Regional and periodic asymmetries in the effect of Russia-Ukraine war on global stock markets.

This study aims to investigate regional and periodic asymmetries in the impact of the outbreak of the Russia-Ukraine war on global equity markets. Employing the event study methodology, the current study examines global stock market reactions within a 61-day window centred around the event day, i.e., February 24, 2022. MSCI equity indices of 47 sample countries have been utilized to ensure uniformity in the index development methodology. They provide broader coverage of global equity markets by including large and mid-cap companies, representing approximately 85% of the free float-adjusted market capitalization for each sampled country. The study extends the event window to 61 days to assess the enduring effects of the war over a relatively longer period. The research delineates regional and periodic asymmetries and posits that the impact of the war on a market is contingent upon its geographical proximity and trade relations with Russia and Ukraine. Additionally, the impact is stronger during a shorter window surrounding the event date but diminishes over the extended period.

End-to-End Multi-task Learning Architecture for Brain Tumor Analysis with Uncertainty Estimation in MRI Images.

Brain tumors are a threat to life for every other human being, be it adults or children. Gliomas are one of the deadliest brain tumors with an extremely difficult diagnosis. The reason is their complex and heterogenous structure which gives rise to subjective as well as objective errors. Their manual segmentation is a laborious task due to their complex structure and irregular appearance. To cater to all these issues, a lot of research has been done and is going on to develop AI-based solutions that can help doctors and radiologists in the effective diagnosis of gliomas with the least subjective and objective errors, but an end-to-end system is still missing. An all-in-one framework has been proposed in this research. The developed end-to-end multi-task learning (MTL) architecture with a feature attention module can classify, segment, and predict the overall survival of gliomas by leveraging task relationships between similar tasks. Uncertainty estimation has also been incorporated into the framework to enhance the confidence level of healthcare practitioners. Extensive experimentation was performed by using combinations of MRI sequences. Brain tumor segmentation (BraTS) challenge datasets of 2019 and 2020 were used for experimental purposes. Results of the best model with four sequences show 95.1% accuracy for classification, 86.3% dice score for segmentation, and a mean absolute error (MAE) of 456.59 for survival prediction on the test data. It is evident from the results that deep learning-based MTL models have the potential to automate the whole brain tumor analysis process and give efficient results with least inference time without human intervention. Uncertainty quantification confirms the idea that more data can improve the generalization ability and in turn can produce more accurate results with less uncertainty. The proposed model has the potential to be utilized in a clinical setup for the initial screening of glioma patients.

Impact of minimal lumen segmentation uncertainty on patient-specific coronary simulations: A look at FFRCT.

We examined the effect of minimal lumen segmentation uncertainty on Fractional Flow Reserve obtained from Coronary Computed Tomography Angiography FFR CT $$ \left({\mathrm{FFR}}_{\mathrm{CT}}\right) $$ . A total of 14 patient-specific coronary models with different stenosis locations and degrees of severity were enrolled in this study. The optimal segmented coronary lumens were disturbed using intra ± 6 % $$ \left(\pm 6\%\right) $$ and inter-operator ± 15 % $$ \left(\pm 15\%\right) $$ variations on the segmentation threshold. FFR CT $$ {\mathrm{FFR}}_{\mathrm{CT}} $$ was evaluated in each case by 3D-OD CFD simulations. The findings suggest that the sensitivity of FFR CT $$ {\mathrm{FFR}}_{\mathrm{CT}} $$ to this type of uncertainty increases distally and with the stenosis severity. Cases with moderate or severe distal coronary lesions should undergo either exact and thorough segmentation operations or invasive FFR measurements, particularly if the FFR CT $$ {\mathrm{FFR}}_{\mathrm{CT}} $$ is close to the cutoff (0.80). Therefore, we conclude that it is crucial to consider the lesion's location and degree of severity when evaluating FFR CT $$ {\mathrm{FFR}}_{\mathrm{CT}} $$ results.

Development of a corn flour certified reference material for the accurate determination of zearalenone.

A certified reference material (CRM, KRISS 108-01-002) for zearalenone in corn flour was developed to assure reliable and accurate measurements in testing laboratories. Commercially available corn flour underwent freeze-drying, pulverization, sieving, and homogenization. The final product was packed in amber bottles, approximately 14 g per unit, and preserved at -70 °C. 13C18-Zearalenone was used as an internal standard (IS) for the certification of zearalenone by isotope-dilution liquid chromatography-tandem mass spectrometry (ID-LC‒MS/MS) and for the analysis of α-zearalenol, ß-zearalenol, and zearalanone by LC‒MS/MS. The prepared CRM was sufficiently homogeneous, as the among-unit relative standard deviation for each mycotoxin ranged from 2.2 to 5.7 %. Additionally, the stability of the mycotoxins in the CRM was evaluated under different temperature conditions and scheduled test periods, including storage at -70°C, -20°C, and 4°C and room temperature for up to 12 months, 6 months, and 1 month, respectively. The content of each target mycotoxin in the CRM remained stable throughout the monitoring period at each temperature. Zearalenone content (153.6 ± 8.0 µg/kg) was assigned as the certified value. Meanwhile, the contents of α-zearalenol (1.30 ± 0.17 µg/kg), ß-zearalenol (4.75 ± 0.33 µg/kg), and zearalanone (2.09 ± 0.16 µg/kg) were provided as informative values.

Uncertainty salience reduces the accessibility of episodic future thoughts.

We live in uncertain times and how this pervasive sense of uncertainty affects our ability to think about the future remains largely unexplored. This study aims to investigate the effects of uncertainty salience on episodic future thinking-the ability to mentally represent specific future events. Experiment 1 assessed the impact of uncertainty on the accessibility of episodic future thoughts using an event fluency task. Participants were randomly assigned to either an uncertainty induction or control condition, and then were asked to imagine as many future events as possible that could happen in different time periods. The results showed that participants in the uncertainty condition produced fewer events, suggesting that uncertainty salience reduced the accessibility of episodic future thoughts. Experiment 2 investigated in further detail the mechanisms of production of episodic future thoughts that are affected by uncertainty. The results showed that uncertainty primarily reduced the accessibility of previously formed future thoughts (i.e., memories of the future) rather than affecting the ability to generatively think about the future and construct events. These findings shed new light on the impact of uncertainty on episodic future thinking, paving the way to further investigation into its implications for decision-making and future-oriented behavior.

Using the R package popharvest to assess the sustainability of offtake in birds.

The R package popharvest was designed to help assess the sustainability of offtake in birds when only limited demographic information is available. In this article, we describe some basics of harvest theory and then discuss several considerations when using the different approaches in popharvest to assess whether observed harvests are unsustainable. Throughout, we emphasize the importance of distinguishing between the scientific and policy aspects of managing offtake. The principal product of popharvest is a sustainable harvest index (SHI), which can indicate whether the harvest is unsustainable but not the converse. SHI is estimated based on a simple, scalar model of logistic population growth, whose parameters may be estimated using limited knowledge of demography. Uncertainty in demography leads to a distribution of SHI values and it is the purview of the decision-maker to determine what amounts to an acceptable risk when failing to reject the null hypothesis of sustainability. The attitude toward risk, in turn, will likely depend on the decision-maker's objective(s) in managing offtake. The management objective as specified in popharvest is a social construct, informed by biology, but ultimately it is an expression of social values that usually vary among stakeholders. We therefore suggest that any standardization of criteria for management objectives in popharvest will necessarily be subjective and, thus, hard to defend in diverse decision-making situations. Because of its ease of use, diverse functionalities, and a minimal requirement of demographic information, we expect the use of popharvest to become widespread. Nonetheless, we suggest that while popharvest provides a useful platform for rapid assessments of sustainability, it cannot substitute for sufficient expertise and experience in harvest theory and management.

The impact of economy policy uncertainty and oil price shocks on G20 banks' stock performance: Wavelet coherence and non-parametric causality in quantiles approach.

This study employs nonparametric causality-in-quantiles and wavelet coherence techniques to examine the impact of economic policy uncertainty and oil price variations on bank stocks in twelve prominent global economies. The results reveal that the effects of both economic policy uncertainty and oil prices on bank stock values vary significantly across countries and over time. Notably, during stress periods, we observe an inverse relationship between economic policy uncertainty and bank stocks in multiple countries, namely, Brazil, Canada, France, India, Russia, and the USA, with Japan exhibiting a particularly strong and long-term adverse correlation. Similarly, the influence of oil prices is primarily observed during crisis periods, but it demonstrates a substantial co-movement with bank stocks across the sample countries except Brazil. Our empirical analysis holds valuable implications for policymakers, bankers, investors, and portfolio managers.

Brain-inspired semantic data augmentation for multi-style images.

Data augmentation is an effective technique for automatically expanding training data in deep learning. Brain-inspired methods are approaches that draw inspiration from the functionality and structure of the human brain and apply these mechanisms and principles to artificial intelligence and computer science. When there is a large style difference between training data and testing data, common data augmentation methods cannot effectively enhance the generalization performance of the deep model. To solve this problem, we improve modeling Domain Shifts with Uncertainty (DSU) and propose a new brain-inspired computer vision image data augmentation method which consists of two key components, namely, using Robust statistics and controlling the Coefficient of variance for DSU (RCDSU) and Feature Data Augmentation (FeatureDA). RCDSU calculates feature statistics (mean and standard deviation) with robust statistics to weaken the influence of outliers, making the statistics close to the real values and improving the robustness of deep learning models. By controlling the coefficient of variance, RCDSU makes the feature statistics shift with semantic preservation and increases shift range. FeatureDA controls the coefficient of variance similarly to generate the augmented features with semantics unchanged and increase the coverage of augmented features. RCDSU and FeatureDA are proposed to perform style transfer and content transfer in the feature space, and improve the generalization ability of the model at the style and content level respectively. On Photo, Art Painting, Cartoon, and Sketch (PACS) multi-style classification task, RCDSU plus FeatureDA achieves competitive accuracy. After adding Gaussian noise to PACS dataset, RCDSU plus FeatureDA shows strong robustness against outliers. FeatureDA achieves excellent results on CIFAR-100 image classification task. RCDSU plus FeatureDA can be applied as a novel brain-inspired semantic data augmentation method with implicit robot automation which is suitable for datasets with large style differences between training and testing data.

Satellite NO2 Retrieval Complicated by Aerosol Composition over Global Urban Agglomerations: Seasonal Variations and Long-Term Trends (2001-2018).

Tropospheric nitrogen dioxide (NO2) poses a serious threat to the environmental quality and public health. Satellite NO2 observations have been continuously used to monitor NO2 variations and improve model performances. However, the accuracy of satellite NO2 retrieval depends on the knowledge of aerosol optical properties, in particular for urban agglomerations accompanied by significant changes in aerosol characteristics. In this study, we investigate the impacts of aerosol composition on tropospheric NO2 retrieval for an 18 year global data set from Global Ozone Monitoring Experiment (GOME)-series satellite sensors. With a focus on cloud-free scenes dominated by the presence of aerosols, individual aerosol composition affects the uncertainties of tropospheric NO2 columns through impacts on the aerosol loading amount, relative vertical distribution of aerosol and NO2, aerosol absorption properties, and surface albedo determination. Among aerosol compositions, secondary inorganic aerosol mostly dominates the NO2 uncertainty by up to 43.5% in urban agglomerations, while organic aerosols contribute significantly to the NO2 uncertainty by -8.9 to 37.3% during biomass burning seasons. The possible contrary influences from different aerosol species highlight the importance and complexity of aerosol correction on tropospheric NO2 retrieval and indicate the need for a full picture of aerosol properties. This is of particular importance for interpreting seasonal variations or long-term trends of tropospheric NO2 columns as well as for mitigating ozone and fine particulate matter pollution.

The mediating effect of uncertainty in illness between heart failure symptoms and health-related quality of life among rural patients with heart failure: A multi-center cross-sectional study.

BACKGROUND: The health-related quality of life (HRQoL) of patients with heart failure (HF) in rural settings in China remains unclear. Limited studies explored the mediating effect of uncertainty in illness between heart failure symptoms and HRQoL in this population. OBJECTIVES: To explore the status of HRQoL in rural patients with HF; assess the impact of HF symptoms and uncertainty in illness on HRQoL; and examine the mediating effect of uncertainty in illness on the relationship between symptoms and HRQoL in rural patients with HF. METHODS: Overall, 298 rural patients with HF were recruited from five township hospitals of Taishan and Jinzhong City in China between November 2021 and August 2022. Three variables, namely HF symptoms, uncertainty in illness, and HRQoL were measured using three validated scales. RESULTS: The average score of HRQoL in rural patients with HF was 43.19. Of the participants, 60.4 %, 35.23 %, and 4.37 % exhibited poor, moderate, and good HRQoL, respectively. The HF symptoms (ß = -0.47) and uncertainty in illness (ß = -0.34) directly influenced HRQoL. Moreover, the HF symptoms also indirectly affected HRQoL through uncertainty in illness (ß = -0.07). The indirect effect accounted for 12.96 % of the total effect of HF symptoms on HRQoL. CONCLUSION: Rural patients with HF exhibited poor HRQoL. In this population, HF symptoms and uncertainty in illness were negatively associated with HRQoL. Uncertainty in illness mediated the relationship between HF symptoms and HRQoL. Tailored healthcare services should be developed for the rural population to alleviate HF symptoms, reduce uncertainty in illness, and enhance their HRQoL.

Impact of covariate model building methods on their clinical relevance evaluation in population pharmacokinetic analyses: comparison of the full model, stepwise covariate model (SCM) and SCM+ approaches.

Covariate analysis in population pharmacokinetics is key for adjusting doses for patients. The main objective of this work was to compare the adequacy of various modeling approaches on covariate clinical relevance decision-making. The full model, stepwise covariate model (SCM) and SCM+ PsN algorithms were compared in a clinical trial simulation of a 383-patient population pharmacokinetic study mixing rich and sparse designs. A one-compartment model with first-order absorption was used. A base model including a body weight effect on CL/F and V/F and a covariate model including 4 additional covariates-parameters relationships were simulated. As for forest plots, ratios between covariates at a specific value and that of a typical individual were calculated with their 90% confidence interval (CI90) using standard errors. Covariates on CL, V and KA were considered relevant if their CI90 fell completely outside the reference area [0.8-1.2]. All approaches provided unbiased covariate ratio estimates. For covariates with a simulated effect, the 3 approaches correctly identify their clinical relevance. However, significant covariates were missed in up to 15% of cases with SCM/SCM+. For covariate with no simulated effects, the full model mainly identified them as non-relevant or with insufficient information while SCM/SCM+ mainly did not select them. SCM/SCM+ assume that non-selected covariates are non-relevant when it could be due to insufficient information, whereas the full model does not make this assumption and is faster. This study must be extended to other methods and completed by a more complex high-dimensional simulation framework.

Creating a Multiply Imputed Value Set for the EQ-5D-5L in Canada: State-Level Misspecification Terms Are Needed to Characterize Parameter Uncertainty Correctly.

BACKGROUND: Parameter uncertainty in EQ-5D-5L value sets often exceeds the instrument's minimum important difference, yet this is routinely ignored. Multiple imputation (MI) accounts for parameter uncertainty in the value set; however, no valuation study has implemented this methodology. Our objective was to create a Canadian MI value set for the EQ-5D-5L, thus enabling users to account for parameter uncertainty in the value set. METHODS: Using the Canadian EQ-5D-5L valuation study (N = 1,073), we first refit the original model followed by models with state-level misspecification. Models were compared based on the adequacy of 95% credible interval (CrI) coverage for out-of-sample predictions. Using the best-fitting model, we took 100 draws from the posterior distribution to create 100 imputed value sets. We examined how much the standard error of the estimated mean health utilities increased after accounting for parameter uncertainty in the value set by using the MI and original value sets to score 2 data sets: 1) a sample of 1,208 individuals from the Canadian general public and 2) a sample of 401 women with breast cancer. RESULTS: The selected model with state-level misspecification outperformed the original model (95% CrI coverage: 94.2% v. 11.6%). We observed wider standard errors for the estimated mean utilities on using the MI value set for both the Canadian general public (MI: 0.0091; original: 0.0035) and patients with breast cancer (MI: 0.0169; original: 0.0066). DISCUSSION AND CONCLUSIONS: We provide 1) the first MI value sets for the EQ-5D-5L and 2) code to construct MI value sets while accounting for state-level model misspecification. Our study suggests that ignoring parameter uncertainty in value sets leads to falsely narrow SEs. HIGHLIGHTS: Value sets for health state utility instruments are estimated subject to parameter uncertainty; this parameter uncertainty may exceed the minimum important difference of the instrument, yet it is not fully captured using current methods.This study creates the first multiply imputed value set for a multiattribute utility instrument, the EQ-5D-5L, to fully capture this parameter uncertainty.We apply the multiply imputed value set to 2 data sets from 1) the Canadian general public and 2) women with invasive breast cancer.Scoring the EQ-5D-5L using a multiply imputed value set led to wider standard error estimates, suggesting that the current practice of ignoring parameter uncertainty in the value set leads to falsely low standard errors.Our work will be of interest to methodologists and developers of the EQ-5D-5L and users of the EQ-5D-5L, such as health economists, researchers, and policy makers.

Fear, Perceived Threat, and Anxiety During COVID-19 Pandemic: The Mediating Role of Cognitive Control/Flexibility and Intolerance of Uncertainty. / COVID-19 Pandemisinde Korku, Algilanan Tehdit ve Kaygi: Bilissel Kontrol/Esneklik ve Belirsizlige Tahammülsüzlügün Aracilik Rolü.

OBJECTIVE: In this study, we aimed to evaluate the relationship between fear of COVID-19, perceived threat of COVID-19, anxiety, cognitive control/flexibility, and intolerance to uncertainty. In addition, the mediating role of cognitive control/flexibility and intolerance to uncertainty were investigated. METHOD: 224 volunteers aged between 18-55 years were included in the study. Cognitive Control and Felxibility Scale, Intolerance of Uncertainty Scale, Fear of COVID-19 Scale, Perceived COVID-19 Threat Form and Beck Anxiety Scales were administered to all participants via an online environment. In this context, Pearson correlation, linear regression, and mediation analyzes were performed. RESULTS: There were significant relationships among Cognitive Control and Flexibility, Intolerance of Uncertainty, Beck Anxiety, fear of COVID-19, perceived COVID-19 threat (p<0,01). Linear regression analysis showed that the Beck Anxiety Scale, Intolerance of Uncertainty and Cognitive Control/ Flexibility Scale scores significantly predicted fear of COVID-19 and the perceived threat of COVID-19 (p<0,001). In addition, mediation analysis revealed that intolerance to uncertainty and cognitive control/flexibility are mediating factors between anxiety and the perceived threat of COVID-19 (p<0,01). CONCLUSION: There is a relationship between fear of COVID-19 and perception of threat, anxiety, intolerance of uncertainty, and cognitive control mechanisms. In accordance with these findings, psychosocial support and therapy programs can be created based on cognitive control/flexibility and intolerance of uncertainty in order to increase the mental health well-being of individuals.

Global indicators of the environmental impacts of invasive alien species and their information adequacy.

Monitoring the extent to which invasive alien species (IAS) negatively impact the environment is crucial for understanding and mitigating biological invasions. Indeed, such information is vital for achieving Target 6 of the Kunming-Montreal Global Biodiversity Framework. However, to-date indicators for tracking the environmental impacts of IAS have been either lacking or insufficient. Capitalizing on advances in data availability and impact assessment protocols, we developed environmental impact indicators to track realized and potential impacts of IAS. We also developed an information status indicator to assess the adequacy of the data underlying the impact indicators. We used data on 75 naturalized amphibians from 82 countries to demonstrate the indicators at a global scale. The information status indicator shows variation in the reliability of the data and highlights areas where absence of impact should be interpreted with caution. Impact indicators show that growth in potential impacts are dominated by predatory species, while potential impacts from both predation and disease transmission are distributed worldwide. Using open access data, the indicators are reproducible and adaptable across scales and taxa and can be used to assess global trends and distributions of IAS, assisting authorities in prioritizing control efforts and identifying areas at risk of future invasions. This article is part of the theme issue 'Ecological novelty and planetary stewardship: biodiversity dynamics in a transforming biosphere'.

Characterising information gains and losses when collecting multiple epidemic model outputs.

BACKGROUND: Collaborative comparisons and combinations of epidemic models are used as policy-relevant evidence during epidemic outbreaks. In the process of collecting multiple model projections, such collaborations may gain or lose relevant information. Typically, modellers contribute a probabilistic summary at each time-step. We compared this to directly collecting simulated trajectories. We aimed to explore information on key epidemic quantities; ensemble uncertainty; and performance against data, investigating potential to continuously gain information from a single cross-sectional collection of model results. METHODS: We compared projections from the European COVID-19 Scenario Modelling Hub. Five teams modelled incidence in Belgium, the Netherlands, and Spain. We compared July 2022 projections by incidence, peaks, and cumulative totals. We created a probabilistic ensemble drawn from all trajectories, and compared to ensembles from a median across each model's quantiles, or a linear opinion pool. We measured the predictive accuracy of individual trajectories against observations, using this in a weighted ensemble. We repeated this sequentially against increasing weeks of observed data. We evaluated these ensembles to reflect performance with varying observed data. RESULTS: By collecting modelled trajectories, we showed policy-relevant epidemic characteristics. Trajectories contained a right-skewed distribution well represented by an ensemble of trajectories or a linear opinion pool, but not models' quantile intervals. Ensembles weighted by performance typically retained the range of plausible incidence over time, and in some cases narrowed this by excluding some epidemic shapes. CONCLUSIONS: We observed several information gains from collecting modelled trajectories rather than quantile distributions, including potential for continuously updated information from a single model collection. The value of information gains and losses may vary with each collaborative effort's aims, depending on the needs of projection users. Understanding the differing information potential of methods to collect model projections can support the accuracy, sustainability, and communication of collaborative infectious disease modelling efforts.

A novel application of data-consistent inversion to overcome spurious inference in genome-wide association studies.

The genome-wide association studies (GWAS) typically use linear or logistic regression models to identify associations between phenotypes (traits) and genotypes (genetic variants) of interest. However, the use of regression with the additive assumption has potential limitations. First, the normality assumption of residuals is the one that is rarely seen in practice, and deviation from normality increases the Type-I error rate. Second, building a model based on such an assumption ignores genetic structures, like, dominant, recessive, and protective-risk cases. Ignoring genetic variants may result in spurious conclusions about the associations between a variant and a trait. We propose an assumption-free model built upon data-consistent inversion (DCI), which is a recently developed measure-theoretic framework utilized for uncertainty quantification. This proposed DCI-derived model builds a nonparametric distribution on model inputs that propagates to the distribution of observed data without the required normality assumption of residuals in the regression model. This characteristic enables the proposed DCI-derived model to cover all genetic variants without emphasizing on additivity of the classic-GWAS model. Simulations and a replication GWAS with data from the COPDGene demonstrate the ability of this model to control the Type-I error rate at least as well as the classic-GWAS (additive linear model) approach while having similar or greater power to discover variants in different genetic modes of transmission.

Ecotourism or ecological concerns? Tracing the impact of economic policy uncertainty and natural resources on tourism-driven ecological footprints.

The influence of tourism development and economic policy uncertainties on environmental sustainability is substantial. Promoting responsible tourism and using sustainable tourism practises, like offering eco-friendly lodging, is a key part of protecting natural habitats and lowering carbon footprints. Hence, this study tries to examine the relationship between tourism development, economic policy uncertainty, renewable energy, and natural resources on the ecological footprint of India during 1990-2022. This study applies a novel dynamic ARDL simulation approach for long-run and short-run analyses. The study also employs frequency-domain causality to check the causal relationship between the variables. The result reveals that tourism has a positive effect on the ecological footprint. Similarly, economic policy uncertainty has a positive and significant effect on the ecological footprint in India during the sample period. Additionally, natural resource rent shows a positive effect on the ecological footprint or deteriorating environmental quality in the short and long run in the sample period. However, renewable energy consumption indicates a negative effect on the ecological footprint. The results reveal that TDI and EPU have rejected the null hypothesis of no Granger cause in the long, medium, and short term. While renewable energy has a causal relationship with ecological footprints in both the long run and medium run, it is imperative for India to adopt measures that facilitate the advancement of sustainable tourism, with a particular focus on promoting environmentally friendly lodging options, enhancing public transportation systems, and implementing effective waste management strategies.

Process-Based and Probabilistic Quantification of Co and Ni Mobilization Risks Induced by Managed Aquifer Recharge.

Managed aquifer recharge (MAR) is an increasingly used water management technique that enhances water availability while commonly generating water quality benefits. However, MAR activities may also trigger adverse geochemical reactions, especially during the injection of oxidant-enriched waters into reducing aquifers. Where this occurs, the environmental risks and the viability of mitigating them must be well understood. Here, we develop a rigorous approach for assessing and managing the risks from MAR-induced metal mobilization. First, we develop a process-based reactive transport model to identify and quantify the main hydrogeochemical drivers that control the release of metals and their mobility. We then apply a probabilistic framework to interrogate the inherent uncertainty associated with adjustable model parameters and consider this uncertainty (i) in long-term predictions of groundwater quality changes and (ii) in scenarios that investigate the effectiveness of modifications in the water treatment process to mitigate metal release and mobility. The results suggested that Co, Ni, Zn, and Mn were comobilized during pyrite oxidation and that metal mobility was controlled (i) by the sediment pH buffering capacity and (ii) by the sorption capacity of the native aquifer sediments. Both tested mitigation strategies were shown to be effective at reducing the risk of elevated metal concentrations.

The end-of-life power battery recycling & remanufacturing center location-adjustment problem considering battery capacity and quantity uncertainty.

The booming electric vehicle market has led to an increasing number of end-of-life power batteries. In order to reduce environmental pollution and promote the realization of circular economy, how to fully and effectively recycle the end-of-life power batteries has become an urgent challenge to be solved today. The recycling & remanufacturing center is an extremely important and key facility in the recycling process of used batteries, which ensures that the recycled batteries can be handled in a standardized manner under the conditions of professional facilities. In reality, different adjustment options for existing recycling & remanufacturing centers have a huge impact on the planning of new sites. This paper proposes a mixed-integer linear programming model for the siting problem of battery recycling & remanufacturing centers considering site location-adjustment. The model allows for demolition, renewal, and new construction options in planning for recycling & remanufacturing centers. By adjusting existing sites, this paper provides an efficient allocation of resources under the condition of meeting the demand for recycling of used batteries. Next, under the new model proposed in this paper, the uncertainty of the quantity and capacity of recycled used batteries is considered. By establishing different capacity conditions of batteries under multiple scenarios, a robust model was developed to determine the number and location of recycling & remanufacturing centers, which promotes sustainable development, reduces environmental pollution and effectively copes with the risk of the future quantity of used batteries exceeding expectations. In the final results of the case analysis, our proposed model considering the existing sites adjustment reduces the cost by 3.14% compared to the traditional model, and the average site utilization rate is 15.38% higher than the traditional model. The results show that the model has an effective effect in reducing costs, allocating resources, and improving efficiency, which could provide important support for decision-making in the recycling of used power batteries.

[Psychometric properties of the Hungarian version of the Intolerance of Uncertainty Scale - Short Version (IUS-12)]. / Az alkoholhasználat és az öndetermináció kapcsolata: a Kezelési Önszabályozási Kérdoív pszichometriai jellemzoi magyar mintán.

INTRODUCTION: Intolerance of uncertainty is the tendency to react negatively to an uncertain situation, regardless of the probability of the occurrence of the event and its consequences. Intolerance of uncertainty (IU) can also be conceptualized as a personality trait that is prominent in many anxiety and rumination-related pathologies. A growing body of research highlights its key role in understanding anxiety disorders. METHOD: The aim of present study was to investigate the dimensionality, validity and reliability of the Intolerance of Uncertainty Scale in a large non-clinical sample (N = 1747). Former was analysed by confirmatory factor analysis, the validity by correlation with the Perceived Stress Scale. Reliability was assessed using Cronbach's alpha coefficient and test-retest analysis. RESULTS: Confirmatory factor analysis failed to confirm the hypothesized two-factor structure (CFI = 0.907; TLI = 0.885; RMSEA = 0.103 [90% CI = 0.096-0.110]; SRMR = 0.071). However, the exploratory factor analysis identified the same two factors as in the original study: "Prospective" and "Inhibitory". The scale showed excellent internal reliability (α = 0.897) and test-retest reliability. There was moderate correlation with the Perceived Stress Scale (r = 0.438). CONCLUSION: Based on the results, the Hungarian version of the BTS-12 is a valid and reliable measurement tool. However, before its use in a Hungarian sample, its psychometric properties need to be confirmed by further studies on a large sample. In the future, the questionnaire will be useful in measuring intolerance of uncertainty and may be useful in identifying susceptibility to anxiety disorders.