Search Results (17,450)

The increasing adoption of servitization has led many manufacturing companies to operate simultaneously in manufacturing and service activities, creating dual-sector business models characterized by heightened organizational complexity. Although prior research acknowledges that both internal capabilities and contextual conditions shape organizational outcomes, fewer studies have examined these variables within the same empirical model in companies operating under both manufacturing and service logics. Drawing on the resource-based view and contingency theory, this study examines the effects of organizational culture, organizational commitment, knowledge management, environmental uncertainty, and employee retention on organizational performance in dual-sector companies, while also assessing whether these relationships vary by company size. Survey data were collected from 433 employees working in dual-sector companies and were analyzed using confirmatory factor analysis, covariance-based structural equation modeling, and supplementary hierarchical regression analysis. The findings indicate that environmental uncertainty and employee retention did not receive empirical support as independent direct predictors in the structural model. Organizational commitment, knowledge management, and two dimensions of organizational culture—consistency and adaptability—are significant positive predictors of perceived organizational performance. The moderation analysis does not provide strong evidence that company size changes these relationships, although the interaction suggests that environmental uncertainty may be more consequential in large firms. This study contributes to research on servitization by showing that, in dual-sector companies, performance is most strongly associated with internal capabilities that support coordination, shared meaning, and knowledge integration across manufacturing and service activities. For managers, the results highlight the importance of strengthening commitment, adaptive coordination, and cross-domain knowledge processes rather than relying on retention efforts alone. Full article

This paper addresses the problem of robust identification of gross errors affecting both measurements and network parameters in power system state estimation. The study is conducted within a steady-state framework and focuses on improving bad data identification in the presence of modeling and measurement uncertainties, explicitly accounting for the limited observability of gross errors. Building on an Extended Weighted Least Squares (EWLS) estimator and a theoretically refined eigenvalue-based clustering of dominant error components, a novel Bayesian identification framework is introduced. The proposed Bayesian approach assigns probabilities to competing gross error models, including scenarios involving multiple simultaneous errors, given the observed clusters of dominant errors. This probabilistic formulation enables a systematic and quantitative decision-making process for identifying the most likely sources of gross errors, extending existing deterministic or heuristic approaches. The methodology is evaluated through numerical simulations on the IEEE-14 bus test system, considering several gross error scenarios and significant parameter uncertainties. The results demonstrate that the proposed Bayesian framework enhances the interpretability and discriminative capability of gross error identification, highlighting its potential for robust bad data identification in power system state estimation. Full article

This paper investigates the cooperative formation control problem in unmanned surface vehicles (USVs) with prescribed performance constraints under complex marine conditions including external disturbances, model uncertainties, actuator faults, and input saturation. A novel fault-tolerant control (FTC) algorithm is developed by integrating cooperative learning neural networks (NNs), distributed disturbance observers, and the backstepping technique. Specifically, the learning NNs adaptively approximate system uncertainties, and the learned weight information is shared among vehicles to enhance cooperative cognition. Additionally, an auxiliary dynamic system and an actuator configuration matrix are designed to compensate for input saturation and propeller failures. Theoretical analysis based on the Lyapunov method proves that all signals in the closed-loop system are bounded, and the formation tracking errors strictly remain within the predefined transient and steady-state performance bounds. Finally, simulation experiments involving a group of four USVs validate the proposed algorithm. The results demonstrate that the USVs can rapidly converge to and maintain the desired quadrilateral formation shape despite time-varying disturbances and actuator efficiency loss. Furthermore, comparative simulation results indicate that the proposed cooperative learning FTC scheme significantly reduces velocity tracking error oscillations compared to traditional non-learning methods, explicitly verifying its superior robustness and fault-tolerant capabilities. Full article

Dendrobium Sonia orchid cultivation constitutes a vital commercial industry in Thailand; however, production remains persistently threatened by fungal and bacterial diseases. This study proposes a robust automated framework for orchid disease classification under conditions characterized by high visual uncertainty. A comparative analysis was conducted across four Convolutional Neural Network (CNN) architectures: ResNet-50 and three lightweight counterparts—MobileNetV3-Large, EfficientNetV2-B0, and NASNet-Mobile. All models were optimized using transfer learning, Cosine Decay scheduling, and EarlyStopping on a real-world dataset acquired from commercial orchid farms in Thailand. Experimental results indicate that ResNet-50 attained the highest overall performance (Accuracy: 98.96%, Macro F1: 0.9894, AUC-ROC: 0.9996), while EfficientNetV2-B0 achieved comparable results among the lightweight architectures (Accuracy: 98.47%, Macro F1: 0.9846, AUC-ROC: 0.9985). Importantly, statistical evaluation using the Wilcoxon Signed-Rank Test across five independent trials revealed no statistically significant difference between ResNet-50 and all three lightweight models (p > 0.05). This confirms the practical viability of deploying compact architectures on mobile platforms within smart farming systems without sacrificing diagnostic accuracy. Moreover, integrating Grad-CAM++ enhances interpretability by producing visual explanations that align with expert pathological assessments. This transparency effectively mitigates decision-making ambiguity and strengthens farmer confidence in adopting AI-driven precision agriculture. Full article

Understanding how individuals decide to adopt shelter dogs remains a significant challenge within animal welfare research, as existing studies identify correlates of adoption outcomes without explaining the underlying decision process. This hypothesis introduces a conceptual framework that synthesizes empirical findings from dog adoption studies with interdisciplinary theories to explain how adoption decisions emerge. Using a signal-to-noise perspective, the framework conceptualizes early bond formation between a potential adopter and a dog as a valuation signal that competes with uncertainty arising throughout the process. The functional model describes the adoption process as a lifecycle involving search, visitation, interaction, and decision phases, during which potential adopters seek information, evaluate available dogs, and form perceptions of compatibility. Interdisciplinary decision models, including Prospect Theory and the Diffusion Decision Model, are integrated to explain how information is framed, evaluated, and accumulated until a decision is reached. Empirical findings from human–dog interaction research are used to support the hypothesis that potential adopters evaluate companionship potential based on early bond formation associated with human–dog interactions. The framework offers a broad perspective on how adoption decisions may occur and establishes a theoretical foundation to guide future hypothesis development, measurement, and experimental research in companion animal adoption. Full article

Understanding how volcanic systems evolve over time is a major challenge due to their complex behaviour and constantly changing conditions. This study explores a novel approach to detecting significant changes in multiparametric signals of volcanic unrest by analysing how different types of data, such as ground deformation, gas emissions, temperature, and earthquakes, interact with each other. Focusing on the Solfatara–Pisciarelli volcano system, which is a more active area in the Campi Flegrei Caldera (Southern Italy), we used two advanced methods to identify critical transitions in the system: one to model the nonlinear relationships between variables, and the other to detect key moments when the system’s behaviour shifts. By including time delays between signals (LAG), we found that our model became much more accurate in identifying these changes. In contrast, models that ignored time lags showed higher uncertainty. The results highlight the importance and effectiveness of using integrated multivariate approaches such as Multivariable Fractional Polynomial Analysis (MFPA) and Global Critical Point Analysis (GCPA) to gain deeper insights into the systemic behaviour of the caldera and its temporal evolution within a complex area like the Campi Flegrei over the selected time period. Full article

Accurate estimation of global terrestrial evapotranspiration (ET) is fundamental for understanding the Earth’s water and energy cycles, yet existing multi-source ET products inevitably contain uncertainties that require spatially explicit characterization for optimal data merging or data assimilation. While Quadruple Collocation Analysis (QCA) offers a robust and reference-free approach to quantify uncertainties, its reliability in the ET discipline remains underexplored, and algorithmic non-convergence frequently results in substantial spatial data gaps. To address these limitations, this study evaluated the accuracy of the QCA method using validation errors derived from high-quality FLUXNET sites (N = 55). Moreover, we employed a Random Forest (RF) framework that is driven by 17 environmental variables to generate spatially seamless error maps for four mainstream ET products, i.e., ERA5, GLDAS, GLEAM, and MERRA2, from 2000 to 2020. Results demonstrate that QCA-based errors strongly correlated with ground-based errors as Pearson’s correlation coefficient was >0.3 for all four ET products. Furthermore, the RF model successfully reconstructed the spatial gaps in QCA errors, achieving an exceptionally low mean prediction error of approximately 0.03 mm/day. Based on these seamless maps, the global mean ET error is estimated at roughly 0.3 mm/day, with pronounced high-error clusters emerging in regions such as central Canada and northern Argentina driven by underlying land cover heterogeneity. Ultimately, this seamless gap-filling redefined the global map of product with the lowest estimated collocation error. ERA5 emerged as the superior choice across approximately 45% of the land surface (predominantly in the tropics and mid-to-high latitudes). Meanwhile, before algorithmic gap-filling, GLEAM was optimal across approximately 28% of the valid land pixels; after spatial gap-filling, it proved most effective across approximately 30% of the globe, particularly within arid deserts and glaciated regions. Our work provides useful geographic guidance for optimizing multi-source data merging and land data assimilation frameworks in future global hydrological studies. Full article

Evidence from recent large-scale disruptions indicates that efficiency-centered supply chain designs struggle to sustain operation under persistent and systemic uncertainty. This study introduces the Response and Adaptive Immune-Inspired Supply Chain Immune System (RAIE–SCIS), a continuous-time dynamic framework that extends existing viability and resilience approaches by explicitly modeling inter-temporal adaptation and operational memory within a control-theoretic structure. The framework represents supply chains as multi-layer control systems where structural protection, adaptive regulation, and memory mechanisms jointly shape system response over time. Viability is assessed using time-dependent indicators, including performance trajectories, recovery time, and an adaptation-based viability index. The model is applied to a carbon capture, utilization, and storage (CCUS) supply chain under heterogeneous disruption scenarios. Results show that immune-enabled configurations increase minimum performance levels by 15–30% and reduce recovery times by up to 25% compared to non-adaptive configurations. These improvements are not uniform across scenarios and depend on disturbance structure and recurrence. The analysis reveals that adaptive regulation introduces a trade-off between recovery speed and variability, while memory mechanisms shape recovery dynamics under recurrent disruptions—effects not captured by static or purely reactive models. Their effects become more pronounced when disturbances accumulate or propagate. Full article

The large-scale integration of wind energy introduces increased uncertainty and variability in modern power systems, with direct implications for both system design and operation. In addressing similar aspects, energy storage plays a pivotal role as a key source of system flexibility. However, the design and sizing of storage systems remain challenging, especially under conditions of increased uncertainty. In this context, the present study proposes an alternative methodological framework, based on an inverse sizing pathway, i.e., from operations to design. More specifically, the uncertainty embedded in day-ahead forecasting of residual errors, associated with wind power generation and load demand, is currently exploited as a design-relevant signal, while energy storage is treated explicitly as a risk-hedging mechanism. Forecasting residuals spanning a year of operation are incorporated in the problem through probabilistic modeling, leading to the generation of trajectories that correspond to different risk levels and are managed as design scenarios. Regarding the modeling of uncertainties, the study examines two different strategies, namely a global modeling approach and a k-means clustering strategy. Accordingly, by mapping the interplay between storage capacity, uncertainty levels (or risk tolerance), achieved RES shares and system-level costs, we highlight the role of energy storage as a risk-hedging entity rather than merely a means of energy balancing. Our results to that end demonstrate that the achieved shares of RES exhibit increased sensitivity, even within constrained regions of wind power variation, while storage capacity features distinct zones of hedging value and hedging saturation effects emerging beyond certain storage levels. Moreover, evaluation of the two modeling strategies reflects on their complementary character, with the global modeling approach ensuring continuity and the clustering strategy capturing local asymmetries within different operational regimes. In conclusion, the methodology presented in this study bridges the gap between operational forecasting and long-term system design, offering a risk-aware framework for storage sizing, grounded in actual operational signals rather than relying on stationary historical data and relevant scenarios. Full article

Due to urbanization and industrialization, there are significant regional differences in carbon emissions, making it increasingly urgent and necessary to conduct an in-depth examination of carbon emission trends from energy consumption across various sectors at the provincial level. Taking Hebei Province, a major carbon-emitting province in China, as a case study, we analyzed carbon emissions from three perspectives: historical emissions, influencing factors, and scenario projections. First, we established a carbon emission inventory for energy consumption. Second, using the integrated LMDI-SD-MC framework, we constructed four subsystems economy, society, energy, and technology and employed three scenarios for forecasting. The results show that: (1) Carbon emissions in Hebei Province from 2003 to 2021 exhibited increased trend year by year, with the share of coal and coke decreasing and the share of natural gas increasing. The industry, residential, and transportation sectors accounted for more than 95% of total carbon emissions. (2) In terms of influencing factors, energy intensity and the level of economic development contributed the most significantly, with contribution rates of −75.97% and 195.97%, respectively. (3) Among the scenario projections, the low-carbon development scenario is the most suitable for Hebei Province, enabling the province to achieve its “Dual Carbon” goals as scheduled. Under the baseline development scenario, the peak is reached in 2040. Under the rapid development scenario, carbon emissions will reach 1130.86 10⁶ tons by 2060. (4) Uncertainty analysis using Monte Carlo simulation for all three scenarios showed errors within ±10%, indicating that the model results are robust and interpretable. This study provides a provincial level emission reduction perspective for China to achieve its “Dual Carbon” goals and sustainable development. Full article

Missing modalities remain a major obstacle to the real-world deployment of multimodal learning systems, as incomplete inputs can substantially degrade model performance. Existing methods often suffer from biased imputation under high missing rates and lack uncertainty-aware, differentiated processing. Inspired by three-way decision, a framework for handling uncertainty by adding a deferment option to acceptance and rejection, we propose three-way multimodal learning with severely missing modalities (3WML-SMMs), a novel framework that introduces a three-way decision mechanism into both missing-modality imputation and feature regularization for the first time. Specifically, 3WML-SMM treats variance not merely as a descriptive measure of uncertainty, but as a decision signal for adaptive processing. Based on this idea, the framework incorporates (1) a variance-guided three-way imputation strategy with accept–delay–reject decisions to reduce unreliable reconstruction when only a limited number of complete samples are available and (2) a dimension-wise adaptive feature enhancement module that performs fine-grained regularization according to perturbation uncertainty. Experiments on the CMU Multimodal Opinion Sentiment Intensity (CMU-MOSI) and Multimodal Internet Movie Database (MM-IMDb) datasets show that 3WML-SMM consistently outperforms representative baselines, including reconstruction-based methods, complete-input multimodal methods, and missing-modality-specific methods under severe missing-modality settings, with statistically significant improvements over the multimodal learning with severely missing modality (SMIL) baseline ($p<0.05$). These results demonstrate the effectiveness of the proposed framework, even in extreme settings where only 10% of the text modality is available. Full article

Physical coupling, nonlinearity and uncertainty degrade the dynamic performance of electro-hydraulic servo systems, particularly under conditions involving input delays, leading to reduced trajectory tracking accuracy or even system instability. These factors often fail to meet the high-precision requirements of engineering applications. To effectively address these difficulties, this paper proposes a novel adaptive control protocol for networked electro-hydraulic servo systems. For the minimal communication delay problem of networked electro-hydraulic servo systems, Laplace transform algorithm together with Padé approximation is adopted in this study to remove the delay term from the mathematical system model. Moreover, the matched modeling parametric uncertainty of systems is estimated and compensated by the neural network adaptive method to improve the dynamical performance of the system during the steady state. The controller is designed on the basis of recursive backstepping strategy and the finite-time stability theorem, which can handle system nonlinearity and guarantee transient response. The validity of the proposed theoretical results is proved by Lyapunov stability and the feasibility and superiority are verified via physical simulation. Full article

Vulnerabilities in conventional agri-food supply chains (CAFSCs) necessitate a shift toward resilient, localized production models. Within the Agri-Food 4.0 landscape, urban Controlled-Environment Hydroponic Agriculture (CEHA) systems address these challenges by shortening supply chains and mitigating climate-induced breakdowns. However, structurally aligning Triple Bottom Line (TBL)-oriented stakeholder needs with complex technical specifications remains a critical challenge in sustainable CEHA system design. To address this challenge, the present study proposes a novel framework integrating the Full Consistency Method (FUCOM) and Quality Function Deployment (QFD) within a Finite Interval Type-2 (FIT2) Gaussian fuzzy environment. This approach systematically translates TBL-oriented priorities into precise engineering specifications, mapping 17 stakeholder needs (SNs) to 30 technical design requirements (TDRs) while capturing linguistic uncertainty and hesitation. The findings reveal a clear strategic focus on environmental and social sustainability. Specifically, high product quality, food safety and traceability, consumer acceptance, and minimization of environmental impacts emerge as the primary drivers of CEHA adoption. The QFD translation identifies scalable IoT infrastructure, sensor maintenance and calibration, and AI-enabled decision support as the most critical TDRs. The framework’s reliability and structural robustness were rigorously validated through comprehensive analyses, including Kendall’s W test to confirm expert consensus, alongside a Leave-One-Out (LOO) approach, weight perturbations, and a structural evaluation of TDR intercorrelations. These findings provide a scientifically grounded roadmap for designing sustainable, intelligent urban agricultural systems. Ultimately, this framework offers actionable managerial implications for agribusiness stakeholders to bridge strategic TBL-oriented goals with practical engineering, significantly enhancing Agri-Food 4.0 supply chain resilience. Full article

In recent years, virtual influencers (VIs) have been increasingly used in digital commerce. Despite the rise in VI research, past studies have yet to comprehensively examine the effectiveness of VIs, often focusing only on isolated partial models rather than an integrated framework and boundary conditions that drive consumer responses. This meta-analysis fills this gap by synthesizing 186 effect sizes from 76 studies (N = 64,545) to examine the mechanisms and moderators of purchase intention in VI marketing. The results indicate that human-likeness is a central antecedent that directly and indirectly affects purchase intention through source credibility, customer engagement, and attitude. More importantly, this study challenges prior social proof assumptions by showing that follower size has no significant impact on purchase intention in VI marketing. In addition, purchase intention is independent of a nation’s AI readiness, suggesting a borderless potential for commerce regardless of a country’s technological maturity. This study also examined the moderating effects of product type, consumer age, and uncertainty avoidance culture. Although these moderators showed initial significance, none remained significant after the Benjamini–Hochberg false discovery rate (FDR) correction. Therefore, these effects were viewed as exploratory rather than confirmatory, providing directions for future research. These findings offer new insights for e-commerce managers: success in the metaverse era depends on anthropomorphism and targeted alignment rather than metrics such as follower counts or a nation’s AI readiness. Full article

Cross-Domain Few-Shot Scene Classification (CDFSSC) aims to transfer knowledge from a source domain to a target domain for few-shot classification tasks, and is essential for remote sensing applications involving diverse platforms and dynamic environments. However, distribution discrepancies and category misalignment across domains often introduce high predictive uncertainty, significantly degrading model performance. To address these challenges, an uncertainty-aware cross-domain (UACD) framework is proposed to enhance model reliability by systematically mining uncertainty-related information. Specifically, in the cross-domain training process, a feature-decision consistency regularization (FDCR) structure is designed to stabilize cross-domain training by enforcing consistency at both feature and decision levels. Furthermore, an uncertainty-aware knowledge mining (UKM) policy is introduced to effectively exploit high-uncertainty target samples, mitigating the negative impact of unreliable pseudo-labels and improving representation learning. In the few-shot adaptation stage, an uncertainty-aware predictor is developed to enhance adaptability and decision-making in target tasks. Extensive experiments on 12 cross-domain scenarios demonstrate that the proposed UACD framework consistently achieves superior or competitive performance, with strong robustness and generalization capability across diverse CDFSSC tasks. Full article