Search Results (555)

Fluid-driven fracture processes are central to the development of subsurface energy systems such as geothermal and hydrocarbon reservoirs. Although phase-field formulations have become a widely used tool for describing fracture initiation and growth, the diffuse representation of cracks makes it difficult to resolve flow behavior accurately inside discrete fracture networks (DFNs) and to represent hydro-mechanical coupling in a sharp-interface sense. This study develops a hybrid-dimensional iterative framework for lubrication-flow simulation in deformable fractured geomaterials. By leveraging phase-field point clouds together with non-conforming discretization schemes for both the solid matrix and fracture domains, the proposed framework enables the dynamic reconstruction of evolving fracture networks. The theoretical formulation and numerical implementation of the coupling strategy are presented in detail. Hydraulic benchmark examples verify the performance of the fluid flow solver under various physical conditions. The classical Sneddon problem and Khristianovic–Geertsma–de Klerk (KGD) model are employed to validate the solid deformation solver, confirming accurate predictions of crack opening displacement and mesh independence in fracture width calculation. Additional simulations with complex pre-existing fracture patterns further demonstrate the applicability of the framework to coupled hydro-mechanical analysis in fractured media. Full article

Deep learning has become central to semantic segmentation of three-dimensional medical images. However—despite many published models—their adoption in practice remains limited, as deployment often requires advanced programming skills and familiarity with specific machine learning frameworks. Thus, technical barriers restrict its use to specialized users. To address this, we present SegMed (version 1.0), an open-source, standalone desktop application that provides an end-to-end workflow for deep learning-based medical image segmentation. SegMed supports the loading and inspection of common medical image formats, as well as array-based formats. The application integrates standard preprocessing operations often used in the field and directly supports loading of pretrained segmentation models implemented in both PyTorch (version 2.X) and Keras (version 2.X) and those created using the Medical Open Network for AI framework (version 1.X). Models are automatically inspected to infer required configurations, such as input size and post-processing steps, enabling segmentation with minimal user intervention. Results can be exported as volumetric images or 3D surface meshes for downstream analysis, visualization, or special applications such as virtual reality. SegMed was tested using multiple publicly available pretrained models, demonstrating robustness and flexibility across diverse segmentation tasks. By abstracting low-level implementation details, SegMed lowers technical barriers, promotes reproducibility, and facilitates the integration of AI-assisted segmentation into medical imaging workflows. Full article

This study addresses the impact-induced failure of drilling pump valves caused by uncontrolled disc–seat collisions by proposing a novel valve design incorporating a two-stage buffering mechanism. The design employs a wave spring as the primary buffer and an elastic sealing ring as the secondary buffer, effectively mitigating impact through staged energy dissipation. A nonlinear stiffness model of the wave spring, accounting for the transition between line and surface contact modes, was developed. Strong fluid–structure interaction transients were simulated using dynamic meshing and user-defined functions. A parametric study was conducted by systematically varying cylindrical spring stiffness (7.7–15 N/mm), preload (110–160 N), and wave spring type (D85 to D110). Results show that, compared to a conventional valve, the two-stage mechanism reduces impact velocity by 24.2%, accelerates opening response by 17.9%, and extends the closing phase by 0.28%. Increasing wave spring stiffness (from D85 to D110) decreases opening delay time by 98.7% and attenuates peak velocity by 44.4%. Optimized hybrid spring parameters can minimize closing delay height by 27.3%. By reducing seat erosion and suppressing vibration-induced failure, the two-stage buffering mechanism effectively extends valve service life and enhances operational reliability in high-cycle drilling operations. Full article

Direct collocation transcription is a dominant technique for solving complex optimal control problems, converting continuous dynamics into large-scale, sparse nonlinear programming problems. The computational efficiency of this approach is fundamentally limited by the evaluation of first- and second-order derivatives required by modern optimization algorithms. While general-purpose automatic differentiation tools exist, they often fail to fully exploit the repetitive substructure inherent in trajectory discretization. This paper presents a vectorized, sparse, second-order forward automatic differentiation framework specifically tailored for direct collocation methods. By explicitly distinguishing between scalar and vector nodes within the expression graph, the proposed method leverages the independence of mesh point evaluations to enable Single Instruction, Multiple Data (SIMD) execution and optimize memory access patterns. This structure-aware approach ensures linear time complexity with respect to the number of discretization nodes while maintaining the flexibility to handle complex dependencies. The methodology is implemented in the open-source software package pockit and is validated through three distinct engineering case studies: the aggressive stabilization of a nano-quadrotor, the powered descent guidance of a reusable launch vehicle, and a low-thrust heliocentric orbital transfer. These applications demonstrate the framework’s capability to deliver high-performance derivative computation for large-scale, nonlinear dynamical systems. Full article

Background/Objectives: Locally advanced midface malignant tumors require extensive resection, resulting in complex defects involving bone and multiple soft tissue structures. Reconstructing these substantial defects presents a significant challenge to restore both function and aesthetics. This study aims to compare the functional and aesthetic outcomes of chimeric free flaps versus single free flaps in midface microvascular reconstructions. Methods: This retrospective analysis included fifty consecutive patients with Type III Cordeiro defects who underwent midface reconstruction with free tissue transfer between 2020 and 2024. The cohort included fourteen patients who received prefabricated chimeric flaps and thirty-six patients who received single free flaps. Outcomes were assessed six months postoperatively using a modified University of Washington Quality of Life Questionnaire (UW-QOL), analyzing domains including speech, chewing, sensation, appearance, pain, and social activity. Statistical analysis was performed using the Mann–Whitney U test. Results: In the chimeric flap group, no major flap necrosis or complications were observed. In unadjusted comparisons, the chimeric flap group showed higher transformed UW-QOL scores in several domains. Statistically significant between-group differences were observed for opening and speech (p = 0.004), change in appearance (p = 0.022), sensation (p = 0.011), and social activity (p = 0.006). Aesthetic outcomes, assessed via patient rating of appearance, were also significantly higher in unadjusted comparisons with the chimeric flap approach. Furthermore, in Type IIIa defects, titanium mesh successfully provided reliable orbital support. Conclusions: Chimeric free flaps represent a feasible reconstructive option in selected cases of complex maxillary and midface reconstruction. Their main advantages—providing the proper amount of specific, well-vascularized tissue and offering greater mobility of components— may be associated with more favorable functional, aesthetic, and social outcomes in unadjusted comparisons compared to reconstruction using single free flaps. Full article

In high-speed parachuting, complex turbulent phenomena (i.e., deadly vortices) may cause problems such as parachute inflation delay or even deployment failure. To address these issues, this study develops a high-precision numerical simulation dummy model in which adaptive mesh generation techniques, combined with Euler–Lagrange bidirectional coupling based on a large eddy simulation, are employed to model the multiphase flow field during parachute descent. The key parameters are adjusted, and the numerical model is refined based on wind tunnel experiments and User-Defined Functions. The bidirectional validation of the experimental and simulated data reveals the mechanism of turbulent flow formation and its evolutionary patterns around the parachutist–parachute system for different lateral and descent velocities during the high-speed descent phase. A prediction model based on a multi-information fusion neural network algorithm is further established to address the challenge in special parachuting scenarios whereby vortices in the flow field around the parachutist prevent the parachute from opening. The model integrates the Haar wavelet to extract global low-frequency features that characterize the overall structure and trends, an energy valley optimization algorithm, a convolutional neural network, a bidirectional long short-term memory network, and a self-attention mechanism to achieve one-second-ahead turbulence prediction. With nine physical quantities as inputs and descent velocity as the output indicator, the model has a Root Mean Square Error of 0.085, a Mean Absolute Error of 0.051, and a Mean Absolute Percentage Error of 0.0021. Full article

Historic linear corridors in living-heritage settings concentrate identity, everyday mobility, and visitor experience. Balancing authenticity, adaptability, and publicness therefore benefits from evidence that jointly characterizes long-term physical change, network accessibility, and eye-level interface conditions. Existing assessments often focus on façades or single time slices, leaving limited evidence that relates decades of built-fabric reconfiguration (changes in building footprints, street edges, and open-space fragmentation) to multi-scale accessibility and pedestrian-facing qualities. We propose an integrated and interpretable workflow for the Beishan Street corridor in the West Lake World Heritage core (Hangzhou) over 1929–2024. Scale-sensitive morphological metrics, multi-radius network measures (integration and centrality), and street-view semantic segmentation are aligned at corridor-segment resolution and examined together with segment-level functional intensity derived from POIs using transparent linear models. The results indicate a long-term shift from a lakeshore-led to a road-led spatial logic, followed by post-2000 stabilization near saturation. Average integration increases, while the high-integration tail becomes thinner. In connector-removal scenarios, the eastern segment shows a relative accessibility decline, and a central hinge node emerges as a vulnerability hotspot (bottleneck) where through-movement concentrates. Eye-level profiles differ by segment: the west exhibits maximal canopy and lower sky visibility, the center shows stronger continuous walls around compounds with intermittent forecourt openings, and the east is characterized by compact residential heritage frontage with low vegetation. Segment-level associations suggest that address and wayfinding density tends to co-occur with clearer frontages, wider sky cones, and stronger tree cover. Transportation-related and access/passage facilities tend to co-occur with higher ground-plane legibility, measured as wider and more continuous road and sidewalk surfaces. Medical and government clusters tend to co-occur with lower sky openness. Recommended actions include the following: (1) mesh-aware protection of key connectors and the hinge, (2) segment-specific targets for façade share and ground cues with planned punctuations, (3) tailored interface standards for institutional clusters, (4) scalable address and wayfinding systems, and (5) event staging that preserves effective roadway and sidewalk capacity. Full article

Accurate perception of deformable objects on the lunar surface is essential for autonomous construction and in situ resource utilization (ISRU) missions. However, the lack of representative lunar imagery limits the development of data-driven models for pose estimation and manipulation. We present SynBag 1.0, a large-scale synthetic dataset designed for training and benchmarking six-degree-of-freedom (6-DoF) pose estimation algorithms on regolith-filled construction bags. SynBag 1.0 employs rigid-body representations of bag meshes designed to approximate deformable structures with varied levels of feature richness. The dataset was generated using a novel framework titled MoonBot Studio, built in Unreal Engine 5 with physically consistent lunar lighting, low-gravity dynamics, and dynamic dust occlusion modeled through Niagara particle systems. SynBag 1.0 contains approximately 180,000 labeled samples, including RGB images, dense depth maps, instance segmentation masks, and ground-truth 6-DoF poses in a near-BOP format. To verify dataset usability and annotation consistency, we perform zero-shot 6-DoF pose estimation using a state-of-the-art model on a representative subset of the dataset. Variations span solar azimuth, camera height, elevation, dust state, surface degradation, occlusion level, and terrain type. SynBag 1.0 establishes one of the first open, physically grounded datasets for 6-DoF-object perception in lunar construction and provides a scalable basis for future datasets incorporating soft-body simulation and robotic grasping. Full article

Creating a digital 3D model of museum artefacts has been a common practice for many years. Such models can be used for archiving, research, and marketing purposes, as well as to counteract various types of exclusion. A digital copy created using professional 3D scanners using 3D structured-light scanning (3D SLS) or terrestrial laser scanning technology requires expensive equipment, specialised software for postprocessing, and a trained team. The introduction of mobile phones with Light Detection and Ranging (LiDAR) sensors and the development of appropriate open-access software have enabled the use of phones to generate digital 3D models. This study compares the quality of 3D models created with 3D SLS and mobile LiDAR technologies using three identical small museum artefacts from the Silk Road area of the Samarkand State University museum in Uzbekistan. They were digitised in 2017 and 2025. The results indicate that digital 3D models generated with an iPhone 16 PRO MAX device using Scaniverse LiDAR software are incomplete and thus less versatile. Therefore, they cannot serve as archival models. Their accuracy and quality (mesh density, size, and texture quality), as well as the speed of generating 3D models, make them ideal for marketing purposes and digital tourism. Full article

The sport of orienteering requires athletes to reach specific points marked on a map (called “punching stations”) in the shortest possible time. Currently, the recording of athletes’ passages through the stations is performed offline. In addition to delays in generating intermediate and final rankings, this approach often leads to detection errors and potential cheating related to the lack of authentication of an athlete’s actual passage at a given station. This paper aims to define and design a system enabling three main functionalities: 1. real-time monitoring of athletes’ trajectories through a sensor network connected to control stations; 2. multi-modal authentication of athletes at each station; and 3. immutable certification of each athlete’s passage through blockchain-based recording. System performance is evaluated in terms of wireless network coverage and data collection efficiency across three representative environments: urban, rural, and forested areas. Results are obtained through a measurement campaign for two dedicated wireless technologies: ZigBee for local mesh network and LoRa for long-range links to connect local mesh networks to the cloud over the Internet, which is then accessed by the race organizers. Furthermore, two supporting subsystems are described, addressing athlete authentication and data integrity assurance, as well as a blockchain recording for the overall event management framework. Results are in terms of coverage distances for both technologies, proving highly effective across varied terrains. Field tests demonstrated significant communication capabilities, achieving distances of up to 1800 m in open spaces. Even in challenging, dense wooded environments, the system maintained reliable coverage, reaching transmission distances of up to 600 m. Local ZigBee links between punching stations achieved ranges between 70 and 150 m in forested areas. These findings validate the use of a wireless multi-hop network designed to minimize packet loss and ensure reliable data delivery in competitive scenarios. The feasibility is also investigated in terms of WSN performance, delay analysis and power consumption evaluation. Full article

In high-density urban environments, residential design often faces a conflict between maximizing landscape access and maintaining energy-oriented compactness. This study proposes a target-based visibility analysis framework to optimize high-rise forms under strict performance constraints. Utilizing a Quad-mesh reconstruction strategy and Inverse Targeted Ray-Casting, the method accurately quantifies visibility via the cumulative Landscape Visible Surface (LVS) on the target building and Viewpoint-Specific Surface Visibility Rate (R_v) for precise verification against specific landscape targets. The framework is applied to evaluate three morphological prototypes: Compact Tower, Dispersed Tower, and Slab–Tower Hybrid. Quantitative simulations identified the Slab–Tower Hybrid as the optimal solution, demonstrating superior “Visual Morphological Efficiency.” While maintaining a moderate Shape Coefficient (SC = 0.326) to satisfy energy standards, the Hybrid achieved a cumulative Park-View LVS approximately 1.8 times that of the Compact Tower. Furthermore, environmental simulations indicated the Hybrid fosters stable wind environments (0.4–0.7 m/s) and equitable sunlight distribution. The research concluded that through differentiated massing, high-rise architecture can achieve a synergistic balance between visual openness and physical compactness, transforming view analysis from a passive check into an active design driver. Full article

Accurate modeling of residual limb geometry is essential for prosthetic socket design, yet current scanning techniques can be costly, operator-dependent, or impractical for repeated clinical use. This study presents a fully automated, low-cost photogrammetry workflow capable of generating metrically accurate 3D models of lower-limb residual limbs using video and still images acquired with a standard smartphone or a full-frame digital camera. The pipeline integrates adaptive frame selection, deep learning-based background removal, robust metric scaling via ArUco markers, and open-source Structure-from-Motion and Multi-View Stereo reconstruction, requiring no manual post-processing or proprietary software. Accuracy and repeatability were evaluated using four 3D-printed limb phantoms and high-resolution CT-derived meshes as ground truth. Smartphone video and full-frame camera acquisitions achieved sub-millimeter surface accuracy, volume and perimeter errors within ±1%, and high inter-session repeatability, all within clinically accepted thresholds for prosthetic socket fabrication. In contrast, smartphone still-photo reconstructions showed larger deviations and reduced stability. Acquisition time was under five minutes, and complete reconstruction required approximately 1 h and 30 min. These results demonstrate that smartphone video-based photogrammetry provides a practical, scalable, and clinically viable alternative for residual limb modeling, particularly in resource-constrained or remote care settings. Full article

In low-amplitude and low-frequency vibration environments, the energy harvesting efficiency of self-powered wireless sensor nodes is insufficient, limiting their long-term autonomous operation. To address this issue, a micro piezoelectric–electromagnetic hybrid energy harvester is designed, aiming to enhance energy capture efficiency through structural integration and parameter optimization. The study is conducted entirely through numerical simulations. A coaxial integrated architecture is adopted, combining a piezoelectric cantilever beam array with an electromagnetic induction module. The piezoelectric layer uses lead magnesium niobate–lead titanate (PMN-PT) solid solution material with a thickness of 0.2 mm. The electromagnetic module employs copper wire coils with a diameter of 0.08 mm, winding 1500–3000 turns, paired with N52-type neodymium–iron–boron (NdFeB) permanent magnets. To improve energy conversion efficiency, the optimization parameters include the length-to-thickness ratio of the cantilever beam, the mass of the tip mass, the number of coil turns, and the spacing of the permanent magnets. Each parameter is set at four levels for orthogonal experiments. A multi-physics coupling model is established using ANSYS Workbench 2023, covering structural dynamics, piezoelectric effects, and the electromagnetic induction module. The mesh size is set to 0.1 mm. The energy output characteristics are analyzed under vibration frequencies of 0.3–12 Hz and amplitudes of 0.2–1.0 mm. Simulation results show that the optimized hybrid harvester achieves 45% higher energy conversion efficiency than a single piezoelectric structure and 31% higher than a traditional separated hybrid structure within the 0.3–12 Hz low-frequency range. Under a 6 Hz frequency and 0.6 mm amplitude, the output power density reaches 3.5 mW/cm³, the peak open-circuit voltage is 4.1 V, and the peak short-circuit current is 1.3 mA. Under environmental conditions of 20–88% humidity and −15–65 °C temperature, the device maintains over 94% stability in energy output. After 1.2 million vibration cycles, structural integrity remains above 96%, and energy conversion efficiency decreases by no more than 5%. The proposed coaxial hybrid structure and multi-parameter orthogonal optimization method effectively enhance energy harvesting performance in low-amplitude, low-frequency environments. The simulation design parameters and analysis procedures provide a reference for the development of similar micro hybrid energy harvesters and support the performance optimization of self-powered wireless sensor nodes. Full article

As computational fluid dynamics (CFD) has become more mainstream in production engineering workflows, new demands have been introduced that require high-quality meshes to accurately capture the complex geometries. This evolution has created the need for mesh generation frameworks that help engineers design optimized meshing structures for each new geometry. However, many simulation workflows rely on the experience and intuition of senior engineers rather than systematic frameworks. In this paper, a novel technique for determining mesh convergence is created using machine learning (ML). This method seeks to provide process engineers with a visual feedback mechanism of flow regions that require mesh refinement. The work was accomplished by creating three grid sensitivity studies on various geometries: zero-pressure-gradient flat plate, bump in channel, and axisymmetric free jet. The cases were then simulated using the Reynolds Averaged Navier-Stokes (RANS) models in OpenFOAM (v2306) and had the ML method applied post-hoc using Python (v3.12.6). To apply the method to each case, the flow field was regionalized and clustered using an unsupervised ML model. The ML clustering results were then converted into a similarity score, which compares two grid levels to inform the user whether the region of the flow had converged. To prove this framework, the similarity scores were compared to flow field probes used to determine mesh convergence at key points in the flow. The method was found to be in agreement with the flow field probes on the level of mesh refinement that created convergence. The approach was also seen to provide refinement region recommendations in regions of the flow that align with human intuition of the physics of the flow. Full article

Prolonged tooth loss causes alveolar ridge atrophy, complicating implantation, especially in patients with impaired mineral metabolism. This study aimed to develop a personalized titanium mesh for guided bone regeneration and qualitatively evaluate its local tissue response in a vitamin D3-deficient rabbit model. A titanium mesh design has been developed in the form of a plate-shaped profile frame of a truncated pyramid with a solid upper base and perforated side faces. For testing in a rabbit model with vitamin D3 deficiency, a bone defect was created and repaired in the mandible using hydroxyapatite, an individual titanium mesh and a collagen membrane. Histological analysis was performed in the Laboratory of Digital Microscopic Analysis. The optimized geometry and parameters of the mesh openings contributed to effective vascularization and osteogenesis. In the postoperative period (3, 5 and 7 days), moderate edema and hyperemia were noted with their complete leveling by the 7th day (p < 0.05). According to the histological examination, 3 months after the installation of the titanium mesh, the formation of dense connective tissue with signs of active osteogenesis was observed in the defect area, including zones of mineralized bone trabeculae, osteocytes and osteon elements. The findings of this study indicate acceptable biocompatibility of the developed titanium structure and suggest osteoconductive potential, which, however, needs to be confirmed in controlled, quantitatively powered studies. Full article