Search Results (419)

In mode-locked fiber lasers and optical sensors, in-line filters are essential components. Fiber-core Mach–Zehnder interferometer (MZI) technology has garnered a lot of research interest for the several manufacturing techniques for in-line MZI filters. Although multi-line inscription is frequently needed in existing methods to attain enough waveguide width, this approach adds complexity to production and may result in compromised waveguide quality. In this work, we present an improved single-line direct-writing method that attains similar MZI filtering results to multi-line scan. Additionally, the MZI filter created with the modified single-line direct-writing technique has a smaller insertion loss and requires less direct-writing energy than the previous single-line direct-writing technique. A 516 μm long MZI-based in-line filter was successfully constructed. The results of the characterization showed a central loss dip at 1089.82 nm, a free-spectral range (FSR) of 141.36 nm, an extinction ratio of 19.69 dB, and an insertion loss of 1.122 dB. This method decreased the insertion loss by a factor of 2.7 for an identical extinction ratio and improved the direct-writing efficiency by a factor of 9 for an equivalent FSR with multi-line scan. There was consistency between the experimental and simulation results. We also took measurements of the MZI’s temperature sensitivity. This work shows notable improvements in waveguide quality and ease of manufacture. This accomplishment lays the groundwork for further advancements in integrated mode-locked fiber laser technology. Full article

With the advancement of 3D sensing technologies, point clouds have become a key data format in the construction industry, supporting tasks such as as-built verification and BIM integration. However, robust and accurate edge detection from unstructured point cloud data remains a critical challenge, particularly in architectural environments characterized by structured geometry and variable noise conditions. This study presents a comparative evaluation of two classical edge detection algorithms—Random Sample Consensus (RANSAC) and Density-Based Spatial Clustering of Applications with Noise (DBSCAN)—applied to terrestrial laser-scanned point cloud data of eight rectangular structural columns. After preprocessing with the Statistical Outlier Removal (SOR) algorithm, the algorithms were evaluated using four performance criteria: edge detection quality, BIM-based geometric accuracy (via Cloud-to-Cloud distance), robustness to noise, and density-based performance. Results show that RANSAC consistently achieved higher geometric fidelity and stable detection across varying conditions, while DBSCAN showed greater resilience to residual noise and flexibility under low-density scenarios. Although DBSCAN occasionally outperformed RANSAC in local accuracy, it tended to over-segment edges in high-density regions. These findings underscore the importance of selecting algorithms based on data characteristics and project goals. This study establishes a reproducible framework for classical edge detection in architectural point cloud processing and supports future integration with BIM-based quality control systems. Full article

Streak cameras, essential for ultrahigh temporal resolution diagnostics in laser-driven inertial confinement fusion, underpin the streak tube imaging LiDAR (STIL) system—a flash LiDAR technology offering high spatiotemporal resolution, precise ranging, enhanced sensitivity, and wide field of view. This study establishes a theoretical model of the STIL system, with numerical simulations predicting limits of temporal and spatial resolutions of ~6 ps and 22.8 lp/mm, respectively. Dynamic simulations of laser backscatter signals from targets at varying depths demonstrate an optimal distance reconstruction accuracy of 98%. An experimental STIL platform was developed, with the key parameters calibrated as follows: scanning speed (16.78 ps/pixel), temporal resolution (14.47 ps), and central cathode spatial resolution (20 lp/mm). The system achieved target imaging through streak camera detection of azimuth-resolved intensity profiles, generating raw streak images. Feature extraction and neural network-based three-dimensional (3D) reconstruction algorithms enabled target reconstruction from the time-of-flight data of short laser pulses, achieving a minimum distance reconstruction error of 3.57%. Experimental results validate the capability of the system to detect fast, low-intensity optical signals while acquiring target range information, ultimately achieving high-frame-rate, high-resolution 3D imaging. These advancements position STIL technology as a promising solution for applications that require micron-scale depth discrimination under dynamic conditions. Full article

This paper experimentally investigates the fretting fatigue behavior of metal additive-manufactured Ti-6Al-4V alloy specimens fabricated using the selective laser melting (SLM) method, focusing on damage characterization and fatigue life assessment. Based on the ASTM E466 standard, the test components were manufactured using metal 3D printing technology. Fretting fatigue tests were conducted under varying axial stress levels and contact loads, followed by microscopic examinations using scanning electron microscopy (SEM) to analyze damage mechanisms. A fretting map was developed based on SEM observations, providing insights into damage evolution under different loading conditions. These findings contribute to a better understanding of the relationship between fretting fatigue parameters and failure mechanisms. The developed fretting map and experimental observations provide a foundation for further studies aimed at enhancing the fretting fatigue life assessment of standard specimens for different test parameters. Finally, this paper includes design-oriented evaluation frameworks that can guide engineers in integrating AM components into safety-critical systems under fretting fatigue conditions. Full article

To describe a technique for managing acute corneal hydrops in eyes with keratoconus using dome stromal drainage with intracameral air injection under real-time three-dimensional (3D) microscope-integrated optical coherence tomography (OCT) guidance. We describe a retrospective case series of six eyes from six patients with keratoconus who developed acute corneal hydrops. All eyes underwent intracameral air injection with controlled dome puncture for stromal fluid drainage, without the use of sutures. The procedure was performed using a 3D visualization system that enables integrated and simultaneous viewing of the surgical field and intraoperative OCT scan (a 3D digitally assisted visualization system that displayed a split-screen view of the surgical field and OCT cross-sections simultaneously). Postoperative resolution of edema and improvement in clarity were documented. The resolution of corneal edema allowed for subsequent mushroom-shaped penetrating keratoplasty performed with a femtosecond laser in four eyes of four patients. All six eyes showed significant resolution of corneal edema within 2 to 4 weeks. Stromal clefts collapsed rapidly after drainage. In each case, the thick edema was reduced to a confined leucoma. No intraoperative or postoperative complications were observed. All four eyes that underwent a femtosecond laser-assisted mushroom-shaped penetrating keratoplasty showed optimal anatomical and functional success. Air-assisted dome drainage, combined with simultaneous 3D and OCT visualization, is a safe and effective technique for treating acute corneal hydrops. This technology enables real-time decision-making and enhances surgical precision, opening the door to advanced procedures that are otherwise limited by corneal opacity. Full article

The structural analysis of shelterbelts forms the foundation of their planning and management, yet the scientific and effective quantification of shelterbelt structures requires further investigation. This study developed an innovative heterogeneous analytical framework, integrating three key methodologies: the LeWoS algorithm for wood–leaf separation, TreeQSM for structural reconstruction, and 3D alpha-shape spatial quantification, using terrestrial laser scanning (TLS) technology. This framework was applied to three typical farmland shelterbelts in the Ulan Buh Desert oasis, enabling the first precise quantitative characterization of structural components during the leaf-on stage. The results showed the following to be true: (1) The combined three-algorithm method achieved ≥90.774% relative accuracy in extracting structural parameters for all measured traits except leaf surface area. (2) Branch length, diameter, surface area, and volume decreased progressively from first- to fourth-order branches, while branch angles increased with ascending branch order. (3) The trunk, branch, and leaf components exhibited distinct vertical stratification. Trunk volume and surface area decreased linearly with height, while branch and leaf volumes and surface areas followed an inverted U-shaped distribution. (4) Horizontally, both surface area density (Scd) and volume density (Vcd) in each cube unit exhibited pronounced edge effects. Specifically, the Scd and Vcd were greatest between 0.33 and 0.60 times the shelterbelt’s height (H, i.e., mid-canopy). In contrast, the optical porosity (Op) was at a minimum of 0.43 H to 0.67 H, while the volumetric porosity (Vp) was at a minimum at 0.25 H to 0.50 H. (5) The proposed volumetric stratified porosity (Vsp) metric provides a scientific basis for regional farmland shelterbelt management strategies. This three-dimensional structural analytical framework enables precision silviculture, with particular relevance to strengthening ecological barrier efficacy in arid regions. Full article

Lactiplantibacillus plantarum is a kind of common lactic acid bacteria, which plays an important role in the production of fermented foods. In general, the formation of biofilm is conducive to the adaptability of cells in the face of fierce competition and an increasingly harsh fermentation environment. In this work, optimum conditions for the formation of biofilm by L. plantarum were investigated, and scanning electron microscopy (SEM) and confocal laser scanning microscopy (CLSM) analysis showed the morphology of biofilm cells and 3D architecture of biofilm under different conditions, respectively. In addition, cells in the biofilms showed higher cell viability under heat stress, acid stress, and oxidative stress compared with planktonic cells. RNA-seq technology and TMT-based proteomic technology were employed to reveal the differential expression of profiles between biofilm cells and planktonic cells. The shelter provided by biofilm and the differential expression of genes and proteins involved in PTS, the TCA cycle, alanine, and teichoic acid biosynthesis may be involved in the formation of biofilm cells. The results presented in this study will help to understand the formation of biofilms in L. plantarum and regulate the industrial performance of cells in the food industry. Full article

Technological development has strongly impacted all processes related to the design, construction, and management of real estate assets. In fact, the introduction of the BIM approach has required the application of three-dimensional survey technologies, and in particular the use of LiDAR instruments, both in their static (TLS—terrestrial laser scanner) and dynamic (iMMS—indoor mobile mapping system) implementations. Operators and developers of LiDAR technologies, for the implementation of scan-to-BIM procedures, initially placed particular care on the 3D surveying accuracy obtainable from such tools. The incorporation of RGB sensors into these instruments has progressively expanded LiDAR-based applications from essential topographic surveying to geospatial applications, where the emphasis is no longer on the accurate three-dimensional reconstruction of buildings but on the capability to create three-dimensional image-based visualizations, such as virtual tours, which allow the recognition of assets located in every area of the buildings. Although much has been written about obtaining the best possible accuracy for extensive asset surveying of large-scale building complexes using iMMS systems, it is now essential to develop and define suitable procedures for controlling such kinds of surveying, targeted at specific geospatial applications. We especially address the design, field acquisition, quality control, and mass data management techniques that might be used in such complex environments. This work aims to contribute by defining the technical specifications for the implementation of geospatial mapping of vast asset survey activities involving significant building sites utilizing iMMS instrumentation. Three-dimensional models can also facilitate virtual tours, enable local measurements inside rooms, and particularly support the subsequent integration of self-locating image-based technologies that can efficiently perform field updates of surveyed databases. Full article

Preserving heritage sites is a complex challenge that requires multidisciplinary approaches, combining scientific accuracy with cultural and historical sensitivity. In alignment with UNESCO’s conservation guidelines, this study investigated the airflow dynamics and wind-induced structural effects within ancient architecture using advanced computational fluid dynamics (CFD). The study site was the Na Phra Meru Historical Temple in Ayutthaya, Thailand, where the shear stress transport $k-\omega$ turbulence model was applied to analyze distinctive airflow patterns. A high-precision 3D computational domain was developed using Faro focus laser scanning technology, with the CFD results being validated based on onsite experimental data. The findings provided critical insights into the temple’s ventilation behavior, revealing strong correlations between turbulence characteristics, wind speed, temperature, and relative humidity. Notably, the small slit windows generated complex flow mixing, producing a large internal recirculation zone spanning approximately 70% of the central interior space. In addition to airflow distribution, the study evaluated the aerodynamic forces and rotational moments acting on the structure based on five prevailing wind directions. Based on these results, winds from the east and northeast generated the highest aerodynamic loads and rotational stresses, particularly in the lateral and vertical directions. Overall, the findings highlighted the critical role of airflow and wind-induced forces in the deterioration and long-term stability of heritage buildings. The study demonstrated the value of integrating CFD, environmental data, and structural analysis to bridge the gap between conservation science and engineering practice. Future work will explore further the interactions between wall moisture and the multi-layered pigments in mural paintings to inform preservation practices. Full article

Background/Objectives: The growing interest in personalized medicine has accelerated the exploration of three-dimensional (3D) printing technologies in pharmaceutical applications. This study investigates the potential of selective laser sintering (SLS) as a flexible, small-scale manufacturing method for atomoxetine tablets tailored for individualized therapy, comparing it with conventional direct compression. Methods: Atomoxetine tablets were produced using SLS 3D printing with varying laser scanning speeds and compared to tablets made via a compaction simulator. Formulations were based on hydroxypropyl methylcellulose (HPMC) as the primary matrix former. The physical properties, drug content, disintegration time, and dissolution profiles were evaluated. The structural and chemical integrity were assessed using SEM, FTIR, DSC, and XRPD. Results: The SLS tablets exhibited comparable mechanical properties and drug content to those made by compaction. Lower laser speeds produced harder tablets with slower disintegration, while higher speeds yielded more porous tablets with ultra-rapid drug release (>85% in 15 min). All tablets met the European Pharmacopoeia dissolution criteria. No significant drug–excipient interactions or changes in crystallinity were detected. Conclusions: SLS printing is a viable alternative to traditional tablet manufacturing, offering control over drug release profiles through parameter adjustment. The technique supports the development of high-quality, patient-specific dosage forms and shows promise for broader implementation in personalized pharmaceutical therapy. Full article

This paper presents a novel method, Histogram of Angles in Linked Features (HALF), designed for the segmentation of 3D point cloud data of plants for robust sensing. The proposed method leverages local angular features extracted from 3D measurements obtained via sensing technologies such as laser scanning, LiDAR, or photogrammetry. HALF enables efficient identification of plant structures—leaves, stems, and knots—without requiring large-scale labeled datasets, making it highly suitable for applications in plant phenotyping and structural analysis. To enhance robustness and interpretability, we extend HALF to a convolution-based mathematical framework and introduce the Sequential Competitive Segmentation Algorithm (SCSA) for phytomer-level classification. Experimental results using 3D point cloud data of soybean plants demonstrate the feasibility of our method in sensor-based plant monitoring systems. By providing a low-cost and efficient approach for plant structure analysis, HALF contributes to the advancement of sensor-driven plant phenotyping and precision agriculture. Full article

This study applies surveying and representation techniques to analyze the cavities of the Cumae site, an archeological park located in the Phlegraean Fields in the Campania region, providing a documentary basis for monitoring, maintenance, and enhancement efforts. The process core is the comparative management of the numerical models produced employing technologies such as laser scanning, photogrammetry, and structured light scanning, supported by a georeferenced topographical network. The 3D models produced are used for the extraction of ortho-planes and bidimensional drawings of the various cavities from which to initiate the procedures for redesigning and analyzing the entire artifact. Specific research carried out on the Antro della Sibilla enabled a detailed 3D description of the tuff-carved surfaces, helping the interpretation of the manufacture in the optic of consolidation and musealization interventions. The interdisciplinary approach employed, in which historical–archeological, geological–structural, and diagnostic sciences contributed correlatedly, ensures a comprehensive program of data representation. Full article

In the context of the booming development of precision agriculture and plant phenotyping, plant 3D reconstruction technology has become a research hotspot, with widespread applications in plant growth monitoring, pest and disease detection, and smart agricultural equipment. Given the complex geometric and textural characteristics of plants, traditional 2D image analysis methods are difficult to meet the modeling requirements, highlighting the growing importance of 3D reconstruction technology. This paper reviews active vision techniques (such as structured light, time-of-flight, and laser scanning methods), passive vision techniques (such as stereo vision and structure from motion), and deep learning-based 3D reconstruction methods (such as NeRF, CNN, and 3DGS). These technologies enhance crop analysis accuracy from multiple perspectives, provide strong support for agricultural production, and significantly promote the development of the field of plant research. Full article

This study integrates quantitative scientometric analysis with a qualitative systematic review to comprehensively examine the evolution, core research themes, and emerging trends of three-dimensional (3D) visualization technology in architectural heritage conservation from 2005 to 2024. A total of 813 relevant publications were retrieved from the Web of Science Core Collection and analyzed using CiteSpace to construct a detailed knowledge map of the field. The findings highlight that foundational technologies such as terrestrial laser scanning (TLS), photogrammetry, building information modeling (BIM), and heritage building information modeling (HBIM) have laid a solid technical foundation for accurate heritage documentation and semantic representation. At the same time, the integration of digital twins, the Internet of Things (IoT), artificial intelligence (AI), and immersive technologies has facilitated a shift from static documentation to dynamic perception, real-time analysis, and interactive engagement. The analysis identifies four major research domains: (1) 3D data acquisition and modeling techniques, (2) digital heritage documentation and information management, (3) virtual reconstruction and interactive visualization, and (4) digital transformation and cultural narrative integration. Based on these insights, this study proposes four key directions for future research: advancing intelligence and automation in 3D modeling workflows; enhancing cross-platform interoperability and semantic standardization; realizing the full lifecycle management of architectural heritage; and enhancing cultural narratives through digital expression. This study provides a systematic and in-depth understanding of the role of 3D visualization in architectural heritage conservation. It offers a solid theoretical foundation and strategic guidance for technological innovation, policy development, and interdisciplinary collaboration in the digital heritage field. Full article

The pre-training and fine-tuning paradigm has significantly advanced satellite remote sensing applications. However, its potential remains largely underexplored for airborne laser scanning (ALS), a key technology in domains such as forest management and urban planning. In this study, we address this gap by constructing a large-scale ALS point cloud dataset and evaluating its effectiveness in downstream applications. We first propose a simple, generalizable framework for dataset construction, designed to maximize land cover and terrain diversity while allowing flexible control over dataset size. We instantiate this framework using ALS, land cover, and terrain data collected across the contiguous United States, resulting in a dataset geographically covering 17,000 + ${\mathrm{km}}^2$ (184 billion points) with diverse land cover and terrain types included. As a baseline self-supervised learning model, we adopt BEV-MAE, a state-of-the-art masked autoencoder for 3D outdoor point clouds, and pre-train it on the constructed dataset. The resulting models are fine-tuned for several downstream tasks, including tree species classification, terrain scene recognition, and point cloud semantic segmentation. Our results show that pre-trained models consistently outperform their counterparts trained from scratch across all downstream tasks, demonstrating the strong transferability of the learned representations. Additionally, we find that scaling the dataset using the proposed framework leads to consistent performance improvements, whereas datasets constructed via random sampling fail to achieve comparable gains. Full article