Search Results (19)

Cyclic superelasticity in laser powder bed fusion (LPBF)-fabricated NiTi alloys is strongly influenced by the scale of structural regulation. While conventional post-processing strategies are typically interpreted from a microstructural perspective, the distinct roles of bulk and surface regulation in governing cyclic functional response remain unclear. In this study, heat treatment and laser shock peening (LSP) are employed as representative bulk and surface regulation routes, respectively, to systematically investigate their effects on phase transformation and cyclic superelasticity. The results reveal that heat treatment and LSP operate through fundamentally different regulation modes. Heat treatment acts as a bulk regulation route, reconstructing the overall microstructure, promoting precipitation (NiTi₂ and Ni₄Ti₃), and modifying transformation pathways, which enhances recovery ratio but reduces recoverable strain. In contrast, LSP acts as a surface/subsurface regulation route, inducing gradient grain refinement and near-surface hardening while maintaining a B2-dominated matrix. As a result, the LSP-treated sample exhibits superior cyclic stability, with a stable recoverable strain of 9.93% and a superelastic strain of 5.10% after 10 cycles. These findings demonstrate that cyclic superelasticity is governed not only by phase constitution but also critically by the scale of structural regulation. This work provides a practical framework for selecting post-processing strategies to optimize functional performance in LPBF NiTi alloys. Full article

Physics-based simulation of debris flows over complex terrain is essential for hazard assessment, but repeated numerical integration is costly when many scenarios must be explored. We develop a general deep-learning surrogate modelling framework for two-dimensional (2D) debris-flow propagation, here applied to the Morino–Rendinara area (central Italy) using a three-dimensional (3D) Fourier Neural Operator (FNO) trained on synthetic simulations generated by a validated in-house finite-volume shallow-water solver. The solver reproduces debris-flow propagation over complex terrain and is specifically developed for artificial intelligence (AI) applications. It is based on a depth-averaged 2D formulation using the Harten–Lax–van Leer–Contact (HLLC) approximate Riemann solver, hydrostatic reconstruction, positivity-preserving wet–dry treatment, and Voellmy-type basal friction, and was verified through analytical benchmarks, numerical tests, and back-analyses of real events. The dataset was built from four site-specific release settings derived from real topography, combining different released volumes and bulk densities while preserving local geomorphological and rheological characteristics. Each simulation was stored as a full spatio-temporal tensor and used to train an FNO conditioned on coordinates, topography, friction parameters, bulk density, and initial release thickness. Training used a novel loss to emphasize active-flow areas and improve velocity reconstruction, and was performed using a graphics processing unit (GPU). The surrogate shows effective generalization to within-distribution validation samples, with global relative mean squared errors of 5.49% for flow thickness, 5.34% for velocity component u, and 2.60% for v, and mean $R^2$ values of 0.95, 0.94, and 0.97. For a representative sample, the surrogate predicts the full spatio-temporal solution in $0.52$ s, versus about 47 s for the first-order finite-volume solver, corresponding to a speed-up of about $91\times$, with an even larger gap expected for higher-order solvers, since, whilst the computation time of the solver increases as its complexity increases, the computation time of the FNO remains essentially unchanged. These results indicate that the proposed FNO is a reliable site-specific surrogate for rapid approximation of 2D debris-flow dynamics over real terrain, with potential for uncertainty propagation, Monte Carlo analysis, large-ensemble simulation, and hazard-oriented scenario assessment. Full article

Nickel-rich NMC-811 is a benchmark cathode material for high-energy density lithium-ion batteries due to its high specific capacity (>200 mAh g⁻¹) and operating voltage (~3.8 V). However, its strong surface reactivity toward atmospheric species, particularly moisture and CO₂, poses significant challenges during storage and processing, leading to the formation of LiOH- and Li₂CO₃-rich surface layers. Although the effects of humid air have been widely investigated, a direct comparison between high relative humidity and pure CO₂ exposure remains limited. Here, we systematically examine the morphological, structural, chemical, and electrochemical evolution of commercial NMC-811 electrodes after 5 h exposure to 80% relative humidity or CO₂-saturated atmosphere. Moisture treatment induces substantial surface reconstruction, lattice shrinkage, and increased cation disorder, accompanied by extensive hydroxide and carbonate formation. In contrast, CO₂ exposure mainly modifies the outermost surface layer without significant bulk structural changes. Electrochemical testing reveals that CO₂-treated electrodes display higher initial polarization but quickly recover near-pristine performance, whereas humidity-treated electrodes exhibit persistent kinetic limitations, accelerated capacity fading, and earlier end-of-life. Overall, degradation severity follows the trend: pristine < CO₂ < RH 80%, highlighting the dominant role of moisture in irreversible structural deterioration. Full article

Safe and efficient cargo handling is essential in modern port logistics, where overhead cranes are widely used to move containers, bulk materials, and heavy equipment. Accurate real-time measurement of wire rope inclination is critical for preventing collisions, reducing load sway, and enabling autonomous crane operation under challenging maritime conditions. This paper presents a You Only Look Once v9 (YOLOv9)-based camera–LiDAR fusion system for real-time estimation of the trolley–hook rope inclination angle in overhead cranes. A monocular industrial camera and a YOLOv9 detector provide semantic region-of-interest (ROI) masks for the trolley and hook, while a 3D LiDAR sensor, rigidly mounted and extrinsically calibrated to the camera, provides depth information. LiDAR points projected onto the image and filtered by YOLOv9 bounding boxes allow efficient extraction of safety-critical 3D geometry and reconstruction of the rope vector. Experimental results on an overhead crane testbed show that the proposed fusion estimator achieves an angle RMSE below 1 degree in dynamic swing and low-illumination scenarios, significantly outperforming a camera-only baseline (RMSE ≈ 2.11). These metrically validated results indicate that the proposed detection pipeline offers a robust foundation for intelligent crane monitoring and automation in maritime logistics and smart port operations. Full article

Accurate numerical simulation of debris flows is essential for hazard assessment and early-warning design, yet high-fidelity solvers remain computationally expensive, especially when large ensembles must be explored under epistemic uncertainty in rheology, initial conditions, and topography. At the same time, field observations are typically sparse and heterogeneous, limiting purely data-driven approaches. In this work, we develop a deep-learning Fourier Neural Operator (FNO) as a fast, physics-consistent surrogate for one-dimensional shallow-water debris-flow simulations and demonstrate its application to the Rendinara–Morino system in central Italy. A validated finite-volume solver, equipped with HLLC and Rusanov fluxes, hydrostatic reconstruction, Voellmy-type basal friction, and robust wet–dry treatment, is used to generate a large ensemble of synthetic simulations over longitudinal profiles representative of the study area. The parameter space of bulk density, initial flow thickness, and Voellmy friction coefficients is systematically sampled, and the resulting space–time fields of flow depth and velocity form the training dataset. A two-dimensional FNO in the $(x,t)$ domain is trained to learn the full solution operator, mapping topography, rheological parameters, and initial conditions directly to $h(x,t)$ and $u(x,t)$, thereby acting as a site-specific digital twin of the numerical solver. On a held-out validation set, the surrogate achieves mean relative $L^2$ errors of about 6–$7\%$ for flow depth and 10–$15\%$ for velocity, and it generalizes to an unseen longitudinal profile with comparable accuracy. We further show that targeted reweighting of the training objective significantly improves the prediction of the velocity field without degrading depth accuracy, reducing the velocity error on the unseen profile by more than a factor of two. Finally, the FNO provides speed-ups of approximately $36\times$ with respect to the reference solver at inference time. These results demonstrate that combining physics-based synthetic data with operator-learning architectures enables the construction of accurate, computationally efficient, and site-adapted surrogates for debris-flow hazard analysis in data-scarce environments. Full article

Background and Objectives: Total hip arthroplasty (THA) following pelvic osteotomy for developmental dysplasia of the hip (DDH) is technically challenging due to altered acetabular morphology. This study aimed to compare radiographic cup position and perioperative characteristics of THA after three common pelvic osteotomies—periacetabular osteotomy (PAO), shelf procedure, and Chiari osteotomy—with primary THA in Crowe type I DDH. Methods: A retrospective review identified 25 hips that underwent conversion THA after pelvic osteotomy (PAO = 12, shelf = 8, Chiari = 5) and 25 primary THAs without prior osteotomy. One-to-one matching was performed based on sex (exact match), age (within 5 years), and BMI (within 2 kg/m²) without the use of propensity scores. Cup inclination, radiographic anteversion, center-edge (CE) angle, and cup height were measured on standardized anteroposterior radiographs (ICC = 0.91). Operative time, estimated blood loss, and use of bulk bone grafts or reinforcement rings were reviewed. One-way ANOVA with Dunnett’s post hoc test and chi-square test were used for statistical comparison. Results: Cup inclination, anteversion, and CE angle did not differ significantly among groups. Cup height was significantly greater in the PAO group than in controls (29.0 mm vs. 21.8 mm; p = 0.0075), indicating a more proximal hip center. The Chiari and shelf groups showed upward trends, though not significant. Mean operative time tended to be longer after PAO (123 min vs. 93 min; p = 0.078). Bulk bone grafts and reinforcement rings were more frequently required in the PAO group (17%; p = 0.036 vs. control), and occasionally in Chiari cases, but not in shelf or control groups. Conclusions: THA after PAO is associated with higher cup placement and greater need for reconstructive devices, indicating increased technical complexity. In contrast, shelf and Chiari conversions more closely resemble primary THA. Preoperative planning should consider hip center translation and bone-stock restoration in post-osteotomy THA. Full article

Three-dimensional information acquisition is crucial for the intelligent control and safe operation of bulk material transportation systems. However, existing visual measurement methods face challenges, including difficult stereo matching due to indistinct surface features, error accumulation in multi-camera calibration, and unreliable depth information fusion. This paper proposes a three-dimensional reconstruction method based on multi-camera hierarchical calibration. The method establishes a measurement framework centered on a core camera, enhances material surface features through speckle structured light projection, and implements a ‘monocular-binocular-multi-camera association’ calibration strategy with global optimization to reduce error accumulation. Additionally, a depth information fusion algorithm based on multi-epipolar geometric constraints improves reconstruction completeness through multi-view information integration. Experimental results demonstrate excellent precision with absolute errors within 1 mm for features as small as 15 mm and relative errors between 0.02% and 2.54%. Compared with existing methods, the proposed approach shows advantages in point cloud completeness, reconstruction accuracy, and environmental adaptability, providing reliable technical support for intelligent monitoring of bulk material transportation systems. Full article

The plasma bulk velocity is one of the key parameters describing the behavior of a plasma jet and is relevant for applications such as plasma spraying or electric propulsion. Therefore, different measurement techniques to determine the velocity were developed in the past. This paper presents a novel, non-invasive method for spatially resolved velocity measurements. The method is based on tracking of characteristic features in tomographic reconstructions of the plasma plume. A high-speed image recording system combined with tomographic acquisition is therefore the backbone of this method. The proposed setup captures the object under study from six different directions at a frame rate up to one million frames per second, providing high spatial and temporal resolution. The tomographic reconstructions are then calculated using the ART algorithm to track features in the plasma fluctuations, from which the bulk velocity is determined. The setup was tested with a DC plasma torch operated at reduced pressures in the range of tens of millibars. For the analyzed parameters, the axial velocity reached a maximum of 1061 m/s at a distance of three centimeters from the plasma torch exit and decreased to 919 m/s further downstream at a distance of seven centimeters, which is in good accordance with previous work. Therefore, the proposed diagnostic method can serve as a non-invasive alternative to velocity measurements, providing additional information in the form of a 3D model of the plasma bulk. Full article

In this paper, we study operator reconstruction in a class of holographic tensor networks describing renormalization group flows studied in arXiv:2210.12127. We study examples of 2D bulk holographic tensor networks constructed from Dijkgraaf–Witten theories and find that for both the ${\mathbb{Z}}_n$ group and the $S_3$ group, the number of bulk operators behaving like a generalized free field in the bulk scales as the order of the group. We also generalize our study to 3D bulks and find the same scaling for ${\mathbb{Z}}_n$ theories. However, there is no generalized free field when the bulk comes from more generic fusion categories such as the Fibonacci model. Full article

Single-crystal Ni-rich layered oxide materials LiNi_1−x−yCo_xMn_yO₂ (NCM, 1 – x − y ≥ 0.6) are emerging as promising cathode materials that do not show intergranular cracks as a result of the lack of grain boundaries and anisotropy of the bulk structure, enabling extended cyclability in lithium-ion batteries (LIBs) operating at high voltage. However, SC-NCM materials still suffer from capacity fading upon extended cycling. This degradation of capacity can be attributed to a reconstruction of the surface. A phase transformation from layered structures to disordered spinel/rock-salt structures was found to be responsible for impedance growth and capacity loss. Film-forming additives are a straightforward approach for the mitigation of surface reconstruction via the formation of a robust protection layer at the cathode’s surface. In this work, we investigate various additives on the electrochemical performance of single-crystal LiNi_0.83Co_0.11Mn_0.06O₂ (SC-NCM83). The results demonstrate that the use of 1% lithium difluoroxalate borate (LiDFOB) and 1% lithium difluorophosphate (LiPO₂F₂) additives substantially enhanced the cycling performance (with a capacity retention of 93.6% after 150 cycles) and rate capability in comparison to the baseline electrolyte (72.7%) as well as electrolytes using 1% LiDFOB (90.5%) or 1% LiPO₂F₂ (88.3%) individually. The superior cycling stability of the cell using the combination of both additives was attributed to the formation of a conformal cathode/electrolyte interface (CEI) layer, resulting in a stabilized bulk structure and decreased impedance upon long-term cycling, as evidenced via a combination of state-of-the-art analytical techniques. Full article

Computational complexity reduction is at the basis of a new formulation of many-body quantum states according to tensor network ansatz, originally framed in one-dimensional lattices. In order to include long-range entanglement characterizing phase transitions, the multiscale entanglement renormalization ansatz (MERA) defines a sequence of coarse-grained lattices, obtained by targeting the map of a scale-invariant system into an identical coarse-grained one. The quantum circuit associated with this hierarchical structure includes the definition of causal relations and unitary extensions, leading to the definition of ground subspaces as stabilizer codes. The emerging error correcting codes are referred to logical indices located at the highest hierarchical level and to physical indices yielded by redundancy, framed in the AdS-CFT correspondence as holographic quantum codes with bulk and boundary indices, respectively. In a use-case scenario based on errors consisting of spin erasure, the correction is implemented as the reconstruction of a bulk local operator. Full article

Building Information Modelling (BIM) is a modern approach to managing the process of preparation, realization and operation of building objects including their documentation throughout their life cycle, based on database agenda platform. The aim of our research is to analyze and innovate existing engineering procedures with the aim: 1. to remodel the existing CAD documentation into BIM for the purpose of public procurement, 2. to provide guaranteed data to the IS of the Digital Map of Public Administration and 3. to provide data for the design of new railway structures or their reconstruction. The aim of the case study was to evaluate the effectiveness of remodelling the existing as-built documentation of a railway construction into a common BIM data environment (CDE), in which further subsequent construction agenda should be managed for the remaining period of its life cycle. Using the documentation for construction realization of the railway station Šumice, this 3D documentation was remodeled into the BIM data environment CDE and alternatively also into the 2D GIS environment. The BIM data standard developed by the State Fund for Transport Infrastructure was analyzed during the documentation reworking. An important parameter of the documentation rework was the use of a geodetic reference system fully compatible with the cadastral system in the Czech Republic. It turned out that the general data standard is only partially applicable for railway structures containing many special objects and many objects requiring individual classification. The remodelling of existing graphical data proved faster and more efficient in a GIS environment (layer oriented) compared to the need for 3D remodelling in a BIM CDE (object oriented). Experimental results have demonstrated the effectiveness of remodelling underground technical infrastructure objects, while the visible surface situation is often more effectively captured by current progressive bulk data acquisition technologies. In a CDE environment, existing as-built documentation data can be efficiently stored and administered and progressively, for the procurement and execution of construction, purposefully remodeled only to the extent required in BIM or converted into an exchangeable Digital Technical Map (DTM) format for public administration. Full article

Recent advances in single-cell transposase-accessible chromatin using a sequencing assay (scATAC-seq) allow cellular heterogeneity dissection and regulatory landscape reconstruction with an unprecedented resolution. However, compared to bulk-sequencing, its ultra-high missingness remarkably reduces usable reads in each cell type, resulting in broader, fuzzier peak boundary definitions and limiting our ability to pinpoint functional regions and interpret variant impacts precisely. We propose a weakly supervised learning method, scEpiLock, to directly identify core functional regions from coarse peak labels and quantify variant impacts in a cell-type-specific manner. First, scEpiLock uses a multi-label classifier to predict chromatin accessibility via a deep convolutional neural network. Then, its weakly supervised object detection module further refines the peak boundary definition using gradient-weighted class activation mapping (Grad-CAM). Finally, scEpiLock provides cell-type-specific variant impacts within a given peak region. We applied scEpiLock to various scATAC-seq datasets and found that it achieves an area under receiver operating characteristic curve (AUC) of ~0.9 and an area under precision recall (AUPR) above 0.7. Besides, scEpiLock’s object detection condenses coarse peaks to only ⅓ of their original size while still reporting higher conservation scores. In addition, we applied scEpiLock on brain scATAC-seq data and reported several genome-wide association studies (GWAS) variants disrupting regulatory elements around known risk genes for Alzheimer’s disease, demonstrating its potential to provide cell-type-specific biological insights in disease studies. Full article

There are a number of empirical methods for determining the width of safe maneuvering areas for ships on waterways. These methods are relatively accurate for straight sections of waterways and less accurate for curves of the waterways. When designing waterways in detail or determining the conditions for their safe operation, more accurate methods (e.g., simulations) are used. However, such studies require relatively large expenditure involving the construction of simulation models of various vessels and water areas, and conducting a real-time simulation experiment with a sufficiently large number of maneuvers performed by highly qualified navigators (pilots, captains). This study deals with the problem of determining the width of safe maneuvering areas of ships on the bends of the waterway based on the results of simulation studies. A new empirical method was developed to determine the width of safe maneuvering areas of loaded bulk carriers passing through fairway bends under deteriorated hydro-meteorological conditions. The method was developed on the basis of a generalization of the results of many simulation experiments carried out at various stages of the Świnoujście–Szczecin fairway reconstruction and a determination of the safe operating conditions for ships passing through the fairway. Full article

In this work, an ultrasound computed tomography (USCT) system was employed to investigate the fast-kinetic reactive crystallization process of calcium carbonate. USCT measurements and reconstruction provided key insights into the bulk particle distribution inside the stirred tank reactor and could be used to estimate the settling rate and settling time of the particles. To establish the utility of the USCT system for dynamical crystallization processes, first, the experimental imaging tasks were carried out with the stirred solid beads, as well as the feeding and stirring of the CaCO₃ crystals. The feeding region, the mixing process, and the particles settling time could be detected from USCT data. Reactive crystallization experiments for CO₂ capture were then conducted. Moreover, there was further potential for quantitative characterization of the suspension density in this process. USCT-based reconstructions were investigated for several experimental scenarios and operating conditions. This study demonstrates a real-time monitoring and fault detection application of USCT for reactive crystallization processes. As a robust noninvasive and nonintrusive tool, real-time signal analysis and reconstruction can be beneficial in the development of monitoring and control systems with real-world applications for crystallization processes. A diverse range of experimental studies shown here demonstrate the versatility of the USCT system in process application, hoping to unlock the commercial and industrial utility of the USCT devices. Full article