Search Results (5,377)

This study numerically investigates pavement damage caused by explosions in buried leaking natural gas pipelines using a coupled Lagrangian–Eulerian (CLE) framework in LS-DYNA. The gas phase is described by a Jones–Wilkins–Lee-based equation of state, while soil and pavement are modeled using a pressure-dependent soil model and the Riedel–Hiermaier–Thoma concrete model with strain-based erosion, respectively. The approach is validated against benchmark underground explosion tests in sand and blast tests on reinforced concrete slabs, demonstrating accurate prediction of pressure histories, ejecta evolution, and crater or damage patterns. Parametric analyses are then conducted for different leaked gas masses and pipeline burial depths to quantify shock transmission, soil heave, pavement deflection, and damage evolution. The results indicate that the dynamic response of the pavement structure is most pronounced directly above the detonation point and intensifies significantly with increasing total leaked gas mass. For a total leaked gas mass of 36 kg, the maximum vertical deflection, the peak kinetic energy, and the peak pressure at the bottom interface at this location reach 148.46 mm, 14.64 kJ, and 10.82 MPa, respectively. Moreover, a deflection-based index is introduced to classify pavement response into slight (<20 mm), moderate (20–40 mm), severe (40–80 mm), and collapse (>80 mm) states, and empirical curves are derived to predict damage level from leakage mass and burial depth. Finally, the effectiveness of carbon fiber reinforced polymer (CFRP) strengthening schemes is assessed, showing that top and bottom surface reinforcement with a total CFRP thickness of 2.67 mm could reduce vertical deflection by up to 37.93% and significantly mitigates longitudinal cracking. The results provide a rational basis for safety assessment and blast resistant design of pavement structures above buried gas pipelines. Full article

This paper introduces an advanced framework for modeling and scheduling construction processes using causal inference techniques, with particular emphasis on capturing complex technological and organizational interdependencies. By integrating causal calculus and counterfactual reasoning, the study demonstrates how construction schedules can be analyzed and optimized not only through temporal relationships but also through explicit cause–effect structures. A matrix-based scheduling methodology is presented, incorporating diagonal and reverse-diagonal time couplings consistent with the Time Coupling Method (TCM). The computational procedure is detailed, including the determination of earliest and latest event times, identification of the critical path, and computation of activity floats. Based on an in-depth examination of technological and organizational constraints, eight theorems are formulated and proven, establishing the fundamental properties of a scheduling approach that embeds causal mechanisms. The findings indicate that the integration of causal inference into construction planning enables more accurate identification of factors influencing project duration, enhances synchronization of dependent activities, and minimizes conflicts and idle times. This causally informed framework strengthens decision-making by allowing practitioners to predict the consequences of modifications in project execution strategies. The developed models constitute a robust foundation for future research on leveraging causal inference algorithms and artificial intelligence to advance construction process management. Full article

Background/Objectives: Neuroblastoma (NB) is the most common extracranial solid tumor in children and accounts for 12–15% of pediatric cancer-related deaths. Current multimodal therapies lack specific cellular targets, causing systemic toxicity and drug resistance. The development of innovative tumor-targeted nanoformulations might represent a promising approach to enhance NB diagnosis and antitumor efficacy, while decreasing off targets side effects. Fibronectin extra-domain B (FN-EDB) is upregulated in the tumor microenvironment. Methods: FN-EDB expression was evaluated by immunohistochemical staining in cell line-derived and tumor patient-derived animal models of NB. A gold nanoparticle, decorated with an antibody (Ab) recognizing FN-EDB (L19-AuNP) was developed by the company Nano Flow and its tumor binding was tested by ELISA in vitro and in patient-derived xenograft (PDX) models of NB by photoacoustic imaging in vivo. Results: All animal models of NB used have been shown to express FN-EDB. L19 Ab demonstrated excellent binding specificity to FN-EDB both when used in free form and after conjugation to AuNP. Compared to the non-functionalized (no Ab L19-coupled) AuNP, which showed an increase in PDI and zeta potential over time, making them unsuitable for use in in vivo studies, L19-AuNP demonstrated good stability. In vivo, L19-AuNP specifically homed into PDX models of NB, accumulating better in tumors expressing higher levels of FN-EDB. Negligible distribution to healthy organs occurred. Conclusions: In this preliminary study, L19-AuNP was shown to be a novel diagnostic tool specifically for binding NB expressing FN-EDB, paving the way for the development of theranostic nanoformulations co-encapsulating gold moiety and standard-of-care therapy for NB. Full article

Set in the vocational education and training sector in England, this article draws attention to how top-down, centre–periphery approaches to curriculum design and development in vocational education fail for at least three reasons. First, they misconstrue the nature of knowledge. Second, they lead to perfunctory and fragmented approaches to curriculum design, coupled with mechanistic measures of quality and achievement, which often require little more than “one-off” and superficially assessed demonstrations of performance. Finally, they underplay the role and importance of the teacher as researcher and artist in putting the cultural resources of society to work in creative curriculum design and pedagogy. Teacher artistry is pivotal in animating and heightening the vitality of vocational curricula. It is through this artistry that teachers make theories, ideas and concepts in vocational subjects and disciplines accessible and meaningful to all learners in coherent ways in the contexts of their learning and their lives. The consequences of the epistemic faux pas underpinning centre-to-periphery models of curriculum design and development are highlighted in this article in vocational tutors’ accounts of experiences of problems and issues in curriculum design, development and assessment encountered in their practice. Participants in the research teach in a variety of vocational education settings, including Apprenticeships and Higher-Level Technical Education; English Language at General Certificate of Secondary Education (GCSE) level; Health and Social Care; Information and Communications Technology; Construction (Plumbing); Digital Production, Design and Development and High-Tech Precision Engineering. Data are analysed and reported through systematic, thematic analysis This article draws upon qualitative data derived from a study funded by the Education and Training Foundation (ETF) in England over a two-year period from 2021 to 2023. The research population consists of a group of eight practitioner–researchers working in three colleges of Further Education (FE) and one Industry Training Centre (ITC) in England. All of the teachers of vocational education reported here volunteered to participate in the study. Research methods include semi-structured interviews, analysis of critical incidents and case studies produced by practitioner–researchers from across the FE and Skills sector in England. Full article

Current research on the vibration characteristics of fastener clips primarily employs modal experiments combined with finite element simulations; however, limited attention has been given to the dynamic vibration behavior of clips during actual train operations. This study investigates both the quasi-static and dynamic vibration characteristics using an integrated approach of finite element simulation and dynamic testing. Based on the Vossloh W300-1 fastener system, a three-dimensional model is established. Modal and frequency response analyses, together with field test validation, reveal two significant vibration modes within 0–1000 Hz: a first-order mode at 500 Hz and a second-order mode at 560 Hz. These modes are characterized by vertical overturning of the clip arm. Dynamic testing demonstrates that the dominant frequency of the arm acceleration is strongly correlated with the second-order natural frequency, confirming that wheel–rail excitation readily triggers second-order mode resonance. The study further shows that, at train speeds of 200–350 km/h, rail corrugation with wavelengths of 99.2–173.6 mm induces high-frequency excitation at 560 Hz, resulting in resonance fatigue of the clip. As a mitigation measure, regular rail grinding is recommended to eliminate corrugation at critical wavelengths. Additionally, optimizing the clip structure to avoid resonance frequency bands is proposed. These findings elucidate the coupling mechanism between the vibration characteristics of the clip and dynamic loads, providing theoretical support for the safety evaluation of high-speed rail fastener systems and the vibration-resistant design of clips. Full article

The kinematic and dynamic characteristics of the grinding media during the wet grinding process are investigated using a coupled Discrete Element Method (DEM)–Computational Fluid Dynamics (CFD) approach. Firstly, a coupled DEM-CFD model of the vertical spiral agitator mill is established and validated with experimental torque measurements. Subsequently, a velocity analysis model is established using the vector decomposition method. The cylinder is then divided into multiple regions along its radial and axial directions. The effects of spiral agitator rotational speed, diameter, pitch, and media filling level are investigated with respect to the circumferential velocity, axial velocity, collision frequency, effective energy between media, and energy loss of the grinding media. The average effective energy between media is an innovative metric for evaluating the grinding effect. The results indicate that the peripheral region of the spiral agitator demonstrates superior kinematic and dynamic performance. The rotational speed of the spiral agitator exerts a highly significant influence on the kinematic and dynamic characteristics of the media. With a maximum rise of 0.2 m/s in circumferential velocity and a 16.7 J gain in total energy. The media filling level demonstrates a negligible influence on media kinematics, while it profoundly affects dynamic properties, evidenced by a substantial increase of 83.09 J in the total media–media energy. As the diameter increases, the peak media circumferential velocity shifts outward, and the total media–media energy rises by 5.4 J. The spiral agitator pitch has a minimal impact on both the kinematic and dynamic characteristics of the media. Full article

Community detection in social networks is one of the most important topics of network science. Researchers have developed numerous methods from various perspectives. However, the existing methods often overlook the team information encoded as a special type of user relation in the social network, which plays an important role in community formation and evolution. In this paper, we propose a novel community detection algorithm called Team-aware Community Detection (TaCD). Our model constructs a multi-view network by encoding the user interaction information as the user view and the team information as the team view. To measure the consistency across the two views, we use the Jaccard similarity to establish a cross-view coupling. Based on the constructed 2-view network, we use multi-view modularity to discover team-aware community structure, and solve the optimization problem using the well-known Generalized Louvain approach. Another contribution of this paper is the collection of a new SCHOLAT dataset, which consists of several social networks with team information and is publicly available for testing purposes. Our experimental results on several SCHOLAT networks with team information demonstrate that TaCD outperforms the existing community detection algorithms. Full article

The present work studies the developing network of adiabatic shear bands (ASBs) during dynamic plane strain compression of orthogonal AISI 1045 steel billets, aiming to investigate the ASB trajectories and their evolution mechanism. This paper conducts a finite element (FE) numerical analysis in LS-DYNA software, developing a doubly coupled analysis by combining both structural–thermal and structural–damage couplings. The Modified Johnson–Cook (MJC) formulas are considered for modeling both the material plasticity and damage law, implementing thermo-viscoplastic numerical approaches, while a critical temperature for material failure is further adjusted. Finally, the case study relates to a parametric analysis of specimen aspect ratio, aiming to reveal its effect on the developing ASB network and its propagating characteristics. Full article

This study details the design, fabrication, and experimental validation of a wearable, non-invasive microwave sensor for continuous blood glucose monitoring. It incorporates a crescent-loaded elliptical patch antenna with a complementary split-ring resonator (CSRR) tag unit to greatly improve sensing sensitivity. The sensor operates across multiple resonant frequencies, enabling broadband dielectric characterization of glucose-dependent blood permittivity. Incorporation of the CSRR tag unit leads to a marked improvement in electromagnetic coupling and field confinement, resulting in a substantial increase in sensitivity, achieving 1.14 MHz/mg/dL in resonant frequency shift and 0.015 dB/mg/dL in reflection coefficient sensitivity compared to conventional designs. The sensor was fabricated on an FR-4 substrate and experimentally characterized using a vector network analyzer (VNA), showing strong agreement between simulated and measured S11 responses, with minimal frequency deviations and consistent resonance behavior. Experimental results confirmed improved sensitivity in response to glucose concentration variations over the range of 0–500 mg/dL, validating the sensor’s performance under realistic conditions. Furthermore, a physics-informed deep learning (PI-DL) model was developed to predict glucose concentration directly from measured S11 data. The model achieved enhanced prediction accuracy, with a mean absolute error below 1 mg/dL and a strong generalization across unseen samples, demonstrating the power of combining physical modeling with data-driven approaches. These results confirm that the proposed sensor, enhanced with the CSRR tag unit and supported by a PI-DL framework, offers a promising pathway toward next-generation non-invasive, accurate, and wearable glucose monitoring solutions. Full article

Fused deposition modeling of polyether ether ketone offers distinct advantages for fabricating complex and lightweight structures. Although three principal build orientations theoretically exist for practical 3D engineering components, research on their effects remains limited, especially regarding the influence of the interaction between build orientation and printing path on mechanical performance. This study investigated the tensile and shear properties, as well as the failure mechanisms, of FDM-fabricated PEEK under the coupled effects of build orientation and printing path through mechanical testing, fracture morphology analysis, and statistical methods. The results indicate that the printing path exerts a dominant influence on anisotropic behavior, while the interaction between printing path and build orientation jointly governs the shear failure modes. Under identical printing paths, the elongation at break varied by up to twofold across different build orientations, reaching a maximum of 96%, whereas samples printed with W or T paths exhibited elongations at break below 5%. Although shear and tensile moduli remained largely consistent across build orientations, other mechanical properties demonstrated significant differences. Variations in cross-sectional dimensions induced by build orientation markedly affected tensile performance: the coupled effect of build orientation and printing path was found to render the path repetition frequency a critical factor in determining temperature uniformity within the printed region and the quality of interlayer interfaces, thereby constituting the core mechanism underlying anisotropic behavior. Furthermore, larger cross-sections re-duced tensile modulus but enhanced yield strength and elongation at break, highlight-ing the regulatory role of cross-sectional geometry on mechanical response. Based on these findings, a synergistic optimization strategy integrating printing path, build orientation, and tensile–shear performance is proposed to achieve tailored mechanical properties in FDM-fabricated PEEK components. This approach enables controlled enhancement of structural performance to meet diverse application requirements. Full article

The Digital Twins of the Ocean (DTOs) represent an emerging framework for monitoring, simulating, and predicting ocean dynamics, supporting a range of applications relevant to understanding and responding to the global climate system. By integrating large-scale, multi-sourced datasets with advanced numerical models, DTOs provide a powerful tool for climate science. This review examines the role of machine learning (ML) in advancing DTOs applications, addressing the limitations of traditional methodologies under current conditions of increasing data availability from satellites, in situ sensors, and high-resolution numerical models. We highlight how ML serves as a versatile tool for enhancing DTOs capabilities, including real-time forecasting, correcting model biases, and filling data gaps where conventional approaches fall short. Furthermore, we review surrogate models that aim to complement or replace traditional physical models, offering increasing accuracy and the appeal of much faster inference for forecasts, and the insertion of hybrid models, which couple physics-based simulations with ML algorithms and are proving to be continuously improving in accuracy for complex oceanographic tasks as bigger datasets become available and methodologies evolve. This paper provides a comprehensive review of ML applications within DTOs, focusing on key areas such as water quality and marine biodiversity, ports, marine pollution, fisheries, and renewable energy. The review concludes with a discussion of future research directions and the potential of ML to foster more robust and practical DTOs, ultimately supporting informed decision-making for sustainable ocean management. Full article

In the mission scenario of On-Orbit Assembly (OOA), servicing spacecraft are frequently tasked with towing large-scale, flexible truss structures to designated assembly sites. This process involves complex coupled dynamics between the spacecraft and the flexible payload, which are often unmodeled or unknown, posing significant challenges to control precision. Furthermore, the proximity of other assembled structures in the construction area necessitates strict collision avoidance. To address these challenges, this paper proposes a novel adaptive robust controller for spacecraft thruster-based orbital control that integrates Prescribed Performance Control (PPC) with a Radial Basis Function Neural Network (RBFNN). The PPC framework ensures that the position tracking errors remain within user-predefined, time-varying boundaries, providing an intrinsic mechanism for collision avoidance during the towing of large flexible structures. Concurrently, the RBFNN is employed to approximate the entire unknown nonlinear dynamics of the combined spacecraft-truss system online, effectively compensating for uncertainties arising from the flexibility of the truss and external disturbances. The performance of the proposed controller is validated through both numerical simulations and hardware experiments on a ground-based air-bearing satellite simulator. Simulation results demonstrate the controller’s superior tracking accuracy compared to a conventional PID controller, while strictly adhering to the prescribed error constraints. Experimental results further confirm its effectiveness, showing that the simulator can track a desired trajectory with high precision, with tracking errors converging to approximately 5 mm while consistently remaining within the predefined safety boundaries. The proposed approach provides a robust and safe control solution for complex proximity operations in on-orbit construction, eliminating the need for precise dynamic modeling of flexible payloads. Full article

A central challenge in tight fault-depression reservoirs is understanding how three-dimensional fracture structures control gas storage and flow. This study introduces a data-driven, geologically informed framework that integrates structural-mechanical coupling to decipher fracture networks within the Shahezi Formation. Our model, based on rock failure criteria, achieves quantitative fracture prediction across one-dimensional to three-dimensional scales. This capability overcomes the limitations inherent in single-method approaches for tight, fracture-dominated reservoirs. By synthesizing sedimentary facies-controlled reservoir modeling, sweet-spot inversion, and geo-engineering integration, we establish a predictive system for accurate reservoir assessment. The continental clastic Shahezi Formation is typified by secondary fractures. This study utilizes leverage small-scale data (core, thin section, log) to quantify key parameters (fracture density, aperture), enabling a systematic analysis of fracture typology, heterogeneity, and controls. Building on this foundation, and spatially constrained by large-scale datasets (seismic interpretation, stress-field simulations), we developed a robust fracture development model for deep tight reservoirs. Stress-field modeling delineated fracture-prone zones, where a discrete fracture network (DFN) model was built to characterize 3D fracture geometry and connectivity. Integrating simulated fracture size and aperture-derived permeability allowed us to quantify fracture contribution to total permeability, ultimately mapping favorable targets. The results identify favorable zones primarily in the western sector of the study area, forming an NS-trending, belt-like distribution. They are mainly concentrated around the wells Changshen-4, Changshen-40, and Changshen-41. This distribution is clearly controlled by the Qianshenzijing Fault. Full article

The application of extended object tracking (EOT) in unmanned aerial vehicles (UAVs) has increasingly gained attention in recent years. However, EOT is often corrupted by heavy-tailed measurement noise due to outliers, which can be caused by factors such as UAV interference or partial object occlusion. Student’s t distribution (STD) is widely adopted for modeling this type of noise, and the estimation accuracy of EOT is highly dependent on prior knowledge of the noise. Although existing methods typically assume such prior knowledge is available, this assumption often fails in practice. Furthermore, the fact that the posterior of the measurement noise is estimated leads to coupling. This coupling, which cannot be adequately resolved by existing methods, prevents the direct derivation of variational Bayesian (VB) inference. We propose an adaptive EOT approach that employs a decoupling model to address unknown outliers in UAV tracking. Then, a novel dual-extended distortion model from sensor’s FoV is proposed to address the coupling. Subsequently, the measurement likelihood is formulated as a hierarchical structure, where the degrees of freedom (DoF) and measurement noise covariance matrix (MNCM) are modeled by Gamma and inverse Wishart (IW) distributions, respectively. The hierarchical structure allows the model to account for unknown noise characteristics. Based on these models, we derive an approach recursively for estimation. Finally, the performance of the proposed approach is validated with both simulated and real-world datasets. The results demonstrate the superior effectiveness and robustness of our approach. Full article

Mountaintop towers are highly exposed to self-initiated upward lightning flashes. Accurate estimation of their effective height—the equivalent flat-ground height yielding the same lightning exposure—is essential for reliable exposure assessment, for interpreting and calibrating measurement data at instrumented mountaintop towers, and for comparison with established protection guidelines. This study applies a two-step numerical framework that couples finite-element electrostatic simulations with a leader-inception and propagation model for representative tower–terrain configurations reflecting reference instrumented mountaintop sites in lightning research. For each configuration, the stabilization field, the minimum background electric field enabling continuous upward leader propagation to the cloud base, is determined, from which effective heights are obtained. The simulated results agree with the analytical formulation of Zhou et al. (within ~10%), while simplified or empirical approaches by Shindo, Eriksson, and Pierce exhibit larger deviations, especially for broader mountains. A normalized analysis demonstrates that the tower-to-mountain slenderness ratio (h/a) governs the scaling of effective height, following a power-law dependence with exponent −0.17 (R² = 0.94). This compact relation enables direct estimation of effective height from geometric parameters alone, complementing detailed leader-inception modeling. The findings validate the proposed physics-based framework, quantify the geometric dependence of effective height for mountaintop towers, and provide a foundation for improving lightning-exposure assessments, measurement calibration and design standards for elevated structures. Full article