Search Results (79)

Background: The coordination between the upper and lower limbs is essential for athletic performance. However, the structural features that influence coordination difficulty remain insufficiently understood. Few studies have systematically analyzed how task components such as the directional congruence or rhythm structure affect inter-limb coordination. Objective: This study aimed to clarify the structural factors that influence the difficulty of upper–lower limb coordination tasks under rhythmic constraints and to explore the feasibility of applying such tasks in future coordination assessments. Methods: Eighty-six male high school baseball players performed six Rhythm Jump tasks combining fixed upper limb movements with varying lower limb patterns. The task performance was analyzed using three indices: full task success, partial success, and average successful series. One year later, a follow-up test involving 27 participants was conducted to evaluate the reproducibility and sensitivity to the performance change. Results: The task difficulty was significantly affected by structural features, including directional incongruence, upper limb static holding, and rhythmic asynchrony. The tasks that exhibited these features had lower success rates. Some tasks demonstrated moderate reproducibility and captured subtle longitudinal changes in the performance. Conclusions: The results highlight the key structural factors contributing to coordination difficulty and support the potential applicability of Rhythm Jump tasks as a basis for future assessment tools. Although further validation is necessary, this study provides foundational evidence for the development of practical methods for evaluating inter-limb coordination. Full article

Background and Objectives: Essential tremor (ET) is a common neurological disorder, typically presenting as bilateral, rhythmic, and symmetric kinetic or postural tremors. In contrast, Parkinson’s disease (PD) is a progressive neurodegenerative disorder, characterized by resting tremor, rigidity, bradykinesia, and postural instability. Although both disorders involve tremor, ET and PD differ in clinical presentation and pathophysiology: ET generally involves action tremor and has a strong familial component, while PD more commonly presents with resting tremor and a weaker family history. A subset of ET patients may develop Parkinsonian features over time, although the relationship between ET and subsequent PD remains unclear. Genetic studies have identified only a few pathogenic variants in ET, suggesting it develops as a result of multifactorial genetic and environmental influences rather than simple Mendelian inheritance. ET is also recognized as a risk factor for developing PD, although the underlying mechanisms remain poorly understood. This study aimed to clarify potential genetic overlaps and distinctions in patients diagnosed with both ET and PD. Materials and Methods: We retrospectively analyzed 40 patients with a family history of ET or PD who were initially diagnosed with ET and later developed PD. Genetic screening and clinical assessments were conducted to investigate associated variants and clinical features. Results: Among these 40 patients, 17 different mutations were detected in 16 individuals. Three pathogenic or likely pathogenic variants were identified. The clinical characteristics and treatment responses of these patients were reviewed in relation to their genetic findings. Notably, none of the identified variants had previously been reported in association with PD following ET. Conclusions: A comprehensive clinical and genetic evaluation of ET patients who develop PD may offer insights into the underlying pathophysiology and inform future therapeutic strategies. Our findings support the need for further studies to explore the genetic landscape of patients with overlapping ET and PD features. Full article

Deciphering the structural hierarchy of musical compositions is indispensable for a range of music analysis applications, encompassing feature extraction, data compression, interpretation, and visualization. In this paper, we introduce a quantitative model grounded in fractal theory to evaluate the significance of individual notes within a musical piece. To analyze the quantized note importance, we adopt a rhythm-based approach and propose a series of detection operators informed by fundamental rhythmic combinations. Employing the Mamba model, we carry out recursive detection operations that offer a hierarchic understanding of musical structures. By organizing the composition into a tree data structure, we achieve an ordered layer traversal that highlights the music piece’s multi-dimensional features. Musical compositions often exhibit intrinsic symmetry in their temporal organization, manifested through repetition, variation, and self-similar patterns across scales. Among these symmetry properties, fractality stands out as a prominent characteristic, reflecting recursive structures both rhythmically and melodically. Our model effectively captures this property, providing insights into the fractal-like regularities within music. It also proves effective in musical phrase boundary detection tasks, enhancing the clarity and visualization of musical information. The findings illustrate the model’s potential to advance the quantitative analysis of music hierarchy, promoting novel methodologies in musicological research. Full article

This study explores rhythm metrics as a sociolinguistic marker in Philippine English (PhE), addressing gaps in understanding rhythmic variation in Southeast Asian Englishes. It aims to uncover how rhythmic patterns reflect sociolectal identities within a multilingual context. Using acoustic data from 30 participants in Manila, rhythm metrics (%V, ΔV, ΔC, nPVI, and rPVI) were analyzed to examine rhythmic tendencies. The findings reveal distinct patterns: PhE acrolect aligns with stress-timed rhythms of general American English, PhE basilect reflects syllable-timed features similar to Spanish, and PhE mesolect occupies a hybrid position blending elements of both. By emphasizing rhythm as a key identifier of sociolectal variation, this study advances the understanding of linguistic diversity in World Englishes and provides a novel framework for exploring identity in multilingual settings. Full article

Arabic poetry follows intricate rhythmic patterns known as ‘arūḍ’ (prosody), which makes its automated categorization particularly challenging. While earlier studies primarily relied on conventional machine learning and recurrent neural networks, this work evaluates the effectiveness of transformer-based models—an area not extensively explored for this task. We investigate several pretrained transformer models, including Arabic Bidirectional Encoder Representations from Transformers (Arabic-BERT), BERT base Arabic (AraBERT), Arabic Efficiently Learning an Encoder that Classifies Token Replacements Accurately (AraELECTRA), Computational Approaches to Modeling Arabic BERT (CAMeLBERT), Multi-dialect Arabic BERT (MARBERT), and Modern Arabic BERT (ARBERT), alongside deep learning models such as Bidirectional Long Short-Term Memory (BiLSTM) and Bidirectional Gated Recurrent Units (BiGRU). This study uses half-verse data across 14 m. The CAMeLBERT model achieved the highest performance, with an accuracy of 90.62% and an F1-score of 0.91, outperforming other models. We further analyze feature significance and model behavior using the Local Interpretable Model-Agnostic Explanations (LIME) interpretability technique. The LIME-based analysis highlights key linguistic features that most influence model predictions. These findings demonstrate the strengths and limitations of each method and pave the way for further advancements in Arabic poetry analysis using deep learning. Full article

Hydrothermal activity on the modern seafloor varies depending on the tectonic setting. In particular, the neovolcanic zones (NVZs) along mid-ocean ridges, where magmatism is intense, generally host high-temperature hydrothermal activities. These high-temperature hydrothermal activities on the NVZs can promote the development of many polymetallic sulfide deposits. Currently, many high-temperature hydrothermal activities and sulfide accumulations have been discovered on the NVZs of major mid-ocean ridges worldwide, but relatively few have been found in the Southern Mid-Atlantic Ridge (SMAR), which limits our understanding of the hydrothermal mineralization characteristics on the NVZs of SMAR. Fortunately, in 2015, a new hydrothermal field—Tongguan—developed on the NVZ of the SMAR was discovered. In this study, we conducted mineralogical and sulfur isotope studies on hydrothermal chimney and massive sulfide samples collected from the Tongguan field. We revealed the mineral composition and growth sequence in the chimney structures and sulfides and discovered two different chimney growth patterns featuring rhythmic banding and opal-filled structures. Additionally, sulfur isotopes suggest the presence of mixing between seawater within the oceanic crust and the upwelling hydrothermal fluid in this hydrothermal field. Our investigation revealed small-scale fluid heterogeneities during the submarine hydrothermal mineralization process, which is due to fluctuations in fluid temperatures and mineral deposition within individual vent frameworks. This work provides a reference for further understanding and comprehension of hydrothermal mineralization on the NVZs of SMAR. Full article

Stromatolites, distinctive fossil records within Precambrian strata, are essential for investigating the depositional environments of early Earth and the geological settings conducive to hydrocarbon formation. The Luonan area is located in Shaanxi Province, China, where a large number of stromatolites have been discovered within the Mesoproterozoic Erathem, providing new perspectives on paleoenvironment and reservoir spaces. This study analyzes the morphology of stromatolites, associated microorganisms, mineralogy, and cathodoluminescence from the carbonate rocks of the Jixian System. Carbon and oxygen isotope analyses help reconstruct paleosalinity and climate, enhancing understanding of their petroleum geological significance. Combining carbon and oxygen isotope analysis with the fine observation and description of stromatolite can better reconstruct the paleoenvironmental features of the Mesoproterozoic Era. The results indicated a narrow range of carbon isotope values (δ¹³C: −5.81‰ to −2.43‰; mean: −4.03‰) and oxygen isotope values (δ¹⁸O: −9.06‰ to −5.64‰). The Longjiayuan Formation is characterized by high CaO and MgO content, with low SiO₂ and minimal terrigenous input, in contrast with the Fengjiawan Formation, which exhibits elevated SiO₂ and greater terrigenous material. The Luonan stromatolites display prominent rhythmic laminations, primarily composed of dolomite, indicating a potential for hydrocarbon source rocks. Stromatolite morphologies, including layered, columnar, and wavy forms, reflect varied depositional microfacies. The alternating bright and dark laminae, rich in CaO and CO₂ but differing in Ca²⁺ and Mg²⁺ concentrations, signify seasonal growth cycles. These Mesoproterozoic stromatolites developed in a warm, humid, and stable climatic regime, within a marine anoxic-to-suboxic setting, typically in intertidal or supratidal zones with low hydrodynamic energy. In the southern margin of the North China Craton, stromatolites from the Mesoproterozoic Era are extensively developed and exhibit distinct characteristics. Due to the biogenic alteration of stromatolites, the porosity of the rock increased. These stromatolites have altered the physical properties of the host rocks to some extent, suggesting the possibility of becoming effective hydrocarbon reservoirs. This has significant implications for deep oil and gas exploration, providing valuable guidance for future prospecting efforts. Full article

The Transformer, a deep learning architecture, has shown exceptional adaptability across fields, including music information retrieval (MIR). Transformers excel at capturing global, long-range dependencies in sequences, which is valuable for tracking rhythmic patterns over time. Temporal Convolutional Networks (TCNs), with their dilated convolutions, are effective at processing local, temporal patterns with reduced complexity. Combining these complementary characteristics, global sequence modeling from Transformers and local temporal detail from TCNs enhances beat tracking while reducing the model’s overall complexity. To capture beat intervals of varying lengths and ensure optimal alignment of beat predictions, the model employs a Dynamic Bayesian Network (DBN), followed by Viterbi decoding for effective post-processing. This system is evaluated across diverse public datasets spanning various music genres and styles, achieving performance on par with current state-of-the-art methods yet with fewer trainable parameters. Additionally, we also explore the interpretability of the model using Grad-CAM to visualize the model’s learned features, offering insights into how the TCN-Transformer hybrid captures rhythmic patterns in the data. Full article

This study presents a differential evolution (DE)-based optimization approach for fractional-order convolutional neural networks (FOCNNs) aimed at enhancing the accuracy of groove classification in music. Groove, an essential element in music perception, is typically influenced by rhythmic patterns and acoustic features. While FOCNNs offer a promising method for capturing these subtleties through fractional-order derivatives, they face challenges in efficiently converging to optimal parameters. To address this, DE is applied to optimize the initial weights and biases of FOCNNs, leveraging its robustness and ability to explore a broad solution space. The proposed DE-FOCNN was evaluated on the Janata dataset, which includes pre-rated music tracks. Comparative experiments across various fractional-order values demonstrated that DE-FOCNN achieved superior performance in terms of higher test accuracy and reduced overfitting compared to a standard FOCNN. Specifically, DE-FOCNN showed optimal performance at fractional-order values such as v = 1.4. Further experiments demonstrated that DE-FOCNN achieved higher accuracy and lower variance compared to other popular evolutionary algorithms. This research primarily contributes to the optimization of FOCNNs by introducing a novel DE-based approach for the automated analysis and classification of musical grooves. The DE-FOCNN framework holds promise for addressing other related engineering challenges. Full article

Hypnotic phenomena exhibit significant inter-individual variability, with some individuals consistently demonstrating efficient responses to hypnotic suggestions, while others show limited susceptibility. Recent neurophysiological studies have added to a growing body of research that shows variability in hypnotic susceptibility is linked to distinct neural characteristics. Building on this foundation, our previous work identified that individuals with high and low hypnotic susceptibility can be differentiated based on the arrhythmic activity observed in resting-state electrophysiology (rs-EEG) outside of hypnosis. However, because previous work has largely focused on mean spectral characteristics, our understanding of the variability over time of these features, and how they relate to hypnotic susceptibility, is still limited. Here we address this gap using a time-resolved assessment of rhythmic alpha peaks and arrhythmic components of the EEG spectrum both prior to and following hypnotic induction. Using multivariate pattern classification, we investigated whether these neural features differ between individuals with high and low susceptibility to hypnosis. Specifically, we used multivariate pattern classification to investigate whether these non-stationary neural features could distinguish between individuals with high and low susceptibility to hypnosis before and after a hypnotic induction. Our analytical approach focused on time-resolved spectral decomposition to capture the intricate dynamics of neural oscillations and their non-oscillatory counterpart, as well as Lempel–Ziv complexity. Our results show that variations in the alpha center frequency are indicative of hypnotic susceptibility, but this discrimination is only evident during hypnosis. Highly hypnotic-susceptible individuals exhibit higher variability in alpha peak center frequency. These findings underscore how dynamic changes in neural states related to alpha peak frequency represent a central neurophysiological feature of hypnosis and hypnotic susceptibility. Full article

Background and Objectives: Vertical rhythmic dyskinetic movements that are primarily drug-induced and affect solely the jaw, mouth, and lips without involving the tongue have been historically described as “rabbit” syndrome (RS). Evidence on the unique features and implications of this disorder remains limited. This literature review aims to evaluate the clinical–epidemiological profile, pathological mechanisms, and management of this movement disorder. Materials and Methods: Two reviewers identified and assessed relevant reports in six databases without language restriction published between 1972 and 2024. Results: A total of 85 articles containing 146 cases of RS were found. The mean frequency of RS among adults in psychiatric hospitals was 1.2% (range 0–4.4%). The mean age of affected patients was 49.2 (SD: 17.5), and 63.6% were females. Schizophrenia was the most frequent comorbidity found in 47.6%, followed by bipolar disorder (17.8%), major depressive disorder (10.3%), and obsessive–compulsive disorder (3.7%). Five cases were idiopathic. The most common medications associated with RS were haloperidol (17%), risperidone (14%), aripiprazole (7%), trifluoperazine (5%), and sulpiride (5%). The mean duration of pharmacotherapy before RS was 21.4 weeks (SD: 20.6). RS occurred in association with drug-induced parkinsonism (DIP) in 27.4% and with tardive dyskinesia (TD) in 8.2% of cases. Antipsychotic modification and/or anticholinergic drugs resulted in full or partial recovery in nearly all reported cases in which they were prescribed. Conclusions: RS occurs as a distinct drug-induced syndrome associated primarily but not exclusively with antipsychotics. Distinguishing RS from TD is important because the treatment options for the two disorders are quite different. By contrast, RS may be part of a spectrum of symptoms of DIP with similar course, treatment outcomes, and pathophysiology. Full article

Pansori, a traditional Korean form of musical storytelling, is characterized by performances involving a vocalist and a drummer. It is well-known for the singer’s expressive narrative (aniri) and delicate gesture with fan in hand. The classical Pansori repertoires mostly tell love, satire, and humor, as well as some social lessons. These performances, which can extend from three to five hours, necessitate that the vocalist adheres to precise rhythmic structures. The distinctive rhythms of Pansori are crucial for conveying both the narrative and musical expression effectively. This paper explores the challenge of open-set recognition, aiming to efficiently identify unknown Pansori rhythm patterns while applying the methodology to diverse acoustic datasets, such as sound events and genres. We propose a lightweight deep learning-based encoder–decoder segmentation model, which employs a 2-D log-Mel spectrogram as input for the encoder and produces a frame-based 1-D decision along the temporal axis. This segmentation approach, processing 2-D inputs to classify frame-wise rhythm patterns, proves effective in detecting unknown patterns within time-varying sound streams encountered in daily life. Throughout the training phase, both center and supervised contrastive losses, along with cross-entropy loss, are minimized. This strategy aimed to create a compact cluster structure within the feature space for known classes, thereby facilitating the recognition of unknown rhythm patterns by allocating ample space for their placement within the embedded feature space. Comprehensive experiments utilizing various datasets—including Pansori rhythm patterns (91.8%), synthetic datasets of instrument sounds (95.1%), music genres (76.9%), and sound datasets from DCASE challenges (73.0%)—demonstrate the efficacy of our proposed method to detect unknown events, as evidenced by the AUROC metrics. Full article

Tremor, defined as an “involuntary, rhythmic, oscillatory movement of a body part”, is a key feature of many neurological conditions including Parkinson’s disease and essential tremor. Clinical assessment continues to be performed by visual observation with quantification on clinical scales. Methodologies for objectively quantifying tremor are promising but remain non-standardized across centers. Our center performs full-body behavioral testing with 3D motion capture for clinical and research purposes in patients with Parkinson’s disease, essential tremor, and other conditions. The objective of this study was to assess the ability of several candidate processing pipelines to identify the presence or absence of tremor in kinematic data from patients with confirmed movement disorders and compare them to expert ratings from movement disorders specialists. We curated a database of 2272 separate kinematic data recordings from our center, each of which was contemporaneously annotated as tremor present or absent by a movement physician. We compared the ability of six separate processing pipelines to recreate clinician ratings based on $F1$ score, in addition to accuracy, precision, and recall. The performance across algorithms was generally comparable. The average $F1$ score was $0.84\pm 0.02$ (mean ± SD; range 0.81–0.87). The second highest performing algorithm (cross-validated $F1=0.87$) was a hybrid that used engineered features adapted from an algorithm in longstanding clinical use with a modern Support Vector Machine classifier. Taken together, our results suggest the potential to update legacy clinical decision support systems to incorporate modern machine learning classifiers to create better-performing tools. Full article

Enhancing the naturalness and rhythmicity of generated audio in end-to-end speech synthesis is crucial. The current state-of-the-art (SOTA) model, VITS, utilizes a conditional variational autoencoder architecture. However, it faces challenges, such as limited robustness, due to training solely on text and spectrum data from the training set. Particularly, the posterior encoder struggles with mid- and high-frequency feature extraction, impacting waveform reconstruction. Existing efforts mainly focus on prior encoder enhancements or alignment algorithms, neglecting improvements to spectrum feature extraction. In response, we propose BERTIVITS, a novel model integrating BERT into VITS. Our model features a redesigned posterior encoder with residual connections and utilizes pre-trained models to enhance spectrum feature extraction. Compared to VITS, BERTIVITS shows significant subjective MOS score improvements (0.16 in English, 0.36 in Chinese) and objective Mel-Cepstral coefficient reductions (0.52 in English, 0.49 in Chinese). BERTIVITS is tailored for single-speaker scenarios, improving speech synthesis technology for applications like post-class tutoring or telephone customer service. Full article

Gait, a manifestation of one’s walking pattern, intricately reflects the harmonious interplay of various bodily systems, offering valuable insights into an individual’s health status. However, the current study has shortcomings in the extraction of temporal and spatial dependencies in joint motion, resulting in inefficiencies in pathological gait classification. In this paper, we propose a Frequency Pyramid Graph Convolutional Network (FP-GCN), advocating to complement temporal analysis and further enhance spatial feature extraction. specifically, a spectral decomposition component is adopted to extract gait data with different time frames, which can enhance the detection of rhythmic patterns and velocity variations in human gait and allow a detailed analysis of the temporal features. Furthermore, a novel pyramidal feature extraction approach is developed to analyze the inter-sensor dependencies, which can integrate features from different pathways, enhancing both temporal and spatial feature extraction. Our experimentation on diverse datasets demonstrates the effectiveness of our approach. Notably, FP-GCN achieves an impressive accuracy of 98.78% on public datasets and 96.54% on proprietary data, surpassing existing methodologies and underscoring its potential for advancing pathological gait classification. In summary, our innovative FP-GCN contributes to advancing feature extraction and pathological gait recognition, which may offer potential advancements in healthcare provisions, especially in regions with limited access to medical resources and in home-care environments. This work lays the foundation for further exploration and underscores the importance of remote health monitoring, diagnosis, and personalized interventions. Full article