Search Results (420)

Artificial intelligence is playing an increasingly important role in education. Learning path recommendation is one of the key technologies in artificial intelligence education applications. This paper applies knowledge space theory and fuzzy set theory to study the construction of consistent fuzzy competence spaces and their application to learning path recommendation. With the help of the outer fringe of fuzzy competence states, this paper proves the necessary and sufficient conditions for a fuzzy competence space to be a consistent fuzzy competence space and designs an algorithm for verifying consistent fuzzy competence spaces. It also proposes methods for constructing and reducing consistent fuzzy competence spaces, provides learning path recommendation algorithms from the competence perspective and combined with a disjunctive fuzzy skill mapping, and constructs a bottom-up gradual and effective learning path tree. Simulation experiments are carried out for the construction and reduction in consistent fuzzy competence spaces and for learning path recommendation, and the simulation studies show that the proposed methods achieve significant performance improvement compared with related research and produce a more complete recommendation of gradual and effective learning paths. The research of this paper can provide theoretical foundations and algorithmic references for the development of artificial intelligence education applications such as learning assessment systems and intelligent testing systems. Full article

Long bone fractures are breaks or cracks in a long bone of the body typically caused by trauma like a fall, sport injury, accidents etc. This study investigates the effectiveness of experimental methods for fast and safe healing of long bone fractures in humans, highlighting both their advantages and disadvantages, respectively finding the most effective and safe methods for evaluating the types of fixators that can be used in the consolidation of fractured long bones. As for the preliminary data, numerical methods and applied mathematics were used to address this problem. After collecting of preliminary data there were performed a series of experimental analysis as follows: Electrical Strain Gauges (ESGs); the Moiré Fringes method; Photo-Elasticity, with the particular technique thereof, the so-called Photo-Stress method; Holographic Interferometry (HI); Speckle Pattern Interferometry (ESPI) and Shearography; and Video Image Correlation (VIC), which is also called Digital Image Correlation (DIC). By analyzing different methods, the following two methods resulted to be widely applicable, namely, ESG and DIC/VIC. The findings highlight the net advantages regarding the objective choice of these types of fixators, thereby contributing to a possible extension of these approaches for the benefit of medical surgical practice Full article

As ultra-high resolution liquid crystal displays (LCDs) advance, crosstalk has become a critical challenge due to the reduced spacing of electronic circuits and increased signal frequencies. In particular, vertical crosstalk (V-CT) in vertical-alignment LCDs arises mainly from fringing electric fields generated by data lines, along with secondary contributions from data line–pixel coupling effect, thin-film transistor leakage, and other factors. To resolve V-CT, we propose a memory-efficient compensation algorithm implemented on a field-programmable gate array as a customized timing controller. The proposed algorithm achieves compensation accuracy within 2% while significantly reducing memory requirements. A conventional 7680 × 4320 pixel LCD panel requires approximately 796 MB of memory for compensation data, whereas our method reduces this to only 0.37 MB—a nearly 2000-fold reduction—by referencing only preceding pixel information. This approach enables cost-effective implementation, faster processing, and enhanced image quality. Overall, the proposed method provides a practical and scalable solution for resolving V-CT in 8K LCD panels, establishing a new benchmark for high-resolution display technologies. Full article

We report the design, fabrication, and experimental characterization of an asymmetric loop-terminated Mach–Zehnder interferometer (a-LT-MZI) realized on a silicon nitride (SiN) platform for refractive index (RI) sensing. The LT-MZI architecture incorporates a Sagnac loop that enables bidirectional light propagation, effectively doubling the interaction length without enlarging the device footprint, enhancing sensitivity and improving stability against environmental noise. Subwavelength grating (SWG) waveguides were integrated into the sensing arm to further strengthen light-matter interaction. The fabricated devices exhibited stable and well-defined interference fringes, with uniform wavelength shifts that scaled linearly with changes in the surrounding refractive index. Standard a-LT-MZI structures (ΔL = 300 μm) achieved experimental sensitivities of 288.75–301.25 nm/RIU, while SWG-enhanced devices reached 496–518 nm/RIU, confirming the effectiveness of refractive index engineering. Comparative analysis against previously reported MZI-based sensors highlights the competitive performance of the proposed design. By combining the scalability and CMOS compatibility of silicon nitride with the sensitivity and robustness of the a-LT-MZI architecture, this device provides a compact and versatile platform for next-generation lab-on-chip photonic sensors. It holds strong potential for applications in biochemical diagnostics, medical testing, and environmental monitoring. Full article

The accuracy of phase unwrapping is a decisive factor in achieving high-precision dimensional measurement using the projected fringe profilometry. However, discontinuities at truncation points inevitably lead to phase jumps, especially when measuring objects with complex hollow features, resulting in significantly increased errors. To address this issue, this paper proposes an adaptive phase correction algorithm based on row and column constraints. First, the algorithm identifies the main normal phase distribution region in each column and interpolates abnormal values deviating from this region, ensuring smooth phase distribution in the column direction. Then, it detects each continuous non-zero segment in every row, locates phase jump positions, and performs local corrections. This approach enhances the overall continuity of the phase map and effectively compensates for phase jump errors. Experimental results demonstrate that the proposed method can effectively suppress phase jumps caused by object edges and hollow regions, achieving an absolute error of less than 0.05 mm in measured step height differences in standard blocks. This provides a reliable phase preprocessing solution for the optical measurement of complex-shaped objects. Full article

In high-density cities like Hong Kong, green spaces are often characterized by fragmentation and uneven spatial distribution, which negatively impacts their accessibility and equity. To address this issue, studies have proposed the use of informal green spaces (IGSs) as a supplementary component to formal urban green spaces (UGSs). However, the spatial delineation and quantitative analysis of IGSs remain challenging due to the lack of standardized identification and evaluation methods. Building upon the work of urban theorists Henry Lefebvre and Edward Soja, this study explores informal green spaces as third spaces. This study employed remote sensing and GIS technologies to systematically assess the spatial distribution and benefits of IGSs, categorizing them into four types: Urban Interstitial IGSs, Transitional IGSs, Fringe IGSs, and Riparian IGSs. Subsequently, an evaluation framework was constructed across ecological, social, and economic dimensions to quantify the overall value of IGSs. The results reveal that IGS significantly contributes to ecological regulation, social interaction, and economic potential, particularly in urban areas with limited green resources. This demonstrates that IGSs can serve as a vital complement to formal urban green spaces, playing a key role in alleviating green space inequity, enhancing urban livability, and promoting sustainability. Furthermore, this study provides a scientific foundation for precise identification, benefit assessment, and optimized management of IGSs, supporting effective integration and rational utilization in future urban planning. Full article

This study introduces a novel, low-cost, and non-invasive method for characterizing the surface profile of transparent objects using double digital fringe projection (DDFP). By projecting dual sinusoidal patterns that generate a Moiré effect and applying a frequency-domain Gaussian filter, the system isolates relevant data for accurate phase recovery through the isotropic quadrature transform (IQT). Experimental validation with plastic and acrylic samples confirms the method’s high spatial resolution and robustness against ambient noise. Unlike traditional systems, this technique avoids coherent light sources and complex hardware, improving its accessibility for academic and industrial use in transparent surface metrology. Full article

This study is focused on the hydrothermal synthesis of iron–manganese oxide nanostructures, focusing on the influence of Fe:Mn precursor ratios, temperature, and reaction time on phase formation, morphology, and structural characteristics. Three molar ratios (Fe:Mn = 2:1, 1:1, and 1:2) were explored under variable conditions (80 °C, 120 °C, and 200 °C; 4, 12, and 24 h). X-ray diffraction (XRD) analysis revealed distinct phase selectivity depending on precursor composition: FeMn₂O₄ was obtained with 1:2 ratio, Fe₃Mn₃O₈ with 1:1, and Fe₂MnO₄ with 2:1, each without phase mixing. Scanning electron microscopy (FESEM) showed a pronounced effect of temperature and time on nanoparticle morphology, ranging from compact agglomerates to well-defined rod-like structures at 200 °C/24 h. Dynamic light scattering (DLS) indicated narrow size distributions for samples synthesized at 120 °C/12 h, with hydrodynamic diameters between 20 and 50 nm. Raman spectroscopy confirmed the presence of characteristic vibrational modes of spinel-type structures and validated structural integrity. High-resolution transmission electron microscopy (HRTEM) evidenced well-ordered lattice fringes with interplanar spacings of ~0.48–0.52 nm, consistent with spinel phases and indicative of high crystallinity. These findings demonstrate that controlled atomic binding and thermal parameters enable selective synthesis of pure iron–manganese oxide phases with tailored morphologies, offering a scalable route for designing advanced functional materials in catalysis, energy, and biomedical applications. Full article

Interference lithography (IL) offers high throughput, excellent uniformity, and maskless patterning capabilities. Compared to other methods, IL enables large-area, cost-effective fabrication of periodic structures with subwavelength resolution, which is particularly valuable for sensing applications, enabling the development of more sensitive, high-resolution, and reliable sensors. This review provides a comprehensive analysis of IL from the perspective of optical field control. We first introduce the principles of interference field formation and summarize key system architectures, including Mach–Zehnder and Lloyd’s mirror configurations, as well as advanced schemes such as multi-beam interference and multi-step exposure for complex pattern generation. We then examine how wavefront engineering, polarization modulation, and phase stabilization influence pattern morphology, contrast, and large-area uniformity. To address dynamic drifts caused by environmental perturbations, both passive vibration isolation and active fringe-locking techniques are discussed. For fringe-locking systems, we review methods for drift monitoring, control algorithms, and feedback implementation. These developments enhance the capability of IL systems to deliver nanoscale accuracy under dynamic conditions, which is essential for stable and high-performance sensing. Looking ahead, IL is evolving into a versatile platform for sensor-oriented nanofabrication. By integrating physical modeling, precision optics, and real-time control, IL provides a robust foundation for advancing next-generation sensing technologies with higher sensitivity, resolution, and reliability. Full article

During the detailed design of magnets for the storage ring of Siam Photon Source II (SPS-II), the influence of magnetic crosstalk between adjacent magnets in the compact Double Triple Bend Achromat (DTBA) lattice was investigated. Using Opera-3D magnetostatic simulation, six magnet pairs were analyzed to investigate the changes in magnetic field distribution along the electron trajectory and integrated magnetic field within each magnet aperture. The study employed polynomial and Fourier analyses to calculate multipole field components. Results indicate that magnetic crosstalk affects the field distribution in the region between magnets, particularly for the defocusing quadrupole and dipole magnets (QD2-D01) and the focusing quadrupole and octupole magnets (QF42-OF1) pairs, which have the pole-to-pole distances of 153.37 mm and 116.45 mm, respectively. Although these separations exceed the estimated fringe field regions, deviations of up to 1% in the main field components were observed. Notably, even an unpowered neighboring magnet contributes to magnetic field distortion due to the modified magnetic flux distribution. Crosstalk effects on the higher-order multipole fields are mostly within the acceptable limit, except for the extra quadrupole field from QD2 found in the dipole D01 magnet. This study highlights the effects of magnetic interference in tightly packed lattice and underscores the need to include a complete multipole field data with crosstalk consideration in the SPS-II lattice model in order to ensure an accurate beam dynamics simulation and predict the operating current adjustments for machine commissioning. Full article

Three-dimensional measurement of translucent objects using structured light techniques remained fundamentally challenging due to severe degradation of fringe patterns caused by subsurface scattering, which inevitably introduced phase errors and compromised measurement accuracy. Although deep learning had emerged as a powerful tool for fringe analysis, its practical implementation was hindered by the impractical requirement for large-scale labeled datasets, particularly in scattering-dominant measurement scenarios. To overcome these limitations, we developed a self-learning-based fringe domain conversion method inspired by image style transfer principles, where degraded and ideal fringe patterns were treated as distinct domains for cyclic translation. The proposed framework employed dual generators and discriminators to establish cycle-consistency constraints while incorporating both numerical intensity-based and physical phase-derived optimization targets, effectively suppressing phase errors and improving fringe modulation without requiring paired training data. Experimental validation demonstrated superior performance in reconstructing high-fidelity 3D morphology of translucent objects, establishing this approach as a robust solution for precision metrology of complex scattering media. Full article

Exploring the pollution characteristics and ecological risks of urbanization on lakes in urban fringe areas has guiding significance for the control and scientific management of heavy metal pollution in lakes in urban fringe areas. Taking the West Lake in Kaifeng city as an example, the samples of the sediments and surface water of the lake were collected, and the contents of heavy metals (As, Cd, Cr, Cu, Ni, Pb, and Zn) were measured, assessing the degree and ecological risk of heavy metal pollution using the Geo-Accumulation Index (I_geo) and Potential Ecological Risk Index methods (RI); and the sources of pollution were identified. The results show that the heavy metal concentrations in the surface water of the West Lake in Kaifeng city are generally low; average concentrations of Cd, Cu, Zn, Cr, Ni, Pb, and As in sediments are 3.120, 1.810, 1.700, 1.540, 1.000, 0.990, and 0.430 times higher than the background value of fluvo-aquic soil, respectively. The sequence of the average I_geo from high to low is Cd (1.020) > Cu (0.220) > Zn (0.160) > Cr (0.000) > Pb (−0.610) > Ni (−0.640) > As (−1.850). Among them, contaminations with Pb are classed as moderately polluted; As pollution is relatively light, while other heavy metals are unpolluted. The average Potential Ecological Risk Coefficient (E) values for seven heavy metals are Cd (93.500) > Cu (9.040) > Ni (4.990) > Pb (4.950) > As (4.290) > Cr (3.080) > Zn (1.700). Cd is at a considerable potential ecological risk, while other heavy metals are at low ecological risks. Heavy metal pollution in sediment of West Lake in Kaifeng mainly comes from traffic activities such as yacht machinery wear and gasoline burning. The research findings provide a scientific foundation for developing effective mitigation strategies against heavy metal contamination in peri-urban lacustrine ecosystems. Full article

Urban and peri-urban lakes are increasingly threatened by water quality degradation due to rising anthropogenic pressures and environmental variability. This study proposes an integrated framework that combines multi-source data and machine learning to estimate and monitor three key water quality parameters: turbidity, total dissolved solids (TDS), and biological oxygen demand (BOD). Field measurements from three lakes in West Bengal, India, Rabindra Sarovar, Mirikh Lake, and Hanuman Ghat Lake, were combined with Landsat-8 satellite imagery, meteorological data, and land use information. Three modeling scenarios were developed: (i) using only remote sensing indices, (ii) combining remote sensing indices with meteorological variables, and (iii) integrating remote sensing indices, meteorological data, and land use features. Principal component analysis (PCA) was used to reduce dimensionality and redundancy. Machine learning models, namely, XGBoost, Decision Tree, and Ridge Regression, were trained and evaluated using R² and RMSE (Root Mean Square Error) metrics. The third scenario outperformed the others, with Ridge Regression achieving the highest accuracy for BOD prediction (R² = 0.99). Spatiotemporal patterns revealed persistently high BOD levels along urban lake fringes and post-monsoon spikes in turbidity and TDS, especially in agriculturally influenced zones. These patterns were closely linked to land use practices, rainfall-driven runoff, and point-source pollution. This study underscores the effectiveness of remote sensing and machine learning as scalable tools for real-time water quality monitoring, promoting sustainability through informed lake management strategies in India. Full article

In this article, we propose an equivalent 2-D finite element analysis (FEA) process considering the 3-D flux of an outer rotor permanent magnet synchronous motor (PMSM). In the motor, 3-D flux such as axial leakage flux (ALF) and overhang fringing flux (OFF) are influenced based on design variables. Three-dimensional FEA is required to consider the components of 3-D flux. However, 3-D FEA is inefficient to use during the design process because of time-consuming. Therefore, we propose an equivalent FEA that considers the 3-D flux. First, the effects of ALF and OFF according to design variables such as rotor inner and outer diameter, stack length, and overhang length. Second, the 3-D flux is converted into a coefficient. Finally, it is applied to 2-D FEA. Using the proposed process, motor performance considering 3-D flux can be quickly predicted. The proposed performance prediction process is verified through simulation and experiment. Full article

Posidonia oceanica seagrass, endemic to the Mediterranean Sea, provides ecological goods and ecosystem services of paramount importance. In shallow and sheltered bays, P. oceanica meadows can reach the sea surface, with leaf tips slightly emerging, forming fringing and barrier reefs. During the 20th century, P. oceanica declined conspicuously in the vicinity of large ports and urbanized areas, particularly in the north-western Mediterranean. The main causes of decline are land reclamation, anchoring, bottom trawling, turbidity and pollution. Artificial sand nourishment of beaches has also been called into question, with sand flowing into the sea, burying and destroying neighbouring meadows. A fringing reef of P. oceanica, located at Saint-Mandrier-sur-Mer, near the port of Toulon (Provence, France), is severely degraded. Analysis of aerial photos shows that, since the beginning of the 2000s, it has remained stable in some parts or continued to decline in others. This contrasts with the trend towards recovery, observed in France, thanks to e.g., the legally protected status of P. oceanica, and the reduction of pollution and coastal developments. The sand nourishment of the study beach, renewed every year, with the sand being washed or blown very quickly (within a few months) from the beach into the sea, burying the P. oceanica meadow, seems the most likely explanation. Other factors, such as pollution, trampling by beachgoers and overgrazing, may also play a role in the decline. Full article