Search Results (9,102)

Correlations in complex systems are often obscured by nonstationarity, long-range memory, and heavy-tailed fluctuations, which limit the usefulness of traditional covariance-based analyses. To address these challenges, we construct scale- and fluctuation-dependent correlation matrices using the multifractal detrended cross-correlation coefficient ${\rho }_r$ that selectively emphasizes fluctuations of different amplitudes. We examine the spectral properties of these detrended correlation matrices and compare them to the spectral properties of the matrices calculated in the same way from synthetic Gaussian and q-Gaussian signals. Our results show that detrending, heavy tails, and the fluctuation-order parameter r jointly produce spectra, which substantially depart from the random case even under the absence of cross-correlations in time series. Applying this framework to one-minute returns of 140 major cryptocurrencies from 2021 to 2024 reveals robust collective modes, including a dominant market factor and several sectoral components whose strength depends on the analyzed scale and fluctuation order. After filtering out the market mode, the empirical eigenvalue bulk aligns closely with the limit of random detrended cross-correlations, enabling clear identification of structurally significant outliers. Overall, the study provides a refined spectral baseline for detrended cross-correlations and offers a promising tool for distinguishing genuine interdependencies from noise in complex, nonstationary, heavy-tailed systems. Full article

Background: Foam rolling has become an increasingly popular self-myofascial release (SMR) technique among athletes to prevent injuries, improve recovery, and increase athletic performance. This study investigated how SMR improves mechanical and movement efficiency in recreational road cyclists. Methods: We conducted an exploratory randomized controlled trial (RCT) to investigate the effects of SMR using a foam roller on biomechanical and physiological performance parameters over a six-month period. A total of 32 male participants, aged 26–57 years, with a mean Body Mass Index (BMI) of 24.0 kg/m² (SD = 2.2), were randomly assigned to either an intervention group (n = 16), which incorporated a standardized SMR program into their post-exercise recovery, or a control group (n = 16), which followed the same cycling protocol without SMR. The training program included heart rate-controlled strength endurance intervals. As the primary target, the variables we investigated included torque effectiveness, leg force symmetry, and pedal smoothness. Secondary measurements included submaximal oxygen uptake (VO₂) as well as bioelectrical variables, which we analyzed using classic, repeated-measures ANOVA models and descriptive statistical methods. Results: The analysis revealed significant interaction effects in favor of the intervention group for torque effectiveness (η²_p = 0.434), leg strength symmetry (η²_p = 0.303), and pedal smoothness (η²_p = 0.993). No significant group × time interactions were found for submaximal VO₂ or bioelectrical parameters. Conclusions: Our findings indicate that foam rolling may serve as an effective adjunct to endurance training by enhancing functional neuromuscular performance in cyclists, particularly in torque control and pedal coordination. Its impact on aerobic efficiency and muscle composition appears to be minimal. The results support theoretical models that attribute SMR benefits to proprioceptive, circulatory, and neuromuscular mechanisms rather than structural tissue adaptations. Full article

As digital images proliferate across open networks, securing them against unauthorized access has become imperative. However, many recent image encryption algorithms are limited by weak chaotic dynamics and inadequate cryptographic design. To overcome these, we propose a new 2D coupling-enhanced cubic hyperchaotic map with exponential parameters (2D-CCHM-EP). By incorporating exponential terms and strengthening interdependence among state variables, the 2D-CCHM-EP exhibits strict local expansiveness, effectively suppresses periodic windows, and achieves robust hyperchaotic behavior, validated both theoretically and numerically. It outperforms several recent chaotic maps in key metrics, yielding significantly higher Lyapunov exponents and Kolmogorov–Sinai entropy, and passes all NIST SP 800-22 randomness tests. Leveraging the 2D-CCHM-EP, we further develop a hierarchical significance-aware multi-image encryption algorithm (MIEA-CPHS). The core of MIEA-CPHS is a hierarchical significance-aware encryption strategy that decomposes input images into high-, medium-, and low-significance layers, which undergo three, two, and one round of vector-level adaptive encryption operations. An SHA-384-based hash of the fused data dynamically generates a 48-bit adaptive control parameter, enhancing plaintext sensitivity and enabling integrity verification. Comprehensive security analyses confirm the exceptional performance of MIEA-CPHS: near-zero inter-pixel correlation (<0.0016), near-ideal Shannon entropy (>7.999), and superior plaintext sensitivity (NPCR $\approx 99.61\%$, UACI $\approx 33.46\%$). Remarkably, the hierarchical design and vectorized operations achieve an average encryption throughput of 87.6152 Mbps, striking an outstanding balance between high security and computational efficiency. This makes MIEA-CPHS highly suitable for modern high-throughput applications such as secure cloud storage and real-time media transmission. Full article

As an important basic material for modern industry, the performance and production energy consumption of medium and thick plates have an important impact on engineering quality, industry technological progress and economic benefits. However, traditional process parameter adjustment relies on manual experience, which is difficult to meet the dual needs of efficient production and energy conservation and emission reduction. This paper focuses on the energy consumption optimization problem in the production process of medium and thick plates. Under the premise of meeting the mechanical property constraints, a data-driven process parameter optimization method is proposed. Firstly, a comprehensive energy consumption prediction model for medium and thick plates is established. Secondly, based on historical data and knowledge, a data set covering chemical composition, physical parameters and process parameters is constructed, and a mechanical property prediction model is developed to achieve the prediction of actual performance. On this basis, the energy consumption minimization problem that satisfies mechanical property constraints is modeled as a constrained optimization problem, and a data-inspired initialized particle swarm optimization algorithm is designed to improve the global search capability and local convergence efficiency. Experimental results confirm that the proposed model provides more stable and accurate prediction of mechanical properties than conventional Random Forest and XGBoost models. Furthermore, compared with standard PSO, GA, SA, and ACO algorithms, the data-inspired initialized particle swarm optimization shows faster convergence and better energy-saving performance, demonstrating the overall effectiveness and practical potential of the proposed framework. Full article

Rice husk ash (RHA) offers an eco-friendly way to improve concrete. Owing to the complex mix design of RHA concrete, accurately predicting its strength remains a challenge. This study addresses this need by compiling a dataset of 291 compressive strength records for RHA concrete. Using seven key input variables (e.g., cement, water, and RHA content), three novel hybrid models were developed by integrating the XGBoost algorithm with advanced metaheuristic optimizers: Northern Goshawk Optimization (NGO), Arctic Puffin Optimization (APO), and Catch Fish Optimization Algorithm (CFOA). These hybrid models were compared against classic Random Forest (RF), and Support Vector Regression (SVR), and unoptimized XGBoost models. The results demonstrated that all hybrid models significantly outperformed the unoptimized classic models. The APO–XGBoost model achieved the highest prediction accuracy on the testing set (RMSE = 3.5462, R² = 0.9579 on testing set), followed by CFOA–XGBoost and NGO–XGBoost. Cement content was revealed to be the most influential parameter on compressive strength, as determined by a sensitivity analysis, ahead of both water and coarse aggregate content. This research confirms the superiority of metaheuristic-optimized hybrid models for predicting the strength of RHA concrete, providing a reliable data-driven tool to support its mix design and promote its application in sustainable construction. Full article

Anaerobic digestion (AD) is a nonlinear and disturbance-sensitive process in which instability is often induced by feedstock variability and biological fluctuations. To address this challenge, this study develops an entropy-guided machine learning framework that integrates parameter prediction, uncertainty quantification, and entropy-based evaluation of AD operation. Using six months of industrial data (~10,000 samples), three models—support vector machine (SVM), random forest (RF), and artificial neural network (ANN)—were compared for predicting biogas yield, fermentation temperature, and volatile fatty acid (VFA) concentration. The ANN achieved the highest performance (accuracy = 96%, F1 = 0.95, root mean square error (RMSE) = 1.2 m³/t) and also exhibited the lowest prediction error entropy, indicating reduced uncertainty compared to RF and SVM. Feature entropy and permutation analysis consistently identified feed solids, organic matter, and feed rate as the most influential variables (>85% contribution), in agreement with the RF importance ranking. When applied as a real-time prediction and decision-support tool in the plant (“sensor → prediction → programmable logic controller (PLC)/operation → feedback”), the ANN model was associated with a reduction in gas-yield fluctuation from approximately ±18% to ±5%, a decrease in process entropy, and an improvement in operational stability of about 23%. Techno-economic and life-cycle assessments further indicated a 12–15 USD/t lower operating cost, 8–10% energy savings, and 5–7% CO₂ reduction compared with baseline operation. Overall, this study demonstrates that combining machine learning with entropy-based uncertainty analysis offers a reliable and interpretable pathway for more stable and low-carbon AD operation. Full article

Urban noise is a major environmental concern that affects public health and quality of life, demanding new approaches beyond conventional noise level monitoring. This study investigates the development of an AI-driven Acoustic Event Detection and Classification (AED/C) system designed for urban sound recognition and its integration into smart city application. Using the UrbanSound8K dataset, five acoustic parameters—Mel Frequency Cepstral Coefficients (MFCC), Mel Spectrogram (MS), Spectral Contrast (SC), Tonal Centroid (TC), and Chromagram (Ch)—were mathematically modeled and applied to feature extraction. Their combinations were tested with three classical machine learning algorithms: Support Vector Machines (SVM), Random Forest (RF), Naive Bayes (NB) and a deep learning approach, i.e., Convolutional Neural Networks (CNN). A total of 52 models with the three ML algorithms were analyzed along with 4 models with CNN. The MFCC-based CNN models showed the highest accuracy, achieving up to 92.68% on test data. This achieved accuracy represents approximately +2% improvement compared to prior CNN-based approaches reported in similar studies. Additionally, the number of trained models, 56 in total, exceeds those presented in comparable research, ensuring more robust performance validation and statistical reliability. Real-time validation confirmed the applicability for IoT devices, and a low-cost wireless sensor unit (WSU) was developed with fog and cloud computing for scalable data processing. The constructed WSU demonstrates a cost reduction of at least four times compared to previously developed units, while maintaining good performance, enabling broader deployment potential in smart city applications. The findings demonstrate the potential of AI-based AED/C systems for continuous, source-specific noise classification, supporting sustainable urban planning and improved environmental management in smart cities. Full article

In this paper, selected methods for the statistical assessment of distribution parameters using estimators were briefly described. Selected aspects of the theory of measurement uncertainty, the determination of standard uncertainty of type A, type B, total and expanded were discussed. The structure of the “current—polarization-dependent loss” optical fiber sensor is presented, which can be used to measure current in power lines. The method of measuring polarizing attenuation using an optical reflectometer OTDR is discussed. The results of research deal with the influence of the light wave, optical fiber length and the angle of rotation of the plane of polarization (polarization angle) on the value of polarizing attenuation are presented. Conclusions from the experiment were formulated regarding the selection of optical fiber and optical window so that the polarization angle was within a specific interval. Full article

Background: Wireless Vital Parameter Continuous Monitoring (WVPCM) allows the continuous tracking of patient physiological parameters, facilitating the earlier detection of clinical deterioration, especially in low-intensity care settings. The aim of this study is to evaluate the effectiveness of using WVPCM compared to the usual monitoring of critically ill patients hospitalized in Internal Medicine wards. An investigation of the attitude of health professionals towards the use of new technologies in daily practice to improve patient management was also carried out. Methods: The LIght Monitor Study (LIMS) is a prospective, open-label, randomized, multi-center pilot trial comparing WVPCM and conventional nurse monitoring during the first 72 h of hospitalization. A central randomization unit used computer-generated tables to allocate patients to two different types of monitoring. The main outcome was the occurrence of major complications. The study planned to enroll 296 critically ill patients with a Modified Early Warning Score (MEWS) ≥ 3 and/or National Early Warning Score (NEWS) ≥ 5 across two Internal Medicine (IM) Units in Italy. The investigation of the attitude of nurses towards the use of WVPCM was carried out by using a questionnaire and a qualitative survey. Results: Due to the COVID-19 outbreak, the study was interrupted early and only 135 patients (WVPCM = 68; standard care = 67) were randomized. One patient in the control group was excluded from analysis because of drop-out, leaving 134 patients for intention to treat analysis. No statistically significant differences between standard care and WVPCM were observed in terms of major complications (37.5%, vs. 31.2% p = 0.475), in-hospital mortality (17.5% vs. 11.1%, p = 0.309), and median hospital length of stay (9 vs. 10 days, p = 0.463). WVPCM decreased nursing workload compared to the control, as the average time spent by nurses on the detection of vital signs per patient was 0 min per patient per day compared to 24.4 min (p < 0.001) observed in the control group. Twenty-two percent of patients in the WVPCM group (15/68) experienced discomfort with the device, resulting in its removal. The investigation of nurses involved 16 out of 18 people participating in the study. Opinions on the wireless device for patient monitoring were particularly favorable; most of them considered remote monitoring clearly superior to traditional in-person visits and easy to use after a brief practice period. All participants recognized the safety benefits of the system. Conclusions: The reduced sample size of this pilot study does not allow us to draw any conclusions on the superiority of WVPCM compared to standard care in terms of clinical outcomes. However, we observed a positive trend in the reduction of major complications. Full article

Pine wilt disease (PWD) is one of the fastest-spreading invasive forest pathogens worldwide, causing rapid mortality of infected trees and posing a severe threat to global forest ecosystem security and carbon sink capacity. However, the spatial dynamics and diffusion characteristics of PWD at the stand scale remain poorly understood. In this study, we selected a typical epidemic area in Qingyuan County, Liaoning Province, China, as the study site. By integrating 23 phases of unmanned aerial vehicle (UAV) multispectral imagery, airborne LiDAR data, and field survey observations, we reconstructed the spatiotemporal diffusion process of PWD from 2023 to 2025 and developed a stand-scale, tree-level mortality risk prediction model. Our results show that 50% of transmission events occurred within 17.2 m, and the spatial autocorrelation range was approximately 28 m. The peak of the lethal latency period occurred 17 days after infection, with 40% of mortality events occurring within 11–22 days and 50% of infected trees dying within 40 days. The latency period was significantly shorter in spring and summer than in winter ($p<0.01$). Among tree-level mortality risk prediction approaches, the random forest model performed best, improving overall accuracy by more than 15% compared with other methods and correctly identifying 98.6% of high-risk individuals. The distance to the nearest infected or dead tree was identified as the dominant predictor, followed by tree height and vegetation parameters reflecting host physiological status. This study reveals the spatial diffusion characteristics of PWD at the stand scale and proposes a tree-level risk prediction framework, providing a theoretical foundation and technical support for dynamic monitoring, early warning, and precision management of PWD. Full article

Global climate change has led to frequent extreme weather events such as high temperatures and droughts, severely threatening the heat and water balance during the growing season of summer maize. To adapt to these changes, adjusting planting dates to optimize crop development has become a key agronomic measure for mitigating climate stress and ensuring yield. Against this backdrop, precise monitoring of leaf area index (LAI) is crucial for evaluating the effectiveness of planting date regulation and achieving precision management. To reveal the impact of planting date variations on summer maize LAI inversion and address the limitations of single data sources in comprehensively reflecting complex environmental conditions affecting crop growth, this study examined summer maize at different planting dates across the North China Plain. Through stepwise regression analysis (SRA), multiple vegetation indices (VIs) and 0–2nd order fractional order derivatives (FODs), spectral parameters were dynamically screened. These were then integrated with effective accumulated temperature (EAT) to optimize model inputs. Partial Least Squares Regression (PLSR), Random Forest (RF), Support Vector Regression (SVR), and Adaptive Boosting Regression (AdaBoot) algorithms were employed to construct LAI inversion models for summer maize across different planting dates and mixed planting dates. Results indicate that, compared to empirical VIs and “tri-band” parameters, randomly selected dual-band combination VIs exhibit the strongest correlation with summer maize LAI. Key bands identified through SRA screening concentrated in the 0.7–1.2 order range, primarily distributed across the red edge and near-infrared bands. Multi-feature models incorporating EAT significantly improved retrieval accuracy compared to single-feature models. Optimal models and feature combinations varied across planting dates. Overall, the VIs + EAT combination exhibited the highest stability across all models. Ensemble learning algorithms RF and AdaBoost performed exceptionally well, achieving average R² values of 0.93 and 0.92, respectively. The model accuracy for the 20-day delayed planting (S4) decreased significantly, with an average R² of 0.62, while the average R² for other planting dates exceeded 0.90. This indicates that the altered environmental conditions during the later growth stages of LAI due to delayed planting hindered LAI estimation. This study provides an effective method for estimating summer maize LAI across different planting dates under climate change, offering scientific basis for optimizing adaptive cultivation strategies for maize in the North China Plain. Full article

The representation of 3D shapes from point clouds remains a fundamental challenge in computer vision. A common approach decomposes 3D objects into interpretable geometric primitives, enabling compact, structured, and efficient representations. Building upon prior frameworks, this study introduces an enhanced unsupervised deep learning approach for 3D shape representation using superquadrics. The proposed framework fits a set of superquadric primitives to 3D objects through a fully integrated, differentiable pipeline that enables efficient optimization and parameter learning, directly extracting geometric structure from 3D point clouds without requiring ground-truth segmentation labels. This work introduces three key advancements that substantially improve representation quality, interpretability, and evaluation rigor: (1) A uniform sampling strategy that enhances training stability compared with random sampling used in earlier models; (2) An overlapping loss that penalizes intersections between primitives, reducing redundancy and improving reconstruction coherence; and (3) A novel evaluation framework comprising Primitive Accuracy, Structural Accuracy, and Overlapping Percentage metrics. This new metric design transitions from point-based to structure-aware assessment, enabling fairer and more interpretable comparison across primitive-based models. Comprehensive evaluations on benchmark 3D shape datasets demonstrate that the proposed modifications yield coherent, compact, and semantically consistent shape representations, establishing a robust foundation for interpretable and quantitative evaluation in primitive-based 3D reconstruction. Full article

Increased risk of musculoskeletal injury in galloping racehorses has been linked to decreased stride length and reduced speed over consecutive races prior to the injury. As racetrack curvature influences horses’ maximal speed, we hypothesized it also affects stride parameters. During training sessions, twenty-eight wagon-pulling Thoroughbred Chuckwagon horses were equipped with Global Navigation Satellite System (GNSS) loggers, allowing for identification of speed, stride length (SL) and stride frequency (SF), and average speed, SL and SF were calculated for consecutive 100 m sections. Effects of curvature on speed were investigated with a linear mixed model with speed as output variable, curvature as fixed factor, and horse as random factor. Effects of curvature and speed on stride parameters were investigated with linear mixed models with output variables SL and SF, continuous covariates speed, curvature, and the two-way interaction between curvature and speed as fixed factors, and horse as random factor. Curvature was associated with a significant increase in speed (p = 0.004), decrease in SL (p < 0.001) and increase in SF (p < 0.001), and for SL and SF the magnitude of these effects was dependent on speed (p < 0.001). At a curvature of 60° per 100 m, an increase in speed of 0.264 m/s was found compared to the straight, although this effect is likely confounded by fatigue. At the median speed of 14.5 m/s and a curvature of 60° per 100 m, a SF increase of 0.053 Hz (+2.4%) and a SL reduction of 0.137 m (−2.1%) was found compared to the straight. This is in the same order of magnitude as the 0.10 m SL reduction over consecutive races previously associated with increased injury risk. We conclude that, in Chuckwagon horses, interactions between speed and curvature are affecting stride parameters that have previously been identified as predictors of musculoskeletal injuries. Full article

Elastic closed-cell porous material is widely applied as a class of light sound insulation product. However, it is difficult to accurately predict its soundproof property due to the occurrence of the closed cells. Therefore, a combined theoretical model of Biot’s theory and acoustic field equations has been developed to predict the sound transmission loss (STL) in the mass control region. Five NBR-PVC closed-cell composites with different parameters were selected to verify the prediction model. Their STL measurement values were compared with the data calculated separately by the theoretical model and the Mass Law, whether under normal incidence or under random incidence. The results show that the Mass Law overestimates the sound insulation values of closed-cell porous material. STL prediction values from the theoretical model have more acceptable agreements to the measurement data than those from the Mass Law. The average deviation rates of the theoretical model are less than 4% under the normal incidence condition and are about 2.9% under the random incidence condition. Full article

The Random Domino Automaton—a stochastic cellular automaton forest-fire model—is formulated for the Bethe lattice geometry. The equations describing the stationary state of the system are derived using combinatorial analysis. The special choice of parameters that define the dynamics of the system leads to a solvable reduction in the set of equations. Analysis of the equations shows that by changing the parameter responsible for cluster removal, the size distribution of clusters smoothly transitions from (near) exponential to inverse power, beyond which the system is unstable. The analysis shows the crucial role of combining more than two clusters in elongating the tail of the size distribution generated by the system and, thus, in increasing the range of validity of the inverse power law. We also point out an interesting connection of the proposed model with Catalan-like integer sequences. Full article