Search Results (1,185)

Background: Accurate differentiation of Parkinson’s disease (PD) from healthy aging is crucial for timely intervention and effective management. Postural sway abnormalities are prominent motor features of PD. Quantitative stabilometry and machine learning (ML) offer a promising avenue for developing objective markers to support the diagnostic process. This study aimed to develop and validate high-performance ML models to classify individuals with PD and age-matched healthy older adults (HOAs) using a comprehensive set of stabilometric parameters. Methods: Thirty-seven HOAs (mean age 70 ± 6.8 years) and 26 individuals with idiopathic PD (Hoehn and Yahr stages 2–3, on medication; mean age 66 years ± 2.9 years), all aged 60–80 years, participated. Stabilometric data were collected using a force platform during quiet stance under eyes-open (EO) and eyes-closed (EC) conditions, from which 34 parameters reflecting the time- and frequency-domain characteristics of center-of-pressure (COP) sway were extracted. After data preprocessing, including mean imputation for missing values and feature scaling, three ML classifiers (Random Forest, Gradient Boosting, and Support Vector Machine) were hyperparameter-tuned using GridSearchCV with three-fold cross-validation. An ensemble voting classifier (soft voting) was constructed from these tuned models. Model performance was rigorously evaluated using 15 iterations of stratified train–test splits (70% train and 30% test) and an additional bootstrap procedure of 1000 iterations to derive reliable 95% confidence intervals (CIs). Results: Our optimized ensemble voting classifier achieved excellent discriminative power, distinguishing PD from HOAs with a mean accuracy of 0.91 (95% CI: 0.81–1.00) and a mean Area Under the ROC Curve (AUC ROC) of 0.97 (95% CI: 0.92–1.00). Importantly, feature analysis revealed that anteroposterior sway velocity with eyes open (V-AP) and total sway path with eyes closed (TOD_EC, calculated using COP displacement vectors from its mean position) are the most robust and non-invasive biomarkers for differentiating the groups. Conclusions: An ensemble ML approach leveraging stabilometric features provides a highly accurate, non-invasive method to distinguish PD from healthy aging and may augment clinical assessment and monitoring. Full article

As technologies become increasingly diverse and complex, Intelligent Massage Systems (IMS) are evolving from traditional mechanically executed modes toward personalized and predictive health interventions. However, the field still lacks a unified grading standard for intelligence, making it difficult to quantitatively assess a system’s overall intelligence level. To address this gap, this paper proposes a task-driven six-level (L0–L5) classification framework and constructs a Massage-Driven Task (MDT) model that decomposes the massage process into six subtasks (S1–S6). Building on this, we design a three-dimensional evaluation scheme comprising a Functional Delegation Structure (FDS), an Anomaly Perception Mechanism (APM), and a Human–Machine Interaction Boundary (HMIB), and we select eight key performance indicators to quantify IMS intelligence across the perception–decision–actuation–feedback closed loop. We then determine indicator weights via the Delphi method and the Analytic Hierarchy Process (AHP), and obtain dimension-level scores and a composite intelligence score S0 using normalization and weighted aggregation. Threshold intervals for L0–L5 are defined through equal-interval partitioning combined with expert calibration, and sensitivity is verified on representative samples using ±10% data perturbations. Results show that, within typical error ranges, the proposed grading framework yields stable classification decisions and exhibits strong robustness. The framework not only provides the first reusable quantitative basis for grading IMS intelligence but also supports product design optimization, regulatory certification, and user selection. Full article

This study developed a deep learning–based water level recognition model using Closed-Circuit Television (CCTV) footage. The model focuses on real-time water level recognition in agricultural reservoirs that lack automated water level gauges, with the potential for future extension to flood forecasting applications. Video data collected over approximately two years at the Myeonggyeong Reservoir in Chungcheongbuk-do, South Korea, were utilized. A semantic segmentation approach using the U-Net model was employed to extract water surface areas, followed by the classification of water levels using Convolutional Neural Network (CNN), ResNet, and EfficientNet models. To improve learning efficiency, water level intervals were defined using both equal spacing and the Jenks natural breaks classification method. Among the models, EfficientNet achieved the highest performance with an accuracy of approximately 99%, while ResNet also demonstrated stable learning outcomes. In contrast, CNN showed faster initial convergence but lower accuracy in classifying complex intervals. This study confirms the feasibility of applying vision-based water level prediction technology to flood-prone agricultural reservoirs. Future work will focus on enhancing system performance through low-light video correction, multi-sensor integration, and model optimization using AutoML, thereby contributing to the development of an intelligent, flood-resilient water resource management system. Full article

The characteristics and evolution of fibrous calcite veins in organic-rich shales have gained significant attention due to the recent advancements in shale oil and gas exploration. However, the fibrous calcite veins in the Upper Permian Dalong Formation remain lacking in awareness. To investigate the formation and significance of bedding-parallel fibrous calcite veins in the Dalong Formation, we conducted an extensive study utilizing petrography, geochemistry, isotopic analysis, and fluid inclusion studies on outcrops of the Dalong Formation in South China. Our findings reveal that fibrous calcite veins predominantly develop in the middle section of the Dalong Formation, specifically within the transitional interval between siliceous and calcareous shales, characterized by symmetric, antitaxial fibrous calcite veins. The δ¹³C values of these veins exhibit a broad range (−4.53‰ to +3.39‰) and display a decreasing trend in the directions of fiber growth from the central part, indicating an increased contribution of organic carbon to the calcite veins. Additionally, a consistent increase in trace element concentrations from the central part toward the fiber growth directions suggests a singular fluid source in a relatively closed environment, while other samples exhibit no distinct pattern, possibly due to the mixing of fluids from multiple layers resulting from repeated opening and closing of bedding-parallel fractures in the shales. The notable difference in δEu between the fibers on either side of the median zone indicates that previously formed veins acted as barriers, impeding the mixing of fluids, with the variation in δEu reflecting the differing sedimentary properties of the surrounding rocks. The in situ U-Pb dating of fibrous calcite veins yields an absolute age of 211 ± 23 Ma, signifying formation during the Late Triassic, which correlates with a shale maturity of 1.0‰ to 1.25‰. This integrated study suggests that the geochemical records of fibrous calcite veins document the processes related to overpressure generation and the opening and healing of bedding-parallel fractures within the Dalong Formation. Full article

Sensors are widely used in inland waterway buoys to monitor their position, but the collected data are often affected by noise, outliers, and irregular sampling intervals. To address these challenges, a standardized data processing framework is proposed. Outliers are identified using a hybrid approach combining interquartile range filtering and Isolation Forest algorithm. Interpolation methods are adaptively selected based on time intervals. For short-term gaps, cubic spline interpolation is applied, otherwise, a method that combines dominant periodicity estimation with physical constraints based on power spectral density (PSD) is proposed. An adaptive unscented Kalman filter (AUKF), integrated with the Singer motion model, are applied for denoising, dynamically adjusting to local noise statistics and capturing acceleration dynamics. Afterwards, a set of time-frequency features are extracted, including centrality, directional dispersion, and wavelet transform-based features. Taking the lower Yangtze River as a case study, representative buoys are selected based on dynamic time warping similarity. The features analysis result show that the movement of buoys is closely related to the dynamics dominated by the semi-diurnal tide, and is also affected by runoff and accidents. The method improves the quality and interpretability of buoy motion data, facilitating more robust monitoring and hydrodynamic analysis. Full article

Welding, as a critical process for achieving permanent material joining through localized heating or pressure, is extensively applied in mechanical manufacturing and transportation industries, significantly enhancing the assembly efficiency of complex structures. However, the associated localized high temperatures and rapid cooling often induce uneven thermal expansion and contraction, leading to complex stress evolution and residual stress distributions that compromise dimensional accuracy and structural integrity. In this study, we propose a combined heat source model based on the geometric characteristics of the weld pool to simulate the arc welding process of large flange shafts made of Fe-C-Mn-Cr low-alloy medium carbon steel. Simulations were performed under different welding durations and shaft diameters, and the model was validated through experimental welding tests. The results demonstrate that the proposed model accurately predicts weld pool geometry (depth error of only 2.2%) and temperature field evolution. Meanwhile, experimental and simulated deformations are presented with 95% confidence intervals (95% CI), showing good agreement. Residual stresses were primarily concentrated in the weld and heat-affected zones, exhibiting a typical “increase–steady peak–decrease” distribution along the welding direction. A welding duration of 90 s effectively reduced residual stress differentials perpendicular to the welding direction by 19%, making it more suitable for medium carbon steel components of this scale. The close agreement between simulation and experimental data verifies the model’s reliability and indicates its potential applicability to the welding simulation of other large-scale critical components, thereby providing theoretical support for process optimization. Full article

Background and Objectives: Carotid artery stenosis is an inflammatory vascular disease closely linked to atherosclerosis and associated with inflammatory biomarkers. The neutrophil/albumin ratio (NAR) is a novel promising biomarker in assessing cardiovascular disease severity. This study aimed to evaluate the relationship between NAR and lesion severity in patients with carotid artery stenosis. Materials and Methods: This retrospective, single-center, comparative study included 625 asymptomatic patients who underwent digital subtraction angiography (DSA) for suspected high-grade carotid artery stenosis between 2012 and 2022. Patients were classified into two groups based on stenosis severity: critical carotid artery stenosis (≥70% stenosis) and non-critical carotid artery stenosis (<70%). Only asymptomatic patients were included; patients with symptoms were excluded. NAR was calculated preoperatively as neutrophil count divided by serum albumin. Additional inflammatory markers, such as neutrophil–lymphocyte ratio (NLR) and C-reactive protein (CRP) to albumin ratio (CAR), were also analyzed. Results: Severe carotid artery stenosis was detected in 191 of the patients who underwent DSA. Individuals in the critical carotid artery stenosis group were older and had a higher prevalence of diabetes mellitus and hypertension (51 (45–57) vs. 60 (54–68), p < 0.001; 143 vs. 83, p = 0.025; 193 vs. 104, respectively, p = 0.021), as well as higher neutrophil counts (4.3 (3.2–6.2) vs. 8.1 (4.9–12.5), p < 0.001), NLR (2.2 (1.4–3.2) vs. 4.2 (2.3–8.9), p < 0.001), while CRP (3.8 (1.8–8) vs. 5.7 (3.6–7.6), p = 0.005) and CAR (0.9 (0.5–1.9) vs. 1.6 (0.8–2.1), p < 0.001) values were significantly higher. NAR was higher in patients of the critical carotid artery stenosis group than the non-critical (1.1 (0.8–1.6) vs. 2.1 (1.4–3.2), p < 0.001). Multivariate analysis identified NAR as an independent predictor of carotid artery stenosis (Odds Ratio [OR]: 3.432; 95% Confidence Interval [CI]: 2.116–5.566; p < 0.001). The best cut-off value of NAR for predicting critical carotid artery stenosis was 1.47, which provided 73.8% sensitivity and 70.5% specificity. Conclusions: NAR, which can be easily measured through a simple blood test, demonstrated moderate sensitivity and specificity in predicting critical carotid artery stenosis, suggesting its potential role as a supportive marker in clinical risk assessment. Full article

This paper investigates the interval type-2 (IT2) fuzzy adaptive fixed-time bipartite consensus self-triggered control for multiple quadrotor unmanned aerial vehicles with deferred full-state constraints and input saturation under cooperative-antagonistic interactions. First, a uniform nonlinear transformation function, incorporating a shifting function, is constructed to achieve the deferred asymmetric constraints on the vehicle states and eliminate the restrictions imposed by feasibility criteria. Notably, the proposed framework provides a unified solution for unconstrained, constant/time-varying, and symmetric/asymmetric constraints without necessitating controller reconfiguration. By employing interval type-2 fuzzy logic systems and an improved self-triggered mechanism, an IT2 fuzzy adaptive fixed-time self-triggered controller is designed to allow the control signals to perform on-demand self-updating without the need for additional hardware monitors, effectively mitigating bandwidth over-consumption. Stability analysis indicates that all states in the closed-loop attitude system are fixed-time bounded while strictly adhering to deferred time-varying constraints. Finally, illustrative examples are presented to validate the effectiveness of the proposed control scheme. Full article

This study focuses on the complex pore structure and pronounced heterogeneity of tight sandstone reservoirs in the Linxing area of the Ordos Basin and develops a multi-scale quantitative characterization approach to investigate the coupling mechanism between pore structure and reservoir properties. Six core samples were selected from the Shiqianfeng Formation (depth interval: 1326–1421 m) for detailed analysis. Cast thin sections and scanning electron microscopy (SEM) experiments were employed to characterize pore types and structural features. Nuclear magnetic resonance (NMR) experiments were conducted to obtain T₂ spectra, which were used to classify bound and movable pores, and their corresponding fractal dimensions were calculated separately. In addition, NMR logging data from the corresponding well intervals were integrated to assess the applicability and consistency of the fractal characteristics at the logging scale. The results reveal that the fractal dimension of bound pores shows a positive correlation with porosity, whereas that of movable pores is negatively correlated with permeability, indicating that different scales of pore structural complexity exert distinct influences on reservoir performance. Mineral composition affects the evolution of pore structures through mechanisms such as framework support, dissolution, and pore-filling, thereby further enhancing reservoir heterogeneity. The consistency between logging responses and experimental observations verifies the regional applicability of fractal analysis. Bound pores dominate within the studied interval, and the vertical variation of the PMF/BVI ratio aligns closely with both the NMR T₂ spectra and fractal results. This study demonstrates that fractal dimension is an effective descriptor of structural characteristics across different pore types and provides a theoretical foundation and methodological support for the evaluation of pore complexity and heterogeneity in tight sandstone reservoirs. Full article

The Poincaré plot was introduced as a tool to analyze heart rate variations caused by arrhythmias. Later, it was applied to time series with normal beats. The plot shows the relationship between the inter-beat interval (IBI) of one beat to the next. Several parameters were developed to characterize this relationship. The short and long axis of the fitting ellipse, SD1 and SD2, respectively, their ratio, and their product are used. The difference between the IBI of a beat and m beats later are also studied, SD1(m) and SD2(m). We studied the mathematical relations between heart rate variability measures and the Poincaré measures in the time (standard deviation of IBI, SDNN, root mean square of successive differences, RMSSD) and frequency domain (power in low and high frequency band, and their ratio). We concluded that SD1 and SD2 do not provide new information compared to SDNN and RMSSD. Only the correlation coefficient r(m) provides new information for m > 1. Novel findings are that ln(SD2(m)/SD1(m)) = tanh⁻¹(r(m)), which is an approximately normal distributed transformation of r(m), and that SD1(m) and SD2(m) can be calculated by multiplying the power spectrum by a weighing function that depends on m, revealing the relationship with spectral measures, but also the relationship between SD1(m) and SD2(m). Both lagged parameters are extremely difficult to interpret compared to low and high frequency power, which are more closely related to the functioning of the autonomic nervous system. Full article

Accurate interpretation of qPCR data continues to present significant challenges, particularly at low target concentrations where technical variability, stochastic amplification, and efficiency fluctuations confound quantification. The widespread assumption that qPCR outputs are intrinsically reliable, coupled with inconsistent adherence to best-practice guidelines, has exacerbated issues of reproducibility and contributed to misleading conclusions. This may distort pathogen load quantification in diagnostic settings, whilst in gene expression studies, it can lead to overinterpretation of small fold changes. This study presents a systematic, cross-platform evaluation of qPCR performance across a wide dynamic range using defined reaction mixes and technical replicates. We show that calculated copy numbers can closely match expected values over more than three orders of magnitude, but that variability increases markedly at low input concentrations, often exceeding the magnitude of biologically meaningful differences. We conclude that establishing and reporting confidence intervals from the data itself is essential for transparency and for distinguishing reliable quantification from technical noise. Full article

For interval-valued intuitionistic fuzzy sets featuring complementary symmetry in evaluation relations, this paper proposes a novel, complete fuzzy multi-attribute group decision-making (MAGDM) method that optimizes both expert weights and attribute weights. First, an optimization model is constructed to determine expert weights by minimizing the cumulative difference between individual evaluations and the overall consistent evaluations derived from all experts. Second, based on the Technique for Order Preference by Similarity to an Ideal Solution (TOPSIS), the improved closeness index for evaluating each alternative is obtained. Finally, leveraging entropy theory, a concise and interpretable optimization model is established to determine the attribute weight. This weight is then incorporated into the closeness index to enable the ranking of alternatives. Integrating these features, the complete fuzzy MAGDM algorithm is formulated, effectively combining the strengths of subjective and objective weighting approaches. To conclude, the feasibility and effectiveness of the proposed method are thoroughly verified and compared through detailed examination of two real-world cases. Full article

Avalanches occur frequently in mountainous areas and pose significant threats to roads and infrastructure. Clarifying how terrain conditions influence avalanche initiation and movement is critical to improving hazard assessment and response strategies. This study focused on a wet-snow slab avalanche that occurred on 26 March 2024, in the Simutas region of the northern Tianshan Mountains, Xinjiang, China. The authors combined remote sensing imagery, high-resolution meteorological station observations, field investigations, and numerical simulations (RAMMS::Avalanche) to analyze the avalanche initiation mechanism, dynamic behavior, and path recurrence characteristics. Results indicated that persistent heavy snowfall, rapid warming, and substantial daily temperature fluctuations triggered this avalanche. The predominant southeasterly (SE) winds and the northwest-facing (NW) shaded slopes created favorable leeward snow deposition conditions, increasing snowpack instability. High-resolution meteorological observations provided detailed wind, temperature, and precipitation data near the avalanche release zone, clearly capturing snowpack evolution and meteorological conditions before avalanche initiation. Numerical simulations showed a maximum avalanche flow velocity of 19.22 m/s, maximum flow depth of 12.42 m, and peak dynamic pressure of 129.3 kPa. The simulated avalanche deposition area and depth closely matched field observations. Multi-temporal remote sensing images indicated that avalanche paths in this area remained spatially consistent over time, with recurrence intervals of approximately 2–3 years. The findings highlight the combined role of local meteorological processes and terrain factors in controlling avalanche initiation and dynamics. This research confirmed the effectiveness of integrating remote sensing data, high-resolution meteorological observations, and dynamic modeling, providing scientific evidence for avalanche risk assessment and disaster mitigation in mountain regions. Full article

Background: Varicella remains a prevalent vaccine-preventable disease, but breakthrough infections are increasingly reported. However, long-term, population-based studies investigating the temporal and demographic characteristics of breakthrough varicella remain limited. Methods: This retrospective study analyzed surveillance data from Jinhua City, China, from 2016 to 2024. Varicella case records were obtained from the China Information System for Disease Control and Prevention (CISDCP), while vaccination data were retrieved from the Zhejiang Provincial Immunization Program Information System (ISIS). Breakthrough cases were defined as infections occurring more than 42 days after administration of the varicella vaccine. Differences in breakthrough interval were analyzed across subgroups defined by dose, sex, age, population category, and vaccination type. A bivariate cubic regression model was used to assess the combined effect of initial vaccination age and dose interval on the breakthrough interval. Results: Among 28,778 reported varicella cases, 7373 (25.62%) were classified as breakthrough infections, with a significant upward trend over the 9-year period (p < 0.001). Most cases occurred in school-aged children, especially those aged 6–15 years. One-dose recipients consistently showed shorter breakthrough intervals than two-dose recipients (M = 62.10 vs. 120.10 months, p < 0.001). Breakthrough intervals also differed significantly by sex, age group, population category, and vaccination type (p < 0.05). Regression analysis revealed a negative correlation between the initial vaccination age, the dose interval, and the breakthrough interval (R² = 0.964, p < 0.001), with earlier and closely spaced vaccinations associated with longer protection. Conclusions: The present study demonstrates that a two-dose varicella vaccination schedule, when initiated at an earlier age and administered with a shorter interval between doses, provides more robust and longer-lasting protection. These results offer strong support for incorporating varicella vaccination into China’s National Immunization Program to enhance vaccine coverage and reduce the public health burden associated with breakthrough infections. Full article

Due to climate effects and human influences, wildfire regimes in boreal forests are changing, leading to profound ecological consequences, including shortened fire return intervals and elevated tree mortality. However, a critical knowledge gap exists concerning the spatiotemporal dynamics of fire-induced tree mortality specifically within the vast North American boreal forest, as previous studies have predominantly focused on Mediterranean and tropical forests. Therefore, in this study, we used satellite observation data obtained by the Moderate Resolution Imaging Spectroradiometer (MODIS) Aqua and Terra MCD64A1 and related database data to study the spatial and temporal variability in burned area and forest mortality due to wildfires in North America (Alaska and Canada) over an 18-year period (2003 to 2020). By calculating the satellite reflectance data before and after the fire, fire-driven forest mortality is defined as the ratio of the area of forest loss in a given period relative to the total forest area in that period, i.e., the area of forest loss divided by the total forest area. Our findings have shown average values of burned area and forest mortality close to 8000 km²/yr and 40%, respectively. Burning and tree loss are mainly concentrated between May and September, with a corresponding temporal trend in the occurrence of forest fires and high mortality. In addition, large-scale forest fires were primarily concentrated in Central Canada, which, however, did not show the highest forest mortality (in contrast to the results recorded in Northern Canada). Critically, based on generalized linear models (GLMs), the results showed that fire size and duration, but not the burned area, had significant effects on post-fire forest mortality. Overall, this study shed light on the most sensitive forest areas and time periods to the detrimental effects of forest wildfire in boreal forests of North America, highlighting distinct spatial and temporal vulnerabilities within the boreal forest and demonstrating that fire regimes (size and duration) are primary drivers of ecological impact. These insights are crucial for refining models of boreal forest carbon dynamics, assessing ecosystem resilience under changing fire regimes, and informing targeted forest management and conservation strategies to mitigate wildfire impacts in this globally significant biome. Full article