Search Results (811)

Facial neuromodulation with botulinum toxin has traditionally been approached from the perspective of wrinkle correction. However, facial expressions primarily arise from coordinated muscular interactions that convey both positive and negative emotional valence. A conceptual framework focused on muscular equilibrium rather than wrinkle severity may therefore offer a more comprehensive, reproducible, and clinically meaningful approach. In this article, we propose the Index of Muscular Equilibrium (IME) Framework, a conceptual model for aesthetic neuromodulation that integrates functional muscle mapping, validated severity scales, and a composite IME score to support personalized treatment planning and outcome assessment. The framework is derived from a narrative review of PubMed-indexed literature on facial muscle activity, emotional expression, and validated clinical assessment tools. It combines a Valence Map to classify positive- and negative-valence muscle groups, a standardized evaluation of static and dynamic hypertonus, a conceptual Plan Score to guide selective neuromodulation, and a feedback-based longitudinal workflow (the IME Loop). Together, these components enable structured assessment of muscular imbalance, integration of established wrinkle severity scales, and translation into individualized, function-oriented treatment strategies, with intended benefits including improved objectivity, reproducibility, and patient communication. By reframing treatment success from the duration of muscle blockade to the duration of expressive harmony, the IME Framework introduces testable constructs for future validation and offers a functional perspective on facial neuromodulation aligned with contemporary affective science. Full article

Broken rotor bar faults (BRBFs) in induction motors (IMs) present significant challenges in industrial applications, particularly due to the need for large labeled datasets and fast processing. This study addresses these issues by leveraging transfer learning with classical diagnostic techniques, using experimental 3-phase current and 3-axes vibration signals. The Gramian Angular Field (GAF) technique has been utilized to transform time series data into 2D images, enabling fine-tuning of an EfficientNetB3 model, which achieved 99.83% accuracy in classifying five BRBF severity levels. The proposed strategy also outperforms the state-of-the-art methods using the same experimental data. Similarly, validation with features extracted using Continuous Wavelet Transform (CWT) and Short-Time Fourier Transform (STFT) further confirmed its reliability and superiority. This study also offers enhanced interpretability through Grad-CAM visualizations of the best model, which highlights the critical regions contributing to fault classification. These visualizations enable deeper and simpler understanding of fault mechanisms and support subsequent risk analysis, making the developed model actionable and user-friendly for industrial applications. Full article

In recent years, improving the energy efficiency of induction machines (IM) has become a key research focus, with particular attention to loss reduction. Losses in IM are significantly influenced by two design-related factors: the nonlinear magnetization characteristic and the non-sinusoidal distribution of the magnetomotive force (MMF) in stator slots. These effects lead to harmonic distortions in stator and rotor currents as well as pulsations of the electromagnetic torque. This paper presents a comprehensive harmonic analysis of the interaction between the nonlinear magnetization curve and the non-sinusoidal MMF distribution in induction machines. A mathematical model in phase coordinates was developed, incorporating both effects through the introduction of harmonic components into the magnetizing inductance. The proposed model enables the evaluation of the impact of these phenomena on stator and rotor currents, as well as on the electromagnetic torque. The validity of the model is verified by experimental results, which show close agreement with simulations. The analysis demonstrates that the nonlinearity of the magnetization curve results in the appearance of the third harmonic in stator currents and the second harmonic in torque, while the non-sinusoidal MMF distribution produces the fifth and seventh harmonics in stator currents and the sixth harmonic in torque. Additionally, the study reveals that in no-load conditions, the third harmonics are dominant, whereas with increasing load, their magnitudes decrease, and the amplitudes of the fifth and seventh harmonics increase due to the interaction between stator and rotor currents. The proposed modeling approach provides an effective tool for accurate performance evaluation and design optimization of induction motor drives Full article

With the widespread deployment of automotive millimeter-wave radars, mutual interference and broadband noise severely degrade the signal-to-noise ratio (SNR) of range–Doppler (RD) maps, leading to the loss of weak targets. Existing deep learning methods rely on difficult-to-obtain paired training samples and often cause excessive target smoothing due to a lack of physical constraints. To address these challenges, this paper proposes RIME-Net, a physics-guided unpaired learning framework designed to jointly achieve radar interference mitigation and weak target enhancement. First, based on a cycle-consistent adversarial architecture, we designed the Interference Mitigation Network (IM-Net). IM-Net integrates spectral consistency loss and identity mapping constraints, learning a robust mapping from the interference domain to the clean domain without paired supervision, effectively suppressing low-rank interference and preserving signal integrity. Second, to recover target details attenuated during denoising, we propose the saliency-aware Target Enhancement Network (TE-Net). TE-Net combines multi-scale residual blocks and channel-spatial attention mechanisms, selectively enhancing weak target features based on saliency priors. Extensive experiments on diverse datasets show that RIME-Net significantly outperforms existing supervised and model-driven methods in terms of SINR, recall, and structural similarity, providing a robust solution for reliable radar perception in complex electromagnetic environments. Full article

Rolling bearings are crucial components in CNC machine tool spindles, and their health condition directly affects machining precision and operational reliability. To address the significant challenges of bearing fault diagnosis in industrial environments, this paper proposes an adaptive shapelet-based deep learning model for bearing fault diagnosis. The proposed model integrates three key components: (1) an adaptive multi-scale shapelet extraction module for discriminative pattern learning, (2) a gated parallel CNN with depthwise separable convolutions for multi-scale spatial feature extraction, (3) an enhanced bidirectional long short-term memory network with residual connections for temporal dependency modeling. A composite loss function combining cross-entropy, supervised contrastive learning, and multi-scale consistency regularization is employed for training. To simulate real-world industrial noise conditions, Gaussian, uniform, and impulse noise were injected into the signals. Experiments conducted on the CWRU and IMS datasets demonstrate that, compared with state-of-the-art methods, the proposed approach achieves stronger noise robustness, higher fault classification accuracy, and more stable performance under severe noise contamination. Full article

Background/Objectives: Fluvoxamine is commonly prescribed for depressive disorders in elderly patients, a population that frequently exhibits variable drug responses and increased susceptibility to adverse effects due to age-related physiological changes. CYP2D6 polymorphisms may further affect fluvoxamine pharmacokinetics in elderly patients, complicating dose optimization for this group. Previous pharmacogenetic studies examining the impact of CYP2D6 phenotype on fluvoxamine treatment outcomes have primarily focused on younger adults, leaving a gap in understanding its effects on the elderly. Methods: The impact of CYP2D6 phenotypes on fluvoxamine exposure in geriatrics was evaluated using a physiologically based pharmacokinetic (PBPK) modeling approach incorporating geriatric-specific physiological parameters. Results: The fluvoxamine PBPK model was verified using clinical pharmacokinetic data from younger and older adults, along with phenotype-dependent exposure differences between CYP2D6 poor metabolizers (PMs) and extensive metabolizers (EMs). Simulations showed that steady-state exposure in elderly patients was 1.8-fold higher than those in younger adults, and 2.1-fold higher in CYP2D6 PMs compared with EMs. Based on these simulations, doses of approximately 50 mg/day for PMs, 50–100 mg/day for intermediate metabolizers (IMs), 100 mg/day for EMs, and 150–200 mg/day for ultrarapid metabolizers (UMs) may be appropriate for elderly patients, accompanied by cautious dose escalation and clinical monitoring. Conclusions: These findings suggest that CYP2D6 genotype-guided dosing may be a useful strategy for optimizing fluvoxamine therapy in elderly patients, with the potential to improve treatment outcomes while minimizing the risk of adverse drug reactions in this high-risk population. Full article

In response to the need for cost-effective and resilient drivetrain architectures in renewable energy emulation platforms, this paper proposes a wind turbine emulator (WTE) designed to enhance the operational efficiency of variable-speed wind turbines (WTs) connected to electric generators in power grid applications. The proposed emulator relies on a robust sensorless vector-controlled induction motor (IM) drive fed by a reduced-switch soft–voltage source inverter (Soft-VSI) topology. The proposed control chain combines a second-order super-twisting sliding-mode flux observer, based on stator measurements, with a modified MRAS speed estimator whose Popov hyperstability offers explicit PI tuning and ensures stable sensorless speed convergence. The complete WTE design, from the aerodynamic model to the Soft-VSI induction motor drive, is implemented and evaluated in MATLAB/Simulink environment. A Mexican hat wind speed profile is used to excite the emulator and assess its dynamic behavior under diverse transient conditions. The simulation results demonstrate fast convergence of the estimated flux and speed, stable closed-loop operation when using the estimated speed, and strong robustness against no-loaded and loaded operations and rotor-resistance variations. Moreover, a comparative analysis between the proposed control scheme and a conventional first-order sliding-mode flux observer is carried out to highlight the enhanced flux and speed estimation accuracy, reduced chattering, and improved dynamic robustness of the WTE. The proposed framework provides a flexible tool to support the energy transition through the development of advanced wind energy system control strategies. Full article

Using satellite data products across 41 years, this study explores the combined effect of summer (15 May–15 September) ambient temperatures and relative humidity on the exposure to excessive heat of people living in northern Israel. Specifically, we fused 126 Landsat satellite image data collected between 1984 and 2024 with surface meteorological observations, topographical, and land-use data. The ambient temperature (T_a) was estimated by a Random Forest (RF) regression model, with the Landsat land surface temperature (LST) as its main input. A complete spatial cover of the ambient relative humidity (RH) was obtained by a hybrid model based on Bolton’s equations. Namely, we used (i) T_a estimates of the RF model to obtain the saturation vapor pressure; (ii) a spatial interpolation of dew point temperature measurements by certified meteorological stations across the whole study area; (iii) estimates of the surface air pressure based on a digital elevation model; and (iv) thermodynamic equations for calculating the ambient vapor pressure. Initially, we calculated T_a and RH at noontime in summer at all the grid cells of each Landsat image, accounting for all the qualified Landsat images in each summer. Next, we evaluated the summer-average estimates against corresponding in situ Israel Meteorological Service (IMS) stations’ average summer measurements. Finally, we studied long-term trends over the whole study area, revealing significant summer noontime long-term trends in T_a, RH and the heat index (HI) over the study area (T_a: 0.03–0.14 °C/summer; RH: 0.05–0.18%/summer; HI: 0.08–0.82 °C/summer), as well as changes in their spatial patterns. Full article

Due to the increasing antibiotic resistance of microorganisms, chronic diseases, and cancer, new-generation drugs such as imidazole derivatives are being sought. Recent advances in nanotechnology enable the potential use of nanomaterials, especially nanoparticles, as drug carriers for such compounds, but also systems capable of crossing biological barriers. This study aimed to perform a preliminary toxicological assessment of nanoparticles containing BzIm-DEA ((Z)-5-benzylidene-3-[2-(diethylamino)ethyl]-2-phenyl-3,5-dihydro-4H-imidazol-4-one) embedded in chitosan films, using chicken chorioallantoic membrane (CAM) as an alternative in vivo test. Fertilized chicken eggs were treated with this therapeutic agent at various concentrations of BzIm-DEA and incubated until the 11th day of embryogenesis. No morphological abnormalities, angiogenesis-related disorders, or increased mortality were observed in any of the experimental groups. A significant increase in Apaf-1 mRNA expression was detected in CAM tissue at a dose of D3 BzIm-DEA, while no significant changes were observed for caspase-3 and catalase compared to the control group. Moreover, no changes in gene expression were observed in the liver. Immunohistochemical localization and analysis of PCNA and b-catenin expression in chicken embryonic liver did not reveal any dose-dependent changes. Within the scope of this preliminary assessment, chitosan nanoparticles loaded with BzIm-DEA did not produce gross acute embryotoxicity or major disruptions to angiogenic development under the tested conditions, providing preliminary evidence of biocompatibility as a nanoparticle carrier system. Full article

Accurate anomaly detection in rotating machinery under noisy conditions remains challenging in Prognostics and Health Management (PHM). Existing deep learning autoencoders and attention mechanisms rely primarily on data-driven similarity measures and fail to explicitly incorporate nonlinear dynamical characteristics of degradation. In this study, we propose a Recurrence Quantification Analysis-Aware Attention (RQAA) framework that systematically injects chaos-theoretic descriptors into the attention mechanism of LSTM-based autoencoders for unsupervised anomaly detection. Specifically, RQA metrics including recurrence rate, determinism, laminarity, entropy, and trapping time are computed at the window level and embedded into the query-key-value attention scoring to guide the model toward dynamically informative temporal patterns. Three attention variants are developed to investigate different fusion strategies between learned representations and RQA-driven structural cues. The proposed framework is evaluated on three widely used bearing vibration datasets, which are IMS, CWRU, and HUST. Experimental results demonstrate that RQAA consistently outperforms conventional LSTM autoencoders and classical attention-based models, achieving up to 99.85% F1-score and 99.00% AUC while exhibiting superior robustness in low signal-to-noise scenarios. Further analysis reveals that explicit dynamical guidance enhances anomaly separability and reduces false alarms, particularly in early-stage fault detection. These findings indicate that integrating nonlinear dynamical information directly into attention scoring offers a principled and effective pathway for advancing unsupervised anomaly detection in rotating machinery and safety-critical industrial systems. Full article

The main characteristics of the large number of coffee species are differences in aroma and caffeine content. Labeled blends of Coffea arabica (C. arabica) and Coffea canephora (C. canephora) are common to broaden the flavor profile or enhance the stimulating effect of the beverage. New emerging species such as Coffea liberica (C. liberica) further increase the variability in blends. However, significant price differences between coffee species increase the risk of unlabeled blends and thus influence food quality and safety for consumers. In this study, a prototypic hyphenation of trapped headspace-gas chromatography-ion mobility spectrometry-quadrupole mass spectrometry (THS-GC-IMS-QMS) was used for the detection of characteristic compounds of C. arabica, C. canephora, and C. liberica in green and roasted coffee samples. For the discrimination of coffee species with IMS data, multivariate resolution with multivariate curve resolution–alternating least squares (MCR-ALS) prior to partial least squares–discriminant analysis (PLS-DA) was evaluated. With this approach, the classification accuracy, as well as sensitivity and specificity, of the PLS-DA model was significantly improved from an overall accuracy of 87% without prior feature selection to 92%. As MCR-ALS preserves the physical and chemical properties of the original data, characteristic features were determined for subsequent substance identification. The simultaneously generated QMS data allowed for partial annotation of the characteristic volatile organic compounds (VOC) of roasted coffee. Full article

Background: Virus-like particles (VLPs) represent key tools for the development of vaccines due to their ability to induce a potent immune response to epitopes presented on their surface. However, the decoration of VLPs with a complete heterologous protein on the surface remains a bottleneck for clinical translation due to the complexity of manufacture. We present a novel platform, EPIclip™, for the decoration of VLPs mediated by high-affinity protein binding partners, colicin E7 (ColE7) and immunity protein 7 (Im7), within a single prokaryotic host. We evaluate this approach using a modified hepatitis B core capsid protein and IL-31 as a model epitope. IL-31 is a prominent therapeutic target for the development of pruritic diseases. Methods: We explore the design and development of the platform, including the use of T-cell-stimulating peptides. We demonstrate several small-scale purification methods for the candidate VLP, as well as morphological analysis by transmission electron microscopy (TEM). Further, we vaccinate mice with IL-31-displaying VLPs to evaluate immunogenicity and the ability to prevent IL-31-induced pruritus in vivo. Results: Our results demonstrate that decorated VLPs dosed in mice elicit an IgG response against IL-31 with at least six months of durability. In addition, IL-31-displaying VLPs suppress the development of IL-31-induced pruritus, confirming in vivo target neutralisation. Notably, IL-31-displaying VLPs induce a strong T-cell response against the VLP capsid but not against the cytokine, confirming a B-cell-biased immune response and the absence of detrimental autoreactive T cells. We further demonstrate the translation of this system with an additional virus capsid: tomato aspermy virus (TAV). Conclusions: Taken together, the novel EPIclip™ platform may represent a promising therapeutic approach for pruritic diseases. Additionally, this modular system could be adapted for a wide range of research as well as human and veterinary therapeutic applications. Full article

In recent years, the digital emulation of power systems, such as induction machines, has increased, driven by advances in computational resources and the processing capabilities of digital platforms. These platforms offer a versatile approach to the design, analysis, and optimization of solutions in electric machine drive research. This work presents the design of an Induction Machine (IM) using a digital twin, simulating its performance and behavior under failure conditions using the DQ model. Additionally, this study presents the design and real-time digital emulation of an IM, incorporating a bearing-fault model. The implementation on an FPGA platform enables high-fidelity simulation and analysis of the machine’s performance under both healthy and faulty operating conditions. This approach introduces a distinctive and critical tool for pre-experimental validation, enabling the precise identification of key fault signatures and system responses under real-time conditions, a capability that is not explicitly addressed in existing studies. Quantitative results demonstrate that the digital model implementation is highly accurate in replicating the theoretical IM with a relative error below (<1%). Additionally, through frequency-domain analysis, the signatures of the injected fault can be observed. Full article

Licorice is an important specialty crop that links agricultural production, processing and trade, and rural livelihoods in the arid and semi-arid regions of China. Using UN Comtrade data for HS 130212 from 1990 to 2024, this study evaluates the international Trade Competitiveness of China’s licorice exports and identifies the sources of export growth. A multi-indicator static framework is constructed, combining International Market Share (IMS), the Trade Competitiveness Index (TC), the Revealed Symmetric Comparative Advantage index (RSCA) and the Revealed Competitive Advantage index (CA). The results show that China maintains a relatively large and stable global market share and a persistent net export position, but its comparative and net Competitive Advantages are weaker than those of high-end suppliers such as France and Israel, revealing a pattern of “large scale but weak competitiveness”. To capture dynamic drivers, an extended Constant Market Share (CMS) model is applied to decompose China’s licorice exports into world demand, structural and competitiveness effects. The decomposition indicates that export growth has gradually shifted from being mainly driven by global demand expansion to relying more on improvements in product competitiveness and market reconfiguration, particularly in emerging markets. These findings suggest that upgrading product quality and processing, strengthening standards and branding, and promoting more inclusive value-chain development are essential for transforming China’s licorice exports from scale expansion to high-quality growth and for enhancing rural incomes in producing regions. Full article

Pulse-like ground motions can cause severe damage to buried cast iron (CI) pipelines, which necessitates the selection of optimal seismic intensity measures (IMs) to estimate pipeline repair rates. Such a selection is essential for mitigating uncertainty in the seismic risk assessment of buried CI pipelines. For the first time, this study systematically screens the optimal scalar and vector IMs for buried cast iron pipelines with lead-caulked joints under pulse-like ground motions by a symmetrical evaluation based on the criteria of efficiency, sufficiency, and proficiency, providing a new method for reducing uncertainty in pipeline seismic risk assessment. We initiate the study by selecting 124 pulse-like ground motions from the NGA-West2 database and identifying 19 scalar and 171 vector IMs as potential candidates. A two-dimensional soil–pipe model is introduced, incorporating variability in the sealing capacity of lead-caulked joints along the axial direction. CI pipeline repair rates are calculated across various scaling factors and apparent wave velocities, yielding 1116 datasets pertinent to CI pipeline damage. The repair rate is adopted as the engineering demand parameter (EDP) to evaluate the efficiency, sufficiency, and proficiency of candidate IMs. Through comprehensive analysis, peak ground velocity (PGV) and the combination of PGV and the time interval between 5% and 75% of normalized Arias intensity ([PGV, Ds_5–75]) are determined as the optimal scalar- and vector-IMs, respectively, for assessing the repair rate of buried CI pipelines under pulse-like ground motions. Full article