Search Results (1,420)

In deep-sea environments characterized by global climate change and frequent typhoons, the long-term structural stability of offshore buildings depends on the adaptability of their morphology to complex, multi-directional wind loads. Current offshore engineering predominantly emphasizes passive structural resistance, with a notable lack of research on proactive wind-diversion strategies from a morphological design perspective. Utilizing the PHOENICS-FLAIR platform and the Chen–Kim k-ε turbulence model, this study conducted numerical simulations across eight typical wind direction scenarios. The independence of the medium-mesh scheme was verified through Grid Convergence Index (GCI) analysis, and the high reliability of the numerical model was validated against the AIJ Case A wind tunnel experiments. Quantitative results demonstrate that, compared to the benchmark rectangular prism, the optimized composite polyhedral form featuring “curved sloped facades” performs superiorly under multi-directional conditions: the maximum positive wind pressure is reduced by up to 50%, and the total surface wind pressure differential decreases by 62–65%. This research proves that a polyhedral continuous envelope configuration can achieve balanced aerodynamic performance across all wind directions, providing a feasible direction for the design strategy of offshore buildings to shift from “passive resistance” to “proactive diversion”. Full article

To reveal the real deformation behavior and control mechanism of surrounding rock in hard rock drifts under deep high-stress conditions, a systematic study was conducted involving engineering geological investigation, in situ monitoring of surrounding rock microstrain, and numerical simulation, taking the −1465 m deep main drift of Shaling Gold Mine as the engineering background. Joint and fissure characteristics of the surrounding rock were acquired via the traverse method, and dominant joint sets were identified to evaluate rock mass integrity, providing a geological basis for deformation analysis. On this premise, vibrating wire microstrain sensors were employed to continuously monitor the time-dependent deformation of surrounding rock at different depths in the drift roof and two sidewalls. The strain evolution law of deep hard rock surrounding rock under the combined action of excavation disturbance and high ground stress was systematically analyzed. The results demonstrate that the surrounding rock is dominated by compressive strain in the early stage after excavation, which gradually transforms into tensile strain over time, exhibiting distinct time-dependent deformation characteristics. The deformation magnitude of the surrounding rock decreases significantly with increasing distance from the drift exposure surface, and the overall deformation amplitude of the roof is greater than that of the two sidewalls. Integrating the monitoring results with the surrounding rock structural characteristics, a combined support scheme of “resin rock bolt + wire mesh + shotcrete” was proposed, and its control effect was verified using RS2 numerical simulation. The simulation results indicate that this support system can effectively constrain the near-surface surrounding rock deformation, reduce the degree of stress concentration, and significantly improve drift stability. The research findings provide engineering references for understanding the surrounding rock deformation and optimizing support parameters of deep hard rock drifts in metal mines. Full article

High-Voltage Fragmentation is a novel comminution technology that utilizes shock waves generated in water by nanosecond pulsed voltages with fast rise times (<500 ns) to fracture materials, offering significant advantages in energy efficiency and environmental friendliness. This study established an underwater pulsed discharge experimental platform to meet the fast-rise-time pulse parameter requirements. It analyzed the influence patterns of the needle-mesh electrode gap distance, the needle electrode tip radius of curvature, and water conductivity on shock wave pressure intensity and time-domain characteristics. The research found that the energy conversion efficiency of underwater pulsed discharge is significantly affected by the pre-breakdown process. The peak pressure, impulse, velocity, and rise slope of the shock wave exhibit a trend of initially increasing and then decreasing with increasing needle-mesh electrode gap distance and needle electrode tip radius of curvature. The maximum pressure intensity, maximum equivalent wave velocity, maximum rise slope, and shortest wavefront time occurred at a 20 mm gap distance and a needle electrode tip curvature radius of 0.45 mm. Both pressure intensity and propagation velocity initially increased and then decreased with increasing water conductivity, reaching their maxima at a water conductivity of 340 μS/cm. Water conductivity showed no significant effect on rise slope and wavefront time. Full article

Background: Myoclonus, a sudden brief shock-like involuntary movement, represents a common yet under-recognized manifestation across many inherited metabolic disorders. Although its occurrence has been reported in case series and small cohorts, the overall spectrum, pathophysiological mechanisms, and therapeutic relevance of metabolic myoclonus have not been systematically summarized. Methods: A systematic search of PubMed was conducted for English-language publications from 2014 to 2025 using predefined MeSH terms related to myoclonus, movement disorders, and inborn errors of metabolism. Titles and abstracts were screened independently by three reviewers. After removal of duplicates, 27 articles were included, complemented by 65 additional references addressing individual disorders. Data were organized according to the International Classification of Inherited Metabolic Disorders (ICIMD). Results: Myoclonus was documented across six ICIMD categories, including intermediary metabolism, mitochondrial energy metabolism, lipid metabolism, disorders of complex molecules and organelles, cofactor and mineral metabolism, and metabolic cell signaling disorders. Clinical presentation ranged from isolated jerks to progressive myoclonic epilepsies. Several conditions—such as GLUT1 deficiency, cerebrotendinous xanthomatosis, and folate receptor α deficiency—are treatable through dietary or pharmacological interventions. Conclusions: Recognition of myoclonus as a presenting feature of inherited errors of metabolism (IEMs) is critical for timely diagnosis and treatment. Metabolic screening should be considered in all unexplained cases of myoclonus, particularly when accompanied by developmental delay or systemic abnormalities. Full article

This study introduces a mesoscale modeling methodology for polyurethane-solidified ballast beds (PSBBs) that eliminates reliance on X-ray computed tomography (XCT) and addresses constraints in specimen size, capital cost, and post-processing complexity. The approach couples the Discrete Element Method (DEM) with the Finite Element Method (FEM). A high-fidelity discrete-element geometry is reconstructed from three-dimensional laser scans of ballast particles. The virtual-ray casting algorithm is then employed to identify the spatial distribution of ballast and polyurethane and map this information onto the finite-element mesh, enabling heterogeneous material reconstruction at the mesoscale. The accuracy of the model and mesh convergence are validated through comparisons with laboratory uniaxial compression tests, determining the optimal mesh size to be 0.4 times the minimum particle size (0.4 D_min). Based on this, a parametric study on the effect of sleeper width on ballast bed mechanical responses is conducted, revealing that when the sleeper width is no less than 0.73 times the ballast bed width (0.73 W_b) an optimal balance between stress diffusion and displacement control is achieved. This method demonstrates excellent cross-material applicability and can be extended to mesoscale modeling and performance evaluation of other multiphase particle–binder composite systems. Full article

Triply periodic minimal surface (TPMS) structures provide high surface area to volume ratios and tunable conduction pathways, but predicting their thermal behavior across different metallic materials remains challenging because multi-material experimentation is costly and full-scale simulations require extremely fine meshes to resolve the complex geometry. This study develops a physics-informed neural network (PINN) that reconstructs steady-state temperature fields in TPMS Gyroid structures using only two experimentally measured materials, Aluminum and Silver, which were tested under identical heat flux and flow conditions. The model incorporates conductivity ratio physics, Fourier-based thermal scaling, and complete spatial temperature profiles directly into the learning process to maintain physical consistency. Validation using the complete Aluminum and Silver datasets confirms excellent agreement for Aluminum and strong accuracy for Silver despite its larger temperature gradients. Once trained, the PINN can generalize the learned behavior to nine additional metals using only their conductivity ratios, without requiring new experiments or numerical simulations. A detailed heat transfer analysis is also performed for Magnesium, a lightweight material that is increasingly considered for thermal management applications. Since no published TPMS measurements for Magnesium currently exist, the predicted Nusselt numbers obtained from the PINN-generated temperature fields represent the first model-based evaluation of its convective performance. The results demonstrate that the proposed PINN provides an efficient, accurate, and scalable surrogate model for predicting thermal behavior across multiple metallic TPMS structures and supports the design and selection of materials for advanced porous heat technologies. Full article

The three-dimensional borehole-to-surface transient electromagnetic (BSTEM) method plays a critical role in resolving subsurface conductivity structures under complex geological conditions. However, its application is often constrained by the high computational costs associated with large-scale simulations and fine temporal resolution. In this study, a time-parallel forward modeling strategy is employed by integrating the finite volume method (FVM) with the Multigrid Reduction-in-Time (MGRIT) algorithm. Maxwell’s equations are discretized in space using unstructured octree meshes, while the MGRIT algorithm enables parallelism along the time axis through coarse–fine temporal grid hierarchy and multilevel iterative correction. Numerical experiments on synthetic and field-scale models demonstrate that the MGRIT-based solver significantly reduces computational time compared to conventional direct solvers, particularly when a large number of processors are utilized. In a field-scale hematite mine model, the MGRIT-based solver reduces the total runtime by more than 40% while maintaining numerical accuracy. The method exhibits parallel scalability and is especially advantageous in problems involving a large number of time channels, where simultaneous time-step updates offer substantial performance gains. These results confirm the effectiveness and robustness of the proposed approach for large-scale 3D TEM simulations under complex conditions and provide a practical foundation for future applications in high-resolution electromagnetic modeling and imaging. Full article

The Cave of Altamira (Spain), a UNESCO World Heritage site, contains one of the most fragile and inaccessible Paleolithic rock-art environments in Europe, where geomatics documentation is constrained not only by severe spatial, lighting and safety limitations but also by conservation-driven restrictions on time, access and operational procedures. This study applies a confined-space UAV equipped with LiDAR-based SLAM navigation to document and assess the stability of the vertical rock wall leading to “La Hoya” Hall, a structurally sensitive sector of the cave. Twelve autonomous and assisted flights were conducted, generating dense LiDAR point clouds and video sequences processed through videogrammetry to produce high-resolution 3D meshes. A Mask R-CNN deep learning model was trained on manually segmented images to explore automated crack detection under variable illumination and viewing conditions. The results reveal active fractures, overhanging blocks and sediment accumulations located on inaccessible ledges, demonstrating the capacity of UAV-SLAM workflows to overcome the limitations of traditional surveys in confined subterranean environments. All datasets were integrated into the DiGHER digital twin platform, enabling traceable storage, multitemporal comparison, and collaborative annotation. Overall, the study demonstrates the feasibility of combining UAV-based SLAM mapping, videogrammetry and deep learning segmentation as a reproducible baseline workflow to inform preventive conservation and future multitemporal monitoring in Paleolithic caves and similarly constrained cultural heritage contexts. Full article

Thermoelectric generators (TEGs) based on single-walled carbon nanotubes (SWCNTs) offer a promising approach for powering sensors in wearable systems. However, achieving high performance remains challenging because the high thermal conductivity of SWCNTs limits the temperature gradient within the device. We previously developed flexible SWCNT-TEGs with enhanced heat dissipation by dip-coating SWCNTs onto mesh sheets; however, their performance in real wearable environments had not been evaluated. In this study, we demonstrate the practical operation of these SWCNT-TEGs under conditions such as fingertip contact and cap-based wear. The output voltage increased proportionally with the number of fingers touching the device, and a stable voltage of 6.1 mV was obtained when the TEG was mounted on a cap and worn outdoors at 7 °C. These findings highlight the promising potential of flexible SWCNT-TEGs as power sources for next-generation wearable technologies, including human–computer interaction and health monitoring. Full article

In temperate freshwater ecosystems, zooplankton play a crucial role in the pelagic food web and act as sensitive indicators of environmental change. They respond to shifts in water temperature, hydrodynamic mixing, and short-term meteorological events. This study investigated the epilimnetic zooplankton fauna of Lake Karamurat (Bolu, Türkiye), a small tectonic temperate lake, with a specific focus on the influence of rainfall events and wind speed on community structure. The samples were taken seasonally and horizontally using a plankton net (55 µm mesh size) and were analyzed alongside in situ physico-chemical measurements and meteorological data. In total, 74 zooplankton taxa were identified, comprising 54 rotifer species and 20 crustacean species (16 Cladocera and 4 Copepoda). Testudinella greeni was recorded for the first time in Türkiye, representing a new addition to the Turkish Rotifera fauna. Multivariate analyses revealed that electrical conductivity, water temperature, dissolved oxygen, precipitation, and wind speed were key drivers shaping community composition. The findings suggest that wind-driven surface mixing and episodic rainfall events enhanced vertical redistribution, leading to dominance of rotifers and small-bodied cladocerans in the epilimnion. These findings underscore the critical role of sampling strategy in shallow lakes under dynamic conditions and provide new faunistic insights into the zooplankton diversity of Anatolian lakes. Full article

Numerical simulations of the forming limit diagram (FLD) for SUS430/Al1050/TA1 laminated metal composites (LMCs) are conducted through the crystal plasticity finite element (CPFE) model integrated with the Marciniak–Kuczyński (M–K) theory. Representative volume elements (RVEs) that reconstruct the measured crystallographic texture, as characterized by electron backscatter diffraction (EBSD), are developed. The optimal grain number and mesh density for the RVE are calibrated through convergence analysis by curve-fitting simulated stress–strain responses to the uniaxial tensile data. The established multi-scale model successfully predicts the FLDs of the SUS430/Al1050/TA1 laminated sheet under two stacking sequences, namely, the SUS layer or the TA1 layer in contact with the die. The Nakazima test results validate the effectiveness of the proposed model as an efficient and accurate predictive tool. This study extends the CPFE–MK framework to multi-layer LMCs, overcoming the limitations of conventional single-layer models, which incorporate FCC, BCC, and HCP crystalline structures. Furthermore, the deformation-induced texture evolution under different loading paths is analyzed, establishing the relationship between micro-scale deformation mechanisms and the macro-scale forming behavior. Full article

Periapical lesions (PALs) are a common sequela of pulpal pathology, and accurate radiographic detection is essential for successful endodontic diagnosis and treatment outcome. With recent advancements in Artificial Intelligence (AI), deep learning systems have shown remarkable potential to enhance the diagnostic accuracy of PALs. This study highlights recent evidence on the use of AI-based systems in detecting PALs across various imaging modalities. These include intraoral periapical radiographs (IOPAs), panoramic radiographs (OPGs), and cone-beam computed tomography (CBCT). A literature search was conducted for peer-reviewed studies published from January 2021 to July 2025 evaluating artificial intelligence for detecting periapical lesions on IOPA, OPGs, or CBCT. PubMed/MEDLINE and Google Scholar were searched using relevant MeSH terms, and reference lists were hand screened. Data were extracted on imaging modality, AI model type, sample size, subgroup characteristics, ground truth, and outcomes, and then qualitatively synthesized by imaging modality and clinically relevant moderators (i.e., lesion size, tooth type and anatomical surroundings, root-filling status and effect on clinician’s performance). Thirty-four studies investigating AI models for detecting periapical lesions on IOPA, OPG, and CBCT images were summarized. Reported diagnostic performance was generally high across radiographic modalities. The study results indicated that AI assistance improved clinicians’ performance and reduced interpretation time. Performance varied by clinical context: it was higher for larger lesions and lower around complex surrounding anatomy, such as posterior maxilla. Heterogeneity in datasets, reference standards, and metrics limited pooling and underscores the need for external validation and standardized reporting. Current evidence supports the use of AI as a valuable diagnostic platform adjunct for detecting periapical lesions. However, well-designed, high-quality randomized clinical trials are required to assess the potential implementation of AI in the routine practice of periapical lesion diagnosis. Full article

Background: Vitamin E has been studied for its role in reducing the growth of colorectal cancer (CRC). CRC is a worldwide health concern. A meta-analysis reported that CRC patients have a lower concentration of serum vitamin E, suggesting it to be a risk factor. Although rodent models are widely used in disease research, their application in studying vitamin E as a preventive or therapeutic agent in CRC is not well characterized. To address this gap, we conducted a scoping review to examine the available evidence, adhering to the PRISMA-ScR checklist. Methods: We searched PubMed, Google Scholar, Scopus, and Web of Science (WoS) for full-text English original articles published before May 2024, using Medical Subject Headings (MeSH) terms and free text. The following search string strategy was applied: (Vitamin E OR tocopherol$ OR tocotrienol$) AND (Colo$ cancer OR colo$ carcinoma) AND (Rodentia OR mouse OR Rodent$ OR mice OR murine OR rats OR guinea OR rabbit OR hamsters OR Animal model OR Animal testing OR animals) AND (neoplasm$ OR “tumor mass” OR tumor volume OR tumor weight OR tumor burden). Data were charted into five categories using a standardized, pretested form. The charted data were synthesized using descriptive and narrative methods. Conclusions: This study highlights that γ- and δ-tocopherols, as well as δ-tocotrienol and its metabolites, were reported to reduce tumor volume and formation in various rodent models. While these results are promising, this scoping review identifies a need for further research to address translational barriers such as dosing, bioavailability, and long-term safety before clinical application. Full article

Reinforced concrete (RC) beams strengthened with carbon-fabric-reinforced cementitious matrix (CFRCM) systems have shown potential for restoring flexural performance, yet their effectiveness under different corrosion levels remains insufficiently understood. This study presents a numerical investigation of the flexural behaviour of simply supported RC beams externally strengthened with CFRCM plates. Refined finite element models (FEMs) were developed by explicitly incorporating the steel–concrete bond-slip behaviour, the carbon fabric (CF) mesh–cementitious matrix (CM) interface, and the CFRCM–concrete substrate interaction and were validated against experimental results in terms of failure modes, load–deflection responses, and flexural capacities. A parametric study was then conducted to examine the effects of CFRCM layer number, steel corrosion level, and longitudinal reinforcement ratio. The results indicate that the baseline flexural capacity can be fully restored only when the corrosion level remains below approximately 15%; beyond this threshold, none of the CFRCM configurations achieved full recovery. The influence of the reinforcement ratio was found to depend on corrosion severity, while increasing CFRCM layers enhanced flexural performance but exhibited saturation effects for thicker configurations. In addition, corrosion level and CFRCM thickness jointly influenced the failure mode. Comparisons with design predictions show that bilinear CFRCM constitutive models are conservative, whereas existing FRP-based design codes provide closer agreement with numerical and experimental results. Full article

Utilizing forestry and agricultural byproducts like wood and hemp residues advance sustainable additive manufacturing (AM), while reducing material costs. This study investigated the development and characterization of wood–sodium silicate composites incorporating hemp hurd and hemp fibers for AM applications. Formulations varied by wood fiber type (unsifted, 40 mesh, and pellet), sodium silicate concentration (50–60 wt%), and hemp hurd content (0–15 wt%). Properties evaluated include particle size and bulk density of the constituent materials, rheological behavior, extrusion performance, composite bulk density, and flexural and compressive strengths. Rheology and extrusion were largely influenced by the liquid content. Mixtures with low liquid content (50 wt% sodium silicate) had high motor power and low viscosity. As liquid content increased, motor power decreased, while viscosity increased up to 55 wt% and then decreased at 60 wt%. Mechanical properties correlated with particle size, where finer particles enhanced strength. A cost analysis was conducted using raw material prices to determine the economic feasibility of each formulation. Finally, the formulations were evaluated based on strength-to-cost ratios, extrudability and processability. The formulation with pellet wood fibers, 55 wt% sodium silicate, and 10 wt% hemp hurd achieved a high ratio of 73.0 MPa/$ while maintaining low motor power. This formulation offered additional benefits which are discussed qualitatively. Full article