Search Results (304)

Background: Advances in additive manufacturing and CAD/CAM technologies have expanded the use of 3D-printed orthodontic models in digital aligner workflows. Intraoral scanners (IOS) are critical for accurately capturing attachment geometries and dental morphology during these workflows. However, comparative evidence regarding IOS accuracy in models with complex orthodontic structures remains limited. Therefore, this study aimed to compare the trueness and precision of five IOS using 3D-printed orthodontic models with attachments. Methods: In this in vitro study, thirty independent single-arch 3D-printed models (either maxillary or mandibular) with orthodontic attachments were scanned twice with each IOS. The Smart Optics Vinyl laboratory scanner served as the reference scanner. Scans were aligned and superimposed in CloudCompare, and root mean square (RMS) deviation values were calculated to evaluate accuracy. Nonparametric Kruskal–Wallis and Dunn tests were applied (α = 0.05). Results: Significant differences were found among scanners for both trueness and precision (p < 0.001). Primescan, TRIOS 3, and iTero element 5D demonstrated comparable trueness (p > 0.05) and outperformed Rapideye MI-1000 (p < 0.001). iTero element 2 plus showed slightly lower accuracy but remained clinically acceptable. Primescan achieved the highest precision, significantly exceeding iTero element 2 plus, iTero element 5D, and Rapideye MI-1000 (p < 0.01). TRIOS 3 also exhibited excellent repeatability, comparable to Primescan (p = 1.000). Conclusions: All intraoral scanners, except Rapideye MI-1000, demonstrated accuracy levels generally considered clinically acceptable for digital orthodontic and additive manufacturing workflows. Primescan, TRIOS 3, and iTero element 5D exhibited similarly high trueness, while Primescan showed the most consistent precision. The ability of these scanners to reproduce fine anatomical details may improve the reliability of 3D-printed orthodontic models and in-office aligner production workflows. Full article

To address the high experimental costs and data scarcity inherent in Directed Energy Deposition (DED), this study proposes a data-efficient hybrid optimization framework for the precision manufacturing of Inconel 718 aerospace components. The framework leverages a two-stage strategy to bridge traditional experimental design with advanced machine learning, ensuring robust process optimization even with limited datasets. In the first stage, the Taguchi method (L16 orthogonal array) was employed for coarse-grained screening to identify influential control factors. In the second stage, a Fully Connected Neural Network (FNN) coupled with Bayesian Optimization (BO) was deployed. Crucially, this machine learning component functions as an optimization-oriented trend surrogate rather than a global regressor, successfully guiding the optimization under extreme data scarcity. The optimized process window yielded exceptional structural integrity, achieving a porosity as low as 0.03%. To thoroughly validate its practical efficacy, tensile testing (ASTM E8/E8M) and Rockwell hardness measurements (ASTM E18) were systematically conducted on the optimized specimens. The mechanical characterization demonstrated an average tensile strength of approximately 1358 MPa and a hardness of ~40 HRC. Finally, the framework was successfully validated through the robotic DED fabrication of a complex-geometry aerospace engine combustion chamber casing, bridging laboratory-scale optimization with authentic industrial applications. Full article

In structural engineering, the iterative optimisation of complex timber wall components is fundamentally limited by the prohibitive computational costs of non-linear Finite Element Analysis (FEA). To overcome this bottleneck, this study introduces a highly efficient machine learning-based surrogate model utilising a convolutional encoder–decoder architecture to predict the global buckling load factor (BLF) directly from structural topology. We evaluate three distinct modeling strategies: a Direct prediction model, a Sequential model, and a multitask Dual-Loss model designed to simultaneously predict the BLF and reconstruct spatial tensile stress fields. Experimental results demonstrate that both the Direct and Dual-Loss approaches achieve near-perfect predictive accuracy on in-distribution data, yielding a coefficient of determination (R²) of approximately 0.996. Crucially, these surrogates accelerate inference times by a factor of roughly 12,800 compared to traditional iterative solvers, reducing evaluation times from hours to mere seconds. Furthermore, the models exhibit exceptional robustness and extrapolative capability under rigorous out-of-distribution testing. The models maintain high predictive fidelity when subjected to cross-dataset distributional shifts (R² $\approx 0.94$) and when evaluating intentionally low-performing, highly vulnerable configurations (R²$\approx 0.967$). Extensive validation on structurally disjoint, hold-out wall geometries confirms the models’ ability to generalise to entirely unseen topologies without introducing systematic bias (R²$>0.95$). By successfully internalising the underlying physical principles of load redistribution, this surrogate framework provides a reliable, computationally inexpensive foundation for enabling real-time, autonomous generative design and structural optimisation. Full article

We develop a comparative synthesis of quantum-information geometry beyond complex numbers, with emphasis on what different algebraic frameworks contribute to information-processing structure rather than on their formal novelty alone. The organizing idea is a layer-by-layer test of the standard complex Hilbert-space formalism: each non-complex or deformed framework modifies the scalar field, phase group, projective state space, Born-probability semantics, composition rule, measurement geometry, symmetry algebra or representation category. The central thesis is that such frameworks are physically meaningful when they identify which assumptions make complex quantum mechanics operationally stable: positive probabilities, associative multipartite composition, reversible dynamics, experimentally testable phases, locality constraints, informationally complete measurements, error bases and clear operational semantics. Real quantum theory probes the necessity of complex phases and local tomography; quaternionic quantum mechanics probes non-Abelian phase while retaining associativity and admitting complex embeddings; octonionic proposals probe the boundary where exceptional geometry survives but generic circuit composition is obstructed by non-associativity; Jordan algebras test ordered probabilistic state spaces; Clifford algebras and Bott periodicity provide the spinorial and topological grammar connecting gates, Hopf maps and periodic dimensions; and quantum-group or q-deformed constructions probe coproducts, braiding and representation categories rather than scalar amplitudes. We distinguish three roles that are often conflated: genuine hypercomplex kinematics, Hopf-fibration coordinates for ordinary complex multipartite entanglement, and deformed algebraic or categorical structures. The resulting map separates established equivalence and experimental-constraint results from useful representation tools and speculative programs, while identifying concrete open problems for non-complex quantum information. Full article

High sound pressure level (SPL) acoustic sensing requires miniaturized microphones that can operate under large acoustic loading while maintaining mechanical linearity, sufficient sensing response, and broadband audio frequency behavior. This work targets high-SPL operation and numerically investigates a graphene piezoresistive MEMS microphone based on a membrane-supported beam–island diaphragm. The proposed structure retains a continuous membrane for acoustic load bearing, while the upper beam–island topology redirects deformation-induced strain toward beam root regions where graphene piezoresistors are placed. This design is intended to increase the local strain available for piezoresistive readout without simply relying on larger global diaphragm deflection. Finite-element analysis was used to optimize the diaphragm geometry and evaluate strain enhancement, pressure response linearity, modal behavior, and harmonic response. Under the 170 dB SPL reference condition, the optimized structure increases the peak structural strain from 47.83 με in a thickness-equivalent solid diaphragm to 562.53 με, achieving an approximately 11.8-fold enhancement in local sensing strain while maintaining a highly linear pressure response (R2 > 0.9999). Additionally, the results also show that the sensor exhibits a high first natural frequency of 64.07 kHz and a small response variation of approximately 0.94 dB within the 0–20 kHz target frequency range, indicating excellent dynamic stability and high-fidelity signal transduction characteristics. To connect the structural response with piezoresistive readout, first-order electromechanical output estimation was further performed using representative graphene gauge factors, quarter-bridge readout assumptions, contact resistance correction, and Johnson-noise-limited signal-to-noise ratio estimation. A ±5% geometric tolerance check further indicates that the membrane side length is the most fabrication-sensitive parameter, while the selected design remains generally robust except for reduced linearity margin under positive membrane side-length deviation. These results demonstrate the potential of the proposed graphene-based MEMS microphone for high-SPL broadband acoustic sensing applications in harsh and high-intensity acoustic environments. Full article

Point cloud registration in environments lacking rich textures or containing repetitive structures remains highly susceptible to misalignments. The core challenge lies in balancing the demand for extracting highly distinctive local features with the computational cost of global context modeling. In this paper, we propose a robust registration framework that efficiently combines rotation-equivariant geometric representations with state space models of linear complexity to mitigate feature ambiguity and mismatch. First, a multivariate geometric encoding mechanism is embedded within convolutional layers, enhancing local feature distinctiveness under strict rotation equivariance by explicitly leveraging surface properties. Second, to efficiently establish long-range spatial dependencies, we replace standard dense attention with a hybrid geometry-state aggregation module. This module integrates local geometric self-attention with the Mamba architecture, strengthening focus on overlapping regions without the quadratic computational burden. Finally, we optimize the generated correspondences through a physically consistent hypothesis generator to compute reliable rigid transformation results. On standard benchmarks, our framework demonstrates exceptional robustness to ambiguous matches, achieving a 96.3% registration recall on the 3DMatch dataset and outstanding accuracy on the KITTI dataset. Full article

This study quantified projection-dependent geometric distortion induced by vertical camera angulation in two-dimensional (2D) dental image acquisition and evaluated its implications for integration with three-dimensional (3D) CAD/CAM and artificial intelligence (AI)-driven workflows. To our knowledge, this study is among the first to use quantitative methods to characterize projection-induced distortion across the dental arch as a function of vertical camera angulation. Fourteen fully dentate casts were photographed at nine standardized vertical angulations using a controlled acquisition setup based on the standardized occlusal plane angle (SOPA). Tooth surface areas were measured through digital tracing and analyzed with a mixed-effects model (α = 0.05). Significant associations were identified between vertical camera angulation and measured tooth surface area for all teeth except canines (p < 0.05 for all except canines). Anterior teeth demonstrated increased apparent surface area at superior camera angulations, whereas posterior teeth were more prominently represented at inferior angulations. Central incisors, lateral incisors, and first premolars exhibited maximal visibility above the occlusal plane, while second premolars and molars were more optimally visualized below it. These findings indicate that vertical camera angulation induces non-uniform, region-specific geometric distortion across the dental arch. From a computational perspective, these distortions represent a systematic source of variability in 2D photographic datasets used in CAD/CAM workflows, virtual smile design, and AI-assisted image analysis. Because modern machine learning systems depend on geometrically consistent input data, uncorrected projection-induced distortion may reduce the reliability and generalizability of downstream algorithmic outputs. Accordingly, the present findings establish a quantitative basis for recognizing projection-induced variability in 2D dental photographs and support future development of geometry-aware calibration strategies for 2D-3D digital integration. AI-assisted correction represents a future translational direction contingent upon explicit alignment between acquisition geometry, image formation, and computational modeling. Full article

This paper presents the electromigration (EM) performance of an embedded trace redistribution layer (ETR) in which the Cu trace features a rounded-bottom cross-sectional geometry and is encapsulated by a Ti barrier layer except for the top surface, with an optional top-side Ti cap. The ETR (with and without top-side Ti capping) and the conventional semi-additive-process (SAP) redistribution layer (RDL) are comparatively evaluated in terms of EM reliability. The ETR demonstrates a marked lifetime improvement compared with the SAP RDL. Notably, the Ti-capped ETR exhibits a minimal resistance increase in less than 10% even after a test duration of 4000 h. We discuss the key contributing factors and underlying mechanisms that support these improvements. Transmission electron microscopy (TEM) combined with atomic-percentage mapping confirms the effectiveness of Ti capping as a Cu diffusion barrier, showing continuous Ti coverage and no observable Cu diffusion. Electro-thermal simulations co-locate predicted thermal hot spots with experimentally observed open-failure sites, highlighting temperature-driven EM acceleration and the necessity of a barrier to suppress Cu–polymer interfacial oxidation. Stress simulations, together with EM failure analysis, indicate that the rounded-bottom Cu geometry alleviates local stress concentration and stress gradients, thereby creating conditions favorable for enhanced EM resistance. Full article

Natural products display exceptional chemical diversity and a broad range of mechanisms of action that are not adequately captured by traditional classifications based on target class, pharmacological phenotype, or chemical scaffold. Such classification schemes often lead to fragmented understanding of mechanisms of action, obscuring the unified principles underlying different target systems while failing to recognize the stage-dependent mechanisms exhibited by the same molecule in varying contexts. Here, we propose a unified “space–interface–time” framework to classify the mechanisms of action by examining the physical principles through which natural products reshape the functions of different biomolecules. Within this framework for unifying the classification of natural product mechanisms of action, geometry-driven binding site occupancy and conformational constraints are assigned to the spatial dimension; induction or stabilization of multicomponent complexes and kinetic regulation of state lifetimes are assigned to the interfacial and temporal dimensions, respectively. Finally, we discuss the conceptual and technical challenges of bridging static structural snapshots with dynamic in vivo pharmacology, and highlight emerging opportunities offered by time-resolved structural methods and the integration of molecular dynamics, machine learning, and biophysical workflows for mechanism-guided drug discovery. Full article

Deep mining of geologically challenging deposits, such as narrow, steeply dipping orebodies, is increasingly pursued to meet the rising demand for mineral resources. However, the geotechnical stability of operations in such environments remains a persistent challenge. A paramount concern is the insufficiently understood mechanisms governing the surface subsidence and stability of underground excavations, which diverge significantly from those in flat or gently dipping deposits. This study bridges this gap through an integrated methodology applied to a deep cut-and-fill gold mine in China. We combined nine years (2016–2025) of SBAS-InSAR monitoring, utilizing 120 Sentinel-1 images corrected with precise orbit and atmospheric correction data, with a comprehensive three-dimensional (3D) numerical simulation. The results reveal a unique subsidence pattern: surface subsidence is highly localized, forming an elliptical basin directly above the orebodies, with a footwall movement angle exceeding 90°. Furthermore, the subsidence magnitude showed minimal progression despite increasing mining depth, with a maximum cumulative subsidence of only 9.3 mm. Numerical simulation confirmed these findings and demonstrated that underground shafts and tunnels remained stable under the sequential extraction of multiple orebody levels. This exceptional geotechnical response is attributed to a synergistic mechanism involving the intrinsic geomechanical advantages of the steeply dipping geometry, the low-disturbance nature of narrow-vein mining, and the crucial structural support provided by the backfilling. This study demonstrates the efficacy of cut-and-fill mining for ensuring operational safety and minimizing surface environmental impact in the deep mining of narrow, steeply dipping orebodies, providing critical insights for the sustainable exploitation of deep mineral resources. Full article

Background: Achieving facial harmony in patients with micrognathia requires precise chin augmentation. While conventional ready-made implants often fail to conform to unique mandibular surfaces, expensive patient-specific options like PEEK or Titanium lack intraoperative adjustability. We introduce an innovative, cost-effective workflow utilizing 3D-printed templates to fabricate customized Polymethyl Methacrylate (PMMA) implants, emphasizing their clinical feasibility and intraoperative versatility. Methods: We retrospectively analyzed 20 patients with mild-to-moderate micrognathia (<6 mm advancement) who underwent genioplasty between March 2021 and June 2022. Patient-specific templates were produced via Fused Deposition Modeling (FDM) using low-shrinkage Acrylonitrile Butadiene Styrene (ABS) filament. During surgery, final PMMA implants were molded using these sterilized templates. Accuracy was evaluated by comparing mental advancement across preoperative, virtual simulation, and 6-month postoperative stages using Vectra 3D scanning. Results: Quantitative analysis revealed high fidelity between virtual planning and clinical outcomes. The mean discrepancy in horizontal advancement was only 1.02 mm (Planned: 5.04 mm vs. Actual: 4.02 mm). Statistical analysis showed a strong positive correlation (r = 0.928, p = 0.001). Subjective patient satisfaction was high, with 90% reporting “exceptional” or “very improved” results on the Global Aesthetic Improvement Scale (GAIS). Two cases of transient numbness resolved spontaneously within two months. Conclusions: This workflow combines FDM-based template fabrication with intraoperative PMMA molding, enabling real-time adjustment of implant geometry. The results demonstrate a high level of agreement between virtual planning and postoperative outcomes, supporting the clinical reliability of this approach. It may serve as a practical alternative to conventional CAD/CAM methods, particularly in cases requiring both precision and intraoperative flexibility. Full article

The goal of the present paper is to apply a novel biomimetic design strategy for the analysis, emulation, and technical evaluation of design solutions inspired by the morphogenetic logic of the walnut shell microstructure. The shell consists of specialized cells, called sclereids, which develop protrusions and mechanically interlock with neighboring cells, providing exceptional toughness through increased surface contact. To extract and transfer this biological principle, a generative algorithm was developed using the evolutionary solver Galapagos within the Grasshopper visual programming environment. The algorithm generates protrusions on the interfaces of structural blocks and optimizes their contact surface area while maintaining constant block volume. Additional design constraints, including symmetry and manufacturability considerations, were introduced to improve structural performance and computational efficiency. A series of physical specimens with variations in key geometric parameters, such as protrusion number and height, were fabricated using fused filament fabrication (FFF) with PLA material and evaluated through in-plane and out-of-plane three-point bending tests. The results show that increasing the number of protrusions significantly enhances mechanical performance, while increasing their height improves stiffness and interlocking up to a certain threshold, beyond which structural performance decreases due to stress concentration effects. This behavior can be attributed to improved load transfer and stress distribution across the enlarged interfacial area, as well as progressive mechanical engagement between complementary protrusions. The computational model is in good agreement with the experimental results, confirming the validity of the proposed approach. The study demonstrates that biomimetic optimization of interfacial geometry can enhance the mechanical behavior of interlocking systems and provides a framework for translating biological morphogenetic principles into engineering design applications. Full article

In response to the significant challenges posed by ice accumulation and contamination from various fluids in complex operating conditions for metallic materials, this study utilises picosecond laser precision machining to develop a ‘slippery surface’ featuring a micro-channel network structure. The core innovation of this study lies in the use of laser-machined micrometre-scale array textures to overcome the limitations of traditional isolated pores. These globally interconnected micro-channels serve as highly efficient reservoirs and dynamic transport channels for lubricants, significantly enhancing the interfacial capillary locking force of the lubricant. Experimental results demonstrate that this unique network geometry endows the surface with exceptional fluid replenishment and self-healing properties, enabling it to exhibit outstanding broad-spectrum hydrophobicity towards various fluids—including water, crude oil and ethanol (surface tension range: 17.9–72.0 mN m⁻¹)—with sliding angles consistently below 12°, whilst effectively slowing the dehydration and solidification processes of biological fluids. At a low temperature of −15 °C, the surface achieved an ice formation delay of up to 286 s, with an ice adhesion strength of only 33.9 kPa, ensuring that accumulated ice could be spontaneously detached under minimal external force. Furthermore, the micro-channel network structure serves as a key protective mechanism against mechanical wear, maintaining robust slippery properties even after three hours of high-pressure water jet scouring (Weber number of 300). This reliable interface, achieved through structural management, provides an efficient and scalable platform for addressing the all-weather anti-icing and antifouling requirements of outdoor infrastructure. Full article

Bacterial cellulose (BC) has emerged as a structurally robust, biologically compatible, and highly adaptable biomaterial with significant potential for next-generation wound-care technologies. Its nanofibrillar, extracellular-matrix-like architecture provides exceptional moisture retention, mechanical stability, and conformability, enabling BC to function as an active scaffold rather than a traditional dressing. Advances in chemical modification, composite engineering, and bioactive functionalization, including antimicrobial metals, chitosan, biosurfactants, enzymes, and growth factors, have expanded BC’s therapeutic capabilities. Emerging smart BC dressings integrate biosensors, stimuli-responsive drug release, and 3D-printed architectures tailored to patient-specific wound geometries. Parallel developments in artificial intelligence (AI) are transforming BC production by optimizing bioprocessing, guiding genetic engineering, reducing culture media costs, and enabling real-time quality control, thereby improving scalability and industrial feasibility. These combined innovations position BC as a multifunctional, immunologically instructive, and digitally integrated platform for advanced regenerative wound care. This review reframes BC within the contemporary pathophysiology of chronic wounds, emphasizing its roles in immunomodulation, macrophage polarization, angiogenesis, mechanotransduction, and the disruption of mixed bacterial–fungal biofilms that characterize diabetic foot ulcers and other non-healing wounds. BC hydrogels typically contain >90–99% water and exhibit tensile strengths exceeding 200 MPa, enabling robust mechanical performance in wound environments. Advances in BC composites have demonstrated antimicrobial reductions of 3–5 log units against common chronic-wound pathogens. Full article

Traditional simulation work often starts from the study of the impact of a single factor on device performance to obtain the optimal value of that factor and then regards the combination of the optimal values of each factor as the optimization condition. Obviously, this approach ignores the impacts of the interactions among factors on device performance. To address this issue, this paper uses Quokka3 v2.6.0 and JMP Pro 17.0.0 to perform device simulation and parameter optimization research on tunnel oxide passivated back contact (TBC) solar cells. First, Quokka3 was employed to investigate the effects of silicon wafer properties, rear-side passivation and contact characteristics, and rear-side geometry on the performance of TBC solar cells. Subsequently, a total of 625 simulations were performed by using Quokka3 with four factors (wafer thickness, wafer resistivity, P/N ratio, and pitch) at five levels. Finally, JMP Pro was used to analyze the simulation results statistically. It was found that the pitch, P/N ratio, quadratic power terms, quadratic interaction terms except the interaction between wafer thickness and resistivity, cubic power terms, and some cubic interaction terms all have significant impact on power conversion efficiency (PCE). JMP Pro predicted that the TBC solar cell could achieve the maximum PCE of 26.784% under the conditions of wafer thickness = 143.25 μm, wafer resistivity = 1.09 Ω·cm, P/N ratio = 1.94, and pitch = 380 μm. Full article