Search Results (124)

I address the challenge of extracting reliable insights from large datasets using a simplified model that illustrates how hierarchical classification can distort outcomes. The model consists of discrete pixels labeled red, blue, or white. Red and blue indicate distinct properties, while white represents unclassified or ambiguous data. A macro-color is assigned only if one color holds a strict majority among the pixels. Otherwise, the aggregate is labeled white, reflecting uncertainty. This setup mimics a percolation threshold at fifty percent. Assuming that directly accessing the various proportions from the data of colors is infeasible, I implement a hierarchical coarse-graining procedure. Elements (first pixels, then aggregates) are recursively grouped and reclassified via local majority rules, ultimately producing a single super-aggregate for which the color represents the inferred macro-property of the collection of pixels as a whole. Analytical results supported by simulations show that the process introduces additional white aggregates beyond white pixels, which could be present initially; these arise from groups lacking a clear majority, requiring arbitrary symmetry-breaking decisions to attribute a color to them. While each local resolution may appear minor and inconsequential, their repetitions introduce a growing systematic bias. Even with complete data, unavoidable asymmetries in local rules are shown to skew outcomes. This study highlights a critical limitation of recursive data reduction. Insight extraction is shaped not only by data quality but also by how local ambiguity is handled, resulting in built-in biases. Thus, the related flaws are not due to the data but to structural choices made during local aggregations. Although based on a simple model, these findings expose a high likelihood of inherent flaws in widely used hierarchical classification techniques. Full article

Silicon (Si) and silicon carbide (SiC) are second- and third-generation semiconductor materials with excellent properties that are particularly suitable for applications in scenarios such as high temperature, high voltage, and high frequency. Si/SiC wafers face warpage and bending problems during production, which can seriously affect subsequent processing. Fast, accurate, and comprehensive detection of thickness, thickness variation, and flatness (including bow and warpage) of SiC and Si wafers is an industry-recognized challenge. Frequency scanning interferometry (FSI) can synchronize the upper and lower surfaces and thickness information of transparent parallel thin wafers, but it is still affected by multiple interfacial harmonic reflections, reflectivity asymmetry, and phase modulation uncertainty when measuring SiC thin wafers, which leads to thickness calculation errors and face reconstruction deviations. To this end, this paper proposes a high-precision facet reconstruction method for SiC/Si structures, which combines harmonic spectral domain decomposition, refractive index gradient constraints, and partitioning optimization strategy, and introduces interferometric signal “oversampling” and weighted fusion of multiple sets of data to effectively suppress higher-order harmonic interferences, and to enhance the accuracy of phase resolution. The multi-layer iterative optimization model further enhances the measurement accuracy and robustness of the system. The flatness measurement system constructed based on this method can realize the simultaneous acquisition of three-dimensional top and bottom surfaces on 6-inch Si/SiC wafers, and accurately reconstruct the key parameters, such as flatness, warpage, and thickness variation (TTV). A comparison with the Corning Tropel FlatMaster commercial system shows that this method has high consistency and good applicability. Full article

Existing studies on ontology evolution lack automated mechanisms to balance semantic coherence and adaptability under real-time uncertainties, particularly in resolving spatiotemporal asymmetry and multidimensional coupling imbalances in dam-break scenarios. Traditional methods such as WordNet’s tree symmetry and FrameNet’s frame symmetry fail to formalize dynamic adjustments through quantitative metrics, leading to path dependency and delayed responses. This study addresses this gap by introducing a novel symmetry-entropy-constrained matrix fusion algorithm, which integrates algebraic direct sum operations and Hadamard product with entropy-driven adaptive weighting. The original contribution lies in the symmetry entropy metric, which quantifies structural deviations during fusion to systematically balance semantic stability and adaptability. This work formalizes ontology evolution as a symmetry-driven optimization process. Experimental results demonstrate that shared concepts between ontologies (s = 3) reduce structural asymmetry by 25% compared to ontologies (s = 1), while case studies validate the algorithm’s ability to reconcile discrepancies between theoretical models and practical challenges in evacuation efficiency and crowd dynamics. This advancement promotes the evolution of traditional emergency management systems towards an adaptive intelligent form. Full article

The inherent uncertainty and information asymmetry in financial markets create significant challenges for accurate price forecasting. Although investor sentiment analysis has gained traction in recent research, the temporal dimension of sentiment dynamics remains underexplored. This study develops a novel framework that enhances stock price prediction by integrating time-partitioned investor sentiment, while improving model interpretability via Shapley additive explanations (SHAP) analysis. Employing the ERNIE (enhanced representation through knowledge integration) 3.0 model for sentiment extraction from China’s Eastmoney Guba stock forum, we quantitatively distinguish intraday and post-market investor sentiment then integrate these temporal components with technical indicators through neural network architecture. Our results indicate that temporal sentiment partitioning effectively reduces uncertainty. Empirical evidence demonstrates that our long short-term memory (LSTM) model integrating intraday and post-market sentiment indicators achieves better prediction accuracy, and SHAP analysis reveals the importance of intraday and post-market investor sentiment to stock price prediction models. Implementing quantitative trading strategies based on these insights generates significantly more annualized returns for representative stocks with controlled risk, outperforming sentiment-agnostic and non-temporal sentiment models. This research provides methodological innovations for processing temporal unstructured data in finance, while the SHAP framework offers regulators and investors actionable insights into sentiment-driven market dynamics. Full article

This study investigates whether government subsidies promote R&D cost stickiness in the new energy vehicle (NEV) industry in China—that is, whether public funding encourages firms to retain R&D resources even during periods of declining sales. While prior literature primarily explores the relationship between subsidies and R&D investment levels, it often overlooks firms’ financial position and dynamic cost behaviors. Given that R&D investment has high adjustment costs and is sensitive to cash flows, reductions in R&D spending during downturns may reflect managerial cost asymmetry rather than a crowding-out effect of subsidies. Moreover, government subsidies may serve as a signal of long-term market optimism, motivating managers to retain R&D resources during economic downturns. Using a panel dataset of 573 listed new energy vehicle (NEV) firms in China’s A-share market from 2007 to 2021, we construct a model based on the asymmetric cost behavior framework to empirically assess the impact of government subsidies on R&D cost stickiness. The results show that government subsidies significantly increase the degree of R&D cost stickiness. Serving as a signal of future market optimism, subsidies raise managerial expectations and incentivize decisions to retain R&D-related costs during economic downturns. This positive relationship is more pronounced in firms with high levels of green innovation, large-scale enterprises, and non-state-owned firms. These findings suggest that public funding alleviates managerial pressure to cut R&D expenses amid revenue declines, thereby supporting firms’ long-term innovation strategies. Our study contributes to the cost management literature by highlighting a novel channel through which subsidies influence managerial discretion under uncertainty. It also provides policy implications for the future phase-out of subsidies, emphasizing the need for complementary market mechanisms to sustain innovation investment, particularly for small, young, and financially constrained firms. Full article

This paper examines the impact of various uncertainty channels on stock market returns in Saudi Arabia, with a focus on the Tadawul All Share Index (TASI). It examines factors such as Saudi-specific Geopolitical Risk, Global Oil Price Uncertainty, Climate Policy Uncertainty, and U.S. Monetary Policy Uncertainty. Using monthly data from November 1998 to June 2024 and the Local Projections (LP) methodology, the study examines how these uncertainties impact market returns across various time horizons, taking into account potential structural breaks and nonlinear dynamics. Our findings indicate significant variations in the market’s response to the uncertainty measures across two distinct periods. During the first period, geopolitical risks have a strong positive impact on market returns. Conversely, the second period reveals a reversal, with negative cumulative effects, suggesting a shift in risk–return dynamics. Oil Price Uncertainty consistently exhibits a negative impact in both periods, highlighting the changing nature of oil dependency in the Saudi market. Additionally, Climate Policy Uncertainty is becoming more significant, reflecting increased market sensitivity to global environmental policy changes. Our analysis reveals significant asymmetries in the effects of various uncertainty shocks, with Monetary Policy Uncertainty exhibiting nonlinear effects that peak at intermediate horizons, while commodity-related uncertainties exhibit more persistent impacts. These findings, which remain robust across various tests, offer critical insights for portfolio management, policy formulation, and risk assessment in emerging markets undergoing substantial economic changes. Full article

Amid growing global concerns regarding sustainable governance, understanding the drivers of ESG disclosure is vital for promoting transparency and responsible corporate behavior. This study examines the peer effects of ESG disclosure among 32,187 observations from Chinese A-share listed firms between 2010 and 2021. This research employs an instrumental variable approach based on stock-specific idiosyncratic returns estimated via the Carhart four-factor model to address endogeneity concerns. The results confirm significant peer effects, suggesting that firms adjust ESG practices in response to their industry counterparts. These effects are significantly moderated by firm-level characteristics, including information asymmetry, corporate reputation, and market competition, as well as by external conditions such as economic policy uncertainty, business environment volatility, and institutional quality. This research defines peer groups by industry affiliation and conducts robustness tests using ESG risk clustering to address classification bias. This study contributes to the literature by strengthening causal inference and refining the understanding of peer-driven ESG behavior by integrating institutional theory, signaling theory, and information economics. The findings offer practical implications for policymakers, investors, and corporate managers seeking to promote ESG convergence through peer-driven incentives in diverse regulatory contexts. Full article

The intervention of distributed loads, propelled by the swift advancement of distributed energy sources and the escalating demand for diverse load types encompassing electricity and cooling within virtual power plants (VPPs), has exerted an influence on the symmetry of the grid. Consequently, a quantitative assessment of the annual peak-shaving capability of a VPP is instrumental in mitigating the peak-to-valley difference in the grid, enhancing the operational safety of the grid, and reducing grid asymmetry. This paper presents a peak-shaving optimization method for VPPs, which takes into account renewable energy uncertainty and flexible load demand response. Firstly, wind power (WP), photovoltaic (PV) generation, and demand-side response (DR) are integrated into the VPP framework. Uncertainties related to WP and PV generation are incorporated through the scenario method within deterministic constraints. Secondly, a stochastic programming (SP) model is established for the VPP, with the objective of maximizing the peak-regulation effect and minimizing electricity loss for demand-side users. The case study results indicate that the proposed model effectively tackles peak-regulation optimization across diverse new energy output scenarios and accurately assesses the peak-regulation potential of the power system. Specifically, the proportion of load decrease during peak hours is 18.61%, while the proportion of load increase during off-peak hours is 17.92%. The electricity loss degrees for users are merely 0.209 in summer and 0.167 in winter, respectively. Full article

Efficient ship scheduling in inland waterways is critical for maritime transportation safety and economic viability. However, traditional scheduling methods, primarily based on First Come First Served (FCFS) principles, often produce suboptimal results due to their inability to account for complex spatial–temporal dependencies, directional asymmetries, and varying ship characteristics. This paper introduces SSRL (Ship Scheduling through Reinforcement Learning), a novel framework that addresses these limitations by integrating three complementary components: (1) a Q-learning framework that discovers optimal scheduling policies through environmental interaction rather than predefined rules; (2) a clustering mechanism that reduces the high-dimensional state space by grouping similar ship states; and (3) a sliding window approach that decomposes the scheduling problem into manageable subproblems, enabling real-time decision-making. We evaluated SSRL through extensive experiments using both simulated scenarios and real-world data from the Xiaziliang Restricted Waterway in China. Results demonstrate that SSRL reduces total ship waiting time by 90.6% compared with TSRS, 48.4% compared with FAHP-ES, and 32.6% compared with OSS-SW, with an average reduction of 57.2% across these baseline methods. SSRL maintains superior performance across varying traffic densities and uncertainty conditions, with the optimal information window length of 13–14 ships providing the best balance between solution quality and computational efficiency. Beyond performance improvements, SSRL offers significant practical advantages: it requires minimal computation for online implementation, adapts to dynamic maritime environments without manual reconfiguration, and can potentially be extended to other complex transportation scheduling domains. Full article

This study considers the uncertainty caused by data asymmetry in supply chains, the risks associated with this uncertainty and the need for robustness in the supply chain network. It discusses the construction of a data-driven two-stage distributionally robust mean semi-variance mixed-integer optimization model to address the location optimization problem under conditions of uncertainty in transportation costs and demand. To solve this model, a distributed separation hybrid genetic algorithm is introduced, enabling determination of the optimal location, distribution strategy and expected return for a distribution center in the worst case. Then, a fresh food supply chain is utilized as a case study to analyze the effects of uncertainty on location allocation decisions while deriving pertinent managerial insights. Additionally, compared to traditional stochastic optimization models, the proposed model demonstrates greater robustness in numerical simulations. The algorithm is also benchmarked against other methods, and its effectiveness and stability are validated in terms of the computational time, the number of iterations and the convergence speed. Full article

Information asymmetry between manufacturers and online retailers regarding consumer preferences for store brands profoundly influences operational strategy. By leveraging information technology, online retailers can collect valuable consumer data, creating a strategic dilemma: whether to share this information with manufacturers and, if so, with which manufacturer (national or third-party). This study aims to explore an online retailer’s strategic decisions regarding sharing information with manufacturers, filling a gap in the literature on store brands and consumer preferences. Using game theory, we analyze the interactions among an online retailer, a national manufacturer, and a third-party manufacturer, incorporating the Hotelling model to capture consumer preference and product differentiation. Our findings reveal that information sharing does not consistently benefit the online retailer or manufacturers. Notably, without side payment, the online retailer is unwilling to share information with either manufacturer, and manufacturers do not always gain more from receiving such information—a result that challenges conventional wisdom. However, when side payment is introduced, the online retailer’s willingness to share information depends on key factors: the probability of low brand loyalty (low-type) consumers, the proportion of comparison shoppers, the side payment, and the degree of information uncertainty. These findings provide innovative insights for operations managers, highlighting the critical role of information management in shaping strategic decisions and enhancing the efficacy and financial outcomes of information sharing in the context of store brands. Full article

When the XBeach model is used to simulate beach profiles, the selection of four sensitive parameters—facua, gammax, eps, and gamma—is crucial. Among these, the two key parameters, facua and gamma, are particularly sensitive. However, the XBeach model does not specify the exact choice of these four key parameters, offering only a broad range for each one. In this paper, we investigate the applicability of tuning these four parameters within the XBeach model. We employ Generalized Likelihood Uncertainty Estimation (GLUE) to optimize the model settings. The Brier Skill Score (BSS) for each parameter combination is calculated to quantify the likelihood probability distribution of each parameter. The optimal parameter set (facua = 0.20, gamma = 0.50) was ultimately determined. Here, the facua parameter represents the degree of influence of wave skewness and asymmetry on the direction of sediment transport, while the gamma parameter represents the equivalent random wave in the wave dissipation model and is used to calculate the probability of wave breaking. Six profiles of the southern beach on Chudao Island are selected to validate the results, establishing the XBeach model based on profile measurement data before and after Typhoon “Lekima”. The results indicate that after parameter optimization, the simulation accuracy of XBeach is significantly improved, with the BSS increasing from 0.3 and 0.17 to 0.68 and 0.79 in P1 and P6 profiles, respectively. This paper provides a recommended range for parameter values for future research. Full article

With the progressive increase in online shopping, e-commerce platforms’ sustainability has attracted considerable attention from both academia and the business sector. This study employs a service-dominant logic perspective and incorporates value-in-use into the conceptual model to investigate the factors and mechanisms that influence sustained intention to use e-commerce platforms. Drawing on a survey of 358 college students in China, we reveal that users’ perceived usefulness and perceived enjoyment on e-commerce platforms have a positive effect on their sustaining use intention on platforms, whereas perceived uncertainty exerts a negative influence. Furthermore, the effect of perceived uncertainty on confirmation is less pronounced than that of perceived usefulness and enjoyment. Perceived uncertainty primarily stems from information asymmetry and seller opportunism, with the latter having a more significant impact on perceived uncertainty than the former. These findings enrich the empirical literature on the sustainable development of e-commerce platforms by offering new insights into the development of service-dominant logic and value-in-use theory. Full article

In a highly volatile environment, strengthening resilience is essential for businesses to promote sustainable development, and environmental information disclosure (EID), as a crucial approach for companies to actively practice the concept of green development, has far-reaching impacts on the enhancement of corporate resilience (CR). To explore ways to efficiently enhance the sustainability of enterprises, this research, based on information asymmetry theory, investigates how EID affects CR, using data from China’s A-share-listed companies between 2011 and 2022. The study indicates that the effect of EID on CR was significantly positive at the 1% level. Mediation analysis suggests that this effect is facilitated by heightened investor attention and enhanced corporate innovation. Additionally, the positive impact is more pronounced for firms in high uncertainty environments, high levels of legalization, high levels of digital transformation, non-state-owned firms, small-scale firms, and firms in growth or decline. Based on this, EID is of great significance to enhance the resilience of enterprises, and policymakers, business managers, and investors should take into account their own development situation and the actual environment, and make scientific decisions according to local conditions. Full article

In off-road environments, the lateral rollover stability of articulated unmanned rollers (URs) is critical to ensure operational safety and efficiency. This paper introduces the concept of a rollover energy barrier (REB), a symmetry-based metric that quantifies the energy margin between the current state and the critical rollover threshold of articulated rollers. URs exhibit dynamic asymmetry due to their hydraulic steering systems, which differ significantly from traditional passenger vehicles. To address these challenges, we propose a hierarchical control framework inspired by the principles of dynamic symmetry. This framework integrates Nonlinear Model Predictive Control (NMPC) and Active Disturbance Rejection Control (ADRC): NMPC is used for trajectory planning by incorporating the REB into the cost function, ensuring rollover stability, while ADRC compensates for dynamic asymmetries, model uncertainties, and external disturbances during trajectory tracking. Simulation and experimental results validate the effectiveness of the proposed control strategy in enhancing the rollover stability and tracking performance of the URs under off-road conditions. Full article