Search Results (5,368)

The Lorentz-breaking theory not only modifies the geometric structure of curved spacetime but also significantly alters the quantum dynamics of bosonic and fermionic fields in black hole spacetime, leading to observable physical effects on Hawking temperature and Bekenstein–Hawking entropy. This study establishes the first systematic theoretical framework for entropy modifications of charged accelerating Anti-de Sitter black holes, incorporating gauge-invariant corrections derived from Lorentz-violating quantum field equations in curved spacetime. The obtained analytical expression coherently integrates semi-classical approximations with higher-order quantum perturbative contributions. Furthermore, the methodologies employed and the resultant conclusions are subjected to rigorous analysis, establishing their physical significance for advancing fundamental investigations into black hole entropy. Full article

With the rapid development of advanced machining technologies such as high-speed cutting, dry cutting, and ultra-precision cutting, as well as the widespread application of various difficult-to-machine materials, the surface degradation problems such as wear, oxidation, and delamination faced by tools in the service process have become increasingly prominent, seriously restricting the performance and service life of tools. Nanocoatings, with their distinct nano-effects, provide superior hardness, thermal stability, and tribological properties, making them an effective solution for cutting tools in increasingly demanding working environments. For example, the hardness of the CrAlN/TiSiN nano-multilayer coating can reach 41.59 GPa, which is much higher than that of a single CrAlN coating (34.5–35.8 GPa). This paper summarizes the most common nanocoating material design, coating deposition technologies, performance evaluation indicators, and characterization methods currently used in cutting tools. It also discusses how to improve nanocoating performance using modulation analysis of element content, coating composition, geometric structure, and coating thickness. Finally, this paper considers the future development of nanocoatings for cutting tools in light of recent research hotspots. Full article

The aim of the research presented in the article is to develop a method for modifying compound curves, i.e. geometric systems composed of two (or more) circular arcs with different radii, directed in the same direction and directly connected to each other. These curves are used when connecting two directions of the railway route where one circular arc is impossible due to permanent terrain obstacles. To solve the problem, an analytical method of designing track geometric systems was used, in which individual elements of these systems are described using mathematical equations. The modification itself involves introducing appropriate transition curves between the connecting arcs. Three possibilities for such a connection were presented, resulting from the method of considering conditions related to horizontal curvature of the track axis. A comparative analysis of the obtained solutions was conducted using the developed geometric test system. The analysis was based on the curvature values determined for the considered transition curves, after assuming varying lengths of these curves. For the recommended solution to the problem, it was necessary to verify the practical feasibility of horizontal ordinate values, which could not be too small relative to the implementation error. As stated, to limit the effects of this error, the transition curve lengths should be adjusted to specific geometric situations and excessively short curves should be avoided. As a result of the conducted research, the transition curve determined with strict curvature conditions was determined to be the most advantageous. It maintains curvature continuity along its entire length, there are no abrupt changes in curvature at the edges, and the changes in curvature along the length are much smoother than in the other curves considered. Therefore, this curve should be recommended for practical use. Full article

Warpage is among the most prevalent defects affecting injection molded parts. In this study, we aimed to develop methods to minimize warpage through mold design. Common strategies include matching the cavity geometry to the intended shape of the part, adjusting cavity dimensions to offset material shrinkage, and optimizing the cooling system and critical injection molding parameters. These optimization methods can offer significant improvements, but recently introduced methods that optimize the molded part and mold cavity shape result in higher levels of warpage reduction. In these methods, optimization of the shape of the molded part is achieved by shaping it in the opposite direction of warpage—a method known as inverse contouring. Inverse contouring of molded parts is a design technique in which mold cavities are intentionally modified to incorporate compensatory geometric deviations in regions anticipated to exhibit significant warpage. The final result after molded part ejection and warpage is a significant reduction in deviations between the warped and reference molded part geometries. In this study, a two-step approach for minimizing warpage was used: the first step was optimizing the most significant injection molding parameters, and the second was inverse contouring. In the first step, Response Surface Methodology (RSM) and Autodesk Moldflow Insight 2023 simulations were used to optimize molded part warpage based on three processing parameters: melt temperature, target mold temperature, and coolant temperature. For improved accuracy, a Computer-Aided Design (CAD) model of the warped molded part was exported into ZEISS Inspect 2023 software and aligned with the reference CAD geometry of the molded part. The maximal warpage value after the initial simulation was 1.85 mm based on Autodesk Moldflow Insight simulations and 1.67 mm based on ZEISS Inspect alignment. After RSM optimization, the maximal warpage was 0.73 mm. In the second step, inverse contouring was performed on the molded part, utilizing the initial injection molding simulation results to further reduce warpage. In this step, the CAD model of the redesigned, inverse-contoured molded part was imported into Moldflow Insight to conduct a second iteration of the injection molding simulation. The simulation results were exported into ZEISS Inspect software for a final analysis and comparison with the reference CAD model. The warpage values after inverse contouring were reduced within the range of ±0.30 mm, which represents a significant decrease in warpage of approximately 82%. Both steps are presented in a case study on an injection molded part made of polybutylene terephthalate (PBT) with 30% glass fiber (GF). Full article

This study explores the free vibration behavior of carbon fiber-reinforced sandwich open circular cylindrical shells featuring 3D reentrant auxetic cores (3D RSOCCSs). For theoretical predictions, a model integrating the Rayleigh–Ritz method (RRM) and Reddy’s third-order shear deformation theory (TOSDT) is adopted, whereas the finite element analysis approach is used for simulation predictions. All-composite 3D RSOCCSs specimens are produced via hot-press molding and interlocking assembly, and the modal characteristics of 3D RSOCCSs are obtained through hammer excitation modal tests. The predicted modal properties are in good agreement with the experimental results. In addition, the influences of fiber ply angles and geometric parameters on the natural frequency in the free vibration are thoroughly analyzed, which can offer insights for the vibration analysis of lightweight auxetic metamaterial cylindrical shells and promote their practical use in engineering scenarios focused on vibration mitigation. Full article

Scene text understanding, serving as a cornerstone technology for autonomous navigation, document digitization, and accessibility tools, has witnessed a paradigm shift from traditional methods relying on handcrafted features and multi-stage processing pipelines to contemporary deep learning frameworks capable of learning hierarchical representations directly from raw image inputs. This survey distinctly categorizes modern scene text recognition (STR) methodologies into three principal paradigms: two-stage detection frameworks that employ region proposal networks for precise text localization, single-stage detectors designed to optimize computational efficiency, and specialized architectures tailored to handle arbitrarily shaped text through geometric-aware modeling techniques. Concurrently, an in-depth analysis of text recognition paradigms elucidates the evolutionary trajectory from connectionist temporal classification (CTC) and sequence-to-sequence models to transformer-based architectures, which excel in contextual modeling and demonstrate superior performance. In contrast to prior surveys, this work uniquely emphasizes several key differences and contributions. Firstly, it provides a comprehensive and systematic taxonomy of STR methods, explicitly highlighting the trade-offs between detection accuracy, computational efficiency, and geometric adaptability across different paradigms. Secondly, it delves into the nuances of text recognition, illustrating how transformer-based models have revolutionized the field by capturing long-range dependencies and contextual information, thereby addressing challenges in recognizing complex text layouts and multilingual scripts. Furthermore, the survey pioneers the exploration of critical research frontiers, such as multilingual text adaptation, enhancing model robustness against environmental variations (e.g., lighting conditions, occlusions), and devising data-efficient learning strategies to mitigate the dependency on large-scale annotated datasets. By synthesizing insights from technical advancements across 28 benchmark datasets and standardized evaluation protocols, this study offers researchers a holistic perspective on the current state-of-the-art, persistent challenges, and promising avenues for future research, with the ultimate goal of achieving human-level scene text comprehension. Full article

Simultaneous Localization and Mapping (SLAM) is a fundamental technique in mobile robotics, enabling autonomous navigation and environmental reconstruction. However, dynamic elements in real-world scenes—such as walking pedestrians, moving vehicles, and swinging doors—often degrade SLAM performance by introducing unreliable features that cause localization errors. In this paper, we define dynamic regions as areas in the scene containing moving objects, and dynamic features as the visual features extracted from these regions that may adversely affect localization accuracy. Inspired by biological perception strategies that integrate semantic awareness and geometric cues, we propose Instance-level Point-Line SLAM (IPL-SLAM), a robust visual SLAM framework for dynamic environments. The system employs YOLOv8-based instance segmentation to detect potential dynamic regions and construct semantic priors, while simultaneously extracting point and line features using Oriented FAST (Features from Accelerated Segment Test) and Rotated BRIEF (Binary Robust Independent Elementary Features), collectively known as ORB, and Line Segment Detector (LSD) algorithms. Motion consistency checks and angular deviation analysis are applied to filter dynamic features, and pose optimization is conducted using an adaptive-weight error function. A static semantic point cloud map is further constructed to enhance scene understanding. Experimental results on the TUM RGB-D dataset demonstrate that IPL-SLAM significantly outperforms existing dynamic SLAM systems—including DS-SLAM and ORB-SLAM2—in terms of trajectory accuracy and robustness in complex indoor environments. Full article

As a national treasure, architectural heritage carries multiple value dimensions such as history, technology, art, and culture. With the increasing demand for architectural heritage protection and utilization, the traditional static digital model of architectural heritage based on geometric expression can no longer meet the practical application of multi-stage and multi-level scenarios. To this end, this paper proposes a value-chain-driven multi-level digital twin model of architectural heritage. Based on the three-stage logic of protection, management, and dissemination of value-chain classification, it integrates four types of models: geometry, physics, rules, and behavior. Combined with different hierarchical application levels, the digital model of architectural heritage is refined into a VCLOD (Value-Chain-Driven Level of Detail) detail hierarchy system to achieve a unified expression from spatial form restoration to intelligent response. Through the empirical application of three typical scenarios: the full-area guided tour of the Forbidden City, the exhibition curation of the central axis and the preventive protection of the Meridian Gate, the model shows the following specific results: (1) the efficiency of tourist guidance is improved through real-time personalized path planning; (2) the exhibition planning and visitor experience are improved through dynamic monitoring and interactive management of the exhibition environment; (3) the predictive analysis and preventive protection measures of structural safety are realized, effectively ensuring the structural safety of the Meridian Gate. The research results provide a theoretical basis and practical support for the systematic expression and intelligent evolution of digital twins of architectural heritage. Full article

Digital Twin (DTw) technology is a cornerstone of Industry 4.0, enabling real-time monitoring, predictive maintenance, and performance optimization across diverse industries. A key requirement for effective DTw implementation is high geometric fidelity—ensuring the digital model accurately represents the physical counterpart. Fidelity metrics provide a quantitative means to assess this alignment in terms of geometry, behavior, and performance. Among these, 3D shape descriptors play a central role in evaluating geometric fidelity, offering computational tools to measure shape similarity between physical and digital entities. This paper presents a comprehensive review of 3D shape descriptor methods and their applicability to geometric fidelity assessment in DTw systems. We introduce a structured taxonomy encompassing classical, structural, texture-based, and deep learning-based descriptors, and evaluate each in terms of transformation invariance, robustness to noise, computational efficiency, and suitability for various DTw applications. Building upon this analysis, we propose a conceptual fidelity metric that maps descriptor properties to the specific fidelity requirements of different application domains. This metric serves as a foundational framework for shape-based fidelity evaluation and supports the selection of appropriate descriptors based on system needs. Importantly, this work aligns with and contributes to the emerging ISO 30138 standardization initiative by offering a descriptor-driven approach to fidelity assessment. Through this integration of taxonomy, metric design, and standardization insight, this paper provides a roadmap for more consistent, scalable, and interoperable fidelity measurement in digital twin environments—particularly those demanding high precision and reliability. Full article

Fractal theory provides a powerful tool for quantifying complex geometric patterns such as concrete cracks. The box-counting method is widely employed for fractal dimension (FD) calculation due to its intuitive principles and compatibility with image data. However, two critical limitations persist in existing studies: (1) the selection of scale parameters (including minimum measurement scale and cutoff scale) lacks systematization and exhibits significant arbitrariness; (2) insufficient attention to the sensitivity of counting origins compromises the stability and comparability of FDs, severely limiting reliable engineering application. To address these limitations, this study first employs classical fractal images and crack samples to systematically analyze the impact of four minimum measurement scales (2, $\sqrt{2}$, 3, $\sqrt{3}$) and three cutoff scale coefficients (cutoff-to-minimum image side ratios: 1, 1/2, 1/3) on computational accuracy. Subsequently, the farthest point sampling (FPS) method is adopted to select counting origins, comparing two optimization strategies—Count-FD-Mean (mean of fits from multiple origins) and Count-Min-FD (fit using minimal box counts across scales). Finally, the optimized approach is validated through static loading tests on concrete beams. Key findings demonstrate that: the optimal scale combination (minimum scale: $\sqrt{2}$; cutoff coefficient: 1) yields a mere 0.5% average error from theoretical FDs; the Count-Min-FD strategy delivers the highest stability and closest alignment with theoretical values; FDs of beam cracks increase continuously with loading, exhibiting an exponential correlation with midspan deflection that effectively captures crack evolution; uncalibrated scale parameters and counting strategies may induce >40% errors in inferred mechanical parameters; results stabilize with 40–45 counting origins across three tested fractal patterns. This work advances standardization in fractal analysis, enhances reliability in concrete crack assessment, and provides critical support for the practical application of fractal theory in structural health monitoring and damage evaluation. Full article

The complexity of the processes occurring in both natural and artificial joints necessitates carrying out the analysis on a 3D model based on already existing mathematical models. All the presented numerical calculations define qualitative conclusions about the influence of certain parameters of endoprostheses on the values of stresses and strains arising in polyethylene parts of hip and knee endoprostheses. The obtained results make it possible to reveal “weak points” in the studied models and thus counteract the later effects resulting from premature wear of the endoprosthesis components. The study included a numerical analysis of the stress and strain distribution of polyethylene components of hip and knee endoprostheses working with the most commonly used material associations in this type of solution. The most common are metal alloys and ceramics. The analyses were carried out using ADINA and Autodesk Simulation Mechanical software. Geometric models were designed based on current solutions used by leading endoprosthesis manufacturers. The load models adopted are based on models commonly used in musculoskeletal biomechanics. Particular attention was paid to modeling the resistance due to friction at the hip endoprosthesis node. To build the hip endoprosthesis model, eight-node 3D solid elements were used. Due to the axisymmetric geometry of the model, the resulting discrete model consisted of 10,000 cubic elements described by 10,292 nodes. In the case of the knee endoprosthesis, a finite element mesh was adopted for the calculations, which was built with 3600 3D solid cubic elements and 4312 nodes. The accuracy of the adopted numerical model did not differ from the generally used solutions in this field. Full article

Background: Respiratory syncytial virus (RSV) poses a significant global health threat, particularly to children and the elderly. While progress has been made in RSV vaccine development, gaps remain, especially in protecting the elderly population. BARS13, a recombinant non-glycosylated G protein-based RSV vaccine, has shown promise in preclinical and Phase 1 studies. This phase II trial sought to determine whether escalating doses of BARS13 could enhance immune responses while maintaining safety and tolerability in healthy older adults aged 60–80 years. Methods: This study employed a rigorous randomized, double-blind, placebo-controlled design involving 125 participants across two Australian centers. Participants were randomized in a 3:1 (vaccine–placebo) ratio for Cohorts 1–2 (30 active, 10 placebo each) and a 2:1 ratio for Cohort 3 (30 active, 15 placebo). Cohort 1 (low dose) received 10 µg rRSV-G + 10 µg CsA in one arm + a placebo in the other (Days 1 and 29); Cohort 2 (high dose) received 10 µg rRSV-G + 10 µg CsA in both arms (20 µg total per dose, Days 1 and 29); Cohort 3 (multi-dose) received the same dose as that of Cohort 2 but with a third dose on Day 57. The placebo groups received IM injections in both arms at matching timepoints. The primary endpoints included safety and tolerability assessments, while the secondary endpoints evaluated the RSV G protein-specific IgG antibody concentrations using enzyme-linked immunosorbent assays (ELISAs). Statistical analysis was performed on both the safety and immunogenicity populations. Results: BARS13 was well-tolerated across all cohorts, with no serious adverse events (SAEs) related to the vaccine. The most common adverse events were mild local reactions (pain and tenderness) and systemic reactions (headache and fatigue), which resolved within 24–48 h. Immunogenicity analysis demonstrated a dose-dependent increase in the RSV G protein-specific IgG geometric mean concentrations (GMCs). Cohort 3, which received multiple high-repeat dose administrations, showed the highest immune response, with the IgG GMC rising from 1195.4 IU/mL on Day 1 to 1681.5 IU/mL on Day 113. Response rates were also the highest in Cohort 3, with 86.2% of participants showing an increase in antibody levels by Day 29. Conclusions: BARS13 demonstrated a favorable safety profile and strong immunogenicity in elderly participants, with a clear dose-dependent antibody response. These results support further development of BARS13 as a potential RSV vaccine candidate for the elderly. Further studies are needed to evaluate the long-term efficacy and optimal dosing schedule. Full article

This study investigates the vibrational characteristics of multi-layered graphene sheets with variable thickness (VTGSs) by using molecular dynamics (MD) simulations. It is aimed to determine how the natural frequencies and vibration damping ratios of variable-thickness graphene change with respect to temperature. Atomistic models for six distinct geometries (1L, 3LT, 3LTB, 5LT, 5LTB, and 9LTB) were generated to analyze the influence of structural design and temperature on their natural frequencies. The simulations were performed using the Large-Scale Atomic/Molecular Massively Parallel Simulator (LAMMPS) with an AIREBO potential to represent interatomic carbon interactions. Natural frequencies of all atomistic models were extracted by applying the Fast Fourier Transform (FFT) method to the Velocity Autocorrelation Function (VACF) data obtained from the simulations. In addition, the analysis was conducted at three different temperatures: 250 K, 300 K, and 350 K. Key findings reveal that an increase in the number of graphene layers results in a decrease in the fundamental natural frequency due to the increased mass of the structure. Moreover, it was noted that natural frequencies decrease with increasing temperature. It is attributed to the reduction in structural rigidity at higher thermal energies. These results provide critical insights into how geometric and thermal variations affect the dynamic behavior of complex multi-layered graphene structures. Full article

We studied the hemodynamics of abdominal aortic aneurysms (AAAs) by combining laboratory experiments and numerical simulations, with a focus on potential rupture mechanisms. In particular, we investigated the influence of geometrical features—beyond the commonly used maximum diameter—on flow patterns and the wall shear stress (WSS) distribution. Following our previous in vitro study performed utilizing a symmetrical bulge, we extended the analysis to an asymmetrical aneurysm geometry. Experiments and simulations were conducted under steady flow conditions while varying the Reynolds number over a wide range (490 < Re < 3930), to replicate the flow regimes occurring throughout the cardiac cycle. High-resolution, two-dimensional velocity fields were measured in the lab via image analysis and numerically computed using ANSYS Fluent^®. These data enabled a detailed characterization of both flow patterns and WSS distributions in healthy aorta and within the aneurysmal region. The good agreement between numerical and experimental results, as well as consistency with the literature, validates the adopted approach and supports its use for future investigations into AAA hemodynamics and rupture risk assessment. Full article

Background: Long-term follow-up after endovascular aortic repair (TEVAR) is crucial to detect adverse aortic remodeling, even with modern stent grafts offering enhanced flexibility and durability. Conventional imaging, based on diameter measurements, may fail to identify complications such as endograft migration. Methods: We conducted a longitudinal 3D geometric analysis of thoracic aortic and stent-graft evolution over 10 years in a patient treated for descending thoracic aortic aneurysm (DTAA) by endovascular treatment. A three-dimensional morphological analysis (length, tortuosity, angulation, and diameter) was carried out using advanced imaging software (EndoSize, MATLAB) to track aortic geometry and stent-graft behavior over time. A focused review of the literature on stent-graft migration, its risk factors, complications, and surveillance strategies was also performed. Results: This case illustrates how progressive geometric remodeling—including aortic elongation and increased tortuosity—can lead to delayed stent-graft migration and late type III endoleaks, with an elevated risk of rupture. The 3D analysis revealed early morphological changes that were undetectable using standard diameter-based follow-up. These observations are consistent with published data showing higher migration rates over time, particularly in tortuous anatomies. The literature review further emphasizes the clinical relevance of geometric surveillance, given the high rates of reintervention, morbidity, and mortality associated with stent-graft migration. Conclusions: This study underlines the importance of personalized and geometry-based surveillance after TEVAR. Advanced morphological assessment tools provide valuable insights for the early detection of complications and tailored patient management. Their integration into routine follow-up could help optimize long-term outcomes and prevent life-threatening events such as rupture. Full article