Market liquidity and volatility: Does economic policy uncertainty matter? Evidence from Asian emerging economies.

This research investigates the complex interaction between liquidity and volatility while considering Economic Policy Uncertainty (EPU) as a moderating factor. Using a comprehensive dataset that incorporates various liquidity measures such as market resilience, depth, and breadth, the study examines how changes in liquidity impact volatility in four Asian incipient economies: China, Pakistan, India, and South Korea. By utilizing sophisticated econometric techniques, particularly the System Generalized Method of Moment (GMM), the findings demonstrate a statistically significant inverse relationship between liquidity and volatility. These findings imply that, within the Asian context, lower levels of volatility are correlated with higher market liquidity. By incorporating EPU into the model, the research acknowledges the significant role of economic factors in shaping market dynamics. Stakeholders, decision-makers, and investors can gain valuable insights from this analysis of variables influencing market stability in Asian emerging economies. The study's outcomes can guide policymakers in formulating strategies that promote market stability and improve market microstructure.

The origins of unpredictability in life outcome prediction tasks.

Why are some life outcomes difficult to predict? We investigated this question through in-depth qualitative interviews with 40 families sampled from a multidecade longitudinal study. Our sampling and interviewing process was informed by the earlier efforts of hundreds of researchers to predict life outcomes for participants in this study. The qualitative evidence we uncovered in these interviews combined with a mathematical decomposition of prediction error led us to create a conceptual framework. Our specific evidence and our more general framework suggest that unpredictability should be expected in many life outcome prediction tasks, even in the presence of complex algorithms and large datasets. Our work provides a foundation for future empirical and theoretical work on unpredictability in human lives.

Driving Cognitive Alertness Detecting Using Evoked Multimodal Physiological Signals Based on Uncertain Self-Supervised Learning.

Multimodal physiological signals play a pivotal role in drivers' perception of work stress. However, the scarcity of labels and the multitude of modalities render the utilization of physiological signals for driving cognitive alertness detection challenging. We thus propose a multimodal physiological signal detection model based on self-supervised learning. First, in order to mine the intrinsic information of data and enable data to highlight effective information, we introduce a multiscale entropy (MSE) evoked attention mechanism. Secondly, the multimodal patches undergo processing through a novel cascaded attention mechanism. This attention mechanism is rooted in patch-level interactions within each modality, progressively integrating and interacting with other modalities in a cascading manner, thereby mitigating computational complexity. Moreover, a multimodal uncertainty-aware module is devised to effectively cope with intricate variations in the data. This module enhances its generalization ability through the incorporation of uncertain resampling. Experiments were conducted on the DriveDB dataset and the CogPilot dataset with both the linear probing and the fine-tuning evaluation protocols. Experimental results in subject-dependent setting show that our model significantly outperforms previous competitive baselines. In the linear probing evaluation, our model achieves on average 6.26%, 6.64%, and 7.75% improvements in Accuracy (Acc), Recall (Rec), and F1 Score. It also outperforms other models by 7.96% in Acc, 9.13% in Rec, and 9.2% in F1 using the fine-tuning evaluation. Furthermore, our model also demonstrates robust performance in subject-independent setting.

UDBRNet: A novel uncertainty driven boundary refined network for organ at risk segmentation.

Organ segmentation has become a preliminary task for computer-aided intervention, diagnosis, radiation therapy, and critical robotic surgery. Automatic organ segmentation from medical images is a challenging task due to the inconsistent shape and size of different organs. Besides this, low contrast at the edges of organs due to similar types of tissue confuses the network's ability to segment the contour of organs properly. In this paper, we propose a novel convolution neural network based uncertainty-driven boundary-refined segmentation network (UDBRNet) that segments the organs from CT images. The CT images are segmented first and produce multiple segmentation masks from multi-line segmentation decoder. Uncertain regions are identified from multiple masks and the boundaries of the organs are refined based on uncertainty data. Our method achieves remarkable performance, boasting dice accuracies of 0.80, 0.95, 0.92, and 0.94 for Esophagus, Heart, Trachea, and Aorta respectively on the SegThor dataset, and 0.71, 0.89, 0.85, 0.97, and 0.97 for Esophagus, Spinal Cord, Heart, Left-Lung, and Right-Lung respectively on the LCTSC dataset. These results demonstrate the superiority of our uncertainty-driven boundary refinement technique over state-of-the-art segmentation networks such as UNet, Attention UNet, FC-denseNet, BASNet, UNet++, R2UNet, TransUNet, and DS-TransUNet. UDBRNet presents a promising network for more precise organ segmentation, particularly in challenging, uncertain conditions. The source code of our proposed method will be available at https://github.com/riadhassan/UDBRNet.

Evaluation of uncertainty in personal dose measured using CaSO4:Dy-based TLD badge at different workplaces.

The metrological quality of a measurement is characterised by evaluating the uncertainty in the measurement. In this paper, uncertainty in personal dose measured using individual monitoring CaSO4:Dy-based thermoluminescence dosimeter badge is evaluated by application of the guide to the expression of uncertainty in measurement method. The present dose reporting quantity, whole body dose (WBD) and the proposed quantity, personal dose equivalent, Hp(10) has been used as measurands. The influence of various input quantities on the measurement were analyzed through tests that conform to the requirements of the International Electrotechnical Commission IEC 62387. The study found that the expanded uncertainties for WBD and Hp(10) measurements were 63.4% and 41.4%, respectively, corresponding to a 95% coverage probability for workplace fields covering a wide photon energy range (33-1250 keV). However, the uncertainty estimates were quite lower for the type of workplaces that are identified using the dose evaluation algorithm. The input quantities, namely, the response to a mixture of photon beam qualities and photon energy and angular dependence contribute the most to the total uncertainty.

Prediction of time to contact under perceptual and contextual uncertainties.

Accurately estimating time to contact (TTC) is crucial for successful interactions with moving objects, yet it is challenging under conditions of sensory and contextual uncertainty, such as occlusion. In this study, participants engaged in a prediction motion task, monitoring a target that moved rightward and an occluder. The participants' task was to press a key when they predicted the target would be aligned with the occluder's right edge. We manipulated sensory uncertainty by varying the visible and occluded periods of the target, thereby modulating the time available to integrate sensory information and the duration over which motion must be extrapolated. Additionally, contextual uncertainty was manipulated by having a predictable and unpredictable condition, meaning the occluder either reliably indicated where the moving target would disappear or provided no such indication. Results showed differences in accuracy between the predictable and unpredictable occluder conditions, with different eye movement patterns in each case. Importantly, the ratio of the time the target was visible, which allows for the integration of sensory information, to the occlusion time, which determines perceptual uncertainty, was a key factor in determining performance. This ratio is central to our proposed model, which provides a robust framework for understanding and predicting human performance in dynamic environments with varying degrees of uncertainty.

Intolerance of uncertainty and negative emotions among high school art students during COVID-19 pandemic: a moderated mediation analysis.

Objectives: A number of high school art students experience negative emotions during their preparation for the art college entrance examination, characterized by worries and fear of uncertainty. Therefore, how individual difference factors, such as intolerance of uncertainty, affect the negative emotions of students needs to be examined. Inspired by the integrative model of uncertainty tolerance, the current study seeks to explain the association between intolerance of uncertainty and negative emotions by testing the potential mediating role of psychological capital and the moderating role of family functioning. Patients and methods: A total of 919 Chinese high school art students (Mage = 18.50 years, range = 16-22) participated from November 2022 to December 2022. Convenience sampling strategies were used. The participants were asked to complete the measures of intolerance of uncertainty scale, psychological capital questionnaire, depression anxiety stress scale, and family adaptability and cohesion evaluation scale. The data were analyzed using Pearson's r correlations and moderated mediation analysis. Results: Results showed that intolerance of uncertainty was positively associated with negative emotions but negatively associated with psychological capital, which in turn, was negatively associated with negative emotions. Psychological capital mediated the indirect link of intolerance of uncertainty with negative emotions. Family functioning buffered the impact of psychological capital on negative emotions. Conclusion: This study can enhance our understanding of the intolerance of uncertainty on negative emotions and provide insights on interventions for high school art students' negative emotions for educators. The interventions targeting intolerance of uncertainty, psychological capital and family functioning may be beneficial in reducing the effect of intolerance of uncertainty on negative emotions faced by high school art students.

The relationship between unpredictability in childhood and depression among college students: the mediating roles of coping style and resilience.

BACKGROUND: According to previous studies, unpredictability in childhood could significantly increase the risk of depression in adulthood. Only a few studies have explored the relationship between these two variables in China. This paper aims to explore the relationship between unpredictability in childhood and depression and examine the mediating roles of coping styles and resilience. METHODS: We investigated 601 college students, who had an average age of 19.09 (SD = 2.78) years. Participants completed questionnaires regarding unpredictability in childhood, coping style, resilience, and depression. We analyzed survey data using the bias-corrected bootstrap method. RESULTS: The findings revealed a significant positive association between unpredictability in childhood and depression among college students. Mature coping style, immature coping style, and resilience were found to mediate this relationship independently. Furthermore, the study unveiled a serial mediation process, wherein both mature and immature coping styles, followed by resilience, sequentially mediate the relationship between unpredictability in childhood and depression, underscoring the complex interplay between these variables. CONCLUSIONS: The results indicated that the risk of depression among college students who have experienced unpredictable childhood should be valued. Attention to coping styles and resilience should be paid to decrease depression among college students who have experienced unpredictable childhood.

Readiness for return to work and its influencing factors among head and neck cancer patients: a cross-sectional study.

PURPOSE: This study aims to investigate the Readiness for Return-to-Work (RRTW) of patients with head and neck tumours and to analyse the relationships among self-efficacy, disease uncertainty, psychosocial adaptation, and RRTW in head and neck cancer (HNC) patients. METHODS: A cross-sectional study was conducted with 259 HNC patients with a discharge length of ≥1 month at a tertiary hospital in Liaoning Province. The research tools included a self-designed general information questionnaire, the Readiness for Return-to-Work (RRTW) Scale, the General Self-Efficacy Scale (GSES), the Mishel Uncertainty in Illness Scale (MUIS), and the Self-Reporting Psychosocial Adjustment to Illness Scale (PAIS-SR). Descriptive statistical analysis, the rank sum test, Spearman correlation analysis, and ordered multiple and dichotomous logistic regression analyses were used. RESULTS: The overall RRTW among HNC patients was low (41.9%). HNC patients who did not return to work were mainly in the precontemplation stage (38.1%) and contemplation stage (29.9%). HNC patients who returned to work were mainly in the active maintenance stage (64.2%). Children's status (OR = 0.218, 95% CI 0.068-0.703), self-efficacy (OR = 1.213, 95% CI 1.012-1.454), unpredictability (OR = 0.845, 95% CI 0.720-0.990), occupational environment (OR = 0.787, 95% CI 0.625-0.990), and family environment (OR = 0.798, 95% CI 0.643-0.990) influence the RRTW of HNC patients who have not returned to work. Educational level (OR = 62.196, 95% CI 63.307-68.567), children's status (OR = 0.058, 95% CI 1.004-2.547), self-efficacy (OR = 1.544, 95% CI 3.010-8.715), unpredictability (OR = 0.445, 95% CI 1.271-2.280), and psychological status (OR = 0.340, 95% CI 1.141-2.401) influence the RRTW of HNC patients who have returned to work. CONCLUSION: Children's status, education level, self-efficacy, illness uncertainty, and psychosocial adjustment are crucial to RRTW. This study provides a theoretical basis for formulating intervention measures aimed at improving the RRTW of patients.

Uncertainty and precaution in hunting wolves twice in a year: Reanalysis of Treves and Louchouarn.

Management of wolves is controversial in many jurisdictions where wolves live, which underscores the importance of rigor, transparency, and reproducibility when evaluating outcomes of management actions. Treves and Louchouarn 2022 (hereafter TL) predicted outcomes for various fall 2021 hunting scenarios following Wisconsin's judicially mandated hunting and trapping season in spring 2021, and concluded that even a zero harvest scenario could result in the wolf population declining below the population goal of 350 wolves specified in the 1999 Wisconsin wolf management plan. TL further concluded that with a fall harvest of > 16 wolves there was a "better than average possibility" that the wolf population size would decline below that 350-wolf threshold. We show that these conclusions are incorrect and that they resulted from mathematical errors and selected parameterizations that were consistently biased in the direction that maximized mortality and minimized reproduction (i.e., positively biased adult mortality, negatively biased pup survival, further halving pup survival to November, negatively biased number of breeding packs, and counting harvested wolves twice among the dead). These errors systematically exaggerated declines in predicted population size and resulted in erroneous conclusions that were not based on the best available or unbiased science. Corrected mathematical calculations and more rigorous parameterization resulted in predicted outcomes for the zero harvest scenario that more closely coincided with the empirical population estimates in 2022 following a judicially prevented fall hunt in 2021. Only in scenarios with simulated harvest of 300 or more wolves did probability of crossing the 350-wolf population threshold exceed zero. TL suggested that proponents of some policy positions bear a greater burden of proof than proponents of other positions to show that "their estimates are accurate, precise, and reproducible". In their analysis, TL failed to meet this standard that they demanded of others.

The impact of macroeconomic policy uncertainty on micro-enterprise investment efficiency-Empirical evidence from China.

The impact of macroeconomic policy uncertainty (EPU) on micro-level entities has garnered increasing attention in economic circles. This study examines the influence of EPU on the efficiency of investments made by China's A-share listed companies between 2016 and 2021. Using a panel fixed effect model for analysis, the research reveals that EPU has a notable adverse effect on the investment efficiency of enterprises. Furthermore, it suggests that advancements in digital finance, strong ESG performance, and enhanced entrepreneurial confidence can mitigate this negative impact effectively. The study also highlights that enterprises with lower valuation, shareholder control, limited audit reputation, and no bank connections are more vulnerable to the impact of EPU on investment efficiency compared to those with higher valuation, manager control, strong audit reputation, and bank connections. Consequently, future efforts should be directed towards enhancing the stability and relevance of economic policies, promoting digital finance, and enhancing corporate governance structures.

Robust clustering-based hybrid technique enabling reliable reservoir water quality prediction with uncertainty quantification and spatial analysis.

Machine learning methodology has recently been considered a smart and reliable way to monitor water quality parameters in aquatic environments like reservoirs and lakes. This study employs both individual and hybrid-based techniques to boost the accuracy of dissolved oxygen (DO) and chlorophyll-a (Chl-a) predictions in the Wadi Dayqah Dam located in Oman. At first, an AAQ-RINKO device (CTD+ sensor) was used to collect water quality parameters from different locations and varying depths in the reservoir. Second, the dataset is segmented into homogeneous clusters based on DO and Chl-a parameters by leveraging an optimized K-means algorithm, facilitating precise estimations. Third, ten sophisticated variational-individual data-driven models, namely generalized regression neural network (GRNN), random forest (RF), gaussian process regression (GPR), decision tree (DT), least-squares boosting (LSB), bayesian ridge (BR), support vector regression (SVR), K-nearest neighbors (KNN), multilayer perceptron (MLP), and group method of data handling (GMDH) are employed to estimate DO and Chl-a concentrations. Fourth, to improve prediction accuracy, bayesian model averaging (BMA), entropy weighted (EW), and a new enhanced clustering-based hybrid technique called Entropy-ORNESS are employed to combine model outputs. The Entropy-ORNESS method incorporates a Genetic Algorithm (GA) to determine optimal weights and then combine them with EW weights. Finally, the inclusion of bootstrapping techniques introduces a stochastic assessment of model uncertainty, resulting in a robust estimator model. In the validation phase, the Entropy-ORNESS technique outperforms the independent models among the three fusion-based methods, yielding R2 values of 0.92 and 0.89 for DO and Chl-a clusters, respectively. The proposed hybrid-based methodology demonstrates reduced uncertainty compared to single data-driven models and two combination frameworks, with uncertainty levels of 0.24% and 1.16% for cluster 1 of DO and cluster 2 of Chl-a parameters. As a highlight point, the spatial analysis of DO and Chl-a concentrations exhibit similar pattern variations across varying depths of the dam according to the comparison of field measurements with the best hybrid technique, in which DO concentration values notably decrease during warmer seasons. These findings collectively underscore the potential of the upgraded weighted-based hybrid approach to provide more accurate estimations of DO and Chl-a concentrations in dynamic aquatic environments.

Influence of Illness Uncertainty on Health Behavior in Individuals with Coronary Artery Disease: A Path Analysis.

PURPOSE: This study aimed to investigate the influence of uncertainty-related factors on the health behavior of individuals with coronary artery disease (CAD) based on Mishel's uncertainty in illness theory (UIT). METHODS: We conducted a cross-sectional study and path analysis to investigate uncertainty and factors related to health behavior. The study participants were 228 CAD patients who visited the outpatient cardiology department between September 2020 and June 2021. We used SPSS 25.0 and AMOS 25.0 software to analyze the data. RESULTS: The final model demonstrated a good fit with the data. Eleven of the twelve paths were significant. Uncertainty positively affected danger and negatively affected self-efficacy and opportunity. Danger had a positive effect on perceived risk. Opportunity positively affected social support, self-efficacy, perceived benefit and intention, whereas it negatively affected perceived risk. Social support, self-efficacy, perceived benefit and intention had a positive effect on health behavior. We found that perceived benefit and intention had the most significant direct effects, whereas self-efficacy indirectly affected the relationship between uncertainty and health behavior. CONCLUSION: The path model is suitable for predicting the health behavior of CAD patients who experience uncertainty. When patients experience uncertainty, interventions to increase their self-efficacy are required first. Additionally, we need to develop programs that quickly shift to appraisal uncertainty as an opportunity, increase perceived benefits of health behavior, and improve intentions.

Effects of different observed datasets on the calibration of crop model parameters with GLUE: A case study using the CROPGRO-Soybean phenological model.

Suitable combinations of observed datasets for estimating crop model parameters can reduce the computational cost while ensuring accuracy. This study aims to explore the quantitative influence of different combinations of the observed phenological stages on estimation of cultivar-specific parameters (CPSs). We used the CROPGRO-Soybean phenological model (CSPM) as a case study in combination with the Generalized Likelihood Uncertainty Estimation (GLUE) method. Different combinations of four observed phenological stages, including initial flowering, initial pod, initial grain, and initial maturity stages for five soybean cultivars from Exp. 1 and Exp. 3 described in Table 2 are respectively used to calibrate the CSPs. The CSPM, driven by the optimized CSPs, is then evaluated against two independent phenological datasets from Exp. 2 and Exp. 4 described in Table 2. Root means square error (RMSE) (mean absolute error (MAE), coefficient of determination (R2), and Nash Sutcliffe model efficiency (NSE)) are 15.50 (14.63, 0.96, 0.42), 4.76 (3.92, 0.97, 0.95), 4.69 (3.72, 0.98, 0.95), 3.91 (3.40, 0.99, 0.96) and 12.54 (11.67, 0.95, 0.60), 5.07 (4.61, 0.98, 0.93), 4.97 (4.28, 0.97, 0.94), 4.58 (4.02, 0.98, 0.95) for using one, two, three, and four observed phenological stages in the CSPs estimation. The evaluation results suggest that RMSE and MAE decrease, and R2 and NSE increase with the increase in the number of observed phenological stages used for parameter calibration. However, there is no significant reduction in the RMSEs (MAEs, NSEs) using two, three, and four observed stages. Relatively reliable optimized CSPs for CSMP are obtained by using at least two observed phenological stages balancing calibration effect and computational cost. These findings provide new insight into parameter estimation of crop models.

How and why do doctors communicate diagnostic uncertainty: An experimental vignette study.

BACKGROUND: Diagnostic uncertainty is common, but its communication to patients is under-explored. This study aimed to (1) characterise variation in doctors' communication of diagnostic uncertainty and (2) explore why variation occurred. METHODS: Four written vignettes of clinical scenarios involving diagnostic uncertainty were developed. Doctors were recruited from five hospitals until theoretical saturation was reached (n = 36). Participants read vignettes in a randomised order, and were asked to discuss the diagnosis/plan with an online interviewer, as they would with a 'typical patient'. Semi-structured interviews explored reasons for communication choices. Interview transcripts were coded; quantitative and qualitative (thematic) analyses were undertaken. RESULTS: There was marked variation in doctors' communication: in their discussion about differential diagnoses, their reference to the level of uncertainty in diagnoses/investigations and their acknowledgement of diagnostic uncertainty when safety-netting. Implicit expressions of uncertainty were more common than explicit. Participants expressed both different communication goals (including reducing patient anxiety, building trust, empowering patients and protecting against diagnostic errors) and different perspectives on how to achieve these goals. Training in diagnostic uncertainty communication is rare, but many felt it would be useful. CONCLUSIONS: Significant variation in diagnostic uncertainty communication exists, even in a controlled setting. Differing communication goals-often grounded in conflicting ethical principles, for example, respect for autonomy versus nonmaleficence-and differing ideas on how to prioritise and achieve them may underlie this. The variation in communication behaviours observed has important implications for patient safety and health inequalities. Patient-focused research is required to guide practice. PATIENT OR PUBLIC CONTRIBUTION: In the design stage of the study, two patient and public involvement groups (consisting of members of the public of a range of ages and backgrounds) were consulted to gain an understanding of patient perspectives on the concept of communicating diagnostic uncertainty. Their feedback informed the formulations of the research questions and the choice of vignettes used.

Correlation between uncertainty stress and depression among healthcare professionals in China: a nationwide cross-sectional survey.

OBJECTIVE: This study investigated the correlation between uncertainty stress (US) and depression among healthcare professionals (HCPs) in China. DESIGN, SETTINGS AND PARTICIPANTS: A cross-sectional online survey was conducted by recruiting HCPs from three provinces in China (central, eastern and western) through purposive sampling between 29 September 2022 and 18 January 2023. US was measured using the Life Stress Questionnaire and depression was measured using the Patient Health Questionnaire-9. In total, 2976 questionnaires were deemed valid. PRIMARY AND SECONDARY OUTCOME: This study examined the prevalence of US and depression among HCPs in China; the correlating sociodemographic traits; and the correlation between US and depression. RESULTS: The prevalence of US and depression among HCPs in China was 26.54% (790 out of 2976) and 71.63% (2132 out of 2976). Binary logistic analysis revealed that individuals with graduate degrees (OR: 1.83; 95% CI 1.07 to 3.11; p<0.05), central China (OR: 1.75; 95% CI 1.36 to 2.24; p<0.01), primary medical institutes (OR: 1.33; 95% CI 1.03 to 1.72; p<0.05), secondary medical institutes (OR: 1.30; 95% CI 1.01 to 1.68; p<0.05), an annual income of less than ¥50 000 (OR: 1.85; 95% CI 1.26 to 2.73; p<0.01) and an income range of ¥50 000-¥99 999 (OR: 1.49; 95% CI 1.10 to 2.03; p<0.05) were associated with a higher likelihood of US. The adjusted logistic regression model demonstrated that HCPs with higher US had a greater likelihood of depression (adjusted OR: 5.02; 95% CI 3.88 to 6.50; p<0.01). The increase in the US score was paralleled by an increased depression score (beta (B): 1.32; 95% CI 1.25 to 1.39; p<0.01). CONCLUSION: These findings reveal a significant correlation between US and depression among HCPs and suggest that improving the management of US may help reduce the prevalence of depression among HCPs.

Acute Stress Symptoms, Intolerance of Uncertainty and Coping Strategies in Reaction to the October 7 War.

BACKGROUND: On 7 October 2023, hundreds of armed Hamas fighters breached the security border fence and entered Israel from the Gaza Strip. More than 1400 Israeli citizens were murdered, and 239 individuals were kidnapped. Many Israeli citizens experienced these occurrences as psychologically traumatic events that caused stress and uncertainty. OBJECTIVES: The present study aimed to examine the relationship between exposure to war (in more distant circles), intolerance of uncertainty (IU) and disengaged coping on acute stress symptoms (ASS). First, we examined whether exposure to war and IU were directly associated with ASS. Second, we tested the mediating role of disengaged coping in the relationship among war exposure, IU and ASS. METHODS: This cross-sectional study involved 393 Israeli citizens. Participants answered questionnaires on exposure to war, IU, coping strategies and ASS. RESULTS: The study results indicate that higher exposure and higher levels of IU were directly associated with more intensive ASS, and this association was partially mediated by higher use of disengaged coping strategies. CONCLUSIONS: Individuals during wartime are at risk of experiencing high levels of ASS and developing ASD. However, degree of exposure to war alone was not associated with ASS, but it was related to personal resources and coping strategies.

Uncertainty affects cancer-related fatigue among breast cancer women undergoing peripherally inserted central catheter chemotherapy: the chain mediating role of psychological resilience and self-care.

BACKGROUND: Breast cancer patients undergoing chemotherapy via peripherally inserted central catheter often experience serious behavioral and psychological challenges, with uncertainty and cancer-related fatigue being prevalent issues that profoundly impact prognosis. Therefore, this study aimed to investigate the relationship between uncertainty and cancer-related fatigue by employing a chain mediation model to examine the potential mediating roles of psychological resilience and self-care. METHODS: A cross-sectional study was conducted with 223 breast cancer patients receiving peripherally inserted central catheter chemotherapy at two tertiary affiliated hospitals of China Medical University in Liaoning, China, from February 2021 to December 2022. Participants completed self-reported questionnaires to assess uncertainty, psychological resilience, self-care, and cancer-related fatigue. The collected data were subsequently analyzed using Pearson's correlation analysis, hierarchical regression analysis, and mediation analysis. RESULTS: Uncertainty exhibited a significant positive correlation with cancer-related fatigue (p < 0.01) and a negative correlation with psychological resilience (p < 0.01) and self-care (p < 0.01). Uncertainty was found to impact cancer-related fatigue through three pathways: psychological resilience mediated the relationship between uncertainty and cancer-related fatigue (mediating effect = 0.240, 95% confidence interval: 0.188 to 0.298, effect ratio = 53.22%); self-care also mediated this relationship (mediating effect = 0.080, 95% confidence interval: 0.044 to 0.121, effect ratio = 17.74%); furthermore, there was a significant joint mediating effect of psychological resilience and self-care on the association between uncertainty and cancer-related fatigue (mediating effect = 0.042, 95% confidence interval: 0.021 to 0.068, effect ratio o = 9.31%). CONCLUSION: The findings of this study revealed that uncertainty not only directly influenced cancer-related fatigue, but also operated through the mediating effect of psychological resilience, self-care, and sequential mediation of psychological resilience and self-care. Interventions tailored for breast cancer patients receiving peripherally inserted central catheter chemotherapy should target these factors to help alleviate uncertainty, enhance psychological resilience, and improve self-care practices, thereby ameliorating cancer-related fatigue.

Unpredictable benefits of social information can lead to the evolution of individual differences in social learning.

Human ecological success is often attributed to our capacity for social learning, which facilitates the spread of adaptive behaviours through populations. All humans rely on social learning to acquire culture, but there is substantial variation across societies, between individuals and over developmental time. However, it is unclear why these differences exist. Here, we present an evolutionary model showing that individual variation in social learning can emerge if the benefits of social learning are unpredictable. Unpredictability selects for flexible developmental programmes that allow individuals to update their reliance on social learning based on previous experiences. This developmental flexibility, in turn, causes some individuals in a population to end up consistently relying more heavily on social learning than others. We demonstrate this core evolutionary mechanism across three scenarios of increasing complexity, investigating the impact of different sources of uncertainty about the usefulness of social learning. Our results show how evolution can shape how individuals learn to learn from others, with potentially profound effects on cultural diversity.