Search Results (7,082)

Parkinson’s disease (PD) is a heterogeneous clinical syndrome representing the final stage of a complex and long-lasting neurodegenerative process that involves not only dysfunction of the dopaminergic system but also impairments in other neurotransmitter systems. The diversity of the clinical presentation of PD, together with the existence of Parkinsonian syndromes and atypical Parkinsonism—such as multiple system atrophy (MSA), progressive supranuclear palsy (PSP), and dementia with Lewy bodies (DLB)—has important implications for rehabilitation outcomes and underscores the need for individualized, stage-dependent therapeutic approaches. Juggling is a complex motor activity that integrates cognitive, visuomotor, and balance processes, requiring a high level of concentration, precision, and motor adaptation. In recent years, there has been growing interest in this form of activity as a potential tool for supporting neuroplasticity, cognitive functions, and neurological rehabilitation. The aim of this review was to summarize current scientific evidence on the effects of juggling training on cognitive functions, visuomotor coordination, and balance, as well as to discuss the potential benefits of combining it with controlled hypoxia in patients with Parkinson’s disease (PD). This narrative review additionally considers how disease heterogeneity and stage of progression may influence the effectiveness of such multimodal interventions. This paper reviews the literature concerning the neurophysiological basis of learning to juggle and the mechanisms of brain plasticity, including increases in gray matter volume, improvements in white matter integrity, and reorganization of neuronal networks in motor and associative regions. Attention is drawn to the synergistic potential of combining juggling training with exposure to moderate, controlled hypoxia, which may induce an adaptive response involving the transcription factor HIF-1α, enhance the expression of brain-derived neurotrophic factor (BDNF), and promote angiogenesis and mitochondrial biogenesis. Although juggling and hypoxia are not directly related to training stimuli, both interventions activate overlapping and complementary neuroplastic pathways, providing a conceptual rationale for their parallel consideration and potential integration within future rehabilitation protocols. Juggling delivers task-specific motor–cognitive learning, whereas hypoxia may amplify molecular plasticity signaling, potentially enhancing responsiveness to motor interventions, particularly in patients at early stages of PD when compensatory mechanisms and neuroplastic capacity are relatively preserved. Findings from existing studies suggest that juggling under controlled hypoxic conditions may represent an innovative, safe, and multimodal form of training that supports both cognitive and motor components. Such effects may be particularly relevant in patients at early stages of PD, when compensatory mechanisms and neuroplastic potential are relatively preserved. Such an intervention may contribute to improvements in balance, attention, executive functions, and cognitive flexibility, which is particularly relevant in the context of rehabilitation for patients with neurodegenerative diseases. Importantly, to date, no randomized clinical trials have directly examined juggling performed under controlled hypoxic conditions in PD. Therefore, the present concept should be regarded as translational and exploratory, integrating evidence from juggling-induced neuroplasticity and hypoxia-related physiological adaptations. In this context, the proposed approach represents a proof-of-concept framework for future multimodal interventions rather than an established therapeutic strategy. Available evidence suggests that combining complex sensorimotor skill training with physiological modulation of the internal environment may constitute a novel direction in PD rehabilitation, extending beyond conventional exercise-based models. Despite promising reports, further well-designed clinical studies are needed to determine the optimal training parameters (frequency, intensity, duration, and degree of hypoxia), to evaluate the long-term sustainability of therapeutic effects, and to account for the heterogeneity of PD and related Parkinsonian disorders. Full article

The acoustic technique emerges as a highly promising, cutting-edge solution that can be effectively employed for extinguishing flames in locations where the access to classical fire-protection measures is limited, the available extinguishing agent is severely restricted, or the burning materials are difficult to suppress using currently known methods. The results of the experimental attempts confirmed that low-frequency acoustic waves containing higher even harmonics from the tenth to the sixteenth order (inclusive) can successfully extinguish flames, demonstrating both the feasibility and the novelty of the acoustic technique for fire protection. Moreover, statistical analysis was applied to identify operational boundary values and assess their variability, supporting the optimal selection of system parameters required for rapid and effective flame extinguishing. By integrating an acoustic extinguisher with optional intelligent sensors, including artificial vision, it becomes possible to rapidly detect flames at much greater distances than with conventional smoke and temperature sensors, as well as to automatically extinguish them. In this context, an integrated solution combining acoustic waves with an artificial intelligence module (smart sensor) may be employed for comprehensive fire management, encompassing both fire detection and flame extinguishing. Full article

Subsurface temperature is an important parameter for characterizing oceanic physical processes, and accurate prediction of subsurface temperature is essential for understanding oceanic changes. Existing methods primarily focus on spatial modeling but offer limited characterization of the spatiotemporal structure and frequency features of sea temperature. They also suffer from restricted receptive fields and limited ability to model long-term dependencies. In this study, we propose a deep learning model named Fourier Window Transformer (FWinFormer), which integrates frequency-domain modeling to predict the three-dimensional subsurface temperature over the next 24 days. The model incorporates both temporal and frequency characteristics to enhance prediction accuracy. It consists of three modules: a Spatial Block Encoder, a Translator, and a Spatial Block Decoder. The spatial encoding and decoding modules are designed to extract spatial features, while the Translator models multi-scale temporal features based on the features extracted by the encoding and decoding modules. The input consists of 24 days of historical satellite observations, including sea-surface temperature (SST), salinity (SSS), eastward velocity (SSU), northward velocity (SSV) and height (SSH). We compared the model predictions with reanalysis data and evaluated performance from the perspectives of temporal evolution, spatial distribution, and vertical structure. Additionally, we validated the predicted temperatures against in situ observations. The results show that the model achieves strong and consistent performance across various temporal scales and spatial regions, with MAE, RMSE, and R² values of 0.529, 0.785, and 0.994, respectively, for the 24-day average prediction. Full article

Plants defend against pathogens such as fungi by initiating coordinated structural and chemical responses. Pathogen perception triggers rapid cytosolic calcium influx and calcium oscillations that drive defense gene expression, yet the mechanisms by which these signals encode stressor intensity and propagate systematically remain unclear. Here, we present a microfluidic system to characterize intracellular calcium dynamics in protonemal colonies of the moss Physcomitrium patens (Hedw.) upon precise and reversible exposure to fungal chitin oligosaccharides. Epifluorescent imaging of cells expressing the calcium indicator GCaMP6f revealed a rapid, coordinated calcium response to chitin addition, followed by stereotyped oscillations that subsided quickly upon stimulus removal. We implemented an unbiased image segmentation algorithm using pixel-based k-means clustering to automatically locate regions with specific oscillatory signatures. Calcium dynamics were distinct across adjacent cells, distinguishable by cell type, and significantly modulated by circadian rhythm, adaptation time within the device, and stimulus timing. Cytosolic calcium oscillations, which rose and fell symmetrically within about 60 s, occurred spontaneously during the subjective night and following short adaptation periods. Chitin elicited strong oscillations with increased frequency, amplitude, and duration, and repeated pulses entrained regular, colony-wide oscillations at the stimulation interval. This study complements prior investigations of whole plant and growth tip dynamics and provides a quantitative framework to study calcium signaling in plants, including mechanisms of signal propagation and the role of oscillation frequency on gene expression. Full article

Arc fault is the dominant cause of fire in photovoltaic (PV) systems, making its accurate identification crucial for PV fire prevention. This study investigates the influence of voltage (200, 300, and 400 V) and current (3, 5, 7, 9, and 11 A) on the DC series arc fault characteristics in PV systems obtained through experimental analysis. The results show that voltage variation has a negligible impact on arc fault behavior, while higher current levels substantially increase noise in the arc fault signals. To effectively mitigate noise, this paper proposes a denoising method that combines an improved moss growth optimization algorithm (IMGO) with improved complete ensemble empirical mode decomposition featuring adaptive noise (ICEEMDAN). It is found that the IMGO-ICEEMDAN denoising algorithm can effectively diminish noise in current signals, broaden characteristic frequency bands, and ameliorate arc feature discernibility. Subsequently, an integrated multi-scale spatiotemporal model is developed to extract arc fault features from the denoised signals. The model employs wide deep convolutional neural networks (WDCNNs) and bidirectional long short-term memory (BiLSTM) networks for parallel feature extraction, supplemented by a cross-attention (CA) module to optimize feature integration. The proposed WDCNN-BiLSTM-CA model ultimately achieves a detection accuracy of 99.89%, demonstrating superior detection performance over conventional methods, such as CNN-GRU and 1DCNN-LSTM models. This work provides a reliable framework for arc fault detection and fire risk reduction in PV systems. Full article

This work systematically investigates the coupled effects of femtosecond laser parameters (wavelength: 515 nm, pulse width: 373 fs, laser fluence: 3.18–12.7 J/cm², repetition frequence: 100 kHz) and post-fabrication thermal treatment on the micro/nano-structure evolution and wettability of aluminum alloys. By varying the scanning spacing (20–80 μm) and laser fluence, diverse hierarchical surface morphologies were obtained. At a small scanning spacing of 20 μm, increasing laser fluence causes severe thermal accumulation and structural collapse, with the microstructure height decreasing from 42.68 μm to 20.30 μm and the water contact angle (WCA) dropping from 158.6° to 143.5°, indicating a degradation of the superhydrophobic state. In contrast, at larger spacings (60–80 μm), moderate fluence enhances microstructure depth and roughness, yielding peak WCAs of ~160°, while excessive fluence induces feature coarsening and partial loss of nanoscale textures, leading to reduced wettability. Nanoscale evolution shows that optimized laser conditions promote dense nanoparticle redeposition and stable ridge-like structures. These structures are accompanied by cotton-like features with pore diameters of 50–100 nm and coral-like porous features with pore diameters of 100–200 nm, whereas excessive laser etching damage these nano-structures. Among, a scanning spacing of 40 μm achieves this most robust hierarchical nano-structure, corresponding to a maximum WCA of 162.6°. These results clarify the role of femtosecond laser parameters in regulating micro/nano-structural formation and the subsequent modulation of wettability through thermal treatment, providing a reference for the fabrication of coating-free superhydrophobic aluminum alloy surfaces. Full article

Elevated indoor carbon dioxide (CO₂) concentrations are increasingly recognized as a critical determinant of occupant health and cognitive performance; however, the neurophysiological mechanisms linking CO₂ exposure to daytime sleepiness remain a subject of debate. This study investigated the effects of dynamically rising CO₂ concentrations on subjective sleepiness and neural oscillation patterns in a controlled environment. A within-subject repeated-measures experiment was conducted with 18 healthy university students exposed to CO₂ levels gradually increasing from a baseline of ~800 ppm to ~2000 ppm over a 40-min period. Subjective sleepiness was assessed using the Karolinska Sleepiness Scale (KSS), while electroencephalogram (EEG), heart rate (HR), and heart rate variability (HRV) were monitored continuously. Results indicate a significant increase in subjective sleepiness scores concurrent with rising CO₂ levels (p < 0.001). Power spectral density (PSD) analysis revealed that elevated CO₂ exposure significantly enhanced power in low-frequency EEG bands (δ, θ, and α), indicative of a transition from alertness to drowsiness. Specifically, α-band power peaked between 20 and 30 min (increasing by 0.15 uV² relative to the 0–10 min baseline), while θ and δ bands peaked earlier (10–20 min) and sustained elevated levels through 30 min. Topographic mapping identified the central, parietal, and occipital regions as the primary loci of this low-frequency activity. Additionally, HR and HRV measures showed upward trends, suggesting autonomic modulation. It is noted that low-frequency EEG power declined during the final 30–40 min interval, potentially reflecting physiological saturation or acclimatization. These findings provide objective neurophysiological evidence that dynamic CO₂ accumulation accelerates drowsiness onset, underscoring the necessity of optimized ventilation strategies in educational settings. However, these findings should be interpreted with caution due to the small sample size and the absence of a control group with constant CO₂ concentration. Future studies with larger, diverse samples and stable-concentration control groups are recommended to validate these trends. Full article

Hypertension is a globally prevalent chronic cardiovascular disease, with angiotensin-converting enzyme (ACE) serving as a key target for therapeutic intervention. Synthetic ACE inhibitors have side effects, making natural food-derived ACE-inhibitory peptides a research hotspot owing to safety advantages. Softshell turtle (Pelodiscus sinensis) bone collagen (STBC) has potential bioactivity, but its ACE-inhibitory peptides have not been systematically investigated. This study used computer-aided screening: STBC α1(I) (K7FHL1) and α2(I) (K7G8R1) sequences from UniProt were processed via SignalP 5.0. BIOPEP-UWM analysis showed ACE-inhibitory peptide frequencies of 0.8947 and 0.9261 in the two chains, confirming STBC as a high-quality precursor. Papain-ficin was selected as the optimal enzymatic system via simulation; 105 potential novel peptides were obtained after toxicity/allergenicity prediction. Twenty-seven highly active peptide fragments were screened out via pLM4ACE, and four peptide fragments with relatively high binding energy (QICVCDS, DVWK, IIEY, APMDVG) were identified through molecular docking. These peptides (molecular weight: 536.6–766.9 Da) possessed excellent physicochemical properties and pharmacokinetic characteristics, while bioinformatics analysis revealed that they could target and regulate SRC/HSP90AA1 to modulate the renin-angiotensin system (RAS). This study provides an efficient strategy for the high-value utilization of softshell turtle resources and the development of food-derived ACE-inhibitory peptides. Full article

Convolutional neural networks leverage their efficient ability to extract common features of images, playing a crucial role in numerous computer vision tasks. Key details such as edges and textures in images often present themselves in the form of high-frequency components, which contain rich semantic information and are essential for accurate image recognition and understanding. However, during the downsampling process, these high-frequency components are improperly mapped to low-frequency components, leading to signal aliasing. This aliasing results in the loss of image detail information and blurred features, significantly affecting the precise extraction of image features by convolutional neural networks and ultimately reducing the performance of the model in various tasks. To effectively address this challenge, this study innovatively proposes the Gaussian Filter Convolution (GFC) module. This module ingeniously utilizes convolution kernels with filtering functions, which can specifically suppress the high-frequency components in the image, reducing the occurrence of signal aliasing at its source, thereby significantly alleviating the aliasing artifacts generated during downsampling. Experimental data show that the model integrated with GFC has significant improvements in key indicators such as model accuracy. Full article

Flexible dielectric composite materials capable of converting power frequency electric fields into measurable electrical signals are of great significance in the field of non-contact electric field sensing in power systems. In this paper, graphene/nitride heterojunction powders were prepared using three representative nitrides (AlN, BN, and Si₃N₄) and embedded in polydimethylsiloxane (PDMS) to prepare flexible composite films with a fixed filler content of 5.0 wt%. Scanning electron microscopy (SEM) and energy dispersive spectroscopy (EDS) confirmed the successful formation of the heterojunctions. The results showed that the nitride-related elements (Al, Si and N) were spatially correlated with the graphene-rich regions, thus providing abundant interfacial contact sites. Dielectric spectroscopy (50 Hz–50 kHz) showed that all samples exhibited typical dispersive behavior, with the real part of the dielectric constant decreasing monotonically with increasing frequency, and the loss tangent also decreasing smoothly. Under a 50 Hz parallel-plate electric field, the normalized induced voltage amplitude (PDMS = 1) increases to 1.070 (≈7.0%) for G/PDMS, and further to 1.0723–1.07447 (≈7.23–7.45%) for AlN–G/PDMS, BN–G/PDMS, and Si₃N₄-G/PDMS. DFT calculations confirm that the graphene/nitride interface has a stable structure with negative binding energies (−2.241, −1.773, and −3.062 eV for AlN–G, BN–G, and Si₃N₄–G, respectively). Significant charge redistribution and Mulliken charge transfer (0.0538, 0.2047, and 0.0244 eV, respectively) are present at the interface, accompanied by Fermi level density of states modulation and a small bandgap opening (~0.101 eV) in BN–G. These results collectively support the interfacial polarization-driven mechanism and provide a comparative basis for selecting nitride components in graphene-based heterojunction fillers in flexible dielectric electric field-sensing layers. Full article

Frequency stability of a 509-nm single-frequency laser, a core component combined with an 852-nm single-frequency laser for two-step cesium Rydberg transitions, is critical for quantum control and metrology precision. Utilizing atomic transition as the absolute reference, we achieved laser frequency locking via modulation-free Rydberg two-color polarization spectroscopy (Rydberg–TCPS) and frequency-modulated Rydberg electromagnetically-induced transparency (Rydberg–EIT) spectroscopy with discrete instruments combination and with Red Pitaya FPGA module. The results show that the Red Pitaya FPGA module matches discrete instruments combination in stability, being more compact and only one-tenth the cost. Rydberg–TCPS scheme avoids modulation-induced noise and linewidth broadening, outperforming Rydberg–EIT scheme. The Red Pitaya FPGA module provides a cost-effective, compact solution for Rydberg research, lowering experimental barriers. Full article

In multi-module boost power factor correction (PFC) systems, current ripple is commonly mitigated by applying fixed 180° interleaving between modules; however, this approach relies on matched inductors and ideal symmetry. In practical implementations, inductor mismatch and duty-cycle variations prevent full cancellation, leading to residual ripple that increases losses and electromagnetic interference. To address this issue, several research works have proposed centralized coordination or high-speed communication among units. However, an explicit converter model is necessary, which makes the system more complicated and expensive. To resolve this problem, this paper presents an extremum seeking optimization method for reducing high-frequency ripple in multi-module PFC systems without requiring explicit converter models. The ripple minimization problem is formulated as a nonlinear, time-varying optimization task, where the relative switching phases of the modules are adaptively tuned. The proposed extremum seeking algorithm perturbs the phase shift, evaluates a ripple-based cost function, and updates the phases iteratively. A harmonic analysis is developed to characterize the dependence of ripple on duty ratio, inductor values, and phase displacement. Simulation results show that the method effectively reduces the RMS ripple current across balanced and mismatched operating conditions. In a three-unit system, applying the proposed technique lowered the current THD to 1.29% compared to 1.44% achieved with a fixed phase-shift approach. These findings demonstrate that extremum seeking optimization provides a mathematically rigorous and practically implementable solution for decentralized ripple minimization in multi-module boost PFC systems. Full article

Shadow removal aims to restore photometric, chromatic, and structural consistency between shadowed and non-shadowed image regions. Although weakly supervised shadow removal methods reduce the reliance on densely paired training data, they still struggle to fully exploit appearance priors from non-shadow regions. As a result, their shadow removal outputs often appear unnatural, exhibiting color shifts and loss of fine texture details. To address this issue, we propose an ab-dynamic feature refinement network (AB-DFRNet) for weakly supervised shadow removal that more effectively exploits structural and chromatic symmetry during training. A high-frequency information enhancement (HFIE) module is introduced into the shadow generation subnet to extract and enhance high-frequency components via frequency separation and dense convolutions, thereby facilitating the learning of fine structural symmetry and enriching pseudo-shadow details. In the removal subnet, a dual-attention adaptive fusion (DAAF) module combines global and local attention mechanisms to adaptively recalibrate channel-wise and spatial features, improving multi-scale feature integration. Furthermore, a chrominance-only consistency (COC) loss is designed to minimize differences between the a and b channels of restored regions and their non-shadow references in the Lab color space. This additional color refinement constraint encourages a symmetric distribution of chromatic information and helps the refinement network produce more natural shadow-removed results. Extensive experiments are conducted on three benchmark datasets: ISTD, SRD, and Video Shadow Removal. The results confirm the effectiveness of AB-DFRNet, demonstrating competitive quantitative performance and noticeably better visual quality compared with existing weakly supervised shadow removal methods. Full article

In complex natural scenarios, facial expression recognition often encounters partial occlusions caused by glasses, hand gestures, and hairstyles, making it difficult for models to extract effective features and thereby reducing recognition accuracy. Existing methods often employ attention mechanisms to enhance expression-related features, but they fail to adequately address the issue where high-frequency responses in occluded regions can disperse attention weights (e.g., incorrectly focus on occluded areas), making it challenging to effectively utilize local cues around the occlusions and limiting performance improvement. To address this, this paper proposes a network based on an adaptive attention mechanism (Adaptive Attention Vision Transformer, AA-ViT). First, an Adaptive Attention module (ADA) is designed to dynamically adjust attention scores in occluded regions, enhancing the effective information in features. Next, a Dual-Branch Multi-Layer Perceptron (DB-MLP) replaces the single linear layer to improve feature representation and model classification capability. Additionally, a Random Erasure (RE) strategy is introduced to enhance model robustness. Finally, to address the issue of model training instability caused by class imbalance in the training dataset, a hybrid loss function combining Focal Loss and Cross-Entropy Loss is adopted to ensure training stability. Experimental results show that AA-ViT achieves expression recognition accuracies of 90.66% and 90.01% on the RAF-DB and FERPlus datasets, respectively, representing improvements of 4.58 and 18.9 percentage points over the baseline ViT model, with only a 24.3% increase in parameter count. Compared to existing methods, the proposed approach demonstrates superior performance in occluded facial expression recognition tasks. Full article

Legged robots inspired by animal locomotion require actuators with high power density, fast response, and robust force control, yet traditional valve-controlled hydraulic systems suffer from substantial energy losses and weak regeneration performance. Motivated by role allocation across gait phases in animal legs, where in-air positioning requires far less actuation effort than ground contact support and force modulation, this work proposes a novel gas–oil hybrid servo actuator, denoted GOhsa, for quadruped knee joints. GOhsa utilizes pre-charged high-pressure gas to pressurize hydraulic oil, converting the conventional dual-chamber pressure servo control into a single-chamber configuration while preserving the original piston stroke. This architecture enables bidirectional position–force control, enhances energy regeneration applicability, and improves operational efficiency. Theoretical modeling is conducted to analyze hydraulic stiffness and frequency-response characteristics, and a linearization-based force controller with dynamic compensation is developed to handle system nonlinearities. Experimental validation on a single-leg platform demonstrates significant energy-saving performance: under no-load conditions (simulating the swing phase), GOhsa achieves a maximum power reduction of 79.1%, with average reductions of 15.2% and 11.5% at inflation pressures of 3 MPa and 4 MPa, respectively. Under loaded conditions (simulating the stance phase), the maximum reduction reaches 28.0%, with average savings of 10.0% and 9.8%. Tracking accuracy is comparable to traditional actuators, with reduced maximum errors (13.7 mm/16.5 mm at 3 MPa; 15.0 mm/17.8 mm at 4 MPa) relative to the 16.6 mm and 18.1 mm errors of the conventional system, confirming improved motion stability under load. These results verify that GOhsa provides high control performance with markedly enhanced energy efficiency. Full article