Search Results (4,111)

Biologically related time-series data characterize the dynamic evolution of biological systems, including genetic inheritance, disease diagnosis, and the biological microenvironment. However, accurate prediction of these data remains challenging due to their pronounced time-varying, non-stationary, and noisy characteristics. Existing approaches often fail to capture latent shifts of biologically related time series, limiting both predictive performance and time-varying pattern recognition capability. Thus, in this study, we first propose a time-varying neural network (TVNN) model that combines frequency-domain information with Koopman theory. TVNN-model Koopman transition matrices are used to model global dynamics and local time-varying behaviors for pattern extraction. Secondly, a time-varying pattern recognition large language model (TVPRLLM) is introduced to recognize and classify the extracted time-varying patterns, enabling the identification of potential pattern categories. Thirdly, we have developed a biology-related time-series predictive platform that can offer visualization, data analysis, and predictive services. Experimental results demonstrate that the TVNN model outperforms existing mainstream methods in predicting biology-related time-varying time series, and that it achieves competitive forecasting performance, though its behavior depends strongly on the design of the frequency-domain decomposition. Additional robustness analyses reveal that the choice of Fourier masking strategy can materially affect both RMSE and long-horizon stability. We further show that Koopman-derived time-varying representations are highly discriminative for dynamic state recognition. Full article

India has been witnessing a high growth rate of aggregate income in the current era of globalization. Even though the per capita income is yet to catch up, this led to an improved global status in 2025, with India becoming the fifth largest economy in terms of aggregate GDP. However, the economic gains have been accompanied by a host of environmental problems. In particular, the increase in carbon emissions is emerging as the biggest challenge in achieving the Sustainable Development Goals by 2030. While some national policy initiatives exist, Indian states have also started implementing new public policies for a contextualized environmental management at a sub-national level to curtail the negative impact of carbon emissions on sustainable development. In this context, this study seeks to explore three aspects: first, the characteristics of the series for per capita CO₂ (PCCO₂) emissions, per capita state domestic product (PCGSDP), and per capita R&D (PCR&D) spending aimed at safeguarding the environment in Indian states; second, the prevalence of both enduring and near-term linkages among the three variables in distinct panels; and third, the constantly changing interplay involving income and carbon emissions in the midst of R&D spending for the environment in the Indian states from 2008–2025. While the series for PCGSDP and PCR&D is seen rising along with PCCO₂ in most states, there are some exceptional states like Delhi and Kerala where trends of PCCO₂ are falling. The panel cointegration and VECM results show that the three indicators, viz., income, PCCO₂ and R&D spending, have a stable long-run relationship, and that income and R&D cause CO₂ emissions in all states’ panels and the panel of developed states. Using several polynomials between the income and CO₂ emission nexus over several panels of states and using panel cointegration techniques, the study reveals that static panel fixed effects models are most appropriate in the case of all states’ panels and the panel of developed states to establish an inverted Environmental Kuznets Curve (EKC), and that R&D spending has worked as a significant control variable to justify the declining shape of the EKC. The study recommends a continuous increase in R&D spending by all states of any development stature to achieve sustainable development in the earliest possible time. Full article

Accurate carbon price forecasting is critical for trading decisions, risk management and policy formulation in carbon markets. However, mainstream decomposition-ensemble models suffer from two key drawbacks: point-wise modeling fails to capture long-term temporal dependencies, while independent modeling of decomposed trend and seasonal components leads to serious information loss. To address these limitations, this paper proposes a novel Dual-Path Interactive Attention Network (DPIANet) for carbon price time series forecasting, whose dual-parallel architecture consists of a Dual Interaction Attention (DIA) Block and a Decomposition–Subsequence Interaction Attention (DSIA) Block. First, DPIANet employs a patch-wise partitioning strategy to extract local temporal semantic information inaccessible to traditional point-wise segmentation. The DIA Block jointly captures temporal dependencies between different patches within the same sequence and inter-feature dependencies within the same time step. In parallel, the DSIA Block extracts interactive features between decomposed trend and seasonal subsequences, fusing these features with original subsequences to enhance representation and mitigate decomposition-induced information loss. A dual-layer feature selection method (PMI and XGBoost-SHAP) is adopted to identify key driving factors. Experiments on four representative Chinese regional carbon trading markets covering 2014-2020 show that DPIANet achieves superior prediction performance over state-of-the-art models in terms of MSE and MAE, with competitive robustness across different market characteristics, providing practical decision support for carbon market stakeholders. Full article

Accurately capturing muscle behavior remains a challenging task in computational biomechanics, primarily due to the nonlinear response, anisotropy, and time-dependent characteristics of muscle tissue. In this context, finite element methods have proven to be a suitable framework for representing such complex mechanical behavior. Among the available constitutive approaches, Hill’s three-element model continues to be widely adopted, largely because it offers a reasonable balance between physiological interpretability and computational efficiency. In this work, a parametric and sensitivity-oriented analysis of the Hill three-element muscle model is performed within a finite element formulation originally proposed by Kojić, Mijailović, and Zdravković (1998) and implemented in the PAK software environment. The analysis considers five key parameters, which are varied independently: the stiffness parameter of the series elastic element (α), the corresponding stress scaling parameter (β), the modulus of the parallel elastic element (E), the activation level (a), and the length ratio constant (k). To enable comparison between parameters of different physical nature, normalized sensitivity indices are used. The results show that the activation parameter a has the strongest influence on active force generation, with an increase of 36.4% at the highest considered activation level. In contrast, parameters α and β primarily affect the behavior of the series elastic component, with variations on the order of ±15–18%. It can also be observed that the influence of individual parameters depends on the deformation regime. At lower deformation levels, the response is mainly governed by the parameter E, while α and β become more relevant in the intermediate nonlinear range. At higher deformation levels, the activation parameter a becomes dominant. From a modeling perspective, these findings suggest a structured approach to parameter calibration in Hill-type finite element models. In addition, they provide further insight into the sensitivity characteristics of such formulations within computational biomechanics. Full article

In ubiquitous Wi-Fi sensing, human motion interval segmentation is crucial for applications ranging from basic intrusion detection to advanced activity understanding. Existing methods often treat the Channel State Information (CSI) primarily as time series, overlooking its rich information in the spatial and frequency domains. To address this, we propose a training-free motion segmentation method that exploits the spatiotemporal features of CSI. We first analyze the discriminative spatial distributions of the CSI Ratio on the complex plane and construct a spatiotemporally dual-constrained local density estimator to characterize motion-induced perturbations. To overcome subcarrier selection challenges, we introduce a packet-level asymmetric truncation-based fusion algorithm, which yields a feature representation with a pronounced bimodal histogram. This enables the automatic determination of the optimal segmentation threshold based on the distribution characteristics of the truncated density image. Experiments in typical indoor environments demonstrate that the proposed method achieves high accuracy in both motion event detection and interval localization. Full article

The health status of rolling bearings is critical to the normal operation of rotating machinery. To effectively extract vibration signal features and accurately identify different fault types, a novel method based on enhanced composite multi-scale slope entropy (ECMSE) and a honey badger algorithm-optimized kernel extreme learning machine (HBA–KELM) is proposed. Specifically, ECMSE integrates high-order differences into the composite multi-scale framework to capture high-frequency information while preserving low-frequency characteristics, thereby enhancing the discriminability of time-series representations. Meanwhile, an average coarse-graining strategy is incorporated to achieve a more comprehensive characterization of the signals. The extracted features are then input into the HBA–KELM classifier for fault identification. Experiments conducted on two public and private rolling bearing datasets demonstrate that our method achieves superior performance in distinguishing different fault types and damage levels compared with several existing approaches. Full article

Root zone soil moisture (RZSM) is critical for understanding hydrological processes and monitoring agricultural drought, yet its accurate representation remains challenging in topographically complex regions. Using 40 cm in situ SM observations from 19 ground stations in Yunnan Province, China, during 2008–2012 as the reference, this study systematically evaluated the performance of five widely used multi-source soil moisture (SM) products and their different depth layers, including ERA5-Land, GLDAS Noah, GLEAM, ASCAT H141, and CCI SM. A CCI-derived RZSM proxy generated by exponential filtering, hereafter CCI RZSM, was also included. Product performance was assessed using original and deseasonalized time series, and the effects of land-use type, long-term wetness background, and short-term dry conditions on product performance were explicitly examined. The results showed that the intermediate and deeper layers of ERA5-Land and ASCAT H141, especially the 7–28 cm layers, exhibited better performance in capturing RZSM dynamics, achieving a favorable balance among temporal correlation (r > 0.6), random error and systematic bias. Surface-layer products showed limited direct representativeness, and effective RZSM representativeness differed substantially among nominal product layers. Deseasonalization showed that original-series correlations were partly supported by the shared seasonal wet–dry cycle, whereas most products had weaker skill in tracking non-seasonal RZSM anomalies. Environmental background substantially modulated error structures: stronger positive Bias generally occurred at drier stations, Grassland showed higher positive Bias, Cropland showed greater dispersion, and Forest displayed relatively balanced performance. Under dry conditions, temporal correlations declined for nearly all products, whereas increases in random error were mainly concentrated in surface layers. Exponential filtering improved the temporal consistency of CCI SM in representing RZSM, but the filtering with a fixed characteristic time parameter (T) performed worse than filtering with station-optimized T, indicating limited generalizability in ungauged regions. Overall, RZSM representativeness in Yunnan is jointly controlled by product structure, environmental background, and wet–dry conditions. ERA5-Land and ASCAT H141 intermediate-to-deep layers are therefore more suitable for RZSM anomaly and drought applications in Yunnan Province. Full article

Surface orientation can influence the aerodynamic response of modern soccer balls, particularly in the drag crisis regime. This study quantified the orientation-dependent aerodynamic characteristics of the FIFA World Cup match ball Trionda using a single specimen and examined how these differences affect simulated flight at sea level and 1500 m altitude. Two reproducible reference orientations were defined: a red-panel-centered orientation (Series A) and a seam-junction-centered orientation (Series B). Each reference orientation was rotated by 0°, 90°, and 180°, resulting in six fixed-orientation conditions. Wind tunnel measurements were repeated three times per condition to obtain drag, lift, and side-force coefficients, and two-dimensional non-spinning flight simulations were performed for representative long-kick and free-kick conditions. All six orientations exhibited drag crisis behavior, but the transition response magnitude, subcritical drag level, and supercritical drag state differed among conditions. The representative transition region occurred at approximately Re = 2.0 × 10⁵ to 2.5 × 10⁵. Among the tested conditions, B-90 showed the lowest full-range mean drag coefficient (0.231), whereas A-90 showed the highest (0.266). In the simulations, lower drag orientations consistently produced longer flight ranges, and the B-90 > A-90 ordering was preserved across representative launch conditions and the expanded parametric comparison. These findings indicate that the aerodynamic response of Trionda cannot be represented adequately by a single mean drag coefficient and that surface orientation should be considered in aerodynamic characterization and flight prediction. Full article

Reliable forecasting of seismic activity trends is essential for regional seismic hazard analysis. Based on earthquake catalogs from 1500 to 2026, this study investigates the spatiotemporal evolution of seismic activity in the North-South Seismic Belt using a hybrid framework that integrates Wiener filtering and artificial neural networks. Seismic activity is modeled as a discrete-time stochastic process, and a time series of earthquakes with magnitudes ≥ 6.0 is constructed. Wiener filtering is applied to establish an optimal linear relationship between input and output under the minimum mean square error criterion, and multi-origin extrapolation is employed to predict earthquakes with magnitudes ≥ 7.0 over the next century. The results reveal several stable peaks or peak clusters that agree well with historical strong earthquakes, with prediction errors generally within approximately three years. Sensitivity analyses indicate that longer time series (∼500 years) and higher threshold magnitudes (≥6.0) enhance prediction stability, although the method shows limitations in spatial prediction. To address this issue, a 16–8–4 artificial neural network model is developed, and seismic sequence features are extracted using a sliding time window approach to perform both temporal and spatial forecasting. The artificial neural network achieves high accuracy in temporal prediction (maximum error ≈ 0.5) and outperforms Wiener filtering in spatial prediction, capturing the migration characteristics of seismic activity. The results further suggest that earthquakes with magnitudes ≥ 7.0 are more likely to occur within the latitude range of 30.5–33.0° N in the near future. Full article

Land subsidence is an important challenge faced by coastal cities under rapid urban development. This study focuses on the Haikou–Laocheng area and conducts time-series monitoring of land subsidence using PS-InSAR and SBAS-InSAR based on 42 Sentinel-1 SAR scenes acquired from April 2023 to April 2025, thereby deriving the spatial distribution of cumulative subsidence rates and the evolution patterns of multi-temporal cumulative subsidence. Because only ascending-orbit Sentinel-1 data were used, the reported deformation values are vertical-projected estimates converted from line-of-sight (LOS) displacement under the assumption that horizontal motion is negligible. The reliability of the monitoring results is evaluated through cross-validation between the two methods, assessing their inter-method consistency. The results indicate that the study area is dominated by slight subsidence, with vertical-projected subsidence rates mainly ranging from −6 to 3.7 mm/y, while a few uplift points are locally observed, forming an overall “stable with localized anomalies” deformation pattern. PS-InSAR and SBAS-InSAR show good consistency in overall trends, and both identify a pronounced subsidence bowl in the southwestern part of the study area, where the peak vertical-projected subsidence rates reach −25.1 mm/y and −35.1 mm/y, respectively, with outward banded attenuation. The results suggest that land subsidence in the study area is influenced by both natural factors and human activities. Specifically, rainfall shows a non-synchronous, stage-wise modulation relationship with subsidence evolution, and most high-subsidence zones are distributed in impervious surfaces such as built-up land and transportation corridors, or in low-elevation areas such as farmland. In terms of geological factors, thick, highly compressible soft soils are the primary geological control on the continued development of subsidence. These findings can provide scientific references for the prevention and control of abnormal subsidence and for urban planning and development in the Haikou–Laocheng area. The strengthened discussion clarifies the research gap, planning significance, and limitations of applying dual time-series InSAR in a data-scarce tropical coastal soft-soil setting. Full article

The assessment of non-stationarity in the rainfall process is still a major research topic in the field of applied hydrology. The water cycle is affected by several characteristics of this process: rainfall volume, wet weather duration, their mutual association, and the annual number of events. The method used to sample rainfall variables from the time series may or may not suitably account for their variability. Herein, the rainfall process is analyzed using a bivariate event-based approach, with reference to two rainfall time series recorded at short time steps in different Italian climates. Trends are also estimated. Full article

Background: Cardiac erosion after transcatheter closure of secundum atrial septal defect (ASD) is a rare (0.1–0.3%) but potentially life-threatening complication. Available evidence remains limited to isolated case reports and small case series. Methods: A case-based review was conducted in accordance with CABARET recommendations. PubMed, Scopus, Web of Science, and the Cochrane Central Register of Controlled Trials (CENTRAL) were searched from inception through January 2026. Adult cases with anatomically confirmed aortic or aorto-atrial erosion after transcatheter closure of a secundum ASD were included. Clinical, anatomical, procedural, imaging, management, and outcome data were synthesized descriptively. An illustrative case with aorto-atrial erosion was included. Results: A total of 40 cases, including the present case, were identified. Median age was 39.5 years, and 27 were female. Chest pain was the most common symptom, reported in 16 cases, whereas six patients were asymptomatic at diagnosis. Median time to erosion was 81 days (range, 0.25–4745 days). A deficient rim was reported in 22 patients, and device oversizing in 17 patients. All erosions involved the aortic wall, most frequently at the atrial roof adjacent to the non-coronary sinus. Aorta-right atrial and aorta-left atrial were the predominant anatomical patterns, reported in 21 and 14 patients, respectively. Surgical intervention was required in 36 cases, which consisted of device explantation with atrial and/or aortic repair. Conclusions: Aortic and aorto-atrial erosion after transcatheter secundum ASD closure is an uncommon but severe complication with heterogeneous clinical presentation and timing. Among published erosion cases, female sex, a deficient retro-aortic rim, device oversizing, and mild aortic root dilation were recurrent characteristics. Careful anatomical assessment, multimodality imaging, and continued follow-up remain essential for early recognition of cardiac erosions. Full article

Series arc faults on the DC side of photovoltaic (PV) systems are a critical hazard that can trigger system fires. Conventional contact-based detection methods suffer from cumbersome installation and high retrofit cost, whereas existing non-contact approaches mostly rely on megahertz-level high-frequency sampling and therefore require expensive radio-frequency instrumentation or high-performance computing platforms. As a result, it remains difficult to simultaneously achieve strong interference immunity and real-time performance on low-cost embedded devices with limited resources. To address this engineering paradox between high-frequency sampling and constrained computational capability, this paper proposes a fully embedded, non-contact arc fault detection system based on a 12–80 kHz low-frequency sub-band selection strategy. By exploiting the physical characteristic of broadband energy elevation induced by arc faults, the proposed strategy avoids dependence on high-bandwidth hardware. Guided by this strategy, a Moebius-topology coaxial shielded loop antenna is employed as the near-field sensor, while an ultra-simplified passive analog front end is constructed directly by using the on-chip programmable gain amplifier and analog-to-digital converter of the microcontroller unit, enabling efficient signal acquisition and fast Fourier transform processing within the target sub-band. To cope with complex background noise in the low-frequency range, an environment-adaptive baseline mechanism based on exponential moving average and exponential absolute deviation is developed for dynamic decoupling. In addition, a lightweight INT8-quantized multilayer perceptron is introduced as a nonlinear auxiliary module, thereby forming a robust hybrid decision architecture with complementary rule-based and artificial intelligence components. Experimental results show that, under the tested household, laboratory, and PV-site conditions, the proposed system achieved an overall detection rate of 97%, while the remaining 3% mainly corresponded to failed ignition or non-sustained arc attempts rather than persistent false triggering during normal monitoring. Full article

Wireless Power Transfer (WPT) systems often face performance limitations due to the right-half-plane zero (RHPz) in conventional constant-current-fed Buck converters, which can lead to negative undershoot and a slow dynamic response. In this paper, we propose a Buck converter topology with an additional active switch in series with the input capacitor. This mechanism-level modification effectively mitigates the RHPz. The operating modes, steady-state behavior, and small-signal characteristics of the converter are systematically analyzed. A tailored control strategy enables independent regulation of input and output capacitor charging times, supporting improved voltage regulation. Experimental results indicate that the proposed converter reduces settling time by approximately 83%, substantially suppresses negative undershoot, and maintains stable voltage regulation under reference step changes and load transients. The converter maintains high efficiency while demonstrating improved dynamic performance and stability relative to conventional topologies, providing a practical approach for advanced WPT applications. Full article

In the classical Stochastic Subspace Identification (SSI) method, mode selection is primarily based on frequency stability, damping stability, and mode shape similarity using the Modal Assurance Criterion (MAC). However, these criteria are often insufficient for reliable modal identification in high-noise environments. This study advances beyond the classical approach by introducing a multi-criteria optimization framework for mode evaluation. In addition to the conventional frequency and damping assessments utilized in the classical SSI method, the proposed approach incorporates a range of supplementary structural metrics. These include Density, Cosine Similarity Difference (CSD), Damping Stability (DS), Spatial Roughness (SR), Mode Shape Complexity (MSC), Signal Energy Coherence (SEC), and Normalized Modal Difference (NMD). These metrics are computed within specifically optimized windows on the stabilization diagram. By integrating spatial, phase, and energy-based characteristics of mode shapes alongside traditional metrics such as the MAC, the method enables a more comprehensive and robust mode selection process that surpasses the limitations of relying solely on frequency and damping stability. Compared to the classical SSI, the optimized window approach provides a significant advantage by enabling the reliable selection of consistent modes by considering the continuity and multi-criteria coherence of modes across window transitions. As a result, the elimination of noise modes and the reliable separation of structural modes are established on a more systematic basis. To achieve this, a two-stage optimization strategy is implemented: the first stage determines the optimal frequency window width and minimum mode count threshold, while the second stage utilizes a Multi-Criteria Decision Making (MCDM) framework based on the Technique for Order Preference by Similarity to Ideal Solution (TOPSIS) algorithm to assign optimized weights to the structural metrics and rank the candidate windows accordingly. As a result, the ideal frequency window is identified based on its TOPSIS score and subsequently validated using the MAC, confirming that the selected window corresponds to reliable structural modes. The framework is validated using long-term in situ measurements from a Roller Compacted Concrete (RCC) dam operating under significant environmental and operational noise. The dataset comprises continuous, high-resolution (200 Hz) vibration recordings collected between 1 July 2023 and 30 October 2024. While the calendar duration is limited to several weeks, the uninterrupted 24 h measurements yield a high-density time-series dataset with substantial information content, enabling a statistically meaningful and robust evaluation of modal identification performance under real-world and noisy conditions. The results reveal that relying solely on traditional selection criteria such as pole density and the MAC can often lead to the identification of spurious modes, particularly in noisy environments. In contrast, the proposed TOPSIS-based multi-criteria decision-making framework incorporates a broader range of structural indicators, balancing frequency, damping, spatial, and energy-related metrics to enhance the consistency and reliability of mode selection. This approach proved effective even under high-noise conditions, successfully distinguishing true structural modes from artificial ones. Application of the TOPSIS method to RCC dam data revealed consistent fundamental frequencies at approximately 5–10 Hz, 10 Hz, and 15 Hz, confirming its robustness and suitability for complex structural monitoring tasks. Full article