Search Results (709)

Traditional concert-hall evaluations primarily rely on ISO 3382-1 scalar parameters (e.g., C₅₀ and C₈₀), which summarize temporal energy behavior but provide limited insight into the directional composition of early reflections, particularly in geometrically shadowed seating zones. This paper presents a first-order Ambisonics (FOA)-based 3D acoustic sensing framework to diagnose under-balcony directional imbalance, with emphasis on early vertical-reflection deficiency. Scene-based FOA impulse responses (WXYZ) were measured at 11 audience positions (P1–P11) in the National Concert Hall (Taipei) and analyzed using intensity-based direction-of-arrival (DoA) proxies, axis-resolved directional energy build-up, and a distributional descriptor based on directional spatial entropy. Results are presented at three representative frequencies (125 Hz, 1 kHz, and 4 kHz) and analyzed within full (0–200 ms), early (0–80 ms), and late (80–200 ms) windows. While the magnitude proxy $\mid p^{meas}\left(f\right)\mid$ exhibits strong seat-to-seat variability and does not support a uniform attenuation assumption under the balcony, direction-resolved metrics reveal a consistent under-balcony signature. Specifically, the early–late vertical energy discrepancy $\Delta R_z=R_z^{early}-R_z^{late}$ is persistently negative at under-balcony positions (P7–P11) across all three frequencies, indicating a selective reduction in early vertical contribution relative to the late field. Directional entropy analysis further shows predominantly negative $\Delta H_n=H_n^{early}-H_n^{late}$, with more negative values in the under-balcony group, consistent with stronger early directional constraint in shadowed seats. Spatial trend maps are provided via Gaussian RBF interpolation within the audience domain for visualization only. The proposed FOA-based diagnostic framework provides a practical and physically interpretable approach to identify direction-specific early-reflection deficits that remain masked in conventional scalar evaluations, supporting mechanism-oriented assessment and targeted intervention in geometrically constrained listening areas. Full article

Examining the mechanism of two-way interaction between the air turbine and generator is essential for accurately predicting the performance of oscillating water column (OWC) devices. This study developed a fully integrated model for a back-bent duct buoy device, which incorporated the chamber, impulse turbine, permanent magnet synchronous generator, PI controller, and speed control strategies. The models of chamber–turbine and turbine-control systems were validated separately against wave-flume experimental results under regular and irregular wave conditions. In addition, a comparative study of two control strategies based on Best Efficiency Point Tracking was conducted by analysing key performance parameters at each energy conversion. The mechanism of two-way interaction between the turbine and the generator was elucidated. The integrated model demonstrated a great potential in predicting the conversion performance of wave energy to electrical energy under real sea conditions, as well as testing control strategies and algorithms before physical deployment. Full article

Background: Spirometry-based methacholine challenge testing using the provocative dose causing a 20% decline in forced expiratory volume in 1 s (FEV₁, PD₂₀) is a reference method for assessing airway hyperresponsiveness. Impulse oscillometry (IOS), performed during tidal breathing, may capture airway mechanical changes not fully reflected by spirometry. We compared FEV₁- and IOS-based methacholine responsiveness in a large, real-world adult cohort and examined associations with clinical markers and symptoms. Methods: We analyzed 794 consecutively referred adults undergoing standardized methacholine challenge testing with concurrent spirometry and IOS. IOS positivity was defined as a ≥40% increase in resistance at 5 Hz (ΔR₅ ≥ 40%). Agreement between FEV₁–PD₂₀ positivity (PD₂₀ ≤ 1440 µg) and IOS positivity was evaluated using cross-classification and Cohen’s κ. Associations between continuous responses were assessed using Pearson and Spearman correlations. The relationship between ΔR₅ and the probability of a ≥20% decline in FEV₁ was examined using logistic regression. Predictors of ΔR₅ were assessed using multivariable linear regression. Symptom severity was recorded immediately post-challenge using a five-point Likert scale and related to physiological responses. Results: FEV₁–PD₂₀ classified 37.5% of participants as hyperresponsive, whereas IOS positivity (ΔR₅ ≥ 40%) classified 70.6%. Agreement between methods was limited (κ = 0.09; p < 0.01). ΔFEV₁ and ΔR₅ were weakly correlated (r = −0.287; ρ = −0.306; both p < 0.001; R² = 0.08). A 20% decline in FEV₁ corresponded on average to a 74% increase in R₅, whereas ΔR₅ ≥ 40% corresponded to an average FEV₁ decline of 7.6%. In multivariable models, referral diagnosis group and age independently predicted ΔR₅, whereas FeNO and baseline FEV₁% predicted did not. Baseline FEV₁% predicted modified the ΔFEV₁–ΔR₅ slope (interaction β = −0.0317; p = 0.0028). Post-challenge symptom (5-point Likert) related to MCT was associated with both ΔFEV₁ and IOS responses; ΔFEV₁ showed a stronger linear association with symptoms, whereas IOS measures showed larger stepwise differences across symptom categories. Conclusions: IOS identifies a larger, partly distinct subset of methacholine-responsive individuals compared with conventional FEV₁–PD₂₀ criteria and detects mechanical changes at lower levels of spirometric impairment. Despite limited concordance, IOS provides complementary physiological and symptom-relevant information when used alongside spirometry. Standardized IOS response definitions and prospective validation are needed to establish clinical utility. Full article

To address the issues of sample imbalance, unstable generation quality, and insufficient feature extraction in spectrum anomaly signal detection under complex electromagnetic environments, this paper proposes a VAE-GANGP identification model that integrates a Variational Autoencoder (VAE) with a Gradient Penalty-based Generative Adversarial Network (GAN-GP). First, the VAE is employed to encode the original spectrum, generating structured latent features that follow a standard normal distribution. This replaces the random noise input in traditional GANs, significantly enhancing the semantic consistency of generated samples and training stability. Second, an adversarial training mechanism based on Wasserstein distance with gradient penalty (WGAN-GP) is introduced, effectively mitigating mode collapse and gradient vanishing, thereby improving the model’s capability to fit complex signal distributions. Furthermore, a multi-objective optimization function combining reconstruction error and adversarial loss is constructed, establishing an end-to-end integrated framework for feature learning, signal reconstruction, and anomaly discrimination. Experiments are conducted using a synthetic dataset comprising various modulation types and simulated environments with different signal-to-noise ratios for systematic validation. The results demonstrate that the spectrum data generated by VAE-GANGP closely matches the distribution of real signals. Under AWGN-dominated synthetic test conditions, the model achieves an anomaly detection accuracy of 98.1%. When evaluated under more realistic channel impairments (phase noise, multipath, impulsive interference), the model maintains competitive performance, outperforming existing methods and demonstrating promising potential for practical electromagnetic spectrum monitoring. Its performance significantly surpasses traditional detection methods and single deep learning models, providing a highly reliable and adaptive solution for spatial electromagnetic spectrum anomaly detection. Full article

Background: Prenatal exposure to air pollutants may increase the risk of adverse respiratory outcomes, particularly in offspring of asthmatic mothers. Evidence on wildfire-related PM_2.5 exposure during pregnancy remains limited. This study investigated associations between early gestational wildfire-related PM_2.5 exposure, infant lung function, and respiratory outcomes at 6 years. Methods: Gestational wildfire-related PM_2.5 exposure patterns were characterised using group-based trajectory modelling and linked to infant lung function outcomes. Infant respiratory measurements were obtained at six weeks of age during behaviourally defined quiet sleep using tidal-breathing flow–volume loops (TBFVL). Airway mechanics at six years were assessed by impulse oscillometry (IOS) following international guideline standards. Trajectory modelling of PM_2.5 during gestation was conducted in SAS (PROC TRAJ); all additional statistical analyses were performed in Stata IC 16.1. Results: Increased mean tidal inspiratory flow (MTIF, beta coefficient [β]: 10.51 mL/s, 95% CI: 3.66 to 17.36, p = 0.003) and peak tidal inspiratory flow (PTIF, β: 12.49 mL/s, 95% CI: 2.48 to 22.51, p = 0.014) were observed in infants born to mothers with higher wildfire-related PM_2.5 exposure during early gestation (n = 420; n = 411 not exposed, n = 9 exposed). β-coefficients from infant mixed models were then used as proxy indicators and applied in linear regression models and associated with higher reactance at 5 Hz frequency (n = 73) at 6 years of age (PTIF: β: 9.88 mL/s, 95% CI: 0.10 to 19.67, p = 0.048 and MTIF: β: 13.43 mL/s, 95% CI: 1.43 to 25.44, p = 0.029). PTIF was further associated with asthma diagnoses at 6 years (aOR: 1.36, 95% CI: 1.07 to 1.73, p = 0.012; n = 259; n = 116 asthma). Conclusion: Early gestational exposure to wildfire-related PM_2.5 may be linked with altered respiratory patterns in infancy and differences in airway reactance during childhood. Findings also suggest a relationship with asthma risk, although mechanisms remain uncertain. Full article

The operational safety of rotating machinery heavily relies on the condition of its rolling bearings. However, under strong background noise and variable operating conditions, weak fault-induced impact responses are easily overwhelmed. To address these challenges, this paper proposes a dual-branch cross-modal fault diagnosis framework (STRN-CM) that integrates a Swin Transformer with a one-dimensional wide-kernel deep residual network (1D ResNet). The model develops a complementary structure of heterogeneous features. The enhanced 1D ResNet branch responds directly to the passage of volatile impulse features, which can detect early errors through raw vibrations. The Swin Transformer branch captures long-term periodic texture windows by using time–frequency images, which have an important dependence on time. Also, a Cross-Modal Attention Fusion (CMAF) scheme is introduced. Using high signal-to-noise ratio (SNR) temporal impulse features as query probes, the mechanism dynamically calibrates the response weights of time–frequency features, thereby achieving adaptive denoising and enhancement at the feature level. Experimental results demonstrate that STRN-CM achieves a diagnostic accuracy of 93.04% in harsh −6 dB noise conditions on the Case Western Reserve University (CWRU) dataset. Furthermore, it achieves a 97.99% accuracy on the Paderborn University (PU) dataset, showcasing superior generalization in cross-load and real fatigue damage transfer tasks. It also demonstrates significantly better generalization performance than single-modal networks in cross-load and real fatigue damage transfer tasks. Full article

Background/Objectives: The perceived-time-based model posits that time perception is a critical factor in intertemporal decision-making; however, the mechanisms underlying this influence remain inadequately explored. Despite growing behavioral and neuroimaging findings, no study has directly compared the temporal neural dynamics of individuals who overestimate or underestimate time during intertemporal choices. Methods: This study screened participants with time overestimation or underestimation to examine differences in their electroencephalogram (EEG) activity during an intertemporal choice task. Results: Behavioral results revealed that the time overestimation group selected the smaller-sooner (SS) option at a higher rate than the time underestimation group, exhibiting a myopic decision-making tendency. EEG results revealed that, compared to the time overestimation group, the time underestimation group exhibited a more pronounced N2 amplitude, an enhanced P300 amplitude, and greater beta band oscillations. Within the time overestimation group, the larger-later (LL) option elicited a more negative N2 amplitude than the SS option. Conversely, in the time underestimation group, the LL option elicited a more positive P300 amplitude than the SS option. Conclusions: The results indicate that, during intertemporal decision-making, the time overestimation group experienced more conflict in the LL option, demonstrating lower cognitive control and fewer cognitive resources. This tendency may be driven by a hot system, resulting in more impulsive choices. Full article

Early detection of fire-related risks in lithium-ion batteries (LIBs) remains a critical challenge, as conventional protection mechanisms typically activate only after irreversible degradation or macroscopic failure occurs. In this study, an innovative sensor-based diagnostic framework is proposed for proactive fire prediction in LIBs by simultaneously monitoring low-frequency and high-frequency electrical signatures generated during battery charge–discharge processes. An electromagnetic (EM) antenna sensor and a high-frequency current transformer (HFCT) sensor were employed to capture complementary voltage- and current-based transient signals associated with internal degradation phenomena. Cell-level experiments were conducted under various C-rates and temperature conditions, including high-stress environments, while module-level validation was performed on a 4-series, 1-parallel (4S1P) configuration at a 2C-rate under ambient temperature. Time–frequency characteristics of the measured signals were systematically evaluated using MATLAB-based continuous wavelet transform (CWT) and discrete wavelet transform (DWT) techniques. The results reveal that degradation-induced transient events exhibit non-stationary, impulsive voltage and current signatures with distinct frequency-band localization, which intensify with increasing C-rate, elevated temperature, and aging progression. At the module level, although signal amplitudes were partially attenuated due to current redistribution, characteristic wavelet energy patterns and time–frequency concentrations remained clearly distinguishable, demonstrating the scalability of the proposed approach. The combined EM antenna–HFCT sensing strategy, together with multi-resolution wavelet analysis, enables effective phenomenological differentiation between normal operational noise and incipient internal fault signatures well before conventional thermal or capacity-based indicators become evident. These findings demonstrate feasibility of the proposed method for early-stage fault diagnosis and highlight its potential applicability to advanced battery management systems for proactive fire prevention in large-scale energy storage and electric vehicle applications. Unlike conventional voltage-, temperature-, or gas-based diagnostics, the proposed approach enables the detection of incipient degradation phenomena at the microsecond scale by exploiting complementary low- and high-frequency electrical signatures. This study provides experimental evidence that wavelet-based EM and HFCT sensing can identify MISC-related precursors significantly earlier than conventional battery management indicators. Full article

Given the increasing environmental degradation, this study investigates advanced zinc oxide (ZnO)-based materials for the mineralization of toxic compounds through the combined action of photo- and piezocatalysis. Two complementary strategies were employed to enhance catalytic efficiency. First, ZnO_1−xN_x thin films were deposited by reactive high-power impulse magnetron sputtering (R-HiPIMS) to reduce the band gap energy. Second, flower-like ZnO nanostructures were synthesized using the pulsed thermionic vacuum arc (p-TVA) technique to increase the specific surface area. Both systems were further modified by decoration with Ag₂O nanoparticles to improve charge separation. The R-HiPIMS technique offers significant advantages in terms of precise control over processing parameters, enabling accurate tuning of film properties, including microstructure, chemical composition, and electronic structure. However, films produced via R-HiPIMS generally exhibit lower photo-piezocatalytic activity compared to nanostructured counterparts, primarily due to their comparatively reduced effective surface area and limited charge separation efficiency. In contrast, the p-TVA technique enables the synthesis of nanostructured thin films with substantially enhanced photo-piezocatalytic performance. This improvement is attributed to the increased effective surface area and the promotion of more efficient electron–hole pair separation. The materials were comprehensively characterized in terms of optical properties (UV–Vis spectroscopy), chemical composition and bonding (XPS), crystalline structure (XRD), surface morphology (FE-SEM), and photo-piezocatalytic performance. Catalytic activity was evaluated via the degradation of methylene blue (MB) under visible light irradiation and mechanical vibrations. Nitrogen incorporation in ZnO_1−xN_x thin films led to an increase in photocatalytic efficiency from 20% to 28.7%, while the simultaneous application of light and mechanical stimulation increased efficiency to approximately 50%. Under identical irradiation conditions, Ag₂O-decorated ZnO and Ag₂O-decorated ZnO_1−xN_x exhibited photo-degradation reaction rate constants up to 65% higher than bare counterparts, attributed to reduced electron–hole recombination. ZnO nanostructures achieved degradation efficiencies of 59%, rising to 88.3% with Ag₂O decoration under solar illumination for 120 min. When combined with mechanical vibrations, after 60 min, the degradation efficiencies reached 93% for ZnO and 98% for Ag₂O/ZnO systems. A photodegradation mechanism of Ag₂O NPs-decorated ZnO heterostructures was proposed. Full article

Background: Interoception has been proposed as a key mechanism underlying impulsive behaviours, including maladaptive eating. However, the brain mechanisms supporting the interaction between interoception and impulsivity across different reward types remain unclear. This study investigated whether modulating the right insula and the dorsal anterior cingulate cortex (dACC) using high-definition transcranial direct current stimulation (HD-tDCS) could affect interoceptive accuracy and impulsive decision-making. Methods: Model-based HD-tDCS montages were defined to target the right insula and dACC. Two behavioural paradigms were administered: (i) the heartbeat detection task (HBD) to assess interoceptive accuracy and (ii) two versions of the delay discounting (DD) task with food and monetary rewards to measure impulsivity. Heart rate variability (HRV) was recorded as an index of autonomic activity. HD-tDCS was delivered online during the HBD, while DD tasks were completed offline. Twenty-four participants took part in four sessions in a within-subject design: baseline DD tasks, anodal HD-tDCS targeting the insula, dACC, or sham stimulation. Results: Stimulation of both the insula and dACC reduced participants’ ability to detect synchronous heartbeat while improving accuracy in exteroceptive trials. Discounting rates significantly increased following insula stimulation. Moreover, HD-tDCS effects on DD performance varied depending on reward type. Conclusions: These findings suggest differential contributions of the dACC and insula in interoceptive and exteroceptive processing and support the effect of HD-tDCS combined with interoceptive tasks to modulate impulsive decision-making. Reward-specific effects highlight the importance of stimulus type when designing interventions for impulsive eating behaviours. Full article

This study examined the role of work–family conflict as a linking mechanism between difficulties in emotion regulation (ER) and perceived parental self-efficacy (PPSE). Participants were 228 working parents (74.6% women; mean age = 45.24, SD = 7.16), who completed self-report measures of ER difficulties, work–family conflict, and PPSE. Lack of emotional awareness and non-acceptance of emotions were positively related to PPSE, but these associations were not significant when work–family conflict dimensions were considered. Difficulties in goal-directed behavior and limited access to ER strategies were associated with lower PSSE, with strain-based conflict emerging as a relevant pathway for difficulties in goal-directed behavior (95% CI [0.04, 2.67]). Difficulties controlling impulsive behaviors and lack of emotional clarity were linked to lower perceived PPSE through behavior-based conflict (95% CI [−2.04, −0.15]; [−2.01, −0.09], respectively). Overall, the findings suggest that specific ER difficulties may reduce parents’ confidence by increasing strain and behavioral interference between work and family roles. Promoting ER skills may help parents manage these demands more effectively and maintain a stronger sense of parental efficacy. Full article

Background: Early detection of dental caries is essential for effective oral health management. Current diagnostic workflows rely heavily on radiographic imaging, which involves infrastructure requirements, workflow coordination, and resource considerations that may limit frequent use in high-throughput or resource-constrained settings. These contextual factors motivate exploration of adjunct screening concepts that could support front-end triage decisions within existing care pathways. This study evaluates, in simulation, whether modeled tooth-percussion response signals contain sufficient discriminative information to justify further translational and managerial investigation. Implementation costs, workflow optimization, and economic outcomes are not evaluated directly; rather, the objective is to assess whether the technical preconditions for a potentially scalable screening concept are satisfied under controlled in silico conditions. Methods: An in silico model of tooth percussion was developed in which enamel, dentin, and pulp/root structures were represented as a simplified layered mechanical system. Impulse responses generated from simulated tapping were used to compute the modeled surface-vibration response (enamel-layer displacement), which served as a proxy for a measurable percussion-related signal (e.g., contact vibration), rather than a recorded acoustic waveform. Carious conditions were simulated through depth-dependent reductions in stiffness and effective mass and increases in damping to represent enamel and dentin demineralization. A synthetic dataset of labeled simulated signals was generated under varying structural parameters and measurement-noise assumptions. Machine-learning models using Mel-frequency cepstral coefficient (MFCC) features were trained to classify healthy teeth, enamel caries, and dentin caries at a screening (triage) level. Results: Under baseline simulation conditions, the classifier achieved an overall accuracy of 0.97 with balanced macro-averaged F1-score (0.97). Misclassifications occurred primarily between healthy and enamel-caries categories, whereas dentin-caries cases were most consistently identified. When measurement noise and structural variability were increased, performance declined gradually, reaching approximately 0.90 accuracy under the most challenging simulated scenario. These results indicate that discriminative information is present within the modeled signals at a screening (triage) level, meaning that higher-risk categories can be distinguished probabilistically rather than with definitive diagnostic certainty. Sensitivity and specificity trade-offs were not optimized in this study, as the objective was to assess separability rather than to define clinical decision thresholds. Conclusions: Within the constraints of the in silico model, simulated tooth-percussion response signals demonstrated discriminative patterns between healthy, enamel caries, and dentin caries categories at a screening (triage) level. These findings establish technical plausibility under controlled simulation conditions and support further investigation of percussion-based screening as a potential adjunct to clinical assessment. From a healthcare management perspective, the present results address a prerequisite question—whether such signals contain sufficient information to justify translational research, rather than demonstrating workflow optimization, cost reduction, or system-level impact. Clinical validation, threshold optimization, and implementation studies are required before managerial or operational benefits can be evaluated. Full article

Under the rapid development of live commerce, impulse buying has become a core consumption phenomenon, yet its psychological triggering pathways across different consumer groups remain to be fully elucidated. Drawing on the S–O–R framework, this study conceptualizes live-stream interactivity, novelty, and streamer attractiveness as external “stimuli,” and positions immersive experience as the core “organism” mechanism, thereby constructing and testing an integrated “stimulus–experience–response (impulse buying intention)” model. Using a mixed-method approach that combines structural equation modeling (SEM) and fuzzy-set qualitative comparative analysis (fsQCA), the results show that all three live-stream features significantly enhance impulse buying intention, primarily by strengthening immersive experience, with immersion exerting a significant partial mediating effect. Moreover, consumers’ loneliness significantly amplifies the indirect effect of live-stream features on impulse buying via immersive experience. The fsQCA further uncovers multiple equivalent pathways leading to high impulse buying intention, including a strong-experience pattern centered on “streamer attractiveness + immersive experience,” as well as a social compensation pattern centered on “high interactivity + high loneliness.” This study provides a testable theoretical framework, actionable operational strategies, and sustainable ethical guidance for live commerce, offering a pathway for the industry to achieve a “high experience × high conversion × high well-being” triple-win outcome. Full article

The operational reliability of aero-engines is critically dependent on the health of rolling element bearings, while incipient fault diagnosis remains particularly challenging under small-sample conditions. Although multiscale entropy methods are widely used for complexity analysis, conventional coarse-graining strategies suffer from severe information loss and unstable estimation when data are extremely limited. To address this, the primary objective of this study is to develop a robust diagnostic framework that ensures feature consistency and classification stability even with minimal training samples. Specifically, this paper proposes an integrated approach combining Refined Time-shifted Multiscale Rating Entropy (RTSMRaE) with an Animated Oat Optimization (AOO)-optimized Extreme Learning Machine (ELM). By introducing a refined time-shift operator and a dual-weight fusion mechanism, RTSMRaE effectively preserves transient impulsive features across multiple scales while suppressing stochastic fluctuations. Meanwhile, the AOO algorithm is employed to optimize the input weights and hidden biases of the ELM, alleviating performance instability caused by random initialization and improving generalization capability. Experimental validation on both laboratory-scale and real-world aviation bearing datasets demonstrates that the proposed RTSMRaE-AOO-ELM framework achieves a diagnostic accuracy of 99.47% with a standard deviation of ±0.48% using only five training samples per class. These results indicate that the proposed method offers superior diagnostic robustness and computational efficiency, providing a promising solution for intelligent condition monitoring in data-scarce industrial environments. Full article

Can theology be considered a creative science? How can we define creativity in the work of the theologian? This article offers a meta-theological inquiry on the roles of creativity and tradition in innovating theological knowledge. After distinguishing between problem- and solution-driven creativity, I show that both theology and science require a living tradition to test, correct, and stabilize proposals over time. I introduce second-order cybernetics as a heuristic vocabulary through which to view observer-inclusive inquiry in theology. I analyze the main sources of theological novelty: inspiration, prophetic impulse, and charisms, whose discernment and reception shape the incorporation of novelty into Tradition. I argue that, likewise, in second-order cybernetics a system can maintain its identity by adapting to new issues, contexts, and forms of experience through negative and positive feedback mechanisms. These mechanisms preserve coherence in the system and allow for the diffusion and institutionalization of genuine novelty. Full article