Uncertainty In Values Research Articles

Does financial distress impact the dividend payment of Indian firm?

PurposeUnexpected circumstances, like distress or bankruptcy, might impact the time or amount of dividends delivered to investors. Therefore, in the current study, we have attempted to reveal the impact of financial distress (FD) on the dividend decisions of firms working in India.MethodologyIn this study, we looked at secondary data from 78 non-finance firms working in different sectors of the Indian economy that had been collected over five years (2016–2020). Panel data analysis (PDA) determines the relationship between the impact of FD and corporate dividend decisions.FindingsFD has no significant influence on the enterprises' dividend decisions. The association between FD and dividend decisions has also been found to be strongly and positively impacted by one of the interaction variables, environmental, social, and governance.LimitationOne study constraint is that researchers may choose to conduct their research in different industries or areas. Furthermore, researchers may choose to include other factors as interaction terms that they believe are relevant to the domains of FD or dividends.ImplicationThey will be inspired to start effectively managing their finances and debts due to this education. Comprehending these dynamics can aid decision-makers in financial uncertainty and support long-term sustainable value creation for investors, management, and policymakers when making dividend decisions.OriginalityThis scholarly work is novel because of the way its variables are combined, especially the way it looks at how four moderators—PBIT, ESG, LII, and debt ratio, interact with one another. To the best of our knowledge, this specific combination has not yet been studied in the literature, which emphasizes the originality of our research.

Impact assessment of snow thickness, sea ice density and water density in CryoSat-2-derived sea ice thickness

Abstract. Sea ice thickness is an essential climate variable, which is often derived from satellite altimetry freeboard estimates, e.g., by CryoSat-2. In order to convert freeboard to sea ice thickness, assumptions are needed for snow thickness, snow density, sea ice density and water density. These parameters are difficult to observe when co-located in time and space with the satellite-derived freeboard measurements. For this reason, most available CryoSat-2 sea ice thickness products rely on climatologies based on outdated observations and empirical values. Model- and observation-based alternatives to sea ice density and snow thickness values have been suggested in recent years, but their combined influence on the freeboard to sea ice thickness conversion has not been analyzed. This study evaluates model-based spatially varying snow thickness, sea ice density and water density with in situ observations and the associated parameters used in the classical CryoSat-2 sea ice thickness production. The observations used for the comparison are a snow thickness product from Ku- and Ka-band radar, sea ice density observations from airborne campaigns and ice core measurements as well as water density from a large variety of observation platforms included in the World Ocean Atlas. Furthermore, this study calculates the mean sea ice thickness differences resulting from substituting the parameters used in a classical CryoSat-2 sea ice thickness product with model-based values. The evaluation shows that the model-derived snow thickness, sea ice density and water density compare better to observations than the associated parameters used in the CryoSat-2 sea ice thickness product. The parameters were compared to the weekly CryoSat-2 sea ice thickness (SIT) product from the Alfred Wegener Institute, which uses similar values for snow thickness, sea ice density and water density to other available CryoSat-2 SIT products. Furthermore, we find that the model-based snow thickness and sea ice density separately lead to the largest sea ice thickness differences but that, to some extent, their differences cancel out when both parameters are used in combination. For the water density, we find the average and maximum sea ice thickness difference to be small in comparison to the sea ice thickness differences introduced by the snow thickness and sea ice density, but this is not negligible, as currently stated in most studies. We find that the origin of the assumption that water density is negligible in the freeboard to sea ice thickness conversion originates from a study investigating the seasonal Arctic sea ice density variability, not taking into account the spacial variability. Based on our findings, we recommend using either a water density climatology or an uncertainty value of 2.6 kg m−3 instead of the commonly used value of 0 to 0.5 kg m−3 in CryoSat-2 freeboard to sea ice thickness conversion.

Uncertainty-Boosted Robust Video Activity Anticipation.

Video activity anticipation aims to predict what will happen in the future, embracing a broad application prospect ranging from robot vision and autonomous driving. Despite the recent progress, the data uncertainty issue, reflected as the content evolution process and dynamic correlation in event labels, has been somehow ignored. This reduces the model generalization ability and deep understanding on video content, leading to serious error accumulation and degraded performance. In this paper, we address the uncertainty learning problem and propose an uncertainty-boosted robust video activity anticipation framework, which generates uncertainty values to indicate the credibility of the anticipation results. The uncertainty value is used to derive a temperature parameter in the softmax function to modulate the predicted target activity distribution. To guarantee the distribution adjustment, we construct a reasonable target activity label representation by incorporating the activity evolution from the temporal class correlation and the semantic relationship. Moreover, we quantify the uncertainty into relative values by comparing the uncertainty among sample pairs and their temporal-lengths. This relative strategy provides a more accessible way in uncertainty modeling than quantifying the absolute uncertainty values on the whole dataset. Experiments on multiple backbones and benchmarks show our framework achieves promising performance and better robustness/interpretability.

The nexus of overnight trend and asset prices in China

Leveraging the systematic variations in investor clientele within a day, we validate an adapted version of the Hong and Stein (1999) model that addresses the consequences of slow information diffusion in China. The model predicts that overnight returns, rather than total returns, strongly forecast future returns, as informed overnight clientele underreact to value-relevant signals. Empirically, we establish a consistent overnight trend phenomenon: Firms with a strong overnight trend reliably outperform those with a weak overnight trend in the subsequent month. The phenomenon is more pronounced among stocks with higher levels of information asymmetry, valuation uncertainty, and relative mispricing. Furthermore, the overnight trend predicts positively firm fundamentals in the cross section.

Electricity demand uncertainty modeling with Temporal Convolution Neural Network models

This work presents a Temporal Convolution Network (TCN) model for half-hourly, three-hourly and daily-time step to predict electricity demand (G) with associated uncertainties for sites in Southeast Queensland Australia. In addition to multi-step predictions, the TCN model is applied for probabilistic predictions of G where the aleatoric and epistemic uncertainties are quantified using maximum likelihood and Monte Carlo Dropout methodologies. The benchmarks of TCN model include an attention-based, bi-directional, gated recurrent unit, seq2seq, encoder–decoder, recurrent neural networks and natural gradient boosting models. The testing results show that the proposed TCN model attains the lowest relative root mean square error of 5.336-7.547% compared with significantly larger errors for all benchmark models. In respect to the 95% confidence interval using the Diebold–Mariano test statistic and key performance metrics, the proposed TCN model is better than benchmark models, capturing a lower value of total uncertainty, as well as the aleatoric and epistemic uncertainty. The root mean square error and total uncertainty registered for all of the forecast horizons shows that the benchmark models registered relatively larger errors arising from the epistemic uncertainty in predicted electricity demand. The results obtained for TCN, measured by the quality of prediction intervals representing an interval with upper and lower bound errors, registered a greater reliability factor as this model was likely to produce prediction interval that were higher than benchmark models at all prediction intervals. These results demonstrate the effectiveness of TCN approach in electricity demand modelling, and therefore advocates its usefulness in now-casting and forecasting systems.

International intercomparison of Radon 222 activity concentration calibration facilities

The Coalition of International Radon Associations (COIRA) organised an inter-comparison of Rn-222 (radon) activity concentrations reported by calibration laboratories. A set of three AlphaGUARDs were used as transfer reference instruments against which to compare reported Rn-222 activity concentrations. Rn-222 activity concentration calibration facilities (sometimes termed chambers) from seven countries (Australia, US, Czech Republic, Spain, England, Sweden, Canada), and three continents participated in this project. The objective of the study was to provide information useful to calibration chamber operators and public health officials in the improvement of measurement and control systems, the maintenance of performance standards for measurements, and regulatory requirements for calibrations. This work builds upon and expands previous interlaboratory comparisons and provides data for estimating and using calibration uncertainty values as part of overall field error estimates and limits. A simple proportional difference between laboratories is presented here, calculated as the average for N hours of each hour’s difference between the laboratory’s concentration and the average of the three reference instruments. This percent difference ranged from less than 0.5% to just less than 8%. This work demonstrates that the ANSI/AARST standards limit of 8% for the estimated unexpanded (one sigma) individual calibration estimated uncertainty for continuous radon monitor calibration facilities in the US is achievable. However, given the few standards regarding calibration of Rn-222 activity concentration measurement instruments that themselves are often used to calibrate secondary and tertiary Rn-222 calibration facilities, there is a great need for continued interlaboratory comparisons to harmonise and document the calibration of Rn-222 activity concentrations.

Valuing the Societal Impact of Medicines and Other Health Technologies: A User Guide to Current Best Practices.

This study argues that value assessment conducted from a societal perspective should rely on the Generalized Cost-Effectiveness Analysis (GCEA) framework proposed herein. Recently developed value assessment inventories- such as the Second Panel on Cost-Effectiveness's "impact inventory" and International Society of Pharmacoeconomics Outcomes Research (ISPOR) "value flower"- aimed to more comprehensively capture the benefits and costs of new health technologies from a societal perspective. Nevertheless, application of broader value elements in practice has been limited in part because quantifying these elements can be complex, but also because there have been numerous methodological advances since these value inventories have been released (e.g. generalized and risk-adjusted cost effectiveness). To facilitate estimation of treatment value from a societal perspective, this paper provides an updated value inventory- called the GCEA value flower- and a user guide for implementing GCEAfor health economics researchers and practitioners. GCEA considers 15 broader value elements across four categories: (i)uncertainty, (ii) dynamics, (iii)beneficiary, and (iv) additional value components. The uncertainty category incorporates patient risk preferences into value assessment. The dynamics category petals account for the evolution of real-world treatment value (e.g. option value) and includes drug pricing trends (e.g. future genericization). The beneficiary category accounts for the fact health technologies can benefit others (e.g. caregivers) and also that society may care to whom health benefits accrue (e.g. equity). Finally, GCEA incorporates additional broader sources of value (e.g. community spillovers, productivity losses). This GCEA user guide aims to facilitate both the estimation of each of these value elements and the incorporation of these values into health technology assessment when conducted from a societal perspective.

Data Reconciliation for Assessing Compliance of Physicochemical Properties of Petroleum Products in Commercial Transactions

The physicochemical properties of petroleum products in commercial transactions are crucial for quality control in the oil and gas industry. However, different laboratories often produce slightly different measurement results. These variations can be significant when approving or rejecting properties based on regulatory agency and environmental body specifications. A simple arithmetic average is typically used to determine the most probable value in disputes. This study proposed using a Data Reconciliation approach to address the disparity between the projected model and empirical data. An objective function was employed to optimize and evaluate parameters using maximum likelihood estimation, considering the experimental uncertainty values. This study found that the flash point of jet fuel, as determined by the Tag Closed Cup Tester, was within the specified range (maximum of 40 °C). The application of this tool resolved a dispute between a supplier and a customer, as the reconciled value with minimized uncertainty was determined to be 37.5 ± 2.0 °C. Additionally, the study utilized experimental results from 12 accredited laboratories to determine a single reconciled value for the final boiling point of gasoline. Despite the varying experimental uncertainties ranging from 6.0 °C to 13 °C, the reconciled uncertainty was minimized to 2.6 °C. The last case study identified that ASTM D4294 was incompatible with other test methods for evaluating the mass fraction in diesel oil. In this manner, Data Reconciliation enhanced the accuracy and effectively reduced measurement uncertainties, rendering it a potent tool for resolving legal disputes when evaluating the compliance of the physicochemical properties of petroleum products in commercial transactions.

Uncertainty Quantification in the Prediction of Remaining Useful Life Considering Multiple Failure Modes

Abstract Despite the substantive literature on remaining useful life (RUL) prediction, less attention is paid to the influence of epistemic uncertainty and aleatory uncertainty in multiple failure behaviors in the accuracy of RUL. The research question in this study was: can uncertainties be quantified in predicting the RUL of systems with multiple failure modes? The first objective was to quantify the uncertainties in the prediction of RUL, considering known multiple failure modes. This objective used vibration data from accelerated degradation experiments of rolling element bearings. The second objective was to calculate the uncertainties in the prediction of RUL, considering the multiple failure modes as unknown. The experimental data used in this objective were from run-to-failure tests of Li-ion batteries. An analysis was performed on how the uncertainties affect the RUL prediction in systems with known multiple failure modes and systems where the multiple failure modes were unknown. A Bayesian neural network (BNN) was used to quantify epistemic and aleatory uncertainty while predicting RUL. The results of the qualitative uncertainties on RUL in systems with multiple failure modes were presented and discussed. Also, the study yielded an RUL uncertainty quantification model for multiple failure modes. The proposed framework's performance in the RUL prediction was demonstrated. Finally, the epistemic and aleatory uncertainties were quantified in the system's RUL. It was shown that systems that fail due to the same failure mode tend to have similar uncertainty values over time. The results in this paper may lead to the design of more reliable systems that exhibit multiple failure modes.

Online Retailing Operations: Is It Wise to Offer Free Sample Trial and Money-Back Guarantee Together?

In retail operations, free sample trial (FST) and product return with money-back guarantee (MBG) both serve as important strategies to reduce consumers’ worries and valuation uncertainty. This paper is the first study which analytically examines when and whether an online retailer should provide both strategies simultaneously, and when one strategy outperforms the other. We consider an online retailer selling to consumers who are uncertain about the product values when making purchasing decisions. The retailer decides whether to offer MBG or FST. We develop analytical models to examine four strategies: strategy NN (both FST and MBG are not provided), strategy NS (FST is provided and MBG is not offered), strategy MN (FST is not provided and MBG is provided), and strategy MS (both FST and MBG are offered). Comparing among these four strategies, we find that when only FST is offered, the retailer surprisingly should decrease the price if the product cost is low. If the retailer only provides MBG, he will charge a higher price compared with the situation when MBG is not provided. When the size of free sample is very small, strategy NS (MS) is the optimal strategy if the product's cost is low (high). When the size of free sample is large, strategy MN is the optimal strategy. Then, we extend our model to consider three situations, namely: (a) the retailer offers coupons to consumers to experience the free sample, (b) consumer returns incur a cost, and (c) the size of free sample is endogenous. We find the main conclusion remains robust. Our findings may help explain some real-world practices, such as why some retailers (e.g., Amazon and Walmart) provide consumers with low volume toners to decrease the free sample cost; skincare product retailers such as Lancôme, Estee Lauder, and Guerlain implement both MBG and FST. We also interestingly show that a bigger size of free sample may not necessarily benefit consumers.

How Accurate are Your Experimental Data? A More Accessible GUM-Based Methodology for Uncertainty Evaluation

A methodology for evaluating experimental uncertainty is presented. Based on the Guide to the Expression of Uncertainty in Measurement (GUM) in conjunction with a sensitivity analysis, this method readily applies to systems of various degrees of complexity. It consists of three steps: (1) to estimate each uncertainty contribution of the system based on GUM; (2) to determine the sensitivity of the calculated results to variations in each of the input measurands in turn, replacing the partial derivatives of the GUM with a purely numerical approach; and (3) to calculate the overall uncertainty using the error propagation principle. Furthermore, the calculated sensitivity coefficients enable a critical evaluation of the investigated system, allowing the detection of possible targeted improvements. For this reason, the presented method is called “the sensitivity analysis method.” This is applied to three case studies with increasing complexity: a mass calibration procedure, a volume calibration procedure, and a gravimetric densimeter characterized by a multi-parameter nonlinear measuring model. When possible, the results are compared to the GUM uncertainty framework or values available in the literature.

Choosing between additive and conventional manufacturing of spare parts: On the impact of failure rate uncertainties and the tools to reduce them

Recently, there has been great interest in using additive manufacturing (AM) to produce spare parts: allowing to produce spare parts with short lead times and close to the point of use, AM reduces the need for large inventories otherwise required by conventional manufacturing (CM) techniques to deal with intermittent spare parts demand. However, using AM to produce spare parts is limited by two main drawbacks: high production costs and uncertain failure rates arising from AM still being a relatively new production technique. While the former can be counterbalanced by inventory cost reductions, it is unclear how the latter impacts the sourcing option decision (i.e. AM or CM). We aim to fill this gap by studying a periodic review model in which spare parts demands follow a Poisson process. To make our analysis accurate and reliable, we leverage a material science approach to obtain realistic values for the failure rate uncertainties. From the results, it emerges that AM is heavily penalized by failure rate uncertainties much higher than those of CM. Consequently, we then focus on some recent tools developed to reduce AM failure rate uncertainties: porosity assessment and in-situ monitoring. We find that the failure rate uncertainty should be reduced by five and six percentage points to make it worthwhile to invest in porosity assessment and in-situ monitoring, respectively. Finally, we determine under which circumstances each tool is preferred over the other and found that porosity assessment is typically the most competitive uncertainty reduction tool.

A game-based power system planning approach considering real options and coordination of all types of participants

With the ongoing advancement of global power marketization, the increasing diversity of stakeholders in the power market and their interactions have a significant impact on power system planning and operation. Traditional methods employ game theory to describe the interactions among market participants. However, these approaches fail to include all relevant participant types and overlook the long-term uncertainty value of planning schemes. Therefore, this paper proposes a game theory-based power system planning approach considering real options (RO) and coordination of all types of participants. First, the dynamic gaming interaction among supply-transmission-demand is studied, and the independent system operator is integrated into the game planning framework based on the overall perspective of power market operation. Moreover, the new power supply, grid planning, and operation of distributed generation (DG) are taken as decision variables based on the RO theory, and a game decision model of all participants in the power system planning considering the investment uncertainty is constructed. Finally, the iterative search algorithm is employed to solve the model, and the framework is validated based on the IEEE 30-bus system. The results indicate that through collaborative planning involving various market participants, Generation Companies and Transmission Companies expand their investments to maximize profits, while large power consumers reduce their investment in DG and instead increase their direct power purchases to lower costs. Moreover, these participants are inclined to pursue projects with higher asset value volatility, which further enhances their profit. By integrating RO theory with game theory, the proposed approach establishes a comprehensive planning framework that effectively addresses investment uncertainty. This integration enables market participants to make more optimal decisions, ultimately leading to an enhancement in the long-term economic performance of the power system.

OTHR-14 THE AFRICAN NEURO-ONCOLOGY ACCESS DISPARITY (ANAD) INDEX: A PUBLIC DATA-DRIVEN APPROACH TO MAPPING NATIONAL VARIATIONS IN NEURO-ONCOLOGY CARE BARRIERS

Abstract INTRODUCTION The burden of central nervous system tumors is unevenly distributed globally. In Africa, this burden is further compounded by a lack of cancer-focused health system strengthening. The authors sought to quantify these system-level challenges with a novel index. METHODS This prototype index was developed from publicly available data provided by the World Health Organization’s Global Health Observatory, the World Bank Group’s Country Database, and the Institute For Health Metrics and Evaluation’s Global Burden of Disease database. Metrics were selected based on their ability to represent health system components including workforce, service delivery, infrastructure, information management, health financing, and governance. The metrics were normalized either using standardization or min-max scaling. Next, they were weighted iteratively based on importance, relative impact, and expert judgment. Also, the weighted metrics were grouped into facilitators or obstructors. The difference between obstructors and facilitators for each country was percentile ranked to obtain the index score between 0 and 100. Finally, sensitivity analysis was conducted to test best- and worst-case scenarios based on uncertainty values. The t-test and ANOVA were used to evaluate association between country subregion/income and the index. RESULTS Data was found for 51 African countries. Ethiopia (98), Madagascar (96), and Mali (94) had the highest ANAD Index score whereas Gabon (0), Seychelles (2), and Mauritius (4) had the lowest scores. North African countries had lower ANAD Index scores than sub-Saharan African countries (mean (SD) 19 (4) vs. 54 (4), p=0.005). Also, low-income countries had higher ANAD Index scores than lower-middle- and upper-middle-income countries (72 (19) vs. 37 (27) vs. 9 (12), p<0.001). CONCLUSION This prototype will be further tested for face, construct, content, and criterion validity. The authors anticipate that expanding this metric globally will improve its validity while facilitating advocacy and allowing for impact assessment at the system level.

GRACE Satellite Observations of Antarctic Bottom Water Transport Variability

AbstractAntarctic Bottom Water (AABW) formation and transport constitute a key component of the global ocean circulation. Direct observations suggest that AABW volumes and transport rates may be decreasing, but these observations are too temporally or spatially sparse to determine the cause. To address this problem, we develop a new method to reconstruct AABW transport variability using data from the GRACE (Gravity Recovery and Climate Experiment) satellite mission. We use an ocean general circulation model to investigate the relationship between ocean bottom pressure and AABW: we calculate both of these quantities in the model, and link them using a regularized linear regression. Our reconstruction from modeled ocean bottom pressure can capture 65%–90% of modeled AABW transport variability, depending on the ocean basin. When realistic observational uncertainty values are added to the modeled ocean bottom pressure, the reconstruction can still capture 30%–80% of AABW transport variability. Using the same regression values, the reconstruction skill is within the same range in a second, independent, general circulation model. We conclude that our reconstruction method is not unique to the model in which it was developed and can be applied to GRACE satellite observations of ocean bottom pressure. These advances allow us to create the first global reconstruction of AABW transport variability over the satellite era. Our reconstruction provides information on the interannual variability of AABW transport, but more accurate observations are needed to discern AABW transport trends.

Valuation of Ethanol Grade on the Robustness of Acid Number Determination in Fish Oil

The robustness evaluation of the acid number analysis method in fish oil was carried out to see the effect of the type of ethanol used as a solvent in the titration process. The types of ethanol used in this study were pro analysis grade, pharma and food grade, and technical grade. The acid number values (mgKOH/g) in fish oil with various solvents obtained were 2.3955±0.3211 for pro analysis grade; 2.7932±0.2983 for pharma and food grade; 2.7812±0.3362 for technical grade 1; 2.7031±0.3405 for technical grade 2. The acid number for all types of ethanol has a value that is in accordance with the requirements of SNI 8467: 2018 where the acid number value must be less than 3 mgKOH/g. However, if the measurement uncertainty value is included in the calculation, only the pro analysis grade ethanol solvent has an acid number value that is in accordance with the requirements of SNI. The results of the ANOVA test also showed that there was a significant difference in the variation of ethanol types for determining acid numbers because the calculated F value (13.9004) was greater than the F table (3.0984). Therefore, the solvent that must be used in the method of determining acid numbers in fish oil is pro analysis grade ethanol.

Uncertainty guided test-time training for face forgery detection

The rapid development of generative image modeling poses security risks of spreading unreal visual information, even though those techniques make a lot of applications possible in positive aspects. To provide alerts and maintain a secure social environment, forgery detection has been an urgent and crucial solution to deal with this situation and try to avoid any negative effects, especially for human faces, owing to potential severe results when malicious creators spread disinformation widely. In spite of the success of recent works w.r.t. model design and feature engineering, detecting face forgery from novel image creation methods or data distributions remains unresolved, because well-trained models are typically not robust to the distribution shift during test-time. In this work, we aim to alleviate the sensitivity of an existing face forgery detector to new domains, and then boost real-world detection under unknown test situations. In specific, we leverage test examples, selected by uncertainty values, to fine-tune the model before making a final prediction. Therefore, it leads to a test-time training based approach for face forgery detection, that our framework incorporates an uncertainty-driven test sample selection with self-training to adapt a classifier onto target domains. To demonstrate the effectiveness of our framework and compare with previous methods, we conduct extensive experiments on public datasets, including FaceForensics++, Celeb-DF-v2, ForgeryNet and DFDC. Our results clearly show that the proposed framework successfully improves many state-of-the-art methods in terms of better overall performance as well as stronger robustness to novel data distributions.

The interaction between manufacturer’s showroom deployment and online selling formats

Many manufacturers selling through online retail platforms are deploying physical showrooms to facilitate consumers’ learning about their products. To uncover the effects of the manufacturer-deployed showrooms on participants in a platform-based distribution channel, we establish a game-theoretic model wherein a manufacturer sells through an online retailer under either agency selling or wholesale selling, and consumers decide sequentially on showrooming and purchasing. We find that, firstly, despite the merits of showrooms in resolving consumers’ valuation uncertainty, the deployment of showrooms may unexpectedly engender more product returns (i.e., return exacerbation effect), which hurts the retailer under wholesale selling when the consumers’ travel cost is moderate. Second, the manufacturer under each selling format tends to benefit (suffer) from showrooms in the case of low (high) travel cost; in addition, the decrease in commission rate boosts the gain from showroom deployment, making the manufacturer under agency selling more likely to deploy showrooms, especially when the travel cost is moderate. Third, in anticipation of the manufacturer’s (not) deploying showrooms and the corresponding profit impacts, the channel members may adopt varying preferences for the selling format. Notably, they have aligned preferences for agency selling with medium low commission rate, while neither prefers agency selling with relatively high commission rate and travel cost. This study sheds light on the flourishing practice of manufacturer-deployed showrooms, clarifying their impacts on the platform-based distribution channel.

Green and sensitive method combining ultrasonic solvent extraction, stir bar sorptive extraction and thermal desorption-gas chromatography–tandem mass spectrometry for determination of organotin compounds in sediment

A method integrating ultrasonic solvent extraction (USE) and stir bar sorptive extraction (SBSE) with thermal desorption-gas chromatography–tandem mass spectrometry (TD-GC–MS/MS) was developed and validated for determining critical organotin compounds (OTCs)—tributyltin (TBT) and triphenyltin (TPhT)—in sediment. In preparing a 0.5 g sediment sample, two 1-min cycles of US bath solvent extraction (using equal parts CH3COOH and MeOH), were followed by derivatization with NaBEt4 in the presence of a methanolic-aqueous solution of CH3COONa and SBSE for 1 h, achieving analyte recoveries of 64–94 %. Matrix-matched internal standard calibration and the use of an isotopically labelled recovery standard corrected for analyte losses and matrix effect (ME), ensuring accurate results for triorganotins. However, the method was not suitable for mono- and diorganotins due to low and anomalous recoveries caused by ME. Miniaturizing and optimizing the sample preparation reduced chemicals use and waste, contributing to the method being deemed ’an excellent green analysis’ by the Analytical Eco-Scale assessment. The method achieved a limit of quantification (LOQ) of 0.081 ng g−1 and an expanded uncertainty (U) value of 15 % for TBT, meeting the environmental quality standards (EQS) criteria of the European Union Water Framework Directive. Its applicability was demonstrated through the analysis of actual sediment samples and natural matrix certified reference materials.

A novel method for measuring electrolytic conductivity with a polarization impedance control function

The article presents an idealized model of the differential method utilized in conductometry to measure electrolytic conductivity (EC). It identifies the main drawback of the widely used differential method: the inability to control the influence of polarization impedance on EC measurement results. A criterion for the absence of error caused by polarization effects is proposed, involving equality between the ratios of resistance differences and cell constant differences. A novel method for EC measurement, termed the double differential method, is introduced. This method relies on repeated differential impedance measurements and incorporates an additional third impedance measurement of a virtual liquid column. The new approach enables the identification of polarization impedance influence on EC measurements and the calculation of measurement errors for correction. A 3 times lower type A measurement uncertainty value can be achieved by averaging the corrected results.