Reducing the uncertainty in estimating soil microbial-derived carbon storage.

Soil organic carbon (SOC) is the largest carbon pool in terrestrial ecosystems and plays a crucial role in mitigating climate change and enhancing soil productivity. Microbial-derived carbon (MDC) is the main component of the persistent SOC pool. However, current formulas used to estimate the proportional contribution of MDC are plagued by uncertainties due to limited sample sizes and the neglect of bacterial group composition effects. Here, we compiled the comprehensive global dataset and employed machine learning approaches to refine our quantitative understanding of MDC contributions to total carbon storage. Our efforts resulted in a reduction in the relative standard errors in prevailing estimations by an average of 71% and minimized the effect of global variations in bacterial group compositions on estimating MDC. Our estimation indicates that MDC contributes approximately 758 Pg, representing approximately 40% of the global soil carbon stock. Our study updated the formulas of MDC estimation with improving the accuracy and preserving simplicity and practicality. Given the unique biochemistry and functioning of the MDC pool, our study has direct implications for modeling efforts and predicting the land-atmosphere carbon balance under current and future climate scenarios.

Stock assessment models overstate sustainability of the world's fisheries.

Effective fisheries management requires accurate estimates of stock biomass and trends; yet, assumptions in stock assessment models generate high levels of uncertainty and error. For 230 fisheries worldwide, we contrasted stock biomass estimates at the time of assessment with updated hindcast estimates modeled for the same year in later assessments to evaluate systematic over- or underestimation. For stocks that were overfished, low value, or located in regions with rising temperatures, historical biomass estimates were generally overstated compared with updated assessments. Moreover, rising trends reported for overfished stocks were often inaccurate. With consideration of bias identified retrospectively, 85% more stocks than currently recognized have likely collapsed below 10% of maximum historical biomass. The high uncertainty and bias in modeled stock estimates warrants much greater precaution by managers.

Sustainable agility of product development process based on a rough cloud technique: A case study on China's small and medium enterprises.

The importance of incorporating an agile approach into creating sustainable products has been widely discussed. This approach can enhance innovation integration, improve adaptability to changing development circumstances, and increase the efficiency and quality of the product development process. While many agile methods have originated in the software development context and have been formulated based on successful software projects, they often fail due to incorrect procedures and a lack of acceptance, preventing deep integration into the process. Additionally, decision-making for market evaluation is often hindered by unclear and subjective information. Therefore, this study introduces an extended TOPSIS (Technique for Order Performance by Similarity to Ideal Solution) method for sustainable product development. This method leverages the benefits of cloud model theory to address randomness and uncertainty (intrapersonal uncertainty) and the advantages of rough set theory to flexibly handle market demand uncertainty without requiring extra information. The study proposes an integrated weighting method that considers both subjective and objective weights to determine comprehensive criteria weights. It also presents a new framework, named Sustainable Agility of Product Development (SAPD), which aims to evaluate criteria for assessing sustainable product development. To validate the effectiveness of this proposed method, a case study is conducted on small and medium enterprises in China. The obtained results show that the company needs to conduct product structure research and development to realize new product functions.

From symptoms to surgery-A pathway through uncertainty and hope: An interview study of women facing ovarian surgery.

BACKGROUND: Diagnosis of an adnexal mass might be a sign of ovarian cancer, with an overall poor prognosis. This study aimed to explore women's experiences and perceptions of facing ovarian surgery due to an adnexal mass, and expectations on life after surgery. METHODS: Individual in-depth interviews with 15 women facing ovarian surgery due to an adnexal mass. Interviews were analysed using qualitative content analysis. RESULTS: An overarching theme, From symptoms to surgery-a pathway through uncertainty and hope, was identified. The theme was made up of three categories; I. The road to diagnosis, II. Striving for information and guidance, and III. Balancing emotions of hope and fear. The period between discovering the adnexal mass and surgery was often described as chaotic and difficult to manage. However, the diagnostic procedures were mostly described as timely and efficient, and participants felt safe and cared for. Person-centred care was considered crucial when being in this vulnerable situation, and the nurse navigator was described as a key person to approach for any queries. While participants expressed overall satisfaction with the information provided by health professionals, some reported a lack of information regarding the surgery's potential impact on hormonal production and sexuality. Restrictions during the COVID-19 pandemic forced participants to attend healthcare visits alone, and some wished that health professionals had taken more responsibility for informing their relatives. Many participants focused on the positive aspects of the information gained about the adnexal mass, and that the entire situation gave perspective of what was important in life. CONCLUSIONS: Waiting for surgery on a possibly malignant adnexal mass can be very stressful, however person-centred care and the guidance of a nurse navigator can make the process more manageable. To improve women's experience, health professionals may involve relatives more often and make sure to inform of potential hormonal loss and sexuality after ovarian surgery.

Assessing structural uncertainty of biochemical regulatory networks in metabolic pathways under varying data quality.

Ordinary differential equation (ODE) models are powerful tools for studying the dynamics of metabolic pathways. However, key challenges lie in constructing ODE models for metabolic pathways, specifically in our limited knowledge about which metabolite levels control which reaction rates. Identification of these regulatory networks is further complicated by the limited availability of relevant data. Here, we assess the conditions under which it is feasible to accurately identify regulatory networks in metabolic pathways by computationally fitting candidate network models with biochemical systems theory (BST) kinetics to data of varying quality. We use network motifs commonly found in metabolic pathways as a simplified testbed. Key features correlated with the level of difficulty in identifying the correct regulatory network were identified, highlighting the impact of sampling rate, data noise, and data incompleteness on structural uncertainty. We found that for a simple branched network motif with an equal number of metabolites and fluxes, identification of the correct regulatory network can be largely achieved and is robust to missing one of the metabolite profiles. However, with a bi-substrate bi-product reaction or more fluxes than metabolites in the network motif, the identification becomes more challenging. Stronger regulatory interactions and higher metabolite concentrations were found to be correlated with less structural uncertainty. These results could aid efforts to predict whether the true metabolic regulatory network can be computationally identified for a given stoichiometric network topology and dataset quality, thus helping to identify optimal measures to mitigate such identifiability issues in kinetic model development.

A participatory multi-criteria decision analysis framework reveals transition potential towards non-grid wastewater management.

Many public environmental decisions are wicked problems due to high complexity and uncertainty. We test a participatory value-based framework based on multi-criteria decision analysis (MCDA) to tackle such problems. Our framework addresses two important gaps identified in reviews of MCDA applications to environmental problems: including stakeholders and treating uncertainty. We applied our framework in two complex real-world cases concerning a paradigm shift in the wastewater sector; the transition from centralized wastewater systems to decentralized non-grid systems. Non-grid systems may solve some problems of centralized systems by reducing costs, increasing flexibility, and addressing growing demands on environmental issues, especially in rural areas. But non-grid systems have rarely been implemented in OECD countries, because it is unclear whether a transition is recommendable, and whether stakeholders would accept this shift. This problem allows addressing several fundamental research questions. As theoretical contribution, we found that stakeholder participation in MCDA is necessary, because different preferences of stakeholders can lead to different best-performing options in the assessments. Compared to the typical integrated assessment (IA) approach that excludes stakeholders' preferences, the MCDA process led to clearer outcomes. Results indicate that including the uncertainty of predicted consequences of options with Monte Carlo simulation helped discriminate between options and identify best-performing options. Challenging the uncertainty of elicited stakeholder preferences with sensitivity analyses, we found that best-performing options were especially sensitive to the MCDA aggregation model. Despite the high uncertainty, it was possible to suggest robust consensus options that would perform reasonably well for all stakeholders. As practical contribution, results indicated that a transition from the centralized to decentralized non-grid systems seems feasible. Most stakeholders assigned highest weights to environmental protection objectives in decision-making workshops. These stakeholder preferences implemented in MCDA led to a generally better assessment of innovative non-grid systems, especially when including urine source separation. Stakeholders perceived the MCDA process as beneficial and found results plausible. We conclude that the proposed participatory value-based framework is rigorous, but still feasible in practice. The framework is certainly transferable to any context and is open to testing and refinement in various applications to wicked decision problems.

Asymmetric impacts of climate policy uncertainty, investor sentiment on energy prices and renewable energy consumption: Evidence from NARDL and wavelet coherence.

This paper seeks to look into the asymmetric impacts posed by climate policy uncertainty (CPU) and investor sentiment (IS) upon the price of non-renewable energy, specifically natural gas prices, and the consumption of renewable energy, embodied in geothermal energy, biofuels, and fuel ethanol. To this end, the analysis draws on a non-linear autoregressive distributed lag (NARDL) model and wavelet coherence (WTC) technique with monthly data from January 2000 to December 2021. The NARDL results establish an asymmetric association between the variables, where negative shocks to CPU exert a greater effect on each energy variable than positive shocks, while the reverse is true for IS. Furthermore, it has been noticed that CPU and IS exhibit primarily negative correlations with the target variables over the long term, with CPU having a more pronounced effect on natural gas prices than on other forms of renewable energy consumption. Wavelet analysis also reveals that CPU leads the energy variables over the medium to long run, while IS assumes a dominant role in the short to medium run. These momentous findings underscore the importance of this study in informing energy policy formulation and environmental management, as well as optimizing investor portfolios.

Green missing spots: Information entropy on greenhouse gas emission disclosure by Brazilian companies.

This study aims to address a critical gap in the literature by examining the incorporation of uncertainty in measuring carbon emissions using the greenhouse gas (GHG) Protocol methodology across all three scopes. By comprehensively considering the various dimensions of CO2 emissions within the context of organizational activities, our research contributes significantly to the existing body of knowledge. We address challenges such as data quality issues and a high prevalence of missing values by using information entropy, techniques for order preference by similarity to ideal solution (TOPSIS), and an artificial neural network (ANN) to analyze the contextual variables. Our findings, derived from the data sample of 56 companies across 18 sectors and 13 Brazilian states between 2017 and 2019, reveal that Scope 3 emissions exhibit the highest levels of information entropy. Additionally, we highlight the pivotal role of public policies in enhancing the availability of GHG emissions data, which, in turn, positively impacts policy-making practices. By demonstrating the potential for a virtuous cycle between improved information availability and enhanced policy outcomes, our research underscores the importance of addressing uncertainty in carbon emissions measurement for advancing effective climate change mitigation strategies.

How is diagnostic uncertainty communicated and managed in real world primary care settings?

BACKGROUND: Managing diagnostic uncertainty is a major challenge in primary care due to factors such as the absence of definitive tests, variable symptom presentations and disease evolution. Maintaining patient trust during a period of investigative uncertainty, whilst minimising scope for diagnostic error is a challenge. Mismanagement can lead to diagnostic errors, treatment delays, and suboptimal patient outcomes. OBJECTIVE: Our aim was to explore how UK primary care physicians (GPs) address and communicate diagnostic uncertainty in practice. DESIGN: This qualitative study used video and audio-recordings. Verbatim transcripts were coded with a modified, validated tool to capture GPs' actions and communication in primary care consultations that included diagnostic uncertainty. The tool includes items relating to advice regarding new symptoms or symptom deterioration (sometimes called 'safety netting'). Video data was analysed to identify GP and patient body postures during and after the delivery of the management plan. PARTICIPANTS: All patient participants had a consultation with a GP, were over the age of 50 and had (1) at least one new presenting problem or (2) one persistent problem that was undiagnosed. APPROACH: Data collection occurred in GP-patient consultations during 2017-2018 across 7 practices in UK during 2017-2018. KEY RESULTS: GPs used various management strategies to address diagnostic uncertainty, including (1) symptom monitoring without treatment, (2) prescribed treatment with symptom monitoring, and (3) addressing risks that could arise from administrative tasks. GPs did not make management plans for potential treatment side effects. Specificity of uncertainty management plans varied among GPs, with only some offering detailed actions and timescales. The transfer of responsibility for the management plan to patients was usually delivered rather than negotiated, with most patients confirming acceptance before concluding the discussion. CONCLUSIONS: We offer guidance to healthcare professionals, improving awareness of using and communicating management plans for diagnostic uncertainty.

Time to diagnosis for a rare disease: managing medical uncertainty. A qualitative study.

BACKGROUND: People with a rare disease commonly experience long delays from the onset of symptoms to diagnosis. Rare diseases are challenging to diagnose because they are clinically heterogeneous, and many present with non-specific symptoms common to many diseases. We aimed to explore the experiences of people with myositis, primary immunodeficiency (PID), and sarcoidosis from symptom onset to diagnosis to identify factors that might impact receipt of a timely diagnosis. METHODS: This was a qualitative study using semi-structured interviews. Our approach was informed by Interpretive Phenomenological Analysis (IPA). We applied the lens of uncertainty management theory to tease out how patients experience, assess, manage and cope with puzzling and complex health-related issues while seeking a diagnosis in the cases of rare diseases. RESULTS: We conducted interviews with 26 people with a rare disease. Ten participants had been diagnosed with a form of myositis, 8 with a primary immunodeficiency, and 8 with sarcoidosis. Time to diagnosis ranged from 6 months to 12 years (myositis), immediate to over 20 years (PID), and 6 months to 15 years (sarcoidosis). We identified four themes that described the experiences of participants with a rare disease as they sought a diagnosis for their condition: (1) normalising and/or misattributing symptoms; (2) particularising by clinicians; (3) asserting patients' self-knowledge; and (4) working together through the diagnosable moment. CONCLUSIONS: Managing medical uncertainty in the time before diagnosis of a rare disease can be complicated by patients discounting their own symptoms and/or clinicians discounting the scale and impact of those symptoms. Persistence on the part of both clinician and patient is necessary to reach a diagnosis of a rare disease. Strategies such as recognising pattern failure and accommodating self-labelling are key to diagnosis.

River flow intermittence influence biodiversity-stability relationships across spatial scales: Implications for an uncertain future.

Climate change is increasing the proportion of river networks experiencing flow intermittence, which in turn reduces local diversity (i.e., α-diversity) but enhances variation in species composition among sites (i.e., ß-diversity), with potential consequences on ecosystem stability. Indeed, the multiscale theory of stability proposes that regional stability can be attained not only by local processes but also by spatial asynchrony among sites. However, it is still unknown whether and how scale-dependent changes in biodiversity associated with river flow intermittence influence stability across spatial scales. To elucidate this, we here focus on multiple metacommunities of French rivers experiencing contrasting levels of flow intermittence. We clearly show that the relative contribution of spatial asynchrony to regional stability was higher for metacommunities of intermittent than perennial rivers. Surprisingly, spatial asynchrony was mainly linked to asynchronous population dynamics among sites, but not to ß-diversity. This finding was robust for both truly aquatic macroinvertebrates and for taxa that disperse aerially during their adult stages, implying the need to conserve multiple sites across the landscape to attain regional stability in intermittent rivers. By contrast, metacommunities of truly aquatic macroinvertebrates inhabiting perennial rivers were mainly stabilized by local processes. Our study provides novel evidence that metacommunities of perennial and intermittent rivers are stabilized by contrasting processes operating at different spatial scales. We demonstrate that flow intermittence enhances spatial asynchrony among sites, thus resulting in a regional stabilizing effect on intermittent river networks. Considering that climate change is increasing the proportion of intermittent rivers worldwide, our results suggest that managers need to focus on the spatial dynamics of metacommunities more than on local-scale processes to monitor, restore, and conserve freshwater biodiversity.

Foreign Language Learners' Uncertainty Experiences and Uncertainty Management.

Uncertainty is unavoidable in life; it is also an indispensable characteristic of educational settings. The process of learning a foreign language naturally involves interaction and coconstruction of knowledge. During this process, students may encounter uncertainty from a variety of sources, including the course, context, friends, or the teacher. Besides, to communicate meaningfully, the mutual effort of both the listener and the speaker is needed to deal with uncertainty. Considering this, the present study makes an effort to comprehend how learners appreciate and manage uncertainty in language learning environments. Although uncertainty is considered a multidisciplinary research topic, a limited number of studies are found in the literature which examine how students experience uncertainty and how they react to it. Regarding foreign language learning, related research focused on a specific form of uncertainty, Tolerance of Ambiguity, which is conceptualized as a cognitive style. In-class pen-and-paper surveys, reflective journals, video recordings, and stimulated recall interviews were the instruments used to gather data. The findings demonstrate that language learners frequently encounter uncertainty during the language learning process due to course-related, cognitive, and social factors, and learners may appreciate uncertainty in both positive and negative ways. Additionally, learners' emotional reactions to uncertainty depend on their positive or negative appraisals of uncertain situations. By recognizing and embracing uncertainty, language learners will adapt to it and be able to handle it through a variety of techniques. The results suggest that learners employ a variety of strategies, such as reducing, maintaining, and resolving uncertainty as well as ignoring uncertainty.

Adaptive fixed-time TSM for uncertain nonlinear dynamical system under unknown disturbance.

For nonlinear systems subjected to external disturbances, an adaptive terminal sliding mode control (TSM) approach with fixed-time convergence is presented in this paper. The introduction of the fixed-time TSM with the sliding surface and the new Lemma of fixed-time stability are the main topics of discussion. The suggested approach demonstrates quick convergence, smooth and non-singular control input, and stability within a fixed time. Existing fixed-time TSM schemes are often impacted by unknown dynamics such as uncertainty and disturbances. Therefore, the proposed strategy is developed by combining the fixed-time TSM with an adaptive scheme. This adaptive method deals with an uncertain dynamic system when there are external disturbances. The stability of a closed-loop structure in a fixed-time will be shown by the findings of the Lyapunov analysis. Finally, the outcomes of the simulations are shown to evaluate and demonstrate the efficacy of the suggested method. As a result, examples with different cases are provided for a better comparison of suggested and existing control strategies.

Computational modeling and uncertainty prediction of hyperelastic constitutive responses of damaged brain tissue under different temperature and strain rates.

The effect of strain rate and temperature on the hyperelastic material stress-strain characteristics of the damaged porcine brain tissue is evaluated in this present work. The desired constitutive responses are obtained using the commercially available finite element (FE) tool ABAQUS, utilizing 8-noded brick elements. The model's accuracy has been verified by comparing the results from the previously published literature. Further, the stress-strain behavior of the brain tissue is evaluated by varying the damages at various strain rates and temperatures (13, 20, 27, and 37°C) under compression test. Additionally, the sensitivity analysis of the model is computed to check the effect of input parameters, that is, the temperature, strain rate, and damages on the material properties (shear modulus). The modeling and discussion sections enumerate the inclusive features and model capabilities.

Collective action within an environment of unknown unknowns: Experiences with the port of Mars Game.

There is limited research about how groups solve collective action problems in uncertain environments, especially if groups are confronted with unknown unknowns. We aim to develop a more comprehensive view of the characteristics that allow both groups and individuals to navigate such issues more effectively. In this article, we present the results of a new online experiment where individuals make decisions of whether to contribute to the group or pursue self-interest in an environment with high uncertainty, including unknown unknowns. The behavioral game, Port of Mars is framed as a first-generation habitat on Mars where participants have to make decisions on how much to invest in the shared infrastructure to maintain system health and how much to invest in personal goals. Participants can chat during the game, and take surveys before and after the game in order to measure personality attributes and observations from the game. Initial results suggest that a higher average social value orientation and more communication are the key factors that explain why some groups are more successful than others in surviving Port of Mars. Neither other attributes of players nor the group's communication content explain the observed differences between groups.

[STRATEGIES FOR COPING WITH CAPTIVITY - THE CASE OF CIVILIANS KIDNAPPED BY HAMAS].

INTRODUCTION: Studies that focus on coping strategies, especially among civilian hostages, are scant. Research findings may help predict the chances of readjustment upon return from captivity. The present study aimed to examine the challenges that the civilian hostages faced during captivity and how they dealt with the conditions of captivity. The study was based on seventeen interviews conducted with citizens who returned from Hamas captivity in the first release of hostages. All interviews were conducted by experienced journalists and were broadcast on the main television networks between 11.12.2024 and 4.1.2024. Two main themes were identified using inductive thematic analysis-difficulties in captivity (e.g., dealing with uncertainty, dependent relationships, conditions of isolation, and boredom). The second theme included coping strategies (e.g., hope, regaining control, building certainty, etc.). In each of these themes, several sub-themes that detail the difficulties in captivity and the ways of dealing with them were identified. In addition, the citizens who returned from captivity described using two coping strategies: problem-focused, which aims to manage the problem by "removing" or changing the stressful factor (e.g., planning, building certainty), and emotion-focused coping, which aims to reduce or eliminate the emotional stress and emotions that arise following the trauma (such as distraction, appeal to beliefs, humor, etc.). The themes revealed attempts to regain resources while trying to understand the situation, interpret it, reduce the emotional stress, and act in threatening situations effectively in order to make it possible to survive.