Search Results (320)

Background: Albania is undergoing rapid nutrition transition, yet cardiometabolic (CM) risk in young adults is poorly characterized. We report baseline, cross-sectional findings from a WHO-aligned sentinel study examining diet, physical activity and early CM phenotypes, with fat quality examined as a modifiable exposure. Methods: Young adults recruited on campus (n = 262; median age, 21 years; 172 women, 90 men) underwent standardized anthropometry, seated blood pressure (BP) and fasting glucose (FG). Diet was assessed by two interviewer-administered 24 h recalls and activity outlined by the IPAQ-short form. We derived potential renal acid load (PRAL) and a MASLD-oriented nutrient score, computed a composite CM risk score (cCMRS: sex-standardized mean of WHtR, mean arterial pressure, FG) and fitted prespecified energy-partition models for isocaloric +5% of energy substitutions (SFA → PUFA; SFA → MUFA) with Benjamini–Hochberg false discovery rate (FDR) control. Results: Despite normal average BMI (23.4), risk clustering was common: elevated BP in 63% of men and 30% of women, impaired FG (100–125 mg/dL) in almost one third and central adiposity (WHtR ≥ 0.5) in 51% of men and 24% of women. Diets were SFA-rich (~17–19%E), sodium-dense and low in fiber and several micronutrients (e.g., vitamin D, folate, potassium). In isocaloric models, SFA → PUFA was associated with more favorable nutrient signatures: MASLD-oriented score −28% (p < 0.001; FDR-significant) and PRAL −33% (p = 0.007; FDR-borderline/suggestive). Conclusions: A waist-centric CM subphenotype—central adiposity co-occurring with upward BP shifts and intermittent dysglycemia—was detectable in young adults despite normal average BMI, against a background of poor diet quality and low activity. These baseline surveillance signals are not causal effects. Integration into routine with WHO-aligned NCD surveillance is feasible. Prospective follow-up (biomarker calibration, device-based activity, repeated waves) will refine inferences and inform scalable proactive prevention. Full article

Microseismic signals induced by mining activities often have low signal-to-noise ratios, and traditional picking methods are easily affected by noise, making accurate identification of P-wave arrivals difficult. To address this problem, this study proposes an adaptive denoising algorithm based on wavelet-threshold-enhanced Complete Ensemble Empirical Mode Decomposition with Adaptive Noise (CEEMDAN) and develops an automatic P-wave arrival picking method incorporating fractal box dimension features, along with a corresponding accuracy evaluation framework. The raw microseismic signals are decomposed using the improved CEEMDAN method, with high-frequency intrinsic mode functions (IMFs) processed by wavelet-threshold denoising and low- and mid-frequency IMFs retained for reconstruction, effectively suppressing background noise and enhancing signal clarity. Fractal box dimension is applied to characterize waveform complexity over short and long-time windows, and by introducing fractal derivatives and short-long window differences, abrupt changes in local-to-global complexity at P-wave arrivals are revealed. Energy mutation features are extracted using the short-term/long-term average (STA/LTA) energy ratio, and noise segments are standardized via Z-score processing. A multi-feature weighted fusion scoring function is constructed to achieve robust identification of P-wave arrivals. Evaluation metrics, including picking error, mean absolute error, and success rate, are used to comprehensively assess the method’s performance in terms of temporal deviation, statistical consistency, and robustness. Case studies using microseismic data from a mining site show that the proposed method can accurately identify P-wave arrivals under different signal-to-noise conditions, with automatic picking results highly consistent with manual labels, mean errors within the sampling interval (2–4 ms), and a picking success rate exceeding 95%. The method provides a reliable tool for seismic source localization and dynamic hazard prediction in mining microseismic monitoring. Full article

Background: Electrocardiographic (ECG) abnormalities are common in acute pulmonary embolism (PE), but the prognostic significance of P wave morphology remains unclear. Early identification of high-risk patients is critical for guiding therapy and monitoring. Methods: We retrospectively analyzed 300 patients with confirmed PE. P wave morphology (normal, biphasic, notched, peaked) was evaluated for association with in-hospital mortality using chi-square and logistic regression, adjusted for age, sex, PESI score, and oxygen saturation. Results: Mortality differed significantly across P wave groups (χ² = 35.3, df = 3, p < 0.001). In univariate analysis, biphasic (OR 15.38, 95% CI 5.02–47.10, p < 0.001) and peaked (OR 7.21, 95% CI 2.35–22.10, p = 0.001) morphologies were strongly associated with mortality, whereas notched P waves were not (OR 1.44, 95% CI 0.16–12.87, p = 0.743). After adjustment, biphasic (OR 14.87, 95% CI 4.77–46.37, p < 0.001) and peaked (OR 6.58, 95% CI 2.11–20.53, p = 0.001) shapes remained independent predictors. Age, sex, PESI score, and oxygen saturation were not significant in multivariable analysis. Conclusions: Biphasic and peaked P wave morphologies on ECG are strong predictors of in-hospital mortality in patients with PE. Routine assessment of P wave shape may provide a simple tool for early risk stratification, warranting validation in prospective cohorts. Full article

Background: Latent myofascial trigger points (MTrPs) are clinically relevant because they lower local pressure pain thresholds (PPTs), can perturb motor control, and may sustain shoulder symptoms even when overt pain is absent. However, even if previous studies assessed stiffness and mechanosensitivity differences between MTrPs and asymptomatic regions, objective patient-level cut-offs and diagnostic-accuracy metrics to distinguish latent MTrPs from adjacent asymptomatic tissue are lacking. Objective: To quantify the diagnostic accuracy of pressure algometry (PPT) and shear-wave elastography (SWE) for distinguishing latent MTrPs from adjacent asymptomatic tissue. Methods: A single-center cross-sectional study was conducted including 76 volunteers with ≥1 latent infraspinatus MTrP (assessed by following the current Delphi consensus criteria). The most sensitive latent MTrP and a control site 2 cm cranial was measured on the dominant side infraspinatus muscle in each participant. PPT and SWE were acquired with a standardized protocol (long-axis imaging, anisotropy control, minimal probe pressure; three captures per site; 1 cm rectangular ROI; operator blinded to site type). ROC analyses estimated areas under the curve (AUCs), Youden-optimal cut-offs, sensitivity, specificity, and likelihood ratios (LR+/−). Results: Latent MTrPs showed lower PPTs than controls (p < 0.001) and higher stiffness (shear modulus: p = 0.009; shear-wave speed: p = 0.022). PPT yielded AUC = 0.704 with an optimal cut-off of 47.5 N (sensitivity 0.75; specificity 0.592; LR+ 1.84; LR− 0.42), outperforming SWE metrics (shear modulus AUC 0.611; cut-off 23.6 kPa; sensitivity 0.632; specificity 0.605; LR+ 1.60; LR− 0.61; shear-wave speed AUC 0.601; cut-off 2.55 m/s; sensitivity 0.592; specificity 0.632; LR+ 1.61; LR− 0.65). Conclusions: In the infraspinatus, PPT provides moderate discrimination between latent MTrPs and adjacent asymptomatic tissue, whereas resting SWE—despite small mean differences—exhibited lower accuracy. These findings support mechanosensitivity as a primary measurable signal and position SWE as an adjunct. External validation across devices and operators, and multivariable models integrating sensory, imaging, and clinical features, are warranted. Full article

Rockburst disasters in deep underground engineering present significant safety hazards due to complex geological conditions and high in situ stresses. To address the limitations of traditional microseismic (MS) monitoring methods—namely, vulnerability to noise interference, low recognition accuracy, and limited computational efficiency—this study proposes an intelligent real-time monitoring and early warning framework that integrates deep learning, MS monitoring, and Internet of Things (IoT) technologies. The methodology includes db4 wavelet-based signal denoising for preprocessing, an improved Gaussian Mixture Model for automated waveform recognition, a U-Net-based neural network for P-wave arrival picking, and a particle swarm optimization algorithm with Lagrange multipliers for event localization. Furthermore, a cloud-based platform is developed to support automated data processing, three-dimensional visualization, real-time warning dissemination, and multi-user access. Field application in a deep-buried railway tunnel in Southwest China demonstrates the system’s effectiveness, achieving an early warning accuracy of 87.56% during 767 days of continuous monitoring. Comparative verification further indicates that the fine-tuned neural network outperforms manual approaches in waveform picking and event identification. Overall, the proposed system provides a robust, scalable, and intelligent solution for rockburst hazard mitigation in deep underground construction. Full article

Early warning of structural damage from induced seismic events requires rapid and reliable ground motion forecasting. This study presents a novel real-time framework that couples a deep learning approach with structural fragility assessment to generate immediate damage alerts following the onset of seismic shaking. Long Short-Term Memory (LSTM) neural networks are employed to predict full S-wave accelerograms from initial P-wave inputs, trained and tested on accelerometric records from induced seismicity scenarios. The predicted S-wave motion is then used as input for a suite of fragility curves in real time to estimate the probability of structural damage for masonry buildings typical in rural areas of geothermal platforms. The proposed method captures both the temporal evolution of shaking and the structural response potential, offering critical seconds of lead time for automated decision-making systems. Results demonstrate high predictive accuracy of the LSTM model and effective early classification of structural risk. This integrated system provides a practical tool for early warning or rapid response in regions experiencing anthropogenic seismicity, such as those affected by geothermal operations. Full article

The synergistic combination of bismuth and its compounds with the exceptional properties of single-walled carbon nanotubes (SWCNT) was investigated as a sensing platform for the sensitive detection of gallic acid and as a standard for the determination of total phenol. Four bismuth-based electrodes were used for this purpose: SWCNT with bismuth (SWCNT/Bi) or bismuth (III) oxide (SWCNT/Bi₂O₃) and bismuth or bismuth (III) oxide electrodeposited on a glassy carbon electrode (ELF/Bi and ELF/Bi₂O₃), which were morphologically characterized by scanning electron microscopy. Cyclic voltammetry in phosphate electrolyte at different pH values revealed that the SWCNT/Bi₂O₃ electrode exhibited optimal performance for the analytical determination of gallic acid at pH 3. Surface-active carbon nanotubes facilitate the adsorption and accumulation of gallic acid, while the addition of Bi₂O₃ improves electron transfer, resulting in a synergistic enhancement of the oxidation signal. Square-wave voltammetry with SWCNT/Bi₂O₃ electrodes also provided reliable and accurate results and proved to be suitable for the quantitative determination of gallic acid with wide linearity (0.2–80 µM) and sensitivities of 12.5, 2.35, and 0.385 µA µmol⁻¹ dm³ for low, medium, and high concentration ranges, respectively. The limit of detection was 0.06 µmol dm⁻³. Finally, the electrode was successfully applied for gallic acid determination in various seeds. Full article

Microwave frequency comb (MFC) optimization for frequency-modulated continuous-wave (FMCW) generation by period-one (P1) dynamics with dual-loop optical feedback are numerically investigated. The linewidth, the side peak suppression (SPS) ratio, and the comb contrast are adopted to quantitatively evaluate the optimization performance, which directly influence the phase stability, spectral purity and repeatability of the MFC. The results show that intensity modulation of the optical injection can generate a sweepable FMCW signal after photodetection via the optical beat effect. When optical feedback loops are introduced, the single-loop configuration can reduce the phase noise of the FMCW signal whereas a dual-loop configuration exploits the Vernier effect to achieve further linewidth reduction and wide tolerance to the feedback strength. Finally, for both the SPS ratio and comb contrast, the dual-loop configuration achieves a higher SPS ratio and maintains high contrast across a wide range of optical feedback loop delays, which outperforms the loop time tolerance of the single-loop configuration. Full article

Background/Objectives: Glutamatergic neurotransmission is essential for the normal functioning of the retina. Photoreceptor to bipolar and bipolar to ganglion cell signaling is mediated by L-glutamate, which is stored in and released from vesicular glutamate transporter 1 (VGLUT1) containing synaptic vesicles. VGLUT1 is expressed postnatally, P2 onwards, and is required for the glutamatergic retinal wave observed between P10 and P12 in the developing mouse retina. P9–P13 postnatal age is critical for retinal development as VGLUT1 expressing ribbon synapses activate in the outer and inner plexiform layers, and rod/cone mediated visual signaling commences in that period. Although it has been hypothesized that glutamatergic extrinsic signaling drives cell cycle exit and initiates cellular differentiation in the developing retina, it is not clear whether intracellular, synaptic, or extrasynaptic vesicular glutamate release contributes to this process. Recent studies have attempted to decipher VGLUT’s role in retinal development. Here, we investigate the potential effect of genetic loss of VGLUT1 on early postnatal histogenesis and development of retinal neural circuitry. Methods: We employed immunohistochemistry and electrophysiology to ascertain the density of glutamatergic, cholinergic, and dopaminergic cells, spontaneous retinal activity, and light responses in VGLUT1 null retina, and contrasted them with wildtype (WT) and melanopsin null retina. Results: We have demonstrated here that VGLUT1 null retina shows signs of age dependent retinal degeneration, similar to other transgenic mice models with dysfunctional photoreceptor to bipolar cell synapses. The loss of VGLUT1 specifically alters glutamatergic cell density and morphological maturation of retinal ganglion cells. Moreover, VGLUT2 expression is lost in the majority of VGLUT2 cones in the absence of VGLUT1 coexpression, except when VGLUT2 coexpresses transiently with VGLUT3 in these cones, or when VGLUT1 null mice are dark reared. Conclusions: We present the first evidence that synaptic or extrasynaptic postnatal glutamate release from VGLUT1 containing vesicles impacts histogenesis of glutamatergic cells, pruning of retinal ganglion cell dendrites and VGLUT2 expression in cones. Full article

Background: Electrocardiogram (ECG) signals are crucial for cardiovascular diagnosis, but their analysis face challenges from noise contamination, compression difficulties due to their non-stationary nature, and the inherent complexity of its morphological components, particularly for low-amplitude P- and T-waves obscured by noise. Methodology: This study proposes a novel, multi-stage framework for ECG signal denoising, compressing, and component decomposition. The proposed framework leverages the sparsity of ECG signal to denoise and compress these signals using autoencoder-driven dictionary (AE-DD) with matching pursuit. In this work, a data-driven dictionary was developed using a regularized autoencoder. Appropriate trained weights along with matching pursuit were used to compress the denoised ECG segments. This study explored different weight regularization techniques: L1- and L2-regularization. Results: The proposed framework achieves remarkable performance in simultaneous ECG denoising, compression, and morphological decomposition. The L1-DAE model delivers superior noise suppression (SNR improvement up to 18.6 dB at $-3$ dB input SNR) and near-lossless reconstruction ($\mathrm{MSE}<{10}^{-5}$). The L1-AE dictionary enables high-fidelity compression (CR = 28:1 ratio, $\mathrm{MSE}\approx 0.58\times {10}^{-5}$, PRD = 2.1%), outperforming non-regularized models and traditional dictionaries (DCT/wavelets), while its trained weights naturally decompose into interpretable sub-dictionaries for P-wave, QRS complex, and T-wave enabling precise, label-free analysis of ECG components. Moreover, the learned sub-dictionaries naturally decompose into interpretable P-wave, QRS complex, and T-wave components with high accuracy, yielding strong correlation with the original ECG ($r=0.98$, $r=0.99$, and $r=0.95$, respectively) and very low MSE ($1.93\times {10}^{-5}$, $9.26\times {10}^{-4}$, and $3.38\times {10}^{-4}$, respectively). Conclusions: This study introduces a novel autoencoder-driven framework that simultaneously performs ECG denoising, compression, and morphological decomposition. By leveraging L1-regularized autoencoders with matching pursuit, the method effectively enhances signal quality while enabling direct decomposition of ECG signals into clinically relevant components without additional processing. This unified approach offers significant potential for improving automated ECG analysis and facilitating efficient long-term cardiac monitoring. Full article

Interference caused by multiple reflections is a critical issue in transmission measurements using continuous wave (CW) terahertz and sub-terahertz radiation. This study proposes a practical method to reduce interference effects and improve the stability of transmittance measurements. By deriving analytical expressions for interference patterns under both normal and oblique incidence conditions, we demonstrate that oblique incidence simplifies the interference behavior and allows the reliable extraction of transmittance values from maximum and minimum signal intensities. Using a 95 GHz CW oscillator (Model SFD-753114-103-10SF-P1, Eravant, Torrance, CA, USA) and a 1 mm-thick PET sample, we conducted transmission measurements while varying the detector position. The derived method enabled the calculation of interference-free transmittance values that were consistent across different sample positions. This approach offers a practical technique for material characterization, especially in applications such as nondestructive testing and plastic recycling. Full article

The article presents Modified Scaled Adaptive Daqrouq Wavelet (MSADW) as an autonomous wavelet framework to overcome the analysis obstacles of traditional wavelets (Morlet and Daubechies) for signals with non-stationary characteristics. MSADW adjusts its waveform shape and frequency in real time based on the specific characteristics of the signal, allowing it to outperform conventional wavelet methods. The system reaches adaptability through three core methods featuring gradient-dependent scale adjustments for fast transient detection and smooth regions, and instantaneous frequency monitoring achieved by a combination of STFT and Hilbert transforms and an iterative error reduction process using gradient descent and genetic algorithms. Continuous Wavelet Transform (CWT) combined with Discrete Wavelet Transform (DWT) extracts features from ECG and speech signals. Throughout this process, MSADW maintains great time precision to detect transients as well as maintain sensitivity for the audio’s base stability. Testing MSADW in practical use reveals its superior performance because it detects R-peaks accurately within 0.01 s through zero-crossing methods, which combine P/T-wave detection with effective ECG signal segmentation and noise-free reconstructed speech ($\mathit{MSE:} 1.17\times {10}^{-31}$). The localized parameterization framework of MSADW, enabled by feedback refinement, fulfills missing aspects in biomedical signal evaluation and creates space for low-cost real-time evaluation methods for medical devices and arrhythmia and ischemic detection platforms. The theoretical backbone for MSADW establishes itself because this work shows how wavelet analysis can transition toward managing non-stationary and noise-prone domains. Full article

With the rapid evolution of 5G wireless communications, millimeter-wave (mmWave) technology has become a crucial enabler for high-speed, low-latency, and large-scale connectivity. As the critical interface for signal transmission, mmWave antennas directly affect system performance, reliability, and application scope. This paper reviews the current state of mmWave antenna technologies in 5G systems, focusing on antenna types, design considerations, and integration strategies. We discuss how the multiple-input multiple-output (MIMO) architectures and advanced beamforming techniques enhance system capacity and link robustness. State-of-the-art integration methods, such as antenna-in-package (AiP) and chip-level integration, are examined for their importance in achieving compact and high-performance mmWave systems. Material selection and fabrication technologies—including low-loss substrates like polytetrafluoroethylene (PTFE), hydrocarbon-based materials, liquid crystal polymer (LCP), and microwave dielectric ceramics, as well as emerging processes such as low-temperature co-fired ceramics (LTCC), 3D printing, and micro-electro-mechanical systems (MEMS)—are also analyzed. Key challenges include propagation path limitations, power consumption and thermal management in highly integrated systems, cost–performance trade-offs for mass production, and interoperability standardization across vendors. Finally, we outline future research directions, including intelligent beam management, reconfigurable antennas, AI-driven designs, and hybrid mmWave–sub-6 GHz systems, highlighting the vital role of mmWave antennas in shaping next-generation wireless networks. Full article

Motor impairments significantly affect individuals’ ability to perform activities of daily living, reducing autonomy and quality of life. In response to this, robot-assisted rehabilitation has emerged as an effective and practical solution, enabling controlled limb movements and supporting functional recovery. This study presents the development of an upper-limb exoskeleton designed to assist rehabilitation by integrating neurophysiological signal processing and real-time control strategies. The system incorporates a proportional–derivative (PD) controller to execute cyclic flexion and extension movements based on a sinusoidal reference signal, providing repeatability and precision in motion. The exoskeleton integrates a brain–computer interface (BCI) that utilizes electroencephalographic signals for therapy selection and engagement enabling user-driven interaction. The EEG data extraction was possible by using the UltraCortex Mark IV headset, with electrodes positioned according to the international 10–20 system, targeting alpha-band activity in channels O1, O2, P3, P4, Fp1, and Fp2. These channels correspond to occipital (O1, O2), parietal (P3, P4), and frontal pole (Fp1, Fp2) regions, associated with visual processing, sensorimotor integration, and attention-related activity, respectively. This approach enables a more adaptive and personalized rehabilitation experience by allowing the user to influence therapy mode selection through real-time feedback. Experimental evaluation across five subjects showed an overall mean accuracy of 86.25% in alpha wave detection for EEG-based therapy selection. The PD control strategy achieved smooth trajectory tracking with a mean angular error of approximately 1.70°, confirming both the reliability of intention detection and the mechanical precision of the exoskeleton. Also, our core contributions in this research are compared with similar studies inspired by the rehabilitation needs of stroke patients. In this research, the proposed system demonstrates the potential of integrating robotic systems, control theory, and EEG data processing to improve rehabilitation outcomes for individuals with upper-limb motor deficits, particularly post-stroke patients. By focusing the exoskeleton on a single degree of freedom and employing low-cost manufacturing through 3D printing, the system remains affordable across a wide range of economic contexts. This design choice enables deployment in diverse clinical settings, both public and private. Full article

Background: Dopamine (DA) is a critical neurotransmitter that regulates many physiological and behavioral processes. The central dopaminergic system plays a pivotal role in modulating general anesthesia (GA). DA release in the brain is mainly concentrated in the nucleus accumbens (NAc), prefrontal cortex, hypothalamus, and dorsal striatum. Several NAc neuron subtypes are essential for modulating states of consciousness during GA. However, whether NAc DA signal dynamics correlate with different states of consciousness under sevoflurane anesthesia remains to be elucidated. In this study, we measured the dynamic fluctuations of NAc DA levels throughout sevoflurane anesthesia to verify its role. Methods: An intensity-based genetically encoded DA indicator, dLight1.1, was employed to track DA release in the NAc. Fiber photometry combined with electroencephalogram/electromyogram recordings was employed to synchronously track NAc DA signal dynamics across different states of consciousness under sevoflurane anesthesia. Results: Under 2.5% sevoflurane exposure, DA release in the NAc significantly increased during the initial 100 s of sevoflurane induction, which was designated as sevo on-1 (mean ± standard error of the mean [SEM]; baseline vs. sevo on-1, p = 0.0261), and continued to decrease in the subsequent anesthesia maintenance phases (sevo on-1 vs. sevo on-4, p = 0.0070). Following the cessation of sevoflurane administration (with intervals denoted as sevooff), NAc DA gradually returned to baseline levels (sevo on-1 vs. sevo off-1, p = 0.0096; sevo on-1 vs. sevo off-3, p = 0.0490; sevo on-1 vs. sevo off-4, p = 0.0059; sevo on-4 vs. sevo off-4, p = 0.0340; sevo off-1 vs. sevo off-4, p = 0.0451). During the induction phase, NAc DA signal dynamics markedly increased during the pre-loss of consciousness (LOC) period (pre-anesthesia baseline vs. pre-LOC, p = 0.0329) and significantly declined after LOC (pre-LOC vs. post-LOC, p = 0.0094). For the emergence period, NAc DA release exhibited a noticeable increase during the initial period after recovery of consciousness (ROC) (anesthesia baseline vs. post-ROC, p = 0.0103; pre-ROC vs. post-ROC, p = 0.0086). Furthermore, the DA signals peaked rapidly upon the initiation of the burst wave and then gradually attenuated, indicating a positive correlation with the burst wave onset during burst suppression events. Conclusions: Our findings revealed that NAc DA neurotransmitter signal dynamics correlate with different states of consciousness throughout sevoflurane anesthesia. Full article