Search Results (33,888)

This study develops a kinetic model that captures poly-ether-ether-ketone (PEEK) crystallization over a temperature $T$ window from glass transition ($T_g$) to melting ($T_m$) temperature, and across cooling rates from 5 to ~10³ °C/min. The framework is a parallel dual-Nakamura formulation whose isokinetic parameters {$k_i\left(T\right),n_i,w_i\left(T\right)$} are obtained from a bi-level non-linear regression of isothermal crystallization tests conducted using a flash-differential scanning calorimeter (FSC). The weight $w_i\left(T\right)$ partitions the faster primary and slower secondary crystallization and is represented by a physics-based analytical function that captures its dome-shaped temperature dependence. A maximum isothermally achievable enthalpy function is introduced so that the model predicts enthalpy $\Delta H\left(t\right)$ natively under arbitrary thermal profiles. To extend this isothermal backbone to non-isothermal conditions, two explicit cooling-rate-dependent scalars are introduced, $\omega \left(\dot{T}\right)$ and $\chi \left(\dot{T}\right)$, which shift $w_i\left(T\right)$ and limit attainable crystallinity at high cooling rates respectively. Finally, a rate-dependent induction time relation is added to adjust the onset of crystallization. Calibrating these rate functions against non-isothermal experiments, while keeping the isokinetic parameters fixed, yields a single isothermal–non-isothermal model that predicts $\Delta H\left(t\right)$ under arbitrary $T\left(t\right)$ profiles. Model performance is validated using an interrupted FSC experiment with a multi-segment cooling program that mimics a local transient thermal history of PEEK during additive manufacturing. The sample is cooled through successive constant-rate segments with intermittent quench–remelt cycles to probe the accumulated crystallinity along the path. Without additional fitting, the model predicts the measured enthalpy evolution with R² ≈ 0.95. The framework thus provides a practical route for predicting polymer crystallinity under processing-relevant thermal histories. Full article

Sewerage systems are a main element of a city’s infrastructure. Roughness coefficients are fundamental parameters for sewage system operation. The intermittent nature of the flow leads to the appearance of deposits that become an integral part of the sewerage systems. Deposited material not only leads to the loss of hydraulic capacity and decreases the concentration of dissolved oxygen (which is found in direct relation to all quality parameters), but it also results in more transported particles being intercepted. In the design calculations, the roughness coefficient is estimated rather than calculated. It has been demonstrated that the estimation of stress within and above roughness elements improves the predictive capability for the concentration of suspended sediment. In this study, we focused on a local evaluation of the roughness coefficient when the flow velocity is below the minimum self-cleansing velocity. Some authors consider the selection of the most reliable method for estimating bed shear stress to be the main challenge. Other authors have suggested that all possible methods should be applied simultaneously to achieve a reliable bed shear stress estimation, knowing that the roughness coefficient can be determined through the shear boundary stress. We calculate the local roughness coefficient in Manning’s equation using a laboratory model, considering clear water flowing over a solid boundary with consolidated deposits, represented by artificial roughness elements (calibrated hemispheres). The European standard EN 752:2017 specifies a minimum average cross-sectional velocity of 0.7 m/s for pipe self-cleansing. This study established the range of possible roughness coefficient values when the minimum velocity design criterion is not met. The second criterion was to consider acceptable a sediment deposit occupying between 1% and 2% of the collector diameter. Velocity distributions around artificial roughness and statistical parameters of the turbulent flow were obtained using a PIV system. Five methods were implemented and the range of roughness coefficient values varied between 0.007 and 0.023. This variation is closely related to sewer performance. We selected the dissipation method as the primary reference for this study, as it is most closely aligned with the underlying physics of flow over roughness elements. This approach allows for robust validation by correlating multiple characteristic mechanisms of the turbulent cascade. Full article

In classical reliability engineering, failure is a probabilistic structural failure based on lifetime distributions of Weibull models. However, in the control-critical mechanical systems, it is possible that functional failure of the system happens before material failure occurs as a result of control power loss. This paper proposes a Controllability–Reliability Coupling (CRC) model, which redefines the concept of reliability as the stabilizability in the face of progressive degradation. The actuators’ deterioration is modeled using the time-varying input effectiveness factor $\alpha \left(t\right)$, and the actuator is said to be in failure when the minimum singular value of the finite-horizon controllability Gramian becomes less than a stabilizability threshold $\epsilon$. The performance of the simulation indicates that the functional failure is a precursor of structural failure in several degradation conditions. A baseline comparison shows that the CRC metric forecasts loss of controllability at $T_{CRC}=17.0$ s, but the classical Weibull reliability never attains the structural failure threshold even in the time horizon of 20 s. The system retains margins of Lyapunov stability and H infinity robustness are not lost, and it is still stable and attenuates disturbances even when control authority is lost. In practical degradation scenarios, the forecasted CRC failure times are 21.5 s (linear wear), 13.1 s (accelerated fatigue), 23.7 s (intermittent faults), and 24.4 s (shock damage), whereas maintenance recovery abated functional failure completely. In a case study of an industrial robotic joint, at 27.0 s, functional collapse occurred, and at the same time, structural reliability was still above the failure threshold. The findings support the hypothesis that structural survival and functional controllability are distinct concepts. The proposed CRC framework is an approach to control-conscious reliability measure, which can detect early failures and offer proactive maintenance advice in the context of a cyber–physical system. Full article

Low-count positron emission tomography (PET) is inherently affected by Poisson-dominated noise, which degrades image contrast, structural delineation, and quantitative reliability. This study systematically evaluated residual learning-based deep neural networks to investigate the influence of residual block depth on PET image restoration performance under low-count conditions. We employed a physically controlled striatum phantom, fabricated using 3D printing technology, to ensure reproducible acquisition conditions and controlled physical variability. PET images were acquired using a clinical PET/computed tomography (CT) system with list-mode acquisition. Low-count images reconstructed from short-duration acquisition were paired with high-count reference images reconstructed from extended acquisitions. We compared conventional filtering techniques, including median, Wiener, and modified median Wiener filters, with residual network (ResNet)-based models incorporating 8, 16, and 32 residual blocks. Image quality was quantitatively assessed using contrast-to-noise ratio (CNR), coefficient of variation (COV), line profile analysis, universal quality index (UQI), and perceptual image patch similarity (LPIPS). The results demonstrated that ResNet-based restorations substantially outperformed conventional filtering techniques in contrast recovery, signal stability, and structural preservation. The ResNet-16 model achieved the most balanced performance, yielding the highest CNR (9.02) and lowest COV (0.105), while also demonstrating superior structural and perceptual similarity, as indicated by UQI (0.9224) and LPIPS (0.0174), relative to the high-count reference images. Deeper network configurations exhibited diminishing returns and reduced structural consistencies. These findings indicate that an intermediate residual block depth is optimal for low-count PET image restoration and highlight the importance of architectural optimization in deep learning-based PET image enhancement with phantom-based evaluation frameworks. Full article

The complex flow behavior of the metal around the stirring tool during welding directly determines the microstructural evolution, defect formation, and mechanical properties of the welded joint, and thus becomes the core physical process affecting welding quality and process stability. In this study, to characterize the three-dimensional material flow behavior of AZ31 magnesium (Mg) alloy during friction stir welding (FSW), conventional metallographic sectioning was adopted as the primary observation method, and copper foil was used as the marker material. The flow trajectories of the materials after welding were investigated via three configurations of the marker material. The results indicate that three typical characteristic zones exist along the vertical direction, which are the shoulder-affected zone (SAZ), the pin-affected zone (PAZ), and the swirl zone from top to bottom. Specifically, the material in the SAZ is dominated by laminar flow; the PAZ exhibits complex mixed-flow characteristics; while the swirl zone shows an obvious rotational flow pattern. Based on the principles of material mechanics and fluid mechanics, a force-flow coupled “simple flow model around a rotating cylinder” was proposed, which defines three flow modes corresponding to the different characteristic zones within the weld. Full article

The increasing proportion of renewable energy leads to frequent changes in turbine load, making the regulating stage more prone to degradation. Traditional anomaly detection methods lack sufficient sensitivity and generalization. To address this issue, this study proposes a method combining multi-attention gated fusion and physical information learning. A gated fusion mechanism is proposed to adaptively extract and fuse key temporal and feature information. Furthermore, the generalization ability of the model is improved by introducing physical constraints derived from the relationship between pressure, temperature, and valve position. Finally, a dynamic temperature prediction model is established using the multi-output long short-term memory neural network. Experiments using actual power plant data demonstrate that the proposed method effectively improves the accuracy of post-regulating-stage temperature prediction and the sensitivity of anomaly detection. The proposed gating fusion method improves prediction accuracy by 4.6% compared to direct addition, while the fusion of physical information reduces the generalization error by more than 6%. In addition, compared to traditional deep learning and machine learning models, the proposed method improves anomaly detection accuracy by at least 3.9%. This research is of great significance for the safe operation of thermal power units and the power grid. Full article

Against the global carbon neutrality target, predictive maintenance (PdM) of automotive engines represents a core technical strategy to advance the sustainable development of the automotive industry. Conventional single-modal diagnostic approaches for engine abnormal sound defects suffer from low accuracy and weak anti-interference capability. Existing multi-modal fusion methods fail to deeply mine the physical coupling between cross-modal features and often entail excessive model complexity, hindering deployment on resource-constrained on-board edge devices. To resolve these limitations, this study proposes a Physical Prior-Embedded Cross-Modal Attention (PPE-CMA) mechanism for lightweight multi-modal fusion diagnosis of engine abnormal sound defects. First, wavelet packet decomposition (WPD) and mel-frequency cepstral coefficients (MFCC) are integrated to extract time-frequency features from engine audio signals, while a channel-pruned ResNet18 is employed to extract spatial features from engine thermal imaging and vibration visualization images. Second, the PPE-CMA module is designed to adaptively assign attention weights to audio and image features by exploiting the physical coupling between engine fault acoustic and visual characteristics, enabling efficient cross-modal feature fusion with redundant information suppression. A rigorous theoretical derivation is provided to link cosine similarity with the physical correlation of engine fault acoustic-visual features, justifying the attention weight constraint (β = 1 − α) from the perspective of fault feature physical coupling. Third, an improved lightweight XGBoost classifier is constructed for fault classification, and a hybrid data augmentation strategy customized for engine multi-modal data is proposed to address the small-sample challenge in industrial applications. Ablation experiments on ResNet18 pruning ratios verify the optimal trade-off between diagnostic performance and computational efficiency, while feature distribution analysis validates the authenticity and effectiveness of the hybrid augmentation strategy. Experimental results on a self-constructed multi-modal dataset show that the proposed method achieves 98.7% diagnostic accuracy and a 98.2% F1-score, retaining 96.5% accuracy under 90 dB high-level environmental noise, with an end-to-end inference speed of 0.8 ms per sample (including preprocessing, feature extraction, and classification). Cross-engine and cross-domain validation on a 2.0T diesel engine small-sample dataset and the open-source SEMFault-2024 dataset yield average accuracies of 94.8% and 95.2%, respectively, demonstrating strong generalization. This method effectively enhances the accuracy and robustness of engine abnormal sound defect diagnosis, offering a lightweight technical solution for on-board real-time fault diagnosis and in-plant online quality inspection. By reducing engine fault-induced energy loss and spare parts waste, it further promotes energy conservation and emission reduction in the automotive industry. Quantified experimental data on fuel efficiency improvement and carbon emission reduction are provided to substantiate the ecological benefits of the proposed framework. Full article

Virtual tours are increasingly used in education, particularly when access to real environments is limited. This study examined how display mode and representation format affect subjective user experience in an educational virtual tour of a hospital operating room. A within-subject 2 × 2 design compared two representation formats (360° photographs vs. 3D models) and two display modes (desktop PC vs. immersive virtual reality using Meta Quest 2). Eighty-four university students completed the four visualization conditions and evaluated each experience using an adapted version of the QUXiVE questionnaire. Descriptive statistics and internal consistency indices were calculated, and each questionnaire dimension was analyzed using a two-way repeated-measures ANOVA with display mode and representation format as within-subject factors. A significant main effect of display mode was found for presence, engagement, immersion, flow, emotion, judgment, physical consequences, and perceived educational usefulness (all p < 0.001), but not for usability (p = 0.273). A significant main effect of representation format was observed for presence (p = 0.003), emotion (p = 0.018), and perceived educational usefulness (p = 0.015), whereas no significant interaction effects were found. These findings indicate that immersive VR had the strongest and most consistent effect on subjective user experience across both 360° and 3D virtual tours, although it was also associated with higher physical-consequence scores. By contrast, the effect of representation format was more limited. Overall, both approaches appear to be complementary educational resources, depending on pedagogical goals, available infrastructure, and desired levels of interactivity. Full article

Externally excited synchronous machines (EESMs) are a rare-earth-free solution for traction applications. However, variable field excitation and magnetic coupling increase control complexity. Efficient validation of the resulting control functionalities can be carried out using hardware-in-the-loop (HIL) testing, which requires high-fidelity real-time simulation models. This paper presents a semi-analytical, discrete-time EESM model tailored for HIL applications. Nonlinear magnetic saturation and magnetic coupling are captured using an inverted flux–current characteristic combined with a rotating coordinate transformation, which improves resource utilization. Spatial harmonics are included through a Fourier decomposition of the angle-dependent inverse characteristics. Additionally, different loss mechanisms are considered to accurately represent the physical behavior of the machine. The model is parameterized using finite element analysis (FEA) results from a 100$\mathrm{k}$$\mathrm{W}$ salient-pole EESM. It is implemented on a field-programmable gate array to achieve real-time capability at a simulation frequency of $2.5$$\mathrm{M}$$\mathrm{Hz}$. Validation results for the typical operating range show deviations below $0.1$ compared to detailed FEA results, demonstrating accurate real-time simulation of the electromagnetic behavior. Full article

This study explores an integrated multi-physics design approach for a high-speed Interior Permanent Magnet Synchronous Motor (IPMSM) optimized for marine diesel engine Exhaust Gas Recirculation (EGR) blower systems. To satisfy the rigorous operational demands of marine environments, an IPMSM with a rated output of 150 kW and a base speed of 9000 rpm was developed. The design validity was rigorously verified through a comprehensive multi-physics framework using the Finite Element Method (FEM), ensuring a balance between electromagnetic, thermal, and mechanical performance. The investigation established a mathematical model for the IPMSM driven by a Space Vector Pulse-Width Modulation (SVPWM) inverter, facilitating a detailed analysis of steady-state characteristics within the EGR system. To guarantee long-term reliability at high rotational speeds, the study performed an integrated thermal analysis based on precise electrical loss separation and a rotor-dynamic evaluation focusing on unbalanced vibration responses of the shaft. Finally, the proposed design was validated by integrating the IPMSM into a full-scale EGR blower system. Experimental evaluations across the entire operating range confirm that the integrated design successfully achieves the high power density and mechanical robustness required for marine diesel applications. Full article

In her fiction, Joan Didion crafted female protagonists who embodied the strange stirrings documented by Betty Friedan in The Feminine Mystique, as common among mid-century White, educated women. Didion’s protagonists are all daughters, wives, and mothers who come to realize their lives are built on empty compromises. However, in her late 20th-century novels, their awareness leads to actual changes: the Didion Women who confront the void in Democracy and The Last Thing He Wanted find their lives impacted by the machinations of U.S. Cold War policies. These novels specifically trace the impact of American imperialism on wives and daughters at home—those that the policies claimed to protect. These protagonists, and their witnesses, refuse to be passive casualties. Their narration by an embedded professional female journalist adds weight to the journeys of these overlooked women. Through her protagonists of privilege, Didion unflinchingly documents the physical and psychological damages of patriarchy—both personal and political—presenting female models of awareness and resistance. This essay will examine Didion’s Democracy and The Last Thing He Wanted as the capstones of her woman-centered fiction, presenting detailed portraits of matrons who deliberately disentangle themselves from history. Full article

In real-world scenarios, multivariate time series data typically presents a variety of complex characteristics simultaneously, including long-term trends, multiple seasonality, sudden event disturbances and random noise. Owing to remarkable discrepancies among different variables in dimensions, periodic stability and other aspects, and the gradual evolution of these periodic characteristics over time, models are confronted with numerous challenges in handling non-stationarity, multi-scale dynamic variations and heterogeneous fusion of variables. To tackle these problems, this paper proposes a time–frequency parallel fusion framework—TFDG-Net (Time–Frequency Dual-Branch Gated Fusion Network). This framework models the prior information in the frequency domain and the temporal query network in the time domain in parallel, and introduces a channel-wise gating mechanism to achieve more flexible adaptive fusion after data inverse normalization. Such a design enables the model to operate collaboratively on the original physical scale, which not only improves the long-term prediction capability for periodically stable variables, but also effectively suppresses the interference of noise and event-driven factors, thus significantly enhancing prediction accuracy and the robustness of the training process. In multiple long-term prediction benchmark tests covering fields such as energy and finance, compared with various mainstream models, TFDG-Net reduces the mean squared error and mean absolute error by an average of 12.0% and 7.8% respectively. Full article

This paper presents a comprehensive review of the intelligent understanding of power grid drawings, with the aim of enabling reliable and executable grid modeling. First, a unified pipeline is established to describe the transformation from drawings to grid models, covering visual understanding, topology inference, and consistency validation. Second, existing methods are systematically analyzed within this framework, where visual understanding extracts components and textual information and topology inference reconstructs electrical connectivity and network structure. Third, model generation methods are investigated as a critical yet underexplored component, focusing on topology correctness and physical constraint verification. Compared with existing review studies that primarily focus on perception-level tasks such as detection and recognition, this paper explicitly emphasizes the reliability of the resulting models. It highlights that errors in connectivity inference and the lack of validation mechanisms significantly limit practical deployment. Key challenges, including connectivity ambiguity, error propagation, and the absence of standardized validation frameworks, are analyzed. Furthermore, emerging directions such as topology-aware learning and physics-constrained validation are discussed. This review provides a structured perspective on transforming power grid drawings into reliable models and offers insights for future research into power system digitalization. Full article

This review examines the current state of development and application of artificial intelligence (AI) tools for monitoring nutrition and physical activity in individuals with obesity, with a focus on the physiological complexity of energy balance and the role of chrono-nutrition. Energy intake and expenditure are dynamically coupled and circadian-regulated: meal timing and movement patterns influence insulin sensitivity, thermogenesis, and Non-Exercise Activity Thermogenesis within the same day. Traditional monitoring methods suffer from recall bias and low granularity, while isolated sensors operate in data silos, limiting accuracy. Effective solutions require multimodal, continuous, and temporally aligned data streams. Current AI models exhibit critical limitations in obesity-specific contexts: inaccurate gait and energy expenditure estimates due to biomechanical differences, dietary models underestimating glycemic variability, poor performance on mixed dishes, sauces, and culturally diverse foods, and a lack of validation against gold standards such as doubly labelled water (DLW) and weighed food records. This review proposes a paradigm shift toward obesity-specific AI design, including enriched datasets and multimodal integration. Physical activity monitoring faces similar challenges: systematic measurement bias in wearables, sensor placement issues, and algorithms trained on normal-weight cohorts. In the GLP-1/GIP era, if transparency, ethical safeguards, and equitable access are ensured, AI will act as a catalyst for personalized care, remote monitoring, trial optimization, and next-generation drug discovery. In conclusion, the integration of AI with rigorous validation procedures and inclusive sampling strategies is essential to achieve reliable, fair, and clinically relevant monitoring approaches for obesity management. Full article

The modern high-pressure lifestyle has led to an increasing emphasis on the healing construction of residential spaces, while air, as an important environmental factor, has received little attention in terms of situational healing experiences within the context of residential culture. Employing grounded theory, this study develops a theoretical model to explain the mechanism through which indoor air environments influence the healing benefits of residential spaces. Guided by the dynamic interaction process of “physical attributes–embodied cognition–behavioral regulation–social context”, the analysis focuses on human embodied perception and emotional responses to indoor air environments as the foundation for healing effects. It highlights the joint role of behavioral regulation and social context, ultimately affecting four levels of healing benefits. Furthermore, it systematically elaborates a theoretical model for embodied interactive residential air experiences, expanding healing environment theory from a contextual air experience perspective, and providing new research paradigm and insights for promoting healing benefits in residential settings. Full article