Search Results (12,374)

Ecological stoichiometry, as a discipline investigating plant elemental coupling mechanisms, has become a research focus across various ecosystems. However, few studies have examined plant stoichiometric characteristics in the water–land ecotone of plateau karst lake wetlands. It remains unclear whether foliar nutrient contents and stoichiometric ratios in this transitional zone vary with flooding intensity. This study established three sampling gradients (near-water area, middle area, and far-water area) within the water–land ecotone of Caohai Lake wetland in Guizhou Plateau, measuring nutrient concentrations along with their stoichiometric ratios in leaves of three dominant plant species. The results revealed significant interspecific differences in leaf nitrogen (N), phosphorus (P), potassium (K), calcium (Ca) concentrations and N:P ratios among the three dominant species, while significant spatial variations were observed in N concentration and the C:N ratio across sampling locations. Correlation analysis demonstrated significant positive relationships among leaf N, P, and K concentrations, all showing negative correlations with C concentration. Phragmites australis exhibited significantly lower C:N and N:P ratios compared to Scirpus validus and Juncus effusus, suggesting its growth advantage through rapid nutrient acquisition. This species may serve as an efficient phytoremediator for N and P absorption from both soil and water. These findings provide valuable references for vegetation selection in constructed wetlands. Full article

This paper presents an angles-only navigation (AON) method utilizing optical observations of a single satellite with known ephemeris and prior altitude constraints given by an altimeter or known topography, which can enable near-ground platforms to achieve autonomous navigation in GNSS-denied environments. By leveraging a star tracker to measure the line-of-sight (LOS) direction of a satellite against a star background, the observer’s location is resolved via triangulation under geometric constraints. Theoretical error models are derived to analyze the influence of satellite position errors, LOS direction errors, and altitude uncertainties on geolocation accuracy. Numerical simulations validate the error propagation mechanisms, demonstrating that geolocation error is primarily determined by the perpendicular projection of orbital error relative to the LOS, increases linearly with LOS distance, and is sensitive to altitude errors at low elevation angles. Ground-based experiments conducted using Globalstar satellites achieve geolocation accuracy within 250 m (RMS), consistent with theoretical predictions. The proposed method offers a practical, low-cost solution for high-precision passive navigation in maritime and terrestrial applications. Full article

The construction of large-scale infrastructure, such as power facilities, requires extensive use of reinforced concrete. The durability degradation of reinforced concrete structures in chloride environments involves multi-physics coupling effects, chloride ion diffusion, rebar corrosion, and concrete damage. Existing models neglect the coupling mechanisms among these processes and the influence of mesoscale structural characteristics. Therefore, this study proposes a diffusive–mechanical coupled phase field by integrating the phase field, chloride ion diffusion, and mechanical equivalence for rebar corrosion, establishing a multi-physics coupling analysis framework at the mesoscale. The model incorporates heterogeneous meso-structure of concrete and constructs a dynamic coupling function between the phase field damage variable and chloride diffusion coefficient, enabling full-process simulation of corrosion-induced cracking under chloride erosion. Numerical results demonstrate that mesoscale heterogeneity significantly affects crack propagation paths, with increased aggregate content delaying the initiation of rebar corrosion. Moreover, the case with corner-positioned rebar exhibits earlier cracking compared to the case with centrally located rebar. Furthermore, larger clear spacing delays delamination failure. Comparisons with the damage mechanics model and experimental data confirm that the proposed model more accurately captures tortuous crack propagation behavior, especially suitable for evaluating the durability of reinforced concrete components in facilities such as transmission tower foundations, substation structures, and marine power facilities. This research provides a highly accurate numerical tool for predicting the service life of reinforced concrete power infrastructure in chloride environments. Full article

This study investigates the influence of technostress (Tech) on the well-being (WB) of employees in manufacturing sectors employing Industry 4.0 in Turkey, examining the effect of work exhaustion (WE) as a mediator in the association between technostress and well-being. How digital leadership (Dg) moderates these relationships is analyzed and discussed accordingly. This article also presents strategies for digital leaders to mitigate employees’ technostress in the digital transformation era and discusses their positive role. Using the Job Demands–Resources (JD-R) framework and Conservation of Resources (COR) theory, data were gathered from 329 workers employed at three manufacturing firms located in Istanbul. Structural equation modeling (SEM) was employed to test this study’s hypothesis. The results indicate that increased technostress notably reduces employee well-being, primarily because it heightens work exhaustion. Moreover, robust digital leadership effectively lessens these negative impacts, underscoring its value in managing technological stress. This research explains the importance of the Sustainable Development Goal (SDG 3) for better health and well-being practices in workplaces. It suggests practical implications for organizations, including developing digital leadership skills, routinely assessing technostress, and applying targeted actions to sustain employee health during digital shifts. Full article

Modern subway platforms are generally equipped with platform screen door systems to enhance safety, but the gap between the platform screen doors and train doors may cause passengers or objects to become trapped, leading to accidents. Addressing the issues of excessive parameter counts and computational complexity in existing foreign object intrusion detection algorithms, as well as false positives and false negatives for small objects, this article introduces a lightweight deep learning model based on YOLOv11n, named GA-YOLOv11. First, a lightweight GhostConv convolution module is introduced into the backbone network to reduce computational resource waste in irrelevant areas, thereby lowering model complexity and computational load. Additionally, the GAM attention mechanism is incorporated into the head network to enhance the model’s ability to distinguish features, enabling precise identification of object location and category, and significantly reducing the probability of false positives and false negatives. Experimental results demonstrate that in comparison to the original YOLOv11n model, the improved model achieves 3.3%, 3.2%, 1.2%, and 3.5% improvements in precision, recall, mAP@0.5, and mAP@0.5: 0.95, respectively. In contrast to the original YOLOv11n model, the number of parameters and GFLOPs were reduced by 18% and 7.9%, respectfully, while maintaining the same model size. The improved model is more lightweight while ensuring real-time performance and accuracy, designed for detecting foreign objects in subway platform gaps. Full article

This study examines the establishment of China’s national supercomputing centers as an exogenous policy shock. Utilizing data from Chinese manufacturing enterprises listed between 2003 and 2023, it applies a multi-period difference-in-differences (DID) model to assess the impact of computing infrastructure on innovation within Chinese manufacturing enterprises. Results indicate that computing infrastructure significantly enhances manufacturing innovation, a finding that is robust across various tests. This effect is positively moderated by the internal R&D investment of enterprises and the external market share. Heterogeneity analysis reveals that the enhancement effect of computing infrastructure on innovation is more pronounced in non-state-owned enterprises, those located in the eastern region, and those with low ownership concentration. Furthermore, computing infrastructure not only boosts the quantity of innovation but also enhances its quality. This paper offers micro-level evidence for emerging countries to advance sustainable development, transformation, and upgrading of the manufacturing sector through computing infrastructure. Full article

This study examines the pigments and materials used in Girolamo Romanino’s Musicians fresco (1537–1538), located in the Duomo Vecchio in Brescia, with the aim of identifying and analyzing the artist’s colour palette. Ten samples of the pictorial layer and mortar were collected from two frescoes and characterized using microscopic and spectroscopic techniques. Confocal laser scanning microscopy (CLSM) was used to define the best positions where single-point, spectroscopic techniques could be applied. Raman spectroscopy and micro-Fourier transform Infrared spectroscopy (micro-FTIR) were used to detect pigments and organic binders, respectively. X-ray powder diffraction (XRPD) provided additional insights into the mineral composition of the pigmenting layers, in combination with environmental scanning electron microscopy equipped with energy-dispersive spectroscopy (ESEM-EDS). The analysis revealed the use of traditional fresco pigments, including calcite, carbon black, ochres, and copper-based pigments. Smalt, manganese earths, and gold were also identified, reflecting Romanino’s approach to colour and material selection. Additionally, the detection of modern pigments such as titanium white and baryte points to restoration interventions, shedding light on the fresco’s conservation history. This research provides one of the most comprehensive analyses of pigments in Romanino’s works, contributing to a deeper understanding of his artistic practices and contemporary fresco techniques. Full article

Indoor positioning remains a recurrent and significant challenge in research. Unlike outdoor environments, where the Global Positioning System (GPS) provides reliable location information, indoor scenarios lack direct line-of-sight to satellites or cellular towers, rendering GPS inoperative and requiring alternative positioning techniques. Despite numerous approaches, indoor contexts with resource limitations, energy constraints, or physical restrictions continue to suffer from unreliable localization. Many existing methods employ a fixed number of reference anchors, which sets a hard balance between localization accuracy and energy consumption, forcing designers to choose between precise location data and battery life. As a response to this challenge, this paper proposes an energy-aware indoor positioning strategy based on Mobile Ad Hoc Networks (MANETs). The core principle is a self-adaptive control loop that continuously monitors the network’s positioning accuracy. Based on this real-time feedback, the system dynamically adjusts the number of active anchors, increasing them only when accuracy degrades and reducing them to save energy once stability is achieved. The method dynamically estimates relative coordinates by analyzing node encounters and contact durations, from which relative distances are inferred. Generalized Multidimensional Scaling (GMDS) is applied to construct a relative spatial map of the network, which is then transformed into absolute coordinates using reference nodes, known as anchors. The proposal is evaluated in a realistic simulated indoor MANET, assessing positioning accuracy, adaptation dynamics, anchor sensitivity, and energy usage. Results show that the adaptive mechanism achieves higher accuracy than fixed-anchor configurations in most cases, while significantly reducing the average number of required anchors and their associated energy footprint. This makes it suitable for infrastructure-poor, resource-constrained indoor environments where both accuracy and energy efficiency are critical. Full article

Photogrammetry applied to sports provides precise data on athlete positions and time instants, especially with digital motion capture systems. However, detecting and identifying specific events in athletic movements such as discus throwing can be challenging when using only images. For example, with high-speed video, it is difficult to pinpoint the exact frame when events like foot touchdown or takeoff occur, as contact between shoe and ground may span several frames. Inertial measurement units (IMUs) can detect maxima and minima in linear accelerations and angular velocities, helping to accurately determine these specific events in throwing movements. As a result, comparing photogrammetry data with IMU data becomes challenging because of the differences in the methods used to detect events. Even if comparisons can be made with IMU data from other sports researchers, variations in methodologies can invalidate the comparison. To address this, the paper proposes a simple methodology for detecting the five phases of a discus throw using three IMUs located on the thrower’s wrist and on the instep or ankle of the feet. Experiments with three elite male discus throwers are conducted and the results are compared with existing data in the literature. The findings demonstrate that the proposed methodology is effective (100% of phases detected in the experiments without false positives) and reliable (results validated with professional coaches), offering a practical and time- and cost-effective solution for accurately detecting key moments in athletic movements. Full article

This study aims to assess the effect of vegetation angle and density on hydrostatic pressure and acceleration of a downstream house model experimentally. The vegetation cylinders were positioned at angles 30°, 45°, 60° and 90° with respect to the flow and two densities of vegetation conditions, i.e., sparse (G/d = 2.13) and intermediate (G/d = 1.09), where G is the spacing between the model vegetation elements in the cross-stream di-rection and d is the vegetation diameter. The streamwise acceleration of the house model was measured by an X2-2 accelerometer that was located downstream from the vegetation patches. Results show that the perpendicular orientation of the vegetation patch (90°) most effectively reduces hydrodynamic loads, with intermediate density (I90) achieving the highest reductions, i.e., 22.1% for acceleration and 7.4% for pressure impacts. Even sparse vegetation (S90) provided substantial protection, reducing acceleration by 21.9% and pressure by 5.8%. These findings highlight the importance of integrating vegetation density and orientation into flood management designs to enhance both their performance and reliability under varying hydraulic conditions. Full article

The methylation of CpG sites with 5mC mark is a dynamic epigenetic modification. However, the relationship between the methylation and the surrounding genomic sequence context remains poorly explored. Investigation of the allele methylation provides an opportunity to decipher the interplay between differences in the primary DNA sequence and epigenetic variation. Here, we performed high-coverage long-read whole-genome direct DNA sequencing of one individual using Oxford Nanopore technology. We also used Illumina whole-genome sequencing of the parental genomes in order to identify allele-specific methylation sites with a trio-binning approach. We have compared the results of the haplotype-specific methylation detection and revealed that trio binning outperformed other approaches that do not take into account parental information. Also, we analysed the cis-regulatory effects of the genomic variations for influence on CpG methylation. To this end, we have used available Deep Learning models trained on the primary DNA sequence to score the cis-regulatory potential of the genomic loci. We evaluated the functional role of the allele-specific epigenetic changes with respect to gene expression using long-read Nanopore RNA sequencing. Our analysis revealed that the frequency of SNVs near allele-specific methylation positions is approximately four times higher compared to the biallelic methylation positions. In addition, we identified that allele-specific methylation sites are more conserved and enriched at the chromatin states corresponding to bivalent promoters and enhancers. Together, these findings suggest that significant impact on methylation can be encoded in the DNA sequence context. In order to elucidate the effect of the SNVs around sites of allele-specific methylation, we applied the Deep Learning model for detection of the cis-regulatory modules and estimated the impact that a genomic variant brings with respect to changes to the regulatory activity of a DNA loci. We revealed higher cis-regulatory impact variants near differentially methylated sites that we further coupled with transcriptomic long-read sequencing results. Our investigation also highlights technical aspects of allele methylation analysis and the impact of sequencing coverage on the accuracy of genomic phasing. In particular, increasing coverage above 30X does not lead to a significant improvement in allele-specific methylation discovery, and only the addition of trio binning information significantly improves phasing. We investigated genomic variation in a single human individual and coupled computational discovery of cis-regulatory modules with allele-specific methylation (ASM) profiling. In this proof-of-concept analysis, we observed that SNPs located near methylated CpG sites on the same haplotype were enriched for sequence features suggestive of high-impact regulatory potential. This finding—derived from one deeply sequenced genome—illustrates how phased genetic and epigenetic data analyses can jointly put forward a hypotheses about the involvement of regulatory protein machinery in shaping allele-specific epigenetic states. Our investigation provides a methodological framework and candidate loci for future studies of genomic imprinting and cis-mediated epigenetic regulation in humans. Full article

The use of additive manufacturing (AM) has seen increased utilization over the last decade, thanks to well-documented advantages such as lower startup costs, reduced wastage, and the ability to rapidly prototype. The poor surface finish of unprocessed AM components is one of the major drawbacks of this technology, with the research literature suggesting a measurable impact on flow characteristics and burner operability. For instance, surface roughness has been shown to potentially increase resistance to boundary layer flashback—an area of high concern, particularly when utilizing fuels with high hydrogen content. A more detailed understanding of the underlying thermophysical mechanisms is, therefore, required. Computational fluid dynamics can help elucidate the impact of these roughness effects by enabling detailed data interrogation in locations not easily accessible experimentally. In this study, roughness effects on a generic gas turbine swirler were numerically modeled using a low-y+ detached eddy simulation (DES) approach. Three DES models were investigated utilizing a smooth reference case and two rough cases, the latter employing a literature-based and novel equivalent sand-grain roughness (k_s) correlation developed for this work. Existing experimental isothermal and CH₄ data were used to validate the numerical simulations. Detailed investigations into the effects of roughness on flow characteristics, such as swirl number and recirculation zone position, were subsequently performed. The results show that literature-based k_s correlations are unsuitable for the current application. The novel correlation yields more promising outcomes, though its effectiveness depends on the chosen turbulence model. Moreover, it was demonstrated that, for identical k_s values, while trends remained consistent, the extent to which they manifested differed under reacting and isothermal conditions. Full article

The paper presents some design principles of an inductive displacement transducer for measuring the displacement of rock specimens under high hydrostatic pressure. It consists of a single-layer, coreless solenoid mounted directly onto the specimen and connected to an LC oscillator located outside the pressure chamber, in which it serves as the inductive component. The specimen’s deformation changes the coil’s length and inductance, thereby altering the oscillator’s resonant frequency. Paired with a reference coil, the system achieves strain resolution of ~100 nm at pressures exceeding 400 MPa. Sensor design challenges include both electrical parameters (inductance and resistance of the sensor, capacitance of the resonant circuit) and mechanical parameters (number and diameter of coil turns, their positional stability, wire diameter). The basic requirement is to achieve stable oscillations (i.e., a high Q-factor of the resonant circuit) while maintaining maximum sensor sensitivity. Miniaturization of the sensor and minimizing the tensile force at its mounting points on the specimen are also essential. Improvement of certain sensor parameters often leads to the degradation of others; therefore, the design requires a compromise depending on the specific measurement conditions. This article presents the mathematical interdependencies among key sensor parameters, facilitating optimized sensor design. Full article

Due to the increasing demand for accurate and robust GNSS positioning for location-based services (LBS) in urban regions, the impacts prevalent in metropolitan areas, like multipath reflections and various interferences, have become persistent challenges. Consequently, developing effective strategies to address these sophisticated influences has become both a primary research focus and a shared priority. In this paper, the authors explore an approach to identify and mitigate the drawbacks arising from multipath effects in urban positioning. Unlike conventional ways for building complex models, an adaptive data-driven methodology is proposed to identify the fingerprints of a multipath in GNSS observations. This approach utilizes the Fourier transform (FT) to examine code multipath and other error sources in terms of frequency, as represented by the power spectrum. Wavelet decomposition and signal spectrum methods are subsequently applied to seek traces of code multipath in multilayer decompositions. Based on the exhibited multipath features, the impacts of multipath in GNSS observations are detected and mitigated in the reconstructed observations. The proposed method is validated for both static and dynamic positioning scenarios, demonstrating seamless integration with existing positioning models. The feasibility has been verified through a series of experiments and tests under urban environments using navigation terminals and smartphones. Full article

GPS spoofing remains a critical threat in the use of autonomous vehicles. Machine-learning-based detection systems, particularly support vector machines (SVMs), demonstrate high accuracy in their defense against conventional spoofing attacks. However, their robustness against intelligent adversaries remains largely unexplored. In this work, we reveal a critical vulnerability in an SVM-based GPS spoofing detection model by analyzing its decision boundary. Exploiting this weakness, we introduce novel evasion strategies that craft adversarial GPS signals to evade the SVM detector: a data location shift attack and a similarity-based noise attack, along with their combination. Extensive simulations in the CARLA environment demonstrate that a modest positional shift reduces detection accuracy from $99.9\%$ to $20.4\%$, whereas similarity to genuine GPS noise-driven perturbations remain largely undetected, while gradually degrading performance. A critical threshold reveals a nonlinear cancellation effect between similarity and shift, underscoring a fundamental detectability–impact trade-off. To our knowledge, these findings represent the first demonstration of such an evasion attack against SVM-based GPS spoofing defenses, suggesting a need to improve the adversarial robustness of machine-learning-based spoofing detection in vehicular systems. Full article