Search Results (7,750)

To address the issue of low-frequency oscillations in active power and frequency caused by parameter mismatches among multiple grid-forming energy-storage converters in islanded microgrids—where the fixed damping coefficient of conventional VSG control fails to simultaneously achieve satisfactory dynamic response and steady-state accuracy—this paper proposes a VSG control strategy enhanced by cooperative transient damping. The strategy first introduces active power feedback transient damping (TDP) into the active power loop. Although TDP can effectively suppress low-frequency oscillations in active power, it provides insufficient suppression for frequency oscillations induced by angular frequency coupling. Therefore, angular frequency feedback compensation is further incorporated, forming an active power-angular frequency two-degree-of-freedom architecture. This design flexibly adjusts the system damping without compromising steady-state performance, achieving cooperative suppression of both types of oscillations. Simulation and experimental results demonstrate that the proposed strategy significantly suppresses low-frequency oscillations in active power and frequency, effectively improving both the dynamic response performance and steady-state accuracy of the system. Full article

Hepatic fibrosis is a pathological process involving the systemic reconfiguration of the liver microenvironment under chronic liver injury. It is a pathological wound healing response characterized by the excessive deposition of extracellular matrix (ECM) driven by the activation of hepatic stellate cells (HSCs), starting with the capillarization of liver sinusoidal endothelial cells (LSECs). This process can lead to liver structure destruction, portal hypertension, and even liver dysfunction, and is a major driving factor for death related to liver diseases. During this process, hypoxia initiates the transcriptional upregulation of effector molecules such as vascular endothelial growth factor (VEGF) and Lysyl oxidase (LOX) by stabilizing hypoxia-inducible factors (HIFs), inducing changes in LSEC phenotype and activation of HSCs, thus becoming a core driving factor. Activated HSCs not only exacerbate the capillarization of LSECs and tissue hypoxia but also strengthen the transcriptional activity of HIFs through autocrine/paracrine signaling, establishing a positive feedback loop linking hypoxia to HIFs and HSC activation, continuously driving the progression of liver fibrosis and significantly increasing the probability of chronic liver diseases evolving into cirrhosis and the risk of death. This review will analyze the mechanism of liver fibrosis driven by hypoxia, integrate the current treatment landscape, focus on exploring the therapeutic targets related to hypoxia-induced activation of hepatic stellate cells leading to liver fibrosis, and evaluate their translational potential. It is expected to provide information for future effective anti-fibrotic intervention strategies. Full article

Although new AI systems have been developed based on the integration of information from multiple sources under conditions of uncertainty, classical probabilistic models are unable to provide structured, interpretable, and reliable representations in the presence of contextual and order effects. Specifically, the fundamental principles of Kolmogorov’s assumptions underlying the modeling overlook certain common violations in real-world decision-making processes, such as non-commutativity, contextual dependence among agents, and interaction effects between information sources characterized by experiential heterogeneity. A Positive Operator-Valued Measure (POVM) formalism defined on a Hilbert space of latent states forms the basis of this article’s structured epistemic representation framework to support the reliability and interpretability of the black box in AI. The resulting model generalizes the classical epistemic quadruplet: Probability, Plausibility, Credibility, and Possibility within a single geometric framework in which three essential non-classical effect mechanisms emerge—(i) the non-commutativity of information acquisition, (ii) the contextuality arising from incompatible observational frameworks, and (iii) the interference interactions between information acquisition channels. We propose the concept of a quantum-inspired fusion operator (QI-Happenability), which introduces symmetric and antisymmetric feedback interaction terms based on the estimation of ordered residuals. An analysis of the proposed framework is then performed using real data, validating the model on the Efron et al. diabetes regression dataset (N = 442; ten standardized physiological predictors; continuous disease progression target), available in scikit-learn, which showed a 17.4% reduction in mean absolute error (MAE) compared to traditional models and significant improvements over polynomial machine learning baselines, as well as ensemble machine learning methods, within a rigorous cross-validation protocol. Contextual analysis indicates that 68% of cases violate classical bounds (CHSH inequality, p < 0.001), empirically confirming the non-classical structured representation in multi-source data. The results confirm the proposed approach as a simpler, more interpretable, and more reliable alternative to black-box models: this work demonstrates how the use of structured epistemic representations in reasoning under uncertainty preserves formal interpretability while retaining useful semantic information. By linking quantum cognition and applied AI, this work could help lay the groundwork for a new generation of interpretable and reliable decision-making systems. Full article

Geographical indication (GI) products are often understood through terroir, certification, and branding, yet less attention has been paid to the feedback dynamics through which their market value is amplified and destabilized. This paper applies systems thinking to the post-harvest value–trust subsystem of Xinhui Chenpi, a traditional Chinese aged citrus pericarp product with GI protection, to examine how value amplification and systemic vulnerability emerge through interacting feedback mechanisms. Drawing on 28 semi-structured interviews conducted between September 2023 and December 2024, supplemented by participant observation, policy and standard documents, field-based market observations, and contextual media materials, the study develops a preliminary causal loop diagram (CLD) with 15 endogenous feedback variables, 3 boundary value-input variables, 4 exogenous contextual inputs, and 21 causal links (18 endogenous and 3 value-input). The model identifies two reinforcing loops and two balancing loops through which price expectations, holding incentives, credible circulation supply, perceived scarcity, misrepresentation, and trust erosion interact. The trust-erosion loop shows how premium-driven misrepresentation increases claim uncertainty, weakens open-market consumer trust, reduces credible open-market liquidity, and further contracts credible circulation supply. Buyer exit and delayed supply response operate as limited balancing mechanisms because their effects are segment-dependent and constrained by aging and verification delays. The proposed CLD suggests that high-value mechanisms in aging-dependent GI products may also generate structural vulnerabilities, with implications for managing consumer trust, claim verification, and credible circulation in premium markets. Full article

Digital maps increasingly replace paper maps because they are accessible, regularly updated, and customizable. Yet they often weaken spatial orientation skills and create technological dependency. One possible solution is supporting navigation without extra cognitive effort by designing maps that address spatially responsive brain cells. These cells are thought to be involved in the construction of an internal spatial representation. As animal studies have shown that the perception of environmental boundaries contributes to the stabilization of firing behavior, we examined boundary effects on path integration (PI) in screen-based and virtual reality (VR) settings. This allowed us to test whether the effect is robust across formats with different immersion and self-motion feedback. Participants completed PI tasks in a virtual arena while viewing a briefly displayed elevated line, wall, or no artificial boundary. It was expected that perceived boundaries would stabilize the activity of spatially responsive cells, such as grid cells. This is likely to contribute to a reduction in PI errors. Results showed a supportive tendency for the line condition, whereas the wall condition produced the highest errors. This pattern was comparable across both media. The findings suggest that the effect of spatial boundary cues depends on their design and perceptual properties. Full article

Continuous shared control provides an effective approach for intelligent vehicles to balance driving autonomy and system safety boundaries in complex human–machine interaction scenarios. However, existing shared control methods fail to dynamically adapt to the complex and time-varying states of the driver. To address this limitation, a hierarchical shared steering control strategy based on driver states is proposed in this paper. First, an in-vehicle eye tracker is utilized to collect data, and recognition features are extracted based on real-world datasets. Subsequently, a CNN-TCN deep learning algorithm is employed to train a model for identifying five-dimensional driver states. To mitigate excessive intervention and driving experience degradation caused by model misclassifications, a total probability weighting mechanism is developed. This mechanism integrates the real-time confidence distribution output by the neural network with the established baseline safety weights for each driving state, enabling the dynamic and continuous computation of the initial machine control authority. Furthermore, to eliminate high-frequency confidence spikes at the state perception end, a weight-smoothing strategy is designed using an adaptive nonlinear tracking differentiator based on Active Disturbance Rejection Control (ADRC). An autonomous driving controller is then constructed using the Linear Quadratic Regulator (LQR) method to ensure vehicle stability. Finally, Hardware-in-the-Loop (HIL) experiments conducted on a human–machine driving platform with hardware feedback verify the feasibility and superiority of the proposed method. Full article

Digital gatekeeping is increasingly performed by adaptive systems that rank, sequence, package, moderate, and sometimes generate news across repeated interactions. This conceptual analysis asks two questions: how should researchers analyze these systems, and who should be responsible for their effects over time? It brings together gatekeeping theory, research on recommender systems and performative prediction, and scholarship on algorithmic accountability, and it makes three contributions. First, it describes curation through six decision functions (source eligibility, agenda and candidate formation, content generation, packaging, exposure allocation, and moderation) connected by a feedback and optimization layer. Agentic curation is treated as a high-autonomy form of adaptive curation, defined by planning ability, authority to act, reach across functions, and delay before human review, rather than as a separate technology class. Second, it defines cumulative exposure allocation as the normalized distribution of weighted exposure across sources, topics, and population groups over time, and it proposes measures of concentration, breadth, repetition, persistence, and disparity. Third, it links each curation function to the actors who control it, the bodies that oversee it, the evidence they need, the standards they apply, and the remedies they can impose. European Union and United States law illustrate the framework. The analysis shifts evaluation away from isolated outputs and short-term engagement toward patterns of visibility produced by a policy over time, and it proposes testable claims about traceability, concentration, disparity, and constrained multi-objective ranking. Full article

Free generative AI (GenAI) services appear to democratize access, but freemium platform models can create post-access stratification after basic connectivity and initial use have been achieved. This article conceptualizes paid commitment to GenAI as an emergent outcome of a socio-technical system composed of platform tiers, user capabilities, task environments, economic constraints, affective feedback, and institutional access. Using a cross-sectional 2025 survey of 2000 South Korean adults aged 18–69, the study models the observed states of non-use, free use, and paid commitment through sequential logit models. Descriptive results show that 70.6% of respondents used GenAI in the previous year, yet only 20.6% of users, or 14.6% of the total sample, paid for access. Practical AI literacy is positively associated with both initial use and paid commitment, whereas critical AI awareness is associated with initial use but not payment. Negative emotions are negatively associated with initial use but positively associated with paid commitment among users, a pattern consistent with vigilant and intensive engagement. The premium digital divide is defined not as a fourth level of the digital divide, but as a tier-mediated extension of second-level inequality that may condition access to later educational, occupational, or civic benefits. Because the data are cross-sectional and combine different GenAI platforms, the estimates are ecosystem-level associations and do not establish causal direction or platform-specific effects. Full article

Humanoid robots are emerging as flexible robotic resources for autonomous manufacturing systems, where different types of tasks must be assigned to suitable robots while shared production resources are coordinated effectively. However, realistic manufacturing environments involve dynamic task arrivals, event-driven priority changes, heterogeneous robot capabilities, and shared-resource contention. In addition, digital twin-based scheduling may rely on state information that is delayed or uncertain, which can reduce the reliability of scheduling decisions. To address this issue, this paper extends the previously proposed deep reinforcement learning-based concurrency control (DRLCC) framework for robustness-aware scheduling and shared-resource control of a multi-functional humanoid robot team. The extended framework integrates capability-aware task assignment, feasibility-based action masking, and priority ceiling protocol (PCP)-based shared-resource coordination under delayed and uncertain digital twin observations. The framework is evaluated in a humanoid-based autonomous manufacturing scenario using performance indicators including task completion, urgent-task delay, resource contention, humanoid utilization, and robustness degradation under imperfect state feedback. Compared with the greedy ceiling-based baseline, DRLCC reduces high-priority task delay by approximately 10.9%, priority inversions by 42.1%, average waiting time by 73.8%, average block count by 74.0%, and temporary infeasible events by 73.5%, while maintaining comparable humanoid utilization. The robustness analysis further shows that overall task-completion performance remains stable under imperfect digital twin feedback, although coordination-level metrics are more sensitive to observation uncertainty and delay. These results suggest that the extended DRLCC framework can support robustness-aware humanoid robot team scheduling in autonomous manufacturing environments where digital twin observations are delayed or uncertain. Full article

Public responses during health crises are shaped by interacting risk perceptions, affect, trust, information needs, overload, misinformation, and protective behavior. Existing applications of the Risk Information Seeking and Processing (RISP) model are largely static and therefore cannot represent stochastic multichannel exposure, delayed correction, or policy feedback. We develop the Stochastic Probabilistic Dynamic RISP (SP-D-RISP) model, which recasts RISP as a bounded stochastic state-space system. Its symmetry structure is explicit: the channel-allocation mechanism is equivariant under simultaneous relabeling of channels and their parameter blocks, while the multi-agent dynamics are invariant to agent relabeling under exchangeable sampling and a label-independent policy. Channel-specific effects, heterogeneous traits, rumor shocks, and interventions generate symmetry breaking. The model combines softmax–multinomial channel competition, discounted Bayesian trust updating, and policy-coupled state transitions. Projection guarantees feasible states by construction, whereas stronger stochastic stability is conditional on a coefficient-level small-gain criterion. For the stationary bounded-memory specification, this criterion is sufficient for Wasserstein contraction, uniqueness of the invariant distribution, and geometric forgetting of initial conditions. The criterion is formulated at the coefficient level and is kept distinct from finite-horizon simulation diagnostics. For the fully disclosed semi-synthetic coefficient vector, the scenario-specific gain matrices have spectral radii between $0.852765$ and $0.857123$; the worst-case column-sum norm is $0.983948$. Thus, the fixed-policy kernels satisfy the stated contraction certificate. For deterministic time-varying paths, the calculation is used only as a common-path one-step certificate, and for the threshold-adaptive rule, it is used only mode by mode rather than as a stationary invariant-law claim. While concentration bounds and Monte Carlo inference quantify population and replication uncertainty, a semi-synthetic experiment with 2500 heterogeneous agents over 90 days examines trust and literacy heterogeneity, clarification delays, communication volume, and intervention portfolios. Within the calibrated SP-D-RISP scenarios, the simulations suggest that higher communication volume may reduce modeled protective behavior when overload effects dominate knowledge gains, delayed clarification may increase transient misinformation, and an integrated portfolio can yield a more favorable simulated outcome profile than the evaluated single-lever strategies. Full article

Kazakhstan faces growing water security and environmental management challenges and needs more practice-oriented environmental science education. This study examines two complementary Kazakhstan-based cases. Case Study 1 analyzes faculty and student surveys from the 2022 Science with a Purpose project, which delivered short courses in water quality, air quality, remediation, sampling methods, and scientific communication. Case Study 2 examines the 2025 pilot adaptation of the Nebraska-based Know Your Well (KYW) participatory-science model. An exploratory comparative two-case-study design drew on survey responses, project documentation, instructional materials, participant feedback, workflow analysis, and two illustrative laboratory water analyses. In Case Study 1, 22 of 25 faculty members responded (88.0%); student response rates were 14 of 18 (77.8%) before and 10 of 18 (55.6%) after the courses. Participants generally rated practice-oriented content positively. In the KYW pilot (N = 15), 14 participants (93.3%) reported that the training met expectations, and 14 (93.3%) considered the skills fully or partially applicable. The pilot demonstrated a feasible workflow linking preparation, field activity, sample handling, and laboratory-supported interpretation. Although the findings do not establish national representativeness, intervention effects, long-term learning outcomes, or large-scale monitoring impact, they support complementary capacity-building pathways for Kazakhstan. Full article

Generative artificial intelligence is increasingly used in higher education to provide explanations, solutions, feedback, and organizational support. Although current debates emphasize academic integrity, productivity, and instructional innovation, less attention has been given to how the timing and form of AI assistance may affect learning after that assistance is removed. This conceptual paper develops the Retrieval Interruption Framework (RIF) through a targeted narrative synthesis of research on retrieval practice, productive failure, scaffolding, cognitive load, cognitive offloading, metacognitive monitoring, the expertise reversal effect, the assistance dilemma, and AI-assisted learning. RIF is proposed as an integrative, AI-specific framework rather than a distinct theory of cognition. It distinguishes retrieval-preserving assistance from retrieval-displacing assistance. Retrieval-preserving assistance supports learners after they have attempted task-relevant recall, self-explanation, problem representation, or solution generation. Retrieval-displacing assistance supplies the targeted explanation, solution, or reasoning structure before an initial learner response. The framework predicts that retrieval-displacing assistance may improve immediate performance while weakening delayed unsupported recall, explanation quality, transfer, or metacognitive calibration, particularly in conceptually demanding tasks and among learners with limited prior knowledge. However, early AI guidance may remain productive when it reduces extraneous cognitive load, promotes active processing, fades over time, and is followed by independent performance. RIF reframes AI integration as a sequencing and instructional-design problem. Future research should compare specific forms of AI-first and learner-first assistance using immediate performance measures and delayed unsupported learning outcomes. Full article

The introduction of robotic-assisted total hip arthroplasty (THA) has had significant effects over and above the more accurate component position it delivers. Diagnosis and templating are improved as a result of the planning CT scan. Complex cases involving abnormal anatomy are simplified surgically by a single ream for the acetabular component and feedback for hip length and offset for the femoral component. The learning curve for surgeons relates primarily to operative time rather than complications. Once planned, robotic systems may reduce variability in component positioning between surgeons with differing levels of experience. Patient Reported Outcome Measures (PROMs) will need to be supplemented with gait lab data and functional tests to prove the added value of robotic-assisted THA. As robotic-assisted surgery (RAS) becomes increasingly reliant on imaging systems and digital workflows, operating theatres face greater complexity in technology integration and intraoperative coordination. Emerging evidence links spatial organisation, equipment positioning, and workflow to operative efficiency, intraoperative safety, and surgical performance. This paper provides an evidence-informed narrative perspective on how advances in robotic-assisted THA are influencing the spatial and functional requirements of operating theatres, thereby affecting clinical performance. As surgical practice advances, there is a need to move towards future-proofed design strategies informed by clinical performance and systems-based evidence. This includes a greater focus on flexibility and integrated digital infrastructure to support evolving technologies, changing team dynamics, and increasing procedural demands. Operating theatres must therefore evolve to enable effective integration of RAS in THA, and support improved patient safety and clinical outcomes. Full article

Amid the accelerated global transition toward sustainable electromobility, Chinese Electric Vehicles (EVs) have forged a complex, evolving communication ecosystem across overseas social media platforms. Conceptualizing global digital discourse as a complex adaptive system (CAS), this study integrates CAS theory with Competitive Framing theory to systematically elucidate the thematic configurations, framework dynamics, and ecological activation mechanisms underlying the international image of Chinese EVs. By integrating unsupervised BERTopic modeling, Large Language Model (LLM) semantic mapping, the Entropy Weight Method (EWM), and Social Network Analysis (SNA), this inquiry operationalizes a comprehensive computational communication framework to mine large-scale behavioral and textual data from YouTube. The empirical findings unveil that: (1) international audience perceptions have broken through the traditional “low-cost manufacturing” stereotype, spontaneously giving rise to a multidimensional, composite cognitive schema centered on smart ecosystems and design experiences; (2) driven by the interplay of rational and irrational user feedback loops, the ecological activation efficiencies across diverse discursive dimensions exhibit pronounced nonlinear variances, characterized by a “strong activation of intelligent ecosystems versus a long-tail stagnation of cost-effectiveness salience”; and (3) positive technological frameworks and negative geopolitical or regulatory risks engage in fierce, adversarial contestation and structural hybridization within a highly volatile network topology, culminating in a unique “dual-core” configuration. Theoretically, this study enriches the scholarly understanding of country-of-origin and corporate brand images through a complex systems lens; methodologically and practically, it offers a high-fidelity, actionable quantitative paradigm for global brand empowerment and targeted cross-border public opinion governance. Full article

Quantum neural architecture search (QNAS) has emerged as a promising approach for automatically designing parameterized quantum circuits (PQCs) for near-term quantum machine learning tasks. However, quantum evolutionary algorithm (QEA)-based QNAS methods often suffer from slow distribution concentration and insufficient update adaptivity in high-dimensional discrete search spaces, which limits both search efficiency and final model performance. To address these issues, this paper proposes Structural Probability Adaptive Quantum Neural Architecture Search (SPA-QNAS), a search-dynamics-enhanced QNAS method built upon the EQNAS benchmark framework. SPA-QNAS introduces two complementary mechanisms into the QPV-driven evolutionary search loop: Structural Probability Enhancement (SPE) and Adaptive Evolutionary Control (AEC). SPE reinforces elite structural decisions to accelerate the concentration of the structural sampling distribution toward high-fitness regions, while AEC adaptively regulates the rotation updates of non-elite individuals according to fitness feedback, thereby improving update stability and suppressing ineffective disturbances. Under the same search space, circuit template, and quantum resource budget as EQNAS, SPA-QNAS is evaluated on the MNIST and Warship benchmark datasets. Experimental results across multiple independent runs demonstrate that SPA-QNAS achieves higher classification accuracy and more stable performance compared with EQNAS. In representative experiments, SPA-QNAS achieves classification accuracies of 99.42% on MNIST and 85.33% on Warship under the same search space and quantum resource budget as EQNAS. These results indicate that improving QPV-based evolutionary update dynamics is an effective way to enhance the stability and robustness of QNAS under fixed quantum resource constraints. Full article