Search Results (140)

The precise characterization of surface rupture zones and associated co-seismic displacement fields from large earthquakes provides critical insights into seismic rupture mechanisms, earthquake dynamics, and hazard assessments. Stereo-photogrammetric digital elevation models (DEMs), produced from high-resolution satellite stereo imagery, offer reliable global datasets that are suitable for the detailed extraction and quantification of vertical co-seismic displacements. In this study, we utilized pre- and post-event WorldView-2 stereo images of the 2024 Ms7.1 Wushi earthquake in Xinjiang to generate DEMs with a spatial resolution of 0.5 m and corresponding terrain point clouds with an average density of approximately 4 points/m². Subsequently, we applied the Iterative Closest Point (ICP) algorithm to perform differencing analysis on these datasets. Special care was taken to reduce influences from terrain changes such as vegetation growth and anthropogenic structures. Ultimately, by maintaining sufficient spatial detail, we obtained a three-dimensional co-seismic displacement field with a resolution of 15 m within grid cells measuring 30 m near the fault trace. The results indicate a clear vertical displacement distribution pattern along the causative sinistral–thrust fault, exhibiting alternating uplift and subsidence zones that follow a characteristic “high-in-center and low-at-ends” profile, along with localized peak displacement clusters. Vertical displacements range from approximately 0.2 to 1.4 m, with a maximum displacement of ~1.46 m located in the piedmont region north of the Qialemati River, near the transition between alluvial fan deposits and bedrock. Horizontal displacement components in the east-west and north-south directions are negligible, consistent with focal mechanism solutions and surface rupture observations from field investigations. The successful extraction of this high-resolution vertical displacement field validates the efficacy of satellite-based high-resolution stereo-imaging methods for overcoming the limitations of GNSS and InSAR techniques in characterizing near-field surface displacements associated with earthquake ruptures. Moreover, this dataset provides robust constraints for investigating fault-slip mechanisms within near-surface geological contexts. Full article

Wide-beam laser direct energy deposition (LDED) has been widely used due to its superior deposition efficiency. To achieve optimal laser-powder coupling, this technique typically employs rectangular powder nozzles. This study establishes a simulation model to systematically investigate the powder flow field characteristics of rectangular symmetric nozzles. Through parametric analysis of powder feeding rate, carrier gas flow rate, and shielding gas flow rate, the effects on powder stream convergence behavior are quantitatively evaluated to maximize powder utilization efficiency. Key findings reveal that, while the powder focal plane position is predominantly determined by nozzle geometry, powder feeding parameters exhibit negligible influence on flow field intersections. The resulting powder spot demonstrates a rectangular profile slightly exceeding the laser spot dimensions, and the powder concentration exhibits a distinctive flat-top distribution along the laser’s slow axis, contrasting with a Gaussian distribution along the scanning direction. Experimental validation through powder collection tests confirms strong agreement with the simulation results. Furthermore, a mathematical model was developed to accurately describe the powder concentration distribution at the focal plane. These findings provide fundamental theoretical guidance for optimizing powder feeding systems in wide-beam LDED applications. Full article

Transcranial focused ultrasound (TcFUS) neuromodulation is hindered by skull-induced acoustic limitations. To enable synergistic multi-region brain stimulation, we designed non-coaxial multi-focal composite Fresnel acoustic lenses, including an overlapping Fresnel lens (OFL) and an alternating-segmented Fresnel lens (ASFL). These lenses convert planar ultrasound into multiple foci. Based on Fresnel theory, acoustic fields were analyzed via simulations and experiments, validating the generation of four non-coaxial foci (10/30 mm focal lengths) from a 1 MHz planar wave using both OFL and ASFL. The influence of lens parameters on focal pressure distribution was investigated, and morphology was quantified using a linear least-squares method. Significant differences in focal morphology and intensity between OFL and ASFL provide crucial guidance for optimizing multi-target TcFUS strategies. Full article

Bessel beams occupy an important position in optical research due to their characteristics of long focal depth, self-healing ability, and diffraction-free propagation. Traditional methods for generating Bessel beams suffer from complexity, a large size, low uniformity, and limited NA. Metasurfaces are considered to be a new technology for the miniaturization of optical devices due to their ability to regulate optical fields at subwavelength scales flexibly. Here, we generated Bessel beams by a complex-amplitude (CA) metasurface. The polarization conversion efficiency was controlled by the geometric size, while the phase value from 0 to 2π was manipulated based on the Pancharatnam–Berry (PB) phase. This approach enabled precise control over the axial intensity distribution of the optical field, which facilitated the generation of sub-millimeter-scale Bessel beams. Axial light field control based on CA metasurfaces has great potential for applications in a variety of fields, such as particle manipulation, large-depth-of-field imaging, and laser processing. Full article

To address the problem of excessive ablation in conventional laser processing caused by the inhomogeneous energy distribution at the focal point, along with the inherent heterogeneity and surface irregularities of textile materials, a new method for laser printing elastic bandage fabrics was developed. We used flat top light sources, short focal field mirrors, and low power lasers instead of the Gaussian light sources, long focal field mirrors, and high-power lasers used in traditional methods. First, the sample was preheated, and the aspherical lens system was designed and simulated. Then, the physical and chemical properties of laser-processed elastic bandage fabrics were investigated. Finally, based on single-factor experiments, orthogonal experimental analysis was conducted to determine the optimal process parameters. The results show that the optimized optical path can effectively improve the uniformity of the temperature field during laser scanning and enhance focusing performance; as energy gradually accumulates, chemical bonds in polymer molecules break; when the elastic bandage fabric is in a highly elastic state, it exhibits appropriate breaking strength and color difference. The best parameters obtained from the single-factor experiment are as follows: laser power range of 25–34 W, scanning speed range of 2200–2800 mm/s, preheating temperature range of 125–200 °C. The best parameters obtained from the orthogonal experiment are as follows: laser power 28 W, scanning speed 2800 mm/s, and the preheating temperature 175 °C. Full article

Magnetoencephalography (MEG) is a non-invasive neuroimaging technique that measures weak magnetic fields generated by neural electrical activity in the brain. Traditional MEG systems use superconducting quantum interference device (SQUID) sensors, which require cryogenic cooling and employ a dense array of sensors to capture magnetic field maps (MFMs) around the head. Recent advancements have introduced optically pumped magnetometers (OPMs) as a promising alternative. Unlike SQUIDs, OPMs do not require cooling and can be placed closer to regions of interest (ROIs). This study aims to optimize the layout of OPM-MEG sensors, maximizing information capture with a limited number of sensors. We applied a sequential selection algorithm (SSA), originally developed for body surface potential mapping in electrocardiography, which requires a large database of full-head MFMs. While modern OPM-MEG systems offer full-head coverage, expected future clinical use will benefit from simplified procedures, where handling a lower number of sensors is easier and more efficient. To explore this, we converted full-head SQUID-MEG measurements of auditory-evoked fields (AEFs) into OPM-MEG layouts with 80 sensor sites. System conversion was done by calculating a current distribution on the brain surface using minimum norm estimation (MNE). We evaluated the SSA’s performance under different protocols, for example, using measurements of single or combined OPM components. We assessed the quality of estimated MFMs using metrics, such as the correlation coefficient (CC), root-mean-square error, and relative error. Additionally, we performed source localization for the highest auditory response (M100) by fitting equivalent current dipoles. Our results show that the first 15 to 20 optimally selected sensors (CC > 0.95, localization error < 1 mm) capture most of the information contained in full-head MFMs. Our main finding is that for event-related fields, such as AEFs, which primarily originate from focal sources, a significantly smaller number of sensors than currently used in conventional MEG systems is sufficient to extract relevant information. Full article

When an unmanned aerial vehicle (UAV) tilts to capture an image of a ground target, variations in object distance may lead to uneven resolution distribution, with the focal length ranging from zero to the full field of view. The field-of-view focal length (FFL), which is a function of the field of view, characterizes the optical properties of the system for each viewing angle. The field-of-view focal length (FFL) quantifies the incremental change in image height resulting from marginal rays exiting the optical system, with infinitesimal angular variations at the field boundary. The optical aberration manifests as an effective focal length variation that exhibits field-dependent characteristics. Through systematic calculation and optimization of the field-of-view focal lengths (FFLs) for ground resolution (GR) control, a mid-wave infrared (MWIR) optical system has been successfully designed, featuring a 10° × 8° field of view (FOV) with an F-number of 3. The optical system implements field-adapted focal length adjustment across distinct viewing angles to ensure consistent ground resolution preservation throughout the full field of view. The designed optical system achieves near-diffraction-limited modulation transfer function (MTF) performance across the full field of view, with all dispersion spots consistently confined within the Airy disk at every viewing angle. The optical system demonstrates superior imaging performance with all dispersion spots confined within the Airy disk radius, fully complying with stringent image quality specifications. Featuring a compact structural configuration, the system exhibits optimal suitability for airborne ground-target reconnaissance applications. Full article

The ultrafast laser processing of three-dimensional structures characterized by highly spatially resolved features is more efficiently realized by implementing adaptive optics. Adaptive optics allow for the correction of optical aberrations, introduced when focusing inside the machined material, by tailoring the focal intensity distribution for the specific texturing task, in a reduced processing time. The aberration corrections by adaptive optics allow for a simplified scan strategy for the selective laser micromachining of transparent materials using depth-independent processing parameters, overcoming the limits related to the previously necessary pulse energy adjustment for different z positions in the material volume. In this paper, recent developments in this field are presented and discussed, mainly focusing on the use of dynamic optical elements—deformable mirrors and liquid crystal spatial light modulators—to obtain a high degree of laser processing control by an in-time correction of optical aberrations on different workpieces and mainly of transparent materials. Full article

With the acceleration of urbanization, the construction of smart cities has become a global focal point, with machine learning technology playing a crucial role in this process. This study aims to conduct a bibliometric analysis of the published research in the fields of smart cities and machine learning, using visualization techniques to reveal the spatiotemporal distribution patterns, research hotspots, and collaborative network structures. The goal is to provide systematic references for academic research and technological innovation in related fields. The results indicate that the development of this field exhibits distinct phases and regional characteristics. From a temporal perspective, research has undergone three stages: initial development, rapid growth, and stable consolidation, with the period from 2017 to 2021 marking a critical phase of rapid expansion. In terms of spatial distribution, countries such as China and the United States are at the forefront of this field, whereas regions like Africa and South America have a relatively low research output due to constraints in research resources and technological infrastructure. A hotspot analysis revealed that research topics are increasingly diverse and dynamically evolving. Issues such as data privacy, cybersecurity, sustainable development, and intelligent transportation have gradually become focal points, reflecting the dual demand of smart city development for technological innovation and green growth. Furthermore, collaboration network analysis indicates that international academic cooperation is becoming increasingly close, with research institutions in China, the United States, and Europe playing a central role in the global collaboration system, thereby promoting technology sharing and interdisciplinary integration. Through a systematic bibliometric analysis, this study identifies key application directions and future development trends in the research on smart cities and machine learning, providing valuable insights for academic research and technological advancements in related fields. Full article

Transcranial electrical stimulation, as a means of neural modulation, is increasingly favored by researchers. The distribution and magnitude of the electric field generated within the brain may directly affect the results of neural modulation. Therefore, it is important to clarify the change trend of the cortical electric field and the determinants of the induced electric field in the endodermis at different ages during the adult life cycle. In this study, we used SimNIBS software to perform MR image segmentation and realistic head model reconstruction on 476 individuals (aged 18 to 88 years old) and calculated the cortical electric field of four electrode montages commonly used in cognitive tasks. We divided all participants into groups by age with a span of 10 years for each group and compared the electric field distribution patterns, electric field intensities, and focalities of the cortexes and regions of interest related to cognitive tasks within groups. The degree of influence of global and local anatomical parameters on the electric field was analyzed using a stepwise regression model. The results showed that, in the cortexes and regions of interest, the variability of electric field distribution patterns was highest in adolescents (<20 years old) and elderly individuals (>80 years old). Moreover, throughout the adult lifespan, the electric field induced by transcranial electrical stimulation did not decrease linearly with age but rather presented a U-shaped pattern. In terms of the entire adult life cycle, compared with global anatomical parameters (intracranial brain tissue volume), local anatomical parameters (such as scalp or skull thickness below the electrode) have a greater impact on the amplitude of the intracranial electric field. Our research results indicated that it is necessary to consider the effects caused by different brain tissues when using transcranial electrical stimulation to modulate or treat individuals of different ages. Full article

This paper investigates the symmetrical layout effect in ultrasonic cleaning via acoustic solid coupling simulation, with emphasis on how the symmetrical arrangement of transducers influences sound pressure distribution. Two specific transducer layout methods are examined: uniform arrangement at the bottom and symmetrical arrangement along the sides. The findings indicate that when the tank length is an integer multiple of one-quarter of the acoustic wavelength, the symmetrical side arrangement markedly enhances the sound pressure level within the tank and optimizes the propagation and reflection of acoustic waves. In megasonic cleaning, focusing is achieved through a 7 × 7 transducer array by precisely controlling the phase, and the symmetrical arrangement ensures uniform sound pressure distribution. By integrating 1 MHz megasonic sources from both focused and unfocused configurations, the overall sound pressure distribution and peak sound pressure at the focal point are calculated using multi-physics field coupling simulations. A comparative analysis of the sound fields generated by focused and unfocused sources reveals that the focused source can produce significantly higher sound pressure in specific regions. Leveraging the enhanced cleaning capability of the focused acoustic wave in targeted areas while maintaining broad coverage with the unfocused acoustic wave significantly improves the overall cleaning efficiency. Ultrasonic cleaning finds extensive applications in industries such as electronic component manufacturing, medical device sterilization, and automotive parts cleaning. Its efficiency and environmental friendliness make it highly significant for both daily life and industrial production. Full article

Underground engineering construction in the karst regions of Southwestern China has become a focal point of China’s advancing regional urban development. However, construction activities interfere with the karst groundwater environment, which is characterized by irregular pore distributions and complex, variable flow patterns. This study establishes a numerical model of the karst water system traversed by Line 2 of the Guiyang Rail Transit in China. Incorporating hydrogeological conditions and tunnel engineering parameters, the model simulates the effects of tunnel construction on the karst groundwater system. The flow-field distribution of the karst groundwater system is altered at various stages of tunnel construction. During tunnel excavation, a drainage zone centered around the subway forms in the groundwater system, altering the groundwater flow field and causing fluctuations in the groundwater level. During the lining phase, the tunnel area gradually transforms into a waterproof zone. Although the groundwater level gradually recovers under rainfall recharge, the waterproofing effect of the tunnel drives the formation of a new groundwater flow field within the groundwater system, changing both the groundwater level and the original flow field. This work offers support for the coordinated development of underground engineering and environmental protection in karst areas, facilitating sustainable urbanization. Full article

To address the challenges of detecting cotton pests and diseases in natural environments, as well as the similarities in the features exhibited by cotton pests and diseases, a Lightweight Cotton Disease Detection in Natural Environment (LCDDN-YOLO) algorithm is proposed. The LCDDN-YOLO algorithm is based on YOLOv8n, and replaces part of the convolutional layers in the backbone network with Distributed Shift Convolution (DSConv). The BiFPN network is incorporated into the original architecture, adding learnable weights to evaluate the significance of various input features, thereby enhancing detection accuracy. Furthermore, it integrates Partial Convolution (PConv) and Distributed Shift Convolution (DSConv) into the C2f module, called PDS-C2f. Additionally, the CBAM attention mechanism is incorporated into the neck network to improve model performance. A Focal-EIoU loss function is also integrated to optimize the model’s training process. Experimental results show that compared to YOLOv8, the LCDDN-YOLO model reduces the number of parameters by 12.9% and the floating-point operations (FLOPs) by 9.9%, while precision, mAP@50, and recall improve by 4.6%, 6.5%, and 7.8%, respectively, reaching 89.5%, 85.4%, and 80.2%. In summary, the LCDDN-YOLO model offers excellent detection accuracy and speed, making it effective for pest and disease control in cotton fields, particularly in lightweight computing scenarios. Full article

Industrial process heat typically requires large amounts of fossil fuels. Solar energy, while abundant and free, has low energy density, and so large collector areas are needed to meet thermal needs. Land costs in developed areas are often prohibitively high, making rooftop-based concentrating solar power (CSP) attractive. However, limited rooftop space and the low energy density of solar power are usually insufficient to meet a facility’s demands. Maximizing annual CSP energy generation within a bounded rooftop space is necessary to mitigate fossil fuel consumption. This is a different optimization objective than minimizing the Levelized Cost of Energy (LCOE) in typical open-land, utility-scale heliostat layout optimization. Innovative designs are necessary, such as compact, energy-dense central receiver systems with non-flat heliostat field topographies that use spatially efficient Tilt–Roll heliostats or multi-rooftop and multi-height distributed urban systems. A novel ray-tracing simulation tool was developed to evaluate these unique scenarios. For compact systems, optimized annual energy production occurred with maximum heliostat spatial density, and the best non-flat heliostat field topography found is a shallow section of a parabolic cylinder with an East–West focal axis, yielding a 10% optical energy improvement. Tightly packed Tilt–Roll heliostats showed a double improvement in optical energy at the receiver compared to Azimuth–Elevation heliostats. Full article

Transfer learning has garnered significant interest in the field of bearing fault diagnosis under varying operational conditions due to its robust generalization capabilities. However, real-world diagnostic scenarios frequently encounter data imbalances, which complicates the learning of the classification boundary for the minority class within the diagnostic model. To address this challenge, we propose a normalization-guided and gradient-weighted unsupervised domain adaptation network (NG-UDAN) for intelligent bearing fault diagnosis, aimed at tackling inter-domain feature shifts and intra-domain category imbalances. Firstly, the proposed network integrates a residual feature extractor with the Domain Normalization (DN) module to enhance domain-invariant feature extraction. Subsequently, the Local Maximum Mean Discrepancy (LMMD) loss is utilized to minimize the conditional distributional differences between the source and target domains. Finally, the Gradient-Weighted Focal Loss (GWFL) is specifically designed to address the issue of class imbalance. Experiments conducted across three imbalanced scenarios using the Case Western Reserve University (CWRU) and Paderborn University (PU) datasets demonstrate that NG-UDAN is effective in both single-source and mixed-source domain adaptation. Furthermore, comparisons with alternative methods validate the superiority of this approach in managing class imbalances under varying working conditions. Full article