Abstract The rapid development of large language models (LLMs) has introduced a new paradigm for decision-making in mechanical system health management. However, due to the critical lack of interpretable data embeddings and uncertainty-aware, the direct application of LLMs in industrial settings remains ineffective. Therefore, the key to successful LLMs implementation lies in analyzing uncertainty in data embeddings to enhance reliability. This paper proposes a novel model called Probabilistic Hierarchical Perception Networks (PHPN), which first leverages convolutional kernels to extract local features characterizing vibration semantics and then employs a Transformer backbone to capture their global semantic relationships. Furthermore, a probabilistic Bayesian approach is incorporated to enhance the model's perception of prediction uncertainty. In addition, a complete interpretable fault diagnosis framework is proposed to enable the model to automatically quantify and interpret sources of uncertainty when dealing with fault diagnosis tasks. By effectively separating aleatoric and epistemic uncertainty, the framework can identify the sources of unknown factors in the data, thereby improving the accuracy and confidence of the diagnosis. The experimental part is tested on an independently constructed structural fault dataset, which verifies the effectiveness and generalizability of the method proposed in this paper. Comparison results with existing methods show that PHPN can provide more comprehensive diagnostic results and better uncertainty separation performance. Meanwhile, the proposed method can also provide uncertainty analysis and risk assessment for LLMs in industrial health management.

ABSTRACT This study empirically investigates the impact of firms’ uncertainty perception on cross-regional investments and its mechanisms using data on Chinese A-share listed firms and their subsidiaries from 2010 to 2021 and firm-level economic policy uncertainty perception. The results indicate that a high uncertainty perception significantly inhibits cross-regional investments. Mechanism analysis shows that rising uncertainty heightens investment risks, intensifies firms’ financing constraints, hampers firms’ ability to engage in institutional arbitrage, and induces firms to postpone investments and adopt a wait-and-see strategy, thereby suppressing cross-regional investment. Heterogeneity analysis reveals that the suppressing effect of uncertainty perception on cross-regional investments is concentrated among non-state-owned, technology-intensive, and small firms. Further, we find that uncertainty perception significantly discourages domestic cross-regional investments, while significantly promoting cross-border investments. Moreover, higher uncertainty in the province in which the parent company is located significantly reduces intra-provincial cross-regional investments, while stimulating inter-provincial investments. This indicates that when local uncertainty intensifies, firms reduce local resource reallocation instead of pursuing inter-provincial or cross-border investments to hedge against regional risks and seek more stable external environments. This study provides policy implications for governments to facilitate cross-regional capital flows and foster regional economic growth.

ABSTRACT Firms’ perceptions of economic policy uncertainty (FEPU) exhibit heterogeneity, and current measures of firm-level uncertainty fail to capture their sentimental judgements about uncertainty. This paper constructs an innovative FEPU index based on textual information from listed firms’ annual reports and employs textual sentiment analysis. Furthermore, this paper integrates matching data from the China Customs Import and Export Trade Database and listed firms to examine how FEPU affects export performance from a resource reallocation perspective. Empirical analysis shows that when FEPU increases, export value and quality decline, but it has no significant effect on improving export product diversification or export market concentration. The analysis reveals that FEPU influences export performance by reducing industrial robot investment (IRI), improving labour allocation efficiency (LAE), and minifying imported intermediate product input (IIPI). Higher levels of internationalization of innovation (IIN) and global value chain embedding (GVCE) significantly mitigate the weakening effect of FEPU on export performance. The results, which differentiate between firm ownership and export trade type, indicate that FEPU has a significant inhibitory effect on the export performance of SOE and general trade types, while the export performance of private enterprises and processing trade products is not significantly affected by FEPU.

Parental illness uncertainty significantly impacts the quality of life of caregivers of ill children, particularly for those whose children require mechanical ventilation due to severe pneumonia-a population facing unique clinical and psychological stressors. This study aimed to describe the current status of illness uncertainty and analyze its influencing factors among parents of children with pneumonia undergoing mechanical ventilation. A cross-sectional survey design was used. Consecutive sampling was adopted to recruit parents of children with pneumonia receiving mechanical ventilation in the Pediatric Intensive Care Unit (PICU) of a tertiary A-level children's hospital in Jiangsu, China. Data were collected using the Chinese version of the Parental Perception of Uncertainty Scale (PPUS) and a self-designed general information questionnaire. Questionnaires were administered 48-72h after mechanical ventilation initiation, with independent completion by parents or researcher assistance for those with reading difficulties. Statistical analyses included Pearson/Spearman correlation and multivariate linear regression. not applicable. A total of 208 parents were included in the final analysis. The total score of parental illness uncertainty was (122.41 ± 18.06). Parental illness uncertainty scores were significantly correlated with parental age, place of residence, educational level, marital status, per capita monthly household income, and number of children (all p < 0.05). Multivariate linear regression analysis identified these six variables as independent influencing factors, collectively explaining 59.2% of the variance in illness uncertainty (R²=0.592, F = 24.688, p < 0.001). Parents of children with pneumonia undergoing mechanical ventilation exhibit a high level of illness uncertainty, with unpredictability and ambiguity as the primary contributing dimensions. Clinical nursing interventions should target these high-risk groups, implementing individualized strategies such as simplified health education (for low-education parents), one-on-one emotional counseling (for single-child and divorced parents), and social resource linkage (for rural and low-income families) to reduce illness uncertainty and enhance family coping capacities.

Implicit sensorimotor adaptation-the automatic correction of movement errors-is driven by a perceptual prediction error, the mismatch between the perceived movement outcome and its intended goal. Although perceptual uncertainty is known to attenuate adaptation, the impact of goal uncertainty on adaptation remains unknown. We used a visuomotor adaptation task that isolates implicit adaptation (n = 180), manipulating goal uncertainty by varying how precisely the goal's midpoint could be identified. Display format was varied independently to control for the objective size and luminance of visual features, and targets were hidden at movement onset, ensuring that identical visual input at the moment the error was experienced. We found that goal uncertainty significantly attenuated implicit adaptation, independent of low-level visual and kinematic features. Together, these results demonstrate that a precise internal representation of the goal is critical for sensorimotor adaptation, motivating a fundamental revision of motor learning models to explicitly incorporate goal uncertainty as a key determinant of performance.NEW & NOTEWORTHY Implicit adaptation depends not only on feedback uncertainty but also on goal uncertainty. In a large-scale online visuomotor rotation study (n = 180), increasing spatial ambiguity in the target midpoint-while keeping visual input constant-systematically attenuated implicit adaptation. These results call for a revision of standard models of motor adaptation, positioning the precision of the motor goal as a fundamental component in modulating motor learning.

Increasing visual uncertainty modulates multisensory decision-making.

Firm-level economic policy uncertainty perception and stock volatility dynamics: Evidence from a panel GARCH-MIDAS model

While innovation-driven development has become essential for economic transformation, the specific mechanisms through which government procurement enhances enterprises’ new quality productivity remain underexplored, particularly regarding how procurement policies facilitate technological innovation, human capital development, and digital transformation. Using comprehensive data from Chinese A-share listed companies from 2015 to 2022, this study employs fixed effects models and instrumental variable approaches within a “capability-willingness-action” theoretical framework to examine government procurement’s causal impact on new quality productivity. Results demonstrate that government procurement significantly increases firms’ new quality productivity, operating through three distinct transmission mechanisms: first, by alleviating financing constraints and reducing debt costs to enhance innovation capability; second, by reducing uncertainty perception and incentivizing digital transformation to strengthen development willingness; and third, by establishing technical standards and quality requirements to promote concrete actions in R&amp;D investment, talent cultivation, and innovation output. Heterogeneity analysis reveals particularly strong effects among state-owned enterprises, growth-stage firms, and companies with superior internal control quality. These findings provide systematic evidence of government procurement’s multidimensional impact on new quality productivity, offering crucial insights for optimizing procurement policy design and advancing innovation-driven economic development.

Abstract. This study examines consumer animosity toward Gojek’s ride-hailing and delivery services in Indonesia during the COVID 19 pandemic. Rising case numbers, the national health emergency, and Large Scale Social Restrictions (PSBB) heightened perceptions of danger and uncertainty, reshaping consumer mobility and purchasing patterns. A conceptual model was developed linking perceived danger and service failures to negative word of mouth (NWOM), animosity toward the firm, and boycott intentions. The framework also highlights the role of digital platforms in accelerating NWOM during crisis conditions. Findings suggest that stricter government restrictions and higher perceived uncertainty intensify the pathway from animosity to NWOM and avoidance of Gojek services. This study expands consumer animosity research into domestic platform-based services and provides insights for policymakers and platform operators on reducing perceived risk, improving service reliability, and strengthening communication during public health crises.

Abstract In dynamic crowded environments, uncertainty in perception can cause significant impact with mobile robot's measurement systems, thereby posing challenges to the efficiency and safety of their autonomous exploration. To this end, this paper proposes a goal-driven autonomous exploration strategy integrating "vision-radar" multi-modal perception with adaptive navigation skill-switching. First, YOLOv11 is used to detect pedestrians in RGB-D images to construct a pedestrian position vector, and multi-modal feature fusion is performed with radar data to enable the robot to obtain rich and reliable perception information. Subsequently, the radar data is segmented to obtain points of interest (PoI) in potential exploration directions, and the optimal PoI is selected based on the clustering information of obstacle features to guide the robot in exploration. On this basis, a hierarchical reinforcement learning (HRL) mechanism with adaptive skill-switching is developed, in which the high-level policy utilizes the Ring Key (RK) radar descriptor for inter-temporal similarity comparison to screen high-confidence pedestrian location vector and radar data, and determines the decision-making timing, and then employs a gate recurrent unit (GRU) network to extract the temporal features and outputs the skill vectors to regulate the reward weights of low-level. A series of simulation studies and experimental tests show that our method not merely ensures the efficiency and safety of mobile robot in exploration, but also exhibits good generalization to environments with different pedestrian densities and structures.

The earthquakes that struck Kahramanmaraş on February 6, 2023, known as the “disaster of the century” and recorded as one of the most devastating natural disasters in Turkey’s recent history, not only caused massive loss of life and destruction but also left profound psychological and emotional scars on individuals and society. This study examines the post-disaster trauma, psychological resilience, and emotional healing processes through a narrative research approach. Using qualitative research methods, in-depth semi-structured interviews were conducted with 11 participants selected through purposive and snowball sampling techniques. The participants’ pre-disaster, disaster-time, and post-disaster experiences were analyzed chronologically, focusing on resilience strategies, coping mechanisms, family and community support systems, as well as perceptions of insecurity and uncertainty. Findings reveal that despite their losses, survivors strengthened family bonds and emphasized the need for social solidarity, while feelings of fear, anxiety, and distrust remained persistent. The study highlights both individual and collective pathways to recovery, offers important insights for post-disaster psychosocial support policies, and underscores the unique contribution of narrative analysis to disaster studies.

The autonomous flight capability of Unmanned Aerial Vehicles (UAVs) in complex dynamic environments highly depends on the accurate perception of the environment and their own state by onboard sensors. However, limited by sensor noise, model mismatch, observation heterogeneity, and external environmental disturbances, the output of onboard sensors inherently possesses significant uncertainty and randomness. How to transform imperfect, multi-source, and asynchronous sensor observations into reliable state estimation and environmental cognition results through statistical modeling and inference methods has become one of the core issues in UAV perception system research. This paper systematically reviews the development of research related to UAV onboard sensors from a statistical perspective, focusing on the application and evolution of statistical modeling, state estimation, and multi-sensor fusion methods in UAV perception systems. First, it summarizes the typical statistical observation models and noise characteristics of inertial sensors, satellite navigation, vision, lidar, and novel neuromorphic sensors, and analyzes key statistical issues such as random walk, non-Gaussian noise, and time-dependent errors. Subsequently, based on the Bayesian state estimation framework, this paper systematically reviews the application progress of Kalman filtering, error state filtering, particle filtering, and robust statistical methods in UAV navigation and localization, and compares and analyzes the statistical nature of loosely coupled and tightly coupled multi-sensor fusion strategies. Building upon this, it further discusses joint probabilistic modeling methods for heterogeneous sensors such as vision, inertial, and radar, as well as the fusion trend of statistical learning and deep models in high-dimensional perception tasks. Finally, this paper summarizes the role and limitations of statistical methods in UAV airborne sensor research and looks forward to future development directions oriented towards uncertainty perception (the capability to explicitly quantify the reliability of perception results), risk-constrained decision-making (strategies that incorporate estimation variance into control loops to ensure operational safety), and integrated sensing-computing architectures.

New-quality productive forces are an indispensable part of enterprises' high-quality development, while the perception of uncertainty brought about by economic policies is one of the biggest challenges facing enterprises. To study the relationship between the perception of economic policy uncertainty and new-quality productive forces, this paper selects data from Chinese A-share listed companies from 2013 to 2023, and uses text analysis to obtain enterprise-level data on the perception of economic policy uncertainty. The results show that the perception of economic policy uncertainty promotes the development of enterprises' new-quality productive forces. This study not only expands new pathways for the impact between the perception of economic policy uncertainty and new-quality productive forces, but also provides a new perspective for the development of enterprises' new-quality productive forces.

Abstract Introduction: Uncertainty is a natural part of life, but for young adults, it can be particularly challenging as they navigate major life transitions, whether in career choices, relationships, or personal growth. When individuals perceive uncertain situations as threatening, they may attempt to minimize discomfort by avoiding or suppressing thoughts about the future. This study examines how perceiving uncertainty as a threat contributes to cognitive avoidance, with a focus on the mediating roles of intolerance of uncertainty and future anxiety among young adults in Pakistan. Method: This study comprised of 475 university students (males (n=186) and females (n=289). The study utilized cross-sectional survey design in which standardized instruments were used. Results: The results from structural equation modeling showed that intolerance of uncertainty acts as a mediator in the association between threat appraisal and cognitive avoidance. Furthermore, future anxiety was found to be a nonsignificant mediator between threat appraisal and cognitive avoidance. Conclusion: The findings from this study provide practical insights into underlying factors that influence fear of unknown among young adults in Pakistan. Perception of uncertainty as threatening and increased intolerance of uncertainty can lead to maladaptive avoidance patterns. Understanding these patterns can provide insight into how young people cope with uncertainty

Perception of economic policy uncertainty and inefficient investment

Firm-level perception of uncertainty and corporate default risk: Evidence from China’s listed firms

Sensory input is often ambiguous, leading to uncertain interpretations of the external world. Estimates of perceptual uncertainty might be useful in guiding behavior, but it remains unclear whether humans explicitly represent uncertainty in naturalistic settings, and whether any such representations are normatively correct. Progress has been hindered by the absence of stimulus-computable models that estimate uncertainty. We developed a class of task-optimized models that generate probability distributions over perceptual estimates. To assess whether human uncertainty representations align with the model’s, we compared human confidence judgments, which might indirectly reflect uncertainty representations, to confidence judgments extracted from the model’s uncertainty. In both sound localization and pitch perception, human confidence varied systematically, being lower for stimuli that produced more variable estimates across trials. Human confidence tracked model confidence across conditions, suggesting that human uncertainty representations accurately reflect the actual uncertainty of perceptual estimation. The modeling framework is extensible to other perceptual domains.

ABSTRACT This study investigates how non‐elderly consumers psychologically internalize aging risk (AR)—a context‐specific perception of future health, financial, and social uncertainty—into private medical insurance (PMI) purchase intentions (PI). Integrating Social Influence Theory (SIT) with the Theory of Planned Behavior (TPB), the study conceptualizes AR as a macro‐level antecedent that influences attitudes (AT), subjective norms (SN), and perceived behavioral control (PBC) through informational and normative pathways. Using partial least squares structural equation modeling across three studies in China and Malaysia ( N = 899), results show that AR significantly affects all TPB constructs, with AT emerging as the most robust mediator. Although mediation strength varies across countries, moderation tests confirm that the structural pathways linking AR to PI remain cross‐culturally stable. These findings advance theory by extending TPB with socially embedded risk perceptions and dual‐path influence mechanisms. Practical implications offer tailored strategies for insurers, policymakers, and communicators to address future‐oriented health behaviors in aging societies.

Personalised nutrition based on analysis of biospecimen generates individual-specific dietary recommendations and potentially, improved health. However, the science underpinning these approaches is evolving and uncertain. Additionally, users must provide a biological sample appropriate to the analytic approach being taken. This two-part quasi-experimental study sought to understand the impact of certainty and sample type on affective responses and attitudes to personalised nutrition. Participants (n716) completed a free association task and an attitudinal survey. Participants responded with more positive affect and attitudes to personalised nutrition when the science was characterised as certain. Attitudes to personalised nutrition were not affected by sample type, although contemplating providing a stool sample elicited more negative affective responses than other samples. This suggests that the need to provide a stool sample could be a barrier to microbiome-based personalised nutrition. We consider the implications of our findings in relation to future research and to providers of personalised nutrition.

Personalised nutrition based on analysis of biospecimen generates individual-specific dietary recommendations and potentially, improved health. However, the science underpinning these approaches is evolving and uncertain. Additionally, users must provide a biological sample appropriate to the analytic approach being taken. This two-part quasi-experimental study sought to understand the impact of certainty and sample type on affective responses and attitudes to personalised nutrition. Participants (n716) completed a free association task and an attitudinal survey. Participants responded with more positive affect and attitudes to personalised nutrition when the science was characterised as certain. Attitudes to personalised nutrition were not affected by sample type, although contemplating providing a stool sample elicited more negative affective responses than other samples. This suggests that the need to provide a stool sample could be a barrier to microbiome-based personalised nutrition. We consider the implications of our findings in relation to future research and to providers of personalised nutrition.