Search Results (1,379)

We propose a unified theoretical framework grounded in a Primordial Quantum Field (PQF)—a continuous, non-local informational substrate that precedes space-time and matter. The PQF is represented by a wave functional evolving in an abstract configuration space, where physical properties emerge through the self-organization of complexity. We introduce a novel physical quantity—complexity entropy Sc[ϕ]—which quantifies the structural organization of the PQF. Unlike traditional entropy measures (Shannon, von Neumann, Kolmogorov), Sc[ϕ] captures non-trivial coherence and functional correlations. We demonstrate how complexity gradients induce an emergent geometry, from which spacetime curvature, physical constants, and the arrow of time arise. The model predicts measurable phenomena such as entanglement waves and reinterprets dark energy as informational coherence pressure, suggesting empirical pathways for testing via highly correlated quantum systems. Full article

To address the scientific gap concerning optimal urban wind turbine morphology, this work presents an experimental performance comparison between two small-scale wind turbine designs: a conventional three-blade horizontal-axis wind turbine (HAWT) and a duct-equipped Ayanz-inspired screw-blade turbine. Both configurations were tested in a controlled wind tunnel under steady and transient wind conditions, including synthetic gusts designed to emulate urban wind patterns. The analysis focuses on power output, aerodynamic efficiency (via the power coefficient C_p), dynamic responsiveness, and integration suitability. A key novelty of this study lies in the full-scale experimental comparison between a non-conventional Ayanz screw-blade turbine and a standard three-blade turbine, since experimental data contrasting these two geometries under both steady and gusty urban wind conditions are extremely scarce in the literature. Results show that while the three-blade turbine achieves a higher C_ppeak and greater efficiency near its optimal operating point, the Ayanz turbine exhibits a broader performance plateau and better self-starting behavior under low and fluctuating wind conditions. The Ayanz model also demonstrated smoother power build-up and higher energy capture under specific gust scenarios, especially when wind speed offsets were low. Furthermore, a methodological contribution is made by comparing the C_p vs. tip speed ratio λ curves at multiple wind speeds, providing a novel framework (plateau width analysis) for realistically assessing turbine adaptability and robustness to off-design conditions. These findings provide practical insights for selecting turbine types in variable or urban wind environments and contribute to the design of robust small wind energy systems for deployments in cities. Full article

In the context of mineral resource exploration, spatial prediction must cope with heterogeneous, non-normal data distributions and limited sampling. While conventional geostatistics and standard machine learning provide baseline estimates, they often suffer from excessive smoothing or fail to capture continuous spatial dependencies. This study proposes a geospatially aware Wasserstein conditional Generative Adversarial Network (GSA-cGAN) to complement existing workflows for multivariate mineral interpolation. The framework augments a baseline cGAN with WGAN-GP for stable adversarial training, CoordConv to encode absolute spatial coordinates and Self-Attention to capture long-range spatial dependencies. Eight model configurations were trained on 272 samples from a mineralized zone in the Taebaek Mountains, Korea, and strictly benchmarked against Ordinary/Universal Kriging and multivariate machine learning baselines (Random Forest, XGBoost). Under the adopted experimental design, the full GSA-cGAN achieved the lowest test root mean squared error and highest coefficient of determination, demonstrating a significant performance improvement over the baselines. Furthermore, distribution analysis confirmed that the model effectively overcomes the smoothing limitations of regression-based methods, generating high-resolution 10 m × 10 m maps that preserve statistical variance, hotspot anomalies, and complex spatial patterns. The results indicate that deep generative models can serve as practical decision-support tools for identifying drilling targets and prioritizing follow-up exploration in geologically complex settings. Full article

This study investigates the configurational pathways enabling women in Oman to translate entrepreneurial intentions into technology venture creation. By integrating institutional theory and resource-based view, we develop a novel framework examining how formal institutional support (FIS), informal institutional support (IIS), and digital self-efficacy (DSE) interact in Oman’s conservative context. We emphasize the significant enabling role of work–life balance resources (WLBR) and the cultural legitimacy of spousal endorsement. Our mixed-methods design utilizes survey data from 418 female IT graduates and 20 semi-structured interviews, analyzed through fuzzy-set Qualitative Comparative Analysis (fsQCA). The findings indicate that FIS predicts entrepreneurial intention (β = 0.34, p < 0.001) but not venture creation (OR = 0.85, p = 0.298), revealing a visibility gap in policy implementation. IIS predicts venture creation (OR = 1.43, p = 0.033), with spousal endorsement acting as a cultural legitimacy signal. DSE alone fails to predict venture creation but is vital when combined with WLBR. FsQCA identifies a sufficient configuration pathway characterized by the combination of spousal endorsement, domestic support, DSE, and WLBR with solution consistency of 0.93 and coverage of 0.78. WLBR is a necessary condition with necessity consistency of 0.96, demonstrating that venture creation is improbable without it. Qualitative evidence shows founders reposition conservative norms as legitimacy signals, while non-founders emphasize funding barriers despite policy awareness. We recommend that policymakers subsidize care infrastructure, leverage women-led community networks for targeted outreach, and formalize state-backed legitimacy programs that reduce kinship dependency while building autonomy-focused alternatives. Full article

Real-time emotion monitoring is increasingly relevant in healthcare, automotive, and workplace applications, where adaptive systems can enhance user experience and well-being. This study investigates the feasibility of classifying emotions along the valence–arousal dimensions of the Circumplex Model of Affect using EEG signals acquired from a single mastoid channel positioned near the ear. Twenty-four participants viewed emotion-eliciting videos and self-reported their affective states using the Self-Assessment Manikin. EEG data were recorded with an OpenBCI Cyton board and both spectral and temporal features (including power in multiple frequency bands and entropy-based complexity measures) were extracted from the single ear-channel. A dual analytical framework was adopted: classical statistical analyses (ANOVA, Mann–Whitney U) and artificial neural networks combined with explainable AI methods (Gradient × Input, Integrated Gradients) were used to identify features associated with valence and arousal. Results confirmed the physiological validity of single-channel ear-EEG, and showed that absolute $\beta$- and $\gamma$-band power, spectral ratios, and entropy-based metrics consistently contributed to emotion classification. Overall, the findings demonstrate that reliable and interpretable affective information can be extracted from minimal EEG configurations, supporting their potential for wearable, real-world emotion monitoring. Nonetheless, practical considerations—such as long-term comfort, stability, and wearability of ear-EEG devices—remain important challenges and motivate future research on sustained use in naturalistic environments. Full article

Background: Chinese adolescents face a dual burden of malnutrition, yet the weight-status-specific mechanisms underlying physical activity (PA) participation remain underexplored. Methods: We conducted a cross-sectional study among 1573 adolescents (aged 9–15 years) in Shangrao City, China. Validated scales measured social-ecological factors (family/peer support, physical environment), psychological factors (stage of change, self-efficacy, decisional balance), and PA participation. Data preprocessing utilized full information maximum likelihood to handle missing values. Confirmatory factor analysis was performed to validate the measurement model, followed by multi-group structural equation modeling to analyze pathway configurations across underweight (n = 187), normal-weight (n = 1070), and overweight/obese (n = 316) groups. Mediation effects were tested using bootstrapping with 5000 resamples. Results: Clear weight-specific patterns emerged. Normal-weight adolescents presented a fully functional comprehensive model where PA was predicted by the stage of change (β = 0.211, p < 0.001), friend support (β = 0.120, p < 0.001), self-efficacy (β = 0.092, p < 0.05), and perceived benefits (β = 0.095, p < 0.01). Underweight adolescents primarily relied on internal readiness driven by stage of change (β = 0.270, p < 0.001) and self-efficacy (β = 0.164, p < 0.05), with family support only indirectly influencing participation via psychological mediators. In contrast, overweight/obese adolescents showed a “socially dependent” pattern: friend support directly predicted PA levels (β = 0.136, p < 0.05), significantly enhanced self-efficacy (β = 0.370, p < 0.01), and effectively lowered perceived barriers (β = −0.165, p < 0.05). Additionally, the physical environment strongly impacted perceived benefits (β = 0.471, p < 0.01) but did not translate into action. Conclusions: These findings underscore the significant differences in PA determinants across the spectrum of malnutrition, necessitating targeted public health interventions to support the Healthy China 2030 initiative. Full article

The structural dependence of self-propelled motion in micro/nanomotors is essential for effectively predicting and controlling their dynamic behaviors. In this study, platinum–silica (Pt-SiO₂) micromotors, with structures ranging from spherical Janus to dimer configurations, are fabricated through conventional template-assisted deposition, followed by annealing. These structures are used to investigate how geometry influences motion. Our results demonstrate that the architecture of the Pt-SiO₂ micromotor strongly affects its propulsion mode and trajectory in solution. When immersed in a hydrogen peroxide (H₂O₂) solution, spherical Janus Pt-SiO₂ micromotors exhibit quasi-linear motion, driven by the Pt side (Pt pushing). In contrast, dimeric structures and intermediate forms varied from Janus to dimer display quasi-circular trajectories with continuously changing directions, characteristic of Pt-dragging motion. We reveal that these distinct propulsion behaviors stem from differences in the spatial distribution of Pt on the SiO₂ sphere surface. Variations in Pt distribution alter the exposed silica surface area—rich in hydroxyl groups—which modulates the driving force and causes the resultant force acting on the micromotor to deviate from its mass center axis (or the axis connecting the mass centers of the Pt component and silica sphere), thereby inducing circular motion. This study offers a versatile strategy for fabricating Pt-SiO₂ micromotors with tailored structures and advances the fundamental understanding of structure-dependent self-propulsion mechanisms. Full article

Reading proficiency in early childhood is crucial for academic success and intellectual development. However, more and more children are struggling with reading. According to the last PISA study in Austria, one out of five children is dealing with reading difficulties. The reasons for this are diverse, but an application that tracks children while reading aloud and guides them when they experience difficulties could offer meaningful help. Therefore, this proposal explores a prototyping approach for a core component that tracks children’s reading using a self-supervised Wav2Vec2 model with a limited amount of data. Self-supervised learning allows models to learn general representations from large amounts of unlabeled audio, which can then be fine-tuned on smaller, task-specific datasets, making it especially useful when labeled data is limited. Our model is operating on the phonetic level with the help of the International Phonetic Alphabet (IPA). To implement this, the KidsTALC dataset from the Leibniz University Hannover was used, which contains spontaneous speech recordings of German-speaking children. To enhance the training data and improve robustness, several data augmentation techniques were applied and evaluated, including pitch shifting, formant shifting, and speed variation. The models were trained using different data configurations to compare the effects of data variety and quality on recognition performance. The best model trained in this work achieved a phoneme error rate (PER) of 14.3% and a word error rate (WER) of 31.6% on unseen child speech data, demonstrating the potential of self-supervised models for such use cases. Full article

A systematic spatial resilience analysis of population flow networks in underdeveloped mountain towns is essential for sustainable urban–rural integration. Using mobile signaling data from March 2023, this study constructs a population flow network across 69 towns in the Yimeng Mountainous Region. This study proposes a novel targeted-attack framework based on centrality and assesses structural resilience along the three dimensions of efficiency, transitivity, and connectedness. Population flows exhibit a twin-core north–south structure, characterized by a hub-and-spoke system in the south and a self-stabilizing triangular configuration in the north. The network demonstrates strong spatial agglomeration and heterogeneity, with modular clustering revealing four functional modules shaped by administrative boundaries. It exhibits small-world properties, attributed to high transmission efficiency and strong local clustering. The network shows robust resilience to disruptions. Targeted attacks based on betweenness centrality significantly compromise structural resilience; efficiency, transmission, and connectivity change linearly at low attack intensities but decline sharply at higher levels. Full article

The increasing complexity and digitization of scientific research require Electronic Laboratory Notebooks (ELNs) that are adaptable, sustainable, and compliant across heterogeneous laboratory environments. In response to the limitations of proprietary, inflexible, and cost-intensive ELN solutions, this study systematically derives comprehensive requirements and proposes a modular solution concept for self-configurable ELNs that is explicitly platform-agnostic and broadly accessible. The methodological approach combines a structured requirements analysis with a modular architectural design, followed by theoretical validation through stepwise implementation walkthroughs on Microsoft SharePoint and Google Workspace. These walkthroughs demonstrate the feasibility of deploying self-configurable ELN modules using widely available low-code/no-code tools and native platform extensibility mechanisms. Based on a rigorous literature-driven analysis, key requirements, including modularity, usability, regulatory compliance, interoperability, scalability, auditability, and cost efficiency, are explicitly mapped to concrete architectural features within the proposed framework. The results show that essential ELN functionalities can, in principle, be realized across diverse digital platforms, enabling researchers and local administrators to independently assemble, configure, and adapt ELNs to their specific operational and regulatory contexts. Beyond technical feasibility, the proposed approach fundamentally democratizes ELN deployment and substantially mitigates vendor lock-in by leveraging existing digital infrastructures. Identified limitations, particularly with respect to advanced workflow orchestration and real-time data integration, delineate clear directions for future development. Overall, this work provides a systematic theoretical validation of a modular, self-configurable ELN concept, establishing it as a robust, scalable, and future-ready foundation for digital laboratory infrastructures. Full article

The term ‘conspiracy theory’ is used increasingly frequently, and its meaning is taken as common sense. However, I identify its recent popularisation with the ideology of neoliberalism. Tracing its origins to the theory of Karl Popper, which also influenced neoliberal thought, I show how the concept of the ‘conspiracy theory’ emerged to fulfil a crucial function within neoliberal ideology. Neoliberalism configures social relations as a transparent market of rational, self-interested individuals, free from collective, conspiratorial forms of power like class or state intervention, and at the same time materially depends on conspiratorial forms of power. The concept of the ‘conspiracy theory’ enables the disavowal of conspiratorial forms of power, masking this contradiction and perpetuating neoliberal ideology. This conspiracy-phobic attitude is exemplified by the dismissal of conspiratorial narratives in contemporary academic and popular discourse, alongside other discourses, like critical theory, which similarly challenge neoliberal systems of thought. With developments in neoliberalism (and post-neoliberalism) which suggest the return of explicitly political rather than economic configurations of power, a future in which the label ‘conspiracy theory’ is impotent and conspiratorial forms of power are directly challenged seems possible. Full article

With the increasing penetration of distributed energy resources (DERs) and the rapid development of active distribution networks, the traditional centrally controlled operation mode can no longer meet the flexibility and autonomy requirements under the multi-dimensional coupling of sources, networks, loads, and storage. To achieve regional energy self-balancing and autonomous operation, this paper proposes a partitioned configuration method for energy storage systems (ESSs) in energy-autonomous distribution networks based on autonomous unit division. First, the concept and hierarchical structure of the energy-autonomous distribution network and its autonomous units are clarified, identifying autonomous units as the fundamental carriers of the network’s autonomy. Then, following the principle of “tight coupling within units and loose coupling between units,” a comprehensive indicator system for autonomous unit division is constructed from three aspects: electrical modularity, active power balance, and reactive power balance. An improved genetic algorithm is applied to optimize the division results. Furthermore, based on the obtained division, an ESS partitioned configuration model is developed with the objective of minimizing the total cost, considering the investment and operation costs of ESSs, power purchase cost from the main grid, PV curtailment losses, and network loss cost. The model is solved using the CPLEX solver. Finally, a case study on a typical multi-substation, multi-feeder distribution network verifies the effectiveness of the proposed approach. The results demonstrate that the proposed model effectively improves voltage quality while reducing the total cost by 20.89%, ensuring optimal economic performance of storage configuration and enhancing the autonomy of EADNs. Full article

Transonic shock buffet, characterized by large-amplitude self-sustained shock oscillations arising from shock wave/boundary layer interactions, poses significant challenges to aircraft handling quality and structural integrity. Conventional control strategies for buffet suppression typically require prior knowledge of unstable steady-state solutions or time-averaged flow fields and are only applicable to fixed-flow conditions, rendering them inadequate for realistic flight scenarios involving time-varying parameters. This study proposes a data-driven adaptive control framework for transonic buffet suppression utilizing localized morphing skin as the actuation mechanism. The control system employs a Multi-Layer Perceptron neural network that dynamically adjusts the local skin height based on lift coefficient feedback, with the target lift coefficient determined through a moving average method. Numerical simulations on the NACA0012 airfoil demonstrate that the optimal actuator configuration—a skin length of 0.2c with maximum deformation positioned at 0.65c—achieves effective buffet suppression with minimal settling time. Beyond this baseline case, the proposed method exhibits robust performance across different flow conditions. Furthermore, the controller successfully suppresses buffet under time-varying flow conditions, including simultaneous variations in Mach number and angle of attack. These results demonstrate the potential of the proposed framework for practical aerospace applications. Full article

Background: Quantum Machine Learning (QML) has attracted significant attention in recent years. With quantum computing achievements in computationally costly domains, discovering its potential in improving the performance and efficiency of deep learning models in medical imaging has become a promising field of research. Methods: We investigate QML in healthcare by developing a novel quantum-enhanced U-Net (QU-Net). We experiment with six configurations of parameterized quantum circuits, varying the encoding technique (amplitude vs. angle), depth and entanglement. Using the ISIC-2017 skin cancer dataset, we compare QU-Net with classical U-Net on self-supervised image reconstruction and binary classification of benign and malignant skin cancer, where we combine bottleneck embeddings with patient metadata. Results: Our findings show that amplitude encoding stabilizes training, whereas angle encoding introduces fluctuations. The best performance is obtained with amplitude encoding and one layer. For reconstruction, QU-Net with entanglement converges faster (25 epochs vs. 44) with a lower Mean Squared Error per image (0.00015 vs. 0.00017) on unseen data. For classification, QU-Net with no entanglement embeddings reaches 79.03% F1-score compared with 74.14% for U-Net, despite compressing images to a smaller latent space (7 vs. 128). Conclusions: These results demonstrate that the quantum layer enhances U-Net’s expressive power with efficient data embedding. Full article

Understanding the physical mechanisms of extreme weather and climate events often relies on identifying typical large-scale circulation patterns associated with such extremes. Self-organizing maps (SOMs) have therefore been widely applied in atmospheric and climate studies as an objective clustering tool for circulation pattern classification. However, because SOM necessarily assigns all events to a limited number of representative nodes, individual extreme events with atypical large-scale circulation patterns may be grouped into clusters that do not adequately represent their underlying dynamics. In this study, we examine this interpretational issue using 223 severe January cold events over South Korea during 1949–2021. We show that a substantial fraction of cold events exhibits weak or even conflicting similarity with their assigned SOM node patterns, indicating pronounced within-node heterogeneity. Although optimizing the SOM node configuration and cluster number reduces the proportion of atypical cases, such heterogeneity cannot be fully eliminated. We further apply a pattern-correlation–based post-processing approach (SOM-PC) to explicitly identify and exclude atypical cases, which reduces the number of atypical cold events by approximately 27%. Rather than pointing to limitations of SOM itself, our results underscore potential interpretational pitfalls that can arise when SOM-derived circulation patterns are directly linked to extreme events without evaluating the representativeness of individual cluster members. These findings highlight the importance of applying explicit diagnostics for within-cluster heterogeneity when using SOM or similar data-driven tools to elucidate large-scale circulation patterns underlying localized extreme weather and climate events. Full article