Search Results (108,617)

The standard motion analysis systems are limited to laboratory settings; therefore, an individual’s gait may not be realistic, as it is removed from the day-to-day environment in which people ambulate. The modern world and advanced technologies have driven portable, affordable, and wearable sensors for real-world gait assessment that can be used outside the laboratory and during day-to-day activities. Wearable sensors offer a promising solution; however, despite that, the reliability of many wearable systems, especially under unsupervised and real-world-like conditions, remains insufficiently validated. This study aimed to establish intra- and inter-rater reliability of the inertial sensors as a tool used in gait analysis in a quasi-real-world environment. Ninety-eight healthy participants (52% females) aged 19–33 years took part in this reliability study. The research procedures included two separate measurements of gait analysis at participants’ preferred walking speed, conducted by two raters assessing selected spatiotemporal parameters. The reliability was calculated using intraclass correlation coefficients (ICC), and the bias was assessed using the Bland–Altman method. The analysis of intraclass correlation coefficients (ICC) revealed excellent, or near-excellent, reliability for walking speed, cadence, and stride length between raters (ICC = 0.932–0.941, 0.950–0.957, and 0.916–0.938, respectively) and between measurements (ICC = 0.916–0.928, 0.934–0.938, and 0.888–0.906, respectively). Bland–Altman plots confirmed minimal systematic bias for both inter- and intra-rater assessments, with differences in walking speed below 0.038 km/h, cadence below 0.283 steps/min, and stride length below 0.827 cm. The examined sensors are reliable tools for walking speed, cadence, and stride length in a quasi-real-world environment gait assessment. Future studies should include gait analysis involving random path and direction changes, turns, uneven or slippery surfaces, and natural environments. Additionally, research should consider individuals ambulating with various walking aids, or those with unilateral disorders, such as stroke. Full article

The citrus spiny whitefly Aleurocanthus spiniferus (Quaintance), recently found in Greece, causes severe damage to the leaves and fruits of tree crops, and treatment against it is urgent. In this work, integrated treatments for the management of the A. spiniferus pest on Citrus sinensis (L.) trees, which causes intense damage to orange orchards, were studied. The experiment was carried out in an orange orchard on the Aitoloakarnania plain, an agroforestry ecosystem, and three treatments were set up: (i) a combined treatment comprising the entomopathogenic fungi Beauveria bassiana and Cordyceps fumosorosea, (ii) treatment with the application of a tetramic acid-based formulation, (iii) the control treatment. The damage caused by A. spiniferus was estimated by determining the pest stages on the C. sinensis leaves, samples of which were collected and examined at the entomology laboratory of the Agriculture Faculty of the University of Ioannina for the calculation of populations. The experimental results of this work encourage us to further investigate the use of the treatments against whiteflies, highlighting the potential of EPF for integrated pest management (IPM) in citrus trees. Full article

In this study, thermodynamic properties such as water activity, osmotic coefficient, and saturation points of the aqueous mixture in the system D-Sucrose + Water + ammonium nitrate (AN) were determined at 298.15 K. The measurements were carried out on the mixtures of concentrations of NH₄NO₃ (ranging from 0.1 to 6 mol·kg⁻¹) and D-sucrose (from 0.1 to 4 mol·kg⁻¹) using our hygrometric method. Powder X-ray diffraction (XRD) and attenuated total reflection–Fourier transform infrared (ATR-FTIR) spectroscopy were used to characterize the solid phases crystallized during the supersaturation of the solution. Other thermodynamic quantities such as the solute activity coefficients, excess Gibbs energies, transfer energies, and solute solubilities were calculated using the Pitzer–Simonson–Clegg (PSC) model. The results obtained indicate that at an AN concentration lower than 1 mol·kg⁻¹, the system exhibits increasingly negative deviations from ideality, and that NH₄NO₃ promotes the salting-out effect of sucrose. Full article

Although accurate quantification of forest soil CO₂ emissions is critical for improving global carbon cycle models, traditional chamber and gradient methods often underestimate fluxes under windy conditions. Based on long-term field observations in a subtropical maple forest, we quantified the interaction between canopy-level winds and soil pore air pressure fluctuations in regulating vertical CO₂ profiles. The results demonstrate that canopy winds, rather than subcanopy airflow, dominate deep soil CO₂ dynamics, with stronger explanatory power for concentration variability. The observed “wind-pumping effect” operates through soil pressure fluctuations rather than direct wind speed, thereby enhancing advective CO₂ transport. Soil pore air pressure accounted for 33%–48% of CO₂ variation, far exceeding the influence of near-surface winds. These findings highlight that, even in dense forests with negligible understory airflow, canopy turbulence significantly alters soil–atmosphere carbon exchange. We conclude that integrating soil pore air pressure into flux calculation models is essential for reducing underestimation bias and improving the accuracy of forest carbon cycle assessments. Full article

The use of equipment is an important source of energy consumption and carbon emissions. The energy consumption and carbon emissions generated by different types and quantities of equipment processing the same product vary greatly. At present, most of the equipment selection is only a comparison of the best types, rarely considering the dynamic changes and effects of the production process elements, which makes it difficult to adapt to the needs of sustainable development. In order to solve this problem, we establish a production equipment selection model based on new quality productivity (NQP)-oriented, and establish a unified calculation method for different types of equipment to consider energy consumption and carbon emissions in the production process. The multi-objective egret swarm optimization algorithm (MESOA) is used to calculate the energy consumption and carbon emissions of different devices. A case was conducted, taking the shaft processing as an example. The results show that the optimal energy consumption and carbon emission of the same product produced by different equipment can be calculated under the orientation of new quality productivity. In the later stage of the algorithm, MESOA outperforms NSGA-2 by 5.3%. At last, this paper provides a positive result for equipment selection under the orientation of new quality production. Full article

Image-based geo-localization is a challenging task that aims to determine the geographic location of a ground-level query image captured by an Unmanned Ground Vehicle (UGV) by matching it to geo-tagged nadir-view (top-down) images from an Unmanned Aerial Vehicle (UAV) stored in a reference database. The challenge comes from the perspective inconsistency between matched objects. In this work, we propose a novel metric learning scheme for hard exemplar mining to improve the performance of cross-view geo-localization. Specifically, we introduce a Dynamic Online Cross-Batch (DOCB) hard exemplar mining scheme that solves the problem of the lack of hard exemplars in mini-batches in the middle and late stages of training, which leads to training stagnation. It mines cross-batch hard negative exemplars according to the current network state and reloads them into the network to make the gradient of negative exemplars participating in back-propagation. Since the feature representation of cross-batch negative examples adapts to the current network state, the triplet loss calculation becomes more accurate. Compared with methods only considering the gradient of anchors and positives, adding the gradient of negative exemplars helps us to obtain the correct gradient direction. Therefore, our DOCB scheme can better guide the network to learn valuable metric information. Moreover, we design a simple Siamese-like network called multi-scale feature aggregation (MSFA), which can generate multi-scale feature aggregation by learning and fusing multiple local spatial embeddings. The experimental results demonstrate that our DOCB scheme and MSFA network achieve an accuracy of 95.78% on the CVUSA dataset and 86.34% on the CVACT_val dataset, which outperforms those of other existing methods in the field. Full article

Generation expansion planning is critical for the sustainable development of power systems, particularly with the increasing integration of renewable energy sources like wind power. This paper presents an innovative generation expansion model identifying the optimal strategy for constructing new wind power plants. The model determines the ideal size of wind power generation and strategically allocates wind resources across multi-area power systems to maximize their capacity credit. A novel fuzzy set approach addresses wind power’s inherent uncertainty and variability, which models wind data uncertainty through membership functions for each stochastic parameter. This method enhances the accuracy of capacity credit calculations by effectively capturing the unpredictable nature of wind power. The model uses the Effective Load Carrying Capability (ELCC) as the objective function to measure the additional load that can be reliably supported by wind generation. Additionally, integrating a compressed-air energy storage system (CAESS) is introduced as a novel solution to mitigate the intermittency of wind power, further boosting the wind power plants’ capacity credit. By incorporating an energy storage system (ESS), the model ensures greater resource availability and flexibility. The study evaluates a multi-area power network, where each area has distinct conventional generation capacity, reliability metrics, load profiles, and wind data. A three-interconnected power system case study demonstrates the model’s effectiveness in increasing the load carrying capability of intermittent renewable resources, improving system reliability, and enhancing resilience. This study provides new insights into optimizing renewable energy integration by leveraging advanced uncertainty modeling and energy storage, contributing to the long-term sustainability of power systems. Full article

In this paper, a novel solution- and moving boundary-adaptive Cartesian grid strategy is proposed and used to develop a computational fluid dynamics (CFD) solver. The new Cartesian grid strategy is based on a multi-block structure without grid overlapping or ghost grids in non-fluid areas. In particular, the dynamic grid adaptive operations, as well as the adaptive criteria calculations, are restricted to the grid block boundaries. This reduces the grid adaptation complexity to one dimension lower than that of CFD simulations and also facilitates an intrinsic compatibility with moving boundaries since they are natural grid block boundaries. In addition, an improved hybrid immersed boundary method enforcing a physical constraint of pressure is proposed to robustly implement boundary conditions. The recursively regularized lattice Boltzmann method is applied to solve for fluid dynamics. The performance of the proposed method is validated in simulations of flow induced by a series of two- (2D) and three-dimensional (3D) moving boundaries. Results confirm that the proposed method is adequate to provide efficient and effective dynamical grid refinements for flow solutions and moving boundaries simultaneously. The considered unsteady flow physics are accurately and efficiently reproduced. Particularly, the 3D multiscale flow induced by two tandem flapping wings is simulated at a computational time cost about one order lower than that of a reported adaptive Cartesian strategy. Notably, the grid adaptations only account for a small fraction of CFD time consumption, about 0.5% for pure flow characteristics and 5.0% when moving boundaries are involved. In addition, favorable asymptotic convergence with decreasing minimum grid spacing is observed in the 2D cases. Full article

The DC traction power system adopts the track as the return rail. When the track-to-earth insulation in the subway tunnel deteriorates, stray currents will cause electrochemical corrosion to tunnel steel structures and seriously affect the service life and safety of metro tunnels. Stray currents cannot be directly measured and can only be calculated. Therefore, a calculation model with a hollow circular cross-section structure was proposed, and the stray current distribution in tunnel steel structures was calculated. In addition, the effects of different rail-to-ground transition resistances and adjacent buried metallic pipelines on the stray current distribution of the tunnel steel structures were taken into account. The results show that the total amount of stray current dispersed into the tunnel steel structures and soil is similar. The stray current density distribution in each steel tunnel is related to its location. The total stray current carried by the steel structures of the bottom tunnel segment is 102, 15.7 and 3.1 times higher than that of the top, upper and lower side tunnel segments, respectively. The reduction in the transition resistance and increase in the distance of the train from the traction substation increase the total rail leakage current and have a small effect on the percentage distribution of stray current in tunnel structures. The buried metal pipeline parallel to the tunnel has a lower impact on the total stray current leakage, but can reduce the total stray current in steel structures and drainage net, enlarging the positive stray current scope of some tunnel steel bars, further increasing the stray current density on tunnel steel bars. The results of this study can be used to determine the degree of corrosion of the underground steel tunnels and thereby provide support for corrosion prevention. Full article

Space-based optical imaging detection serves as a crucial means for acquiring characteristic information of space objects, with the quality and resolution of images directly influencing the accuracy of subsequent missions. Addressing the scarcity of datasets in space-based optical imaging, this study introduces a method that combines an improved Phong model and higher-order spherical harmonics (HOSH) for the optical imaging computation of space objects. Utilizing HOSH to fit the light field distribution, this approach comprehensively considers direct sunlight, earthshine, reflected light from other extremely distant celestial bodies, and multiple scattering from object surfaces. Through spectral reflectance experiments, an improved Phong model is developed to calculate the optical scattering characteristics of space objects and to retrieve common material properties such as metallicity, roughness, index of refraction (IOR), and Alpha for four types of satellite surfaces. Additionally, this study designs two sampling methods: a random sampling based on the spherical Fibonacci function (RSSF) and a sequential frame sampling based on predefined trajectories (SSPT). Through numerical analysis of the geometric and radiative rendering pipeline, this method simulates multiple scenarios under both high-resolution and wide-field-of-view operational modes across a range of relative distances. Simulation results validate the effectiveness of the proposed approach, with average rendering speeds of 2.86 s per frame and 1.67 s per frame for the two methods, respectively, demonstrating the capability for real-time rapid imaging while maintaining low computational resource consumption. The data simulation process spans six distinct relative distance intervals, ensuring that multi-scale images retain substantial textural features and are accompanied by attitude labels, thereby providing robust support for algorithms aimed at space object attitude estimation, and 3D reconstruction. Full article

To suppress non-stationary main-lobe interference, we utilized the waveform information of the transmitted signal and proposed an interference suppression method based on a multi-polarized array without the need for calculating the target parameters. This method calculates the steering vector of the target through matched filtering. Additionally, for non-stationary interference whose statistical characteristics change over time, we extract high-energy frequency points from the time–frequency joint domain to obtain the time–frequency covariance matrix for subsequent beamforming. Simulation experiments demonstrate that this method leverages the signal polarization information sensed by the multi-polarized array, effectively suppressing non-stationary main-lobe interference in the polarization domain. This method does not require estimation of the target’s polarization parameters and is more suitable for real-world detection scenarios where the waveform is known. Full article

This paper analyzes the extreme limit of iterated function systems (IFSs) when the number of contractions drops to one and the resulting attractors reduce to a single point. While classical fractals have a strictly positive fractal dimension, the degenerate case $D=0$ has been little explored. Starting from the question “what happens to a fractal when its complexity collapses completely?”, Moran’s similarity equation becomes tautological ($r^s=1$ with solution $s={dim}_M=0$) and that only the Hausdorff and box-counting definitions allow an exact calculation. Based on Banach’s fixed point theorem and these definitions, we prove that the attractor of a degenerate IFS is a singleton with ${dim}_H={dim}_B=0$. We develop a reproducible computational methodology to visualize the collapse in dimensions 1–3 (the Iterated Line Contraction—1D/Iterated Square Contraction—2D/Iterated Cube Contraction—3D families), including deterministic and stochastic variants, and we provide a Python script 3.9. The theoretical and numerical results show that the covering box-counting retains unity across all generations, confirming the zero-dimension element and the stability of the phenomenon under moderate perturbations. We conclude that degenerate fractals are an indispensable benchmark for validating fractal dimension estimators and for studying transitions to attractors with positive dimensions. Full article

This article discusses the potential of using the double-Wiebe function to model combustion in a compression-ignition engine fueled by diesel fuel or its substitutes, such as hydrotreated vegetable oil (HVO) and rapeseed methyl ester (RME), and hydrogen injected into the engine intake manifold. The hydrogen amount ranged from 0 to 35% of the total energy content of the fuels burned. It was found that co-combustion of liquid fuel with hydrogen is characterized by two distinct combustion phases: premixed and diffusion combustion. The premixed phase, occurring just after ignition, is characterized by a rapid combustion rate, which increases with an increase in hydrogen injected. The novelty in this work is the modified formula for a double-Wiebe function and the proposed parameters of this function depending on the amount of hydrogen added for co-combustion with liquid fuel. To model this combustion process, the modified double-Wiebe function was proposed, which can model two phases with different combustion rates. For this purpose, a normalized HRR was calculated, and based on this curve, coefficients for the double-Wiebe function were proposed. Satisfactory consistency with the experiment was achieved at a level determined by the coefficient of determination (R-squared) of above 0.98. It was concluded that the presented double-Wiebe function can be used to model combustion in 0-D and 1-D models for fuels: RME and HVO with hydrogen addition. Full article

Objectives: This exploratory preclinical study aimed to compare the correlations of apparent diffusion coefficient (ADC) and native T1 mapping histogram features with tumor cell proliferation, microvessel density (MVD), and extracellular matrix composition in neuroblastoma xenografts. Methods: Neuroblastoma xenografts (n = 42) were established by subcutaneously injecting three MYCN-amplified/non-amplified human neuroblastoma cell lines (IMR-32, SK-N-BE(2), and SH-SY5Y; n = 14 per group) into female immunodeficient BALB/c-nude mice. Once tumors reached a diameter within the range of 12–15 mm, native T1 mapping and diffusion-weighted imaging were performed using a 3.0T clinical MRI scanner. Tumor cell proliferation and MVD were assessed via immunohistochemical Ki-67 staining and CD31 staining, respectively. Collagen fibers were visualized using Masson staining to calculate the collagen volume fraction (CVF). Pearson correlation coefficients with false discovery rate (FDR) correction were used to evaluate their associations. Results: Significant negative correlations were observed between Ki-67 expression and multiple ADC values after FDR correction, including ADC_10Percentile (r = −0.397, adjusted p = 0.032), ADC_90Percentile (r = −0.394, adjusted p = 0.032), ADC_maximum (r = −0.362, adjusted p = 0.048), ADC_mean (r = −0.421, adjusted p = 0.032), ADC_median (r = −0.422, adjusted p = 0.032), ADC_minimum (r = −0.390, adjusted p = 0.032), and ADC_{rootmeansquared} (r = −0.419, adjusted p = 0.032). In contrast, multiple T1 mapping features showed significant positive correlations with CVF (adjusted p < 0.05). Conclusions: ADC and T1 mapping provide complementary insights into tumor proliferation and extracellular matrix composition in neuroblastoma. These preclinical findings support further research to validate their potential clinical utility. Full article

Surface energy is involved in various thermodynamic processes, providing a driving force for thermodynamic reactions. However, surface energies applied in current engineering calculations are generally measured in J/m², which is unsuitable for thermodynamic analysis. To solve this problem, the calculation formula for surface energies was modified to convert the unit of measurement, transforming the non-thermodynamic measurement unit J/m² into the thermodynamically characterized kJ/mol. The calculated surface energy values measured in kJ/mol are unstable due to the influence of the number of atomic layers (t) in the constructed models. Meanwhile, the problem of determining the surface layer thickness, i.e., the number of atomic layers with surface characteristics (t₀), remains unresolved in surface science. Therefore, the extended Finnis Sinclair (EFS) potential was improved by extending the nearest neighbor range and utilized in analyzing the energy per atom, resulting in the determined number of t₀. These results suggest that selecting the surface layer number corresponding to the first to third nearest-neighbor atoms could be appropriate, and the resulting surface energies in kJ/mol appear reasonable. The validity of this computational method and the origin of nanoporous W activity were confirmed by analyzing the changes in total surface energy before and after nano-treatment using the novel nanosized approach. Full article