensor at a pressure of 20 cmH2O rising pressure. While the largest correction value is 0.54 on the MPX5010DP sensor at a pressure of 25 cmH2O rising pressure. From the data obtained, it can be said that the sensor MPX2010DP more accurate and more stable.

Design Analysis of Portable 1 Channel Infusion Device Analyzer Using Sensor SKU 237545

An infusion pump is a tool used to inject a certain amount of fluid into the patient's body through the patient's veins continuously over a certain period of time. A syringe pump is a tool that functions to push the syringe rod so that it can produce a flow ranging from microliters to milliliters per minute periodically with high accuracy. Very often there are problems with blockages or occlusion when using infusion pumps and syringe pumps. The occlusion limit set is ≤20 PSI according to ECRI. The presence of occlusion in the infusion pump and syringe pump can be identified when there is an alarm buzzer which will sound when a blockage is detected. A 1 Channel Portable Infusion Device Analyzer has been designed using the SKU 237545 Sensor, namely by using a 1 channel flowrate and occlusion sensor and making it portable to be efficient. For this reason, it is necessary to analyze the performance of the tools that have been created. How accurate is it? From the results of performance testing, Oclusion was corrected at 0.242 psi and 0.3 Psi. For flow rate, the largest correction was 2.4 ml/hour and the uncertainty was 6,046 ml/hour. This shows that the accuracy of the design is still quite high and the resulting tool is still not stable, this can be seen from the uncertainty value. The uncertainty that occurs is likely due to the sensitivity of the droplet sensor related to the detection time of the droplet

Comparison of volume and centroid uncertainty of a cylindrical segment by two different methods

Some solids are seldom studied analytically, unlike spheres, cylinders, and parallelepipeds. When researchers are interested in them, approximations by discretised meshes and numerical calculations are proposed. One of those is the cylindrical segment, which has applications in novel tilt-based industrial hydrometers. More specifically, the cylindrical segment has two means of being characterized: by measuring the radius, the middle height, and the cutting angle; and by measuring the radius and both the minimum height and the maximum height. In this study, one equation from the literature about the cylindrical segment is corrected, and the two measurement forms are compared, along with their impact on the uncertainty of the volume and barycenter of this solid. Particular results depend on specific input uncertainties, measurements, and objectives. Still, for most cases, it turned out that measuring the minimum and maximum heights generally provides lower uncertainty values for the evaluated calculations, especially the centroids.

UJI HOMOGENITAS DAN HETEROGENITAS CLUSTER DATA INTERKOMPARASI ANAK TIMBANGAN

Intercomparison of mass quantities is an activity to compare performance between laboratories ofmass quantities and reference laboratories which produces intercomparative data in the form ofconventional mass values and uncertainty values. Clustering of weights was carried out to obtain ananalysis of the laboratory’s participants management. The analysis carried out is very dependent on theaccuracy of the cluster. Clustering using self-organizing maps algorithm, and the results are tested for thelevel of homogeneity within the cluster and the level of heterogeneity between clusters using the icdrate value.The clustering of intercomparison’s data using the self-organizing maps method resulted in an icdratevalue of 0,0713 for an artifact of 1000 g and an icdrate of 0,2889 for an artifact of 200g

Feasibility of Monte Carlo dropout-based uncertainty maps to evaluate deep learning-based synthetic CTs for adaptive proton therapy.

Deep learning has shown promising results to generate MRI-based synthetic CTs and to enable accurate proton dose calculations on MRIs. For clinical implementation of synthetic CTs, quality assurance tools that verify their quality and reliability are required but still lacking. This study aims to evaluate the predictive value of uncertainty maps generated with Monte Carlo dropout (MCD) for verifying proton dose calculations on deep-learning-based synthetic CTs (sCTs) derived from MRIs in online adaptive proton therapy. Two deep-learning models (DCNN and cycleGAN) were trained for CT image synthesis using 101 paired CT-MR images. sCT images were generated using MCD for each model by performing 10 inferences with activated dropout layers. The final sCT was obtained by averaging the inferred sCTs, while the uncertainty map was obtained from the HU variance corresponding to each voxel of 10 sCTs. The resulting uncertainty maps were compared to the observed HU-, range-, WET-, and dose-error maps between the sCT and planning CT. For range and WET errors, the generated uncertainty maps were projected along the 90-degree angle. To evaluate the dose distribution, a mask based on the 5%-isodose curve was applied to only include voxels along the beam paths. Pearson's correlation coefficients were calculated to determine the correlation between the uncertainty maps and HUs, range, WET, and dose errors. To evaluate the dosimetric accuracy of synthetic CTs, clinical proton treatment plans were recalculated and compared to the pCTs RESULTS: Evaluation of the correlation showed an average of r = 0.92 ± 0.03 and r = 0.92 ± 0.03 for errors between uncertainty-HU, r = 0.66 ± 0.09 and r = 0.62 ± 0.06 between uncertainty-range, r = 0.64 ± 0.06 and r = 0.58 ± 0.07 between uncertainty-WET, and r = 0.65 ± 0.09 and r = 0.67 ± 0.07 between uncertainty and dose difference for DCNN and cycleGAN model, respectively. Dosimetric comparison for target volumes showed an average 3%/3mm gamma pass rate of 99.76 ± 0.43 (DCNN) and 99.10 ± 1.27 (cycleGAN). The observed correlations between uncertainty maps and the various metrics (HU, range, WET, and dose errors) demonstrated the potential of MCD-based uncertainty maps as a reliable QA tool to evaluate the accuracy of deep learning-based sCTs.

Measurement uncertainty for < 905 > Uniformity of Dosage Units tests using Monte Carlo and bootstrapping methods – Uncertainties arising from sampling and analytical steps

Uniformity of dosage unit (UDU) test is widely used to assess the quality, safety, and effectiveness of dosage forms in unit doses. An increased variability of the amount of drug (API – Active Pharmaceutical Ingredient) in each dose unit may lead to low quality, unsafety, and ineffective medicines. The aim of this work was to evaluate the measurement uncertainty associated with the acceptance value (AV) using the Monte Carlo and Bootstrapping methods, as well as to estimate the risk of false decisions regarding compliance/non-compliance due to uncertainty. Haloperidon 5 mg tablets and ofloxacin 200 mg tablets were subject to content uniformity (CU) and weight variation (WV) tests, respectively. Measurement uncertainty evaluation of UDU tests considered both uncertainties arising from sampling analytical steps. Uncertainty values were quantified using Monte Carlo (sampling) or bootstrapping (resampling) methods. Confidence intervals at 95% confidence level (CI95%) for AV value obtained for haloperidol 5 mg tablets were found to between 8.1 and15.8 and between 8.1 and 16.9 for Boostrapping and Monte Carlo methods, respectively. There is an increased risk of false conformity assessment for haloperidol UDU test (6.5% and 12.1% risk values for Bootstrapping and Monte Carlo methods). Considering the ofloxacin 200 mg tablets, the CI95% for AV value were found to be between 4.0 and 11.3 and between 4.9 and 11.4 for Bootstrapping and Monte Carlo methods, respectively. Uncertainty arising from sampling and analytical steps were both relevant to the overall uncertainty of AV values. Measurement uncertainty evaluation provided relevant information to support conformity assessments with a reduced risk of false decision making.

Economic policy uncertainty, corporate diversification and firm value: the global evidence

Economic policy uncertainty, corporate diversification and firm value: the global evidence

The Effect of Accuracy and Uncertainty in Test Performance on Optimal Early Detection Survey Budget Allocations

Early detection of pests at a low population prevalence is a key pillar of surveillance for plant health. A novel pest is expected to undergo exponential growth when it first occurs in a host population. This allows for the use of mathematical rules of thumb for the number of samples required to detect presence before it reaches a target prevalence given the diagnostic performance of the tests used. Previous work assumes that pest presence is diagnosed by applying a single diagnostic test to each sample. However, diagnostic decisions are often made based on outcomes of multiple tests applied to the same sample in a consistent test program; for example, an assessment of symptoms followed by a PCR test applied to samples for which symptoms were observed. Each test can have different testing costs, as well as distinct and independent diagnostic performance and uncertainty values. A framework to optimize early detection surveys by minimizing overall costs for test programs that apply up to three diagnostic tests has been developed. The framework assesses the consequences of the test order and logical rule by which diagnoses are determined on costs across a range of pest prevalence. Explicit definitions of uncertainty in key parameters are incorporated to assess the consequences of uncertainty about their true value. Use of the framework is exemplified with two hypothetical case studies exploring the potential impact of selecting a suboptimal test program (based on Xylella fastidiosa) and investment in test improvements (based on lateral flow devices applied to Phytophthora pluvialis). [Formula: see text] Copyright © 2023 The Author(s). This is an open access article distributed under the CC BY-NC-ND 4.0 International license .

Dampak Hukum Kebijakan Pembekuan Lahan Rencana Ibu Kota Negara Terhadap Pemilik Lahan

This study aims to determine the legal impact of the National Capital Plan's Land Freezing Policy on land owners who will make the transition. The problem is what is the legal impact of the National Capital Plan land freezing policy on land owners who will make the transition? This research includes normative legal research. Data in this research was collected using document/library studies and interviews which were processed in a descriptive-qualitative manner using a statutory approach. The results of this research show that the reservation policy in the National Capital Region of East Kalimantan has a regulatory impact, both positive and negative, on landowners who wish to transfer their rights. In essence, people cannot transfer their land even though they already have strong rights and reasons to sell their land, and these reasons are not what the government is worried about. So several things then give rise to legal uncertainty and several values related to land acquisition that conflict between one regulation and another. The government needs to be more specific and mature in formulating policies so that they do not appear authoritarian and reactionary, while at the same time organizing the process of transferring land in the Capital Planning Area to a task force, one of which is conducting investigations.

Mass Ratio Estimates for Overcontact Binaries Using the Derivatives of Light Curves

The photometric mass ratios of eclipsing binaries are usually estimated by light-curve modeling with an iterative method. We propose a new method for estimating the photometric mass ratio of an overcontact binary using the derivatives of a light curve, which provides a reasonable uncertainty value. The method mainly requires only the time interval value between two local extrema found in the third derivative of a light curve with no need of an iterative procedure. We applied the method to a sample of real overcontact binary data and compared the estimated mass ratios with their spectroscopically determined values. The comparison showed that our estimated mass ratios for ∼67% of the samples agreed with their spectroscopic mass ratios within the estimated uncertainties, and the errors for 95% of them are within ±0.1. Our method should be useful for estimating mass ratios for numerous overcontact eclipsing binaries found with existing and future surveys, as well as for the light-curve analysis of each system.

Estimating relative species abundance using fossil data identified to different taxonomic levels

Site‐occupancy modelling is widely used in ecology for understanding species distribution, habitat‐use and community changes but its application is still limited in paleoecology, where incomplete detection is also routine. Here, we make extensive expansions to an earlier multispecies occupancy model used to estimate the dynamics of relative species abundance in fossil communities. These expansions include incorporating counts of individuals at sites, explicitly allowing for the inclusion of specimens assignable to genus – but not species‐level, a situation common in paleontology, and modelling regional presence/absence. We provide simulations to check the performance of this new model, as well as simulations to quantify the benefits of using individual count data versus subsample occupancy data, and model estimates versus face‐value (raw) estimates, respectively. We also provide an empirical case study using occupancy data from a community of marine benthic colonial animals preserved in the Pleistocene of New Zealand. We find that the new model performs well, especially when it comes to recovering relative abundance dynamics and that it is well worth the effort to both collect individual count data and to include individuals unidentified to species‐level in the site‐occupancy modelling framework. This extended model can be widely applied in paleoecological settings and is necessary when both the average and uncertainty values of relative abundance dynamics need to be robustly estimated.

Measurement uncertainty arising from sampling and analytical steps of dissolution test of glyburide tablets

The dissolution test is an essential technique used in pharmaceutical research to assess the rate and extent of drug release from a dosage form into a dissolution medium. Accurate and reliable dissolution test results are crucial to ensure the quality and efficacy of the drug product. The aim of this study was to evaluate the measurement uncertainty arising from the sampling and analytical steps of the dissolution test for glyburide tablets. Dissolution test was performed using 900 mL of phosphate buffer pH 7.3 as dissolution medium and a dissolution apparatus equipped with paddles rotating at 75 rpm for 60 min. Quantification was performed by HPLC-UV. The uncertainty arising from sampling was estimated through classical Analysis of Variance (ANOVA), using 8-sampling targets, two samples for each sampling target and two replicas for each sample, totalizing 32 analyses. Uncertainty arising from analytical steps considered the uncertainty from dissolution step (estimated using Monte Carlo method and regression equation obtained using DoE) and uncertainty from quantification step. The overall uncertainty value was found to be 6.33 %. The sources of uncertainty contributing to the overall measurement uncertainty of the dissolution test were estimated to be 76.09 % from sampling, 22.15 % from the dissolution step, and 1.76 % from the quantification step. The study emphasizes the importance of evaluating measurement uncertainty in dissolution testing to ensure reliable and accurate drug product quality control. The results demonstrate that the dissolution test of glyburide tablets presented high sampling uncertainty and did not pass the dissolution test within the specified limits, probably due to glyburide’s low solubility. Univariate and multivariate rejection limits were calculated in order to ensure reduced particular and total producer’s risk, respectively. This study provides useful insights for pharmaceutical industries to optimize their quality control processes and ensure the efficacy and safety of their products.

Fair Value of Earnouts: Valuation Uncertainty or Managerial Opportunism?

ABSTRACT This study investigates the economic consequences of the IFRS 3 (2008) requirement for fair valuing earnouts. Using a hand-collected sample of earnout fair value estimates in acquisitions completed by Australian firms, we find that a significant portion of acquirers overstate initial earnout liabilities and strategically reverse them as operating gains to boost post-M&amp;A earnings. These overstatements are more pronounced when acquirers face investment- and performance-related pressure but attenuated in the presence of high-quality auditors and debt-financed deals. Acquirers also obfuscate earnout-related disclosures, inhibiting investors’ assessment of earnout values. By doing so, managers extend their tenure. Further analysis reveals that IFRS 3 (2008) leads to a significant increase in both the frequency and magnitude of earnouts in public acquirers’ transactions. Overall, we highlight the accounting benefit of earnouts for acquirers under IFRS 3 (2008), with implications for investors, analysts, auditors, and standard setters. Data Availability: Data are available from the public sources cited in the text. JEL Classifications: G34; M41.

Make it easy: the effect of prospectus readability on IPO performance

PurposeUnderstanding the determinants of a corporate initial public offering (IPO) success is essential for reducing investors' valuation uncertainty when participating in share offerings. In this sense, this study contributes to the existing debate by examining IPO prospectus readability. The authors specifically investigate how clear and more informative insights into pure corporate key financial numbers can lead to a higher valuation for the company after the listing process.Design/methodology/approachThrough a sample of European IPOs, the authors employ a cross-sectional regression to test the relationship between prospectus readability through the Flesch reading ease (FRE) score and companies' market-to-book ratio at the period end date after the listing process.FindingsThe study findings show a positive impact of higher readability on the post-IPO market-to-book ratio. Thus, clear and more informative communication results in stocks being traded at a premium to their book value. This study presents a concrete call for firms to increase corporate documents’ readability to mitigate the risk of withdrawing or spoiling corporate market access. Specifically, enhanced clarity and transparency increase investors' confidence, facilitating a better understanding of companies' intrinsic value and the overall IPO process. The authors conducted several tests to validate the results.Originality/valueTo the best of the authors’ knowledge, this is among the first works to explore the relationship between the readability of corporate prospectus and the sustained IPO success in the European context.

Uncertainty model for automated gunshot residue particle length measurements obtained from electron microscopy images.

Currently, the use of algorithms and computer vision systems for metrological purposes has increased in different areas of knowledge to reduce human error and process deviations, consequently increasing reliability and reducing measurement uncertainties. This study presents a model for estimating the uncertainty of Feret's diameter (DF ) measurements of scanning electron microscopy (SEM) images from regular and irregular gunshot residue (GSR) particles at different magnifications. The data were extracted using the automatic measurement algorithm developed by the Brazilian Institute of Metrology, Quality and Technology (Inmetro). The proposed uncertainty model was based on the recommendations of the guide to the expression of uncertainty in measurement (GUM). The gold standard technique to identify and detect GSR particles is the SEM coupled to energy dispersive X-ray spectroscopy (SEM/EDS), which was used in the study. The low uncertainty values obtained in this study are justified by the refinement of the measurements performed at each stage of digital image procedures. The proposed uncertainty model contributes in an innovative way to the metrological evaluation of regular and irregular GSR particles at different images magnifications. The correct morphometry definition of these particles allows to study their distinction from other possible sources of GSR and, above all, their correlation with the type of ammunition used when firing the firearm. These measurement uncertainty calculations can be applied to any object images acquired by SEM, which provides more confidence in the results of measurements of the object of interest.

Treatability of high-strength real sheep slaughterhouse wastewater using struvite precipitation coupled Fenton's oxidation: The MAPFOX process

Struvite (MAP, magnesium ammonium phosphate hexahydrate, MgNH4PO4.6H2O) precipitation-aided Fenton's OXidation (MAPFOX process) was explored in the treatment of high-strength real sheep slaughterhouse wastewater (RSSW) for the first time under a comprehensive soft-computing-based modeling study. The experimental results showed that under the highest-efficiency conditions (chemical combination of MgCl2.6H2O + NaH2PO4.2H2O, a molar ratio of Mg2+:NH4+-N:PO43–P = 1.2:1:1, a reaction pH of 9.0 ± 0.10, [NH4+-N]0 = 240 ± 20 mg/L, and a reaction time of 15 min), MAP precipitation could effectively remove more than 80 %, 60 %, 55 %, and 70 % of color, total chemical oxygen demand (TCOD), soluble COD (SCOD), and ammonium nitrogen (NH4+-N) from the raw RSSW. The results of the Fenton's oxidation used as the post-treatment unit of the proposed MAPFOX system indicated that the integrated advanced oxidation process (AOP) was able to reduce the residual pollutant levels in the MAP-pretreated RSSW to the relevant discharge standards. Under the subsequent condition of [Fe2+]0 = 90 mmol/L, [H2O2]0 = 180 mmol/L, reaction pH = 3.25, and total reaction time = 60 min, more than 97 % of color, TCOD, SCOD, and NH4+-N could be removed from the RSSW via the Fenton's oxidation after the MAP-based physicochemical treatment. According to SEM micrographs, surface morphology of dehydrated struvite exhibited irregular-shaped and overlapping sharp-edged particles of various sizes with an average size of about 50.9 μm. The Fourier Transform Infrared (FTIR) spectroscopy confirmed the active functional groups and type of bonds for the high-strength RSSW-oriented struvite (heated) within the spectral range of 4000–450 cm−1. Thermogravimetric Analysis (TGA), Derivative Thermogravimetry (DTG), Differential Thermal Analysis (DTA), and Differential Scanning Calorimetry (DSC) of the dehydrated struvite revealed that the weight loss occurred in three temperature zones, the maximum weight loss rate of 0.252 mg/min was recorded at around 224 °C and at time of 20.83 min, and the sample had strong endothermic and medium exothermic peaks at about 241 °C and 679 °C, respectively. The predictive successes of the implemented soft-computing approaches were benchmarked in terms of various statistical goodness-of-fit parameters. The performance assessment indices corroborated the superiority of the support vector machines-Pearson VII universal kernel function (SVM-PUKF)-based model (correlation coefficient (CC) = 0.9999–1.0000), mean absolute error (MAE) = 0.0222–0.0389 %, mean absolute percentage error (MAPE) = 0.0270–0.0506 %, root mean squared error (RMSE) = 0.0258–0.0415 %, coefficient of variation of RMSE (CV(RMSE) = 0.0003–0.0008, Nash–Sutcliffe efficiency (NSE) = 0.9998–1.000, Legates and McCabe's index (LMI) = 0.9894–0.9952) over other data-intelligent models in predicting the pollutant removal efficiencies. The computational results also indicated that the narrowest uncertainty bands (±1.96Se = 0.0537–0.1483 %) and the lowest amounts of expanded uncertainty (U95 = 3.1224–5.3124 %) values for all efficiency sets were achieved for the applied SVM-PUKF-based strategy. This study demonstrated the first-ever and successful application of the proposed MAPFOX process in treatability of the RSSW and capability of the implemented soft-computing strategy in modeling a highly nonlinear treatment system.

PT-TOPSIS methods for multi-attribute group decision making under single-valued neutrosophic sets

Multi-attribute group decision making (MAGDM) is a flexible and complex problem due to multiple factors. Decision makers have different preferences in the face of different risks and benefits. In this paper, it proposed that the extended single-valued neutrosophic TOPSIS method based on the prospect theory (SVN-PT-TOPSIS) can be widely applied to MAGDM. It provides decision-makers with more rigorous decision-making methods. The purpose is to make the results more objective and fair and to reduce subjective errors. This paper is applied in a single-value neutrosophic sets (SVNSs) environment, which contains membership value, uncertainty value and non-membership value, and can effectively reflect the fuzzy decision state of decision makers. It also combines the CRITIC method to determine the weights and considers the psychological state of decision makers according to the prospect theory, which can effectively reduce the influence of subjective preferences on decision making. The innovations of this paper are mainly as follows. Firstly, after the score function determines the positive and negative ideal values, using the distance formula reflects the distance between each single-value neutrosophic number (SVNN) and the positive and negative ideal values. We will feel the overall quality of each scheme more directly. Secondly determine the weights using the CRITIC method based on the distance matrix of each SVNN from the negative ideal. This is not a subjective decision weight, so the influence of personal preferences on the decision can be avoided. Thirdly, the composite profit value and the composite loss value are confirmed based on the prospect theory (PT), taking into account the decision maker’s risk preferences. Finally, use the relative profit loss ratio to rank the programs. After case analysis, other classical decisionmaking methods are selected for comparative analysis of the extended TOPSIS It is confirmed that the SVN-PT-TOPSIS method is reasonable and effective, which is suitable for MAGDM problems.

Optimization of a ultra-high-pressure liquid chromatography-tandem multiple reaction monitoring mass spectrometry method for monitoring haloacetic acids as chlorinated disinfection by-products in the fresh-cut industry

Haloacetic acids (HAAs) are one of the most important chlorinated disinfection by-products generated during water disinfection in the fresh-cut industry, and they can remain in the product, resulting in a consumer health risk. In this study, ultra-high-pressure liquid chromatography-tandem multiple reaction monitoring mass spectrometry (UHPLC-MRM) analysis used for drinking water was optimized and applied for the quantification of nine HAAs (HAA9) in fresh-cut lettuce and process water samples, with the complex matrix interferences for separation, and quantification problems. The method showed good selectivity, specificity and linearity, satisfactory values for trueness (recoveries of 80–116 %), precision (<22 %), and uncertainty (<55 %). Quantification limits varied from 1 to 5 µg L−1 or µg kg−1. The matrix effect for tribromoacetic, bromochloroacetic and chlorodibromoacetic acid was corrected by matrix-matched calibration and standard addition. After storage at -20 °C, only monobromoacetic acid was the HAA which loss happened after 7 days. The application of the methodology in lettuce and process water samples from the industry was successfully implemented. Therefore, this method could be employed for the quality control and regulatory analysis of HAAs in fresh products and process water from the fruit and vegetable industry.