Reconstruction Approach Research Articles

Attitude Behavior Tendencies and Knowledge Orientation as Antecedents of Maritime English Learning: Practical Implications for the International Maritime Industry

Maritime English is a crucial element in the international maritime industry, enabling effective communication and ensuring safety at sea. Considering the importance of English proficiency in the maritime context, this study aims to develop an effective learning model using the Maritime English Reconstruction (MER) approach. This model is designed to enhance the effectiveness of maritime English learning through the application of relevant and contextual reconstruction techniques. The study examines the influence of attitude behavior tendencies, knowledge orientation, and the mediating role of MER on learning effectiveness. This research employs a quantitative method with data collection techniques through simple random sampling. The respondents in this study are final-year students majoring in maritime studies from various state universities in Jakarta and Semarang, Indonesia, with 248 valid questionnaires analyzed. Data analysis is conducted using Partial Least Squares-Structural Equation Modeling (PLS-SEM) with the assistance of SmartPLS 3 software. The results of the study indicate that attitude behavior tendencies and knowledge orientation have a significant influence on MER and learning effectiveness. Furthermore, MER is proven to act as a significant mediator in the relationship between attitude behavior tendencies and learning effectiveness, as well as between knowledge orientation and learning effectiveness. These findings suggest that the MER method plays a vital role in improving the results of maritime English learning by enhancing the application of relevant and contextual reconstruction techniques.

Inferring laminar origins of MEG signals with optically pumped magnetometers (OPMs): a simulation study

Abstract We explore the potential of optically pumped magnetometers (OPMs) to non-invasively infer the laminar origins of neural activity. OPM sensors can be positioned closer to the scalp than conventional cryogenic MEG sensors, opening an avenue to higher spatial resolution when combined with high-precision source space modelling. By simulating the forward model projection of single dipole sources at deep and superficial cortical surfaces onto OPM sensor arrays with varying sensor densities and measurement axes, and employing sparse source reconstruction approaches, we find that laminar inference with OPM arrays is possible at relatively low sensor counts under moderate to high signal-to-noise ratios (SNR). We observe improvements in laminar inference with increasing spatial sampling densities and measurement axes and demonstrate the advantage of placing the sensors closer to the scalp for inferring the laminar origins of cortical sources. However, challenges remain, such as biases towards both the superficial and deep surfaces at very low SNRs and a notable bias towards the deep surface when combining empirical Bayesian beamformer (EBB) source reconstruction with a whole-brain analysis. Adequate SNR through appropriate trial numbers and shielding, as well as precise co-registration, is crucial for reliable laminar inference with OPMs.

Deep learning approach for forensic facial reconstruction depends on unidentified skull

Facial reconstruction, or facial approximation, is an essential problem in a criminal investigation involving reconstructing a victim's face from his skull to determine the victim's identification at a crime scene. Facial approximation plays a crucial part when there is a lack of clues with investigators. Investigators utilize facial approximation to guess the victims' identities. This research attempted to use computer-aided face reconstruction rather than traditional approaches. Traditional methods of face reconstruction include the use of clay or gypsum. Traditional procedures necessitate forensic professionals to rebuild the victim's face. This research uses the convolution neural network skull part with sift (CNNSPS) model is employed to reconstruct facial features from a skull image utilizing public datasets CelebAMask-HQ and MUG500+. The proposed algorithm was tested on unidentified skull databases, and celebrity faces were used. The genuine datasets are not available, which is the key issue in this research.

Deep Neural Networks for Estimating Regularization Parameter in Sparse Time–Frequency Reconstruction

Time–frequency distributions (TFDs) are crucial for analyzing non-stationary signals. Compressive sensing (CS) in the ambiguity domain offers an approach for TFD reconstruction with high performance, but selecting the optimal regularization parameter for various signals remains challenging. Traditional methods for parameter selection, including manual and experimental approaches, as well as existing optimization procedures, can be imprecise and time-consuming. This study introduces a novel approach using deep neural networks (DNNs) to predict regularization parameters based on Wigner–Ville distributions (WVDs). The proposed DNN is trained on a comprehensive dataset of synthetic signals featuring multiple linear and quadratic frequency-modulated components, with variations in component amplitudes and random positions, ensuring wide applicability and robustness. By utilizing DNNs, end-users need only provide the signal’s WVD, eliminating the need for manual parameter selection and lengthy optimization procedures. Comparisons between the reconstructed TFDs using the proposed DNN-based approach and existing optimization methods highlight significant improvements in both reconstruction performance and execution time. The effectiveness of this methodology is validated on noisy synthetic and real-world signals, emphasizing the potential of DNNs to automate regularization parameter determination for CS-based TFD reconstruction in diverse signal environments.

Combining local flap with depressor anguli oris transfer for lower lip reconstruction and orbicularis oris sphincter function restoration.

Despite the versatility of flap designs for lower lip reconstruction, the notion of orbicularis oris (OO) ring integrity was barely stressed. Traditional flap transfer leads to fibrous orientation inconsistency with the remaining OO. Therefore, the aim of this study is to share the experience of combining local flaps with depressor anguli oris (DAO) transfer for lower lip reconstruction and OO sphincter function restoration. 48 patients were included in this study. Four types of local flaps were transferred for lower lip reconstruction. Each technique was modified by adding an additional DAO transfer for OO reconstruction. Functional outcomes were evaluated by grading scales based on clinical examination and observation. Aesthetic outcomes were evaluated by Patient and Observer Scar Assessment Scale. The surgical outcomes were favorable in both functional and aesthetic aspects. No statistically significant differences were observed among different types of flaps. Patients at T1 stage showed statistically significant mouth opening compared with patients at T2 stage (P = 0.004). Patients whose commissure was preserved showed statistically significantly better competence compared with patients whose commissure was sacrificed (P = 0.006). Combining local flap with DAO transfer is a reliable and effective approach for lower lip reconstruction and OO sphincter function restoration. Moreover, the modiolus seems to play a vital role in enhancing competence. Thus, it should be preserved during oncologic ablation if possible. It is vital to match the orientation of the transferred muscle fiber with the orientation of the remaining OO. Therefore, DAO becomes an excellent candidate.

Real-time plasma boundary shape reconstruction using visible camera on EAST tokamak

Abstract Accurate plasma shape reconstruction is crucial for the stable operation of tokamak devices. In this study, a plasma optical boundary reconstruction system is constructed on the Experimental Advanced Superconducting Tokamak (EAST), and real-time shape reconstruction based on an optical method is realized for the first time on EAST. First, a boundary extraction algorithm based on gray features is designed to extract the optical boundary from plasma optical images. Then, to reconstruct the optical boundary in the tokamak coordinate system, a camera calibration algorithm is developed based on feature point matching, and a coordinate mapping algorithm is designed based on plasma geometric features. Then, the system reconstructs the plasma boundary shape based on images acquired by a camera. Finally, the system is deployed in real-time on EAST, and its reconstruction rate meets the requirement of the EAST plasma shape control system. This study validates the feasibility of using an optical boundary shape reconstruction method on a tokamak device for real-time plasma shape reconstruction, providing a new shape reconstruction approach during steady-state discharge in future fusion devices.

Clinical Observations of the Effectiveness of the Masquelet Induced Membrane Technique in the Treatment of Critical Long-Bone Defects of the Lower and Upper Extremities

Background and Objectives: Successful treatment of severe trauma and fractures of the long bones with successful healing and bone union is still a significant challenge for surgeons. Unfortunately, up to 10% of long-bone fractures develop bone healing disorders. The aim of this study was to evaluate the results of treating bone defects with different etiologies in the upper and lower extremities using the induced membrane technique. Materials and Methods: We prospectively evaluated the radiological and clinical outcomes of 45 patients with severe bone defects treated with the induced membrane technique during the period from May 2021 to October 2023. The time to bone defect regeneration, size of the bone defect, and the cost of treatment were evaluated. Functional outcomes were assessed using the Disabilities of the Arm Shoulder and Hand (DASH) scale, SF-36, and the Lower Limb Functional Index (LLFI). Results: The mean follow-up time was 31 months (12–35). There were 20 patients with upper extremity bone defects and 25 with lower extremity bone defects. The mean defect length was 7.9 cm for the upper extremity (3.5–18) and 5.3 cm for the lower extremity (3–11). The mean times to achieve bone union and remodeling were 6.0 months (3–12) and 9 months (3–13) for the upper and lower limbs, respectively. Clinical evaluation at the end of treatment (achieving bone union) showed statistically significant improvements in the DASH, SF-36, and LLFI scales for pre- and postoperative outcomes. There was no statistical significance in the SF-36 clinical scale scores after surgical treatment compared to reconstructive treatment of upper and lower extremity bone defects. Results: The presented reconstructive approach to the treatment of bone defects and healing disorders and extensive analysis demonstrate the effectiveness of the induced membrane technique in a short follow-up period, with a relatively high level of patient comfort and good clinical results in the treatment of severe bone defects with particularly infectious etiologies.

Comprehensive evaluation of management strategies and rupture status in partially thrombosed aneurysms: a systematic review and meta-analysis

BackgroundThis meta-analysis aims to evaluate and compare the clinical and angiographic outcomes of different management strategies for partially thrombosed intracranial aneurysms (PTIAs).MethodsA systematic review was conducted using MEDLINE, Scopus, and...

A Model Development Approach Based on Point Cloud Reconstruction and Mapping Texture Enhancement

To address the challenge of rapid geometric model development in the digital twin industry, this paper presents a comprehensive pipeline for constructing 3D models from images using monocular vision imaging principles. Firstly, a structure-from-motion (SFM) algorithm generates a 3D point cloud from photographs. The feature detection methods scale-invariant feature transform (SIFT), speeded-up robust features (SURF), and KAZE are compared across six datasets, with SIFT proving the most effective (matching rate higher than 0.12). Using K-nearest-neighbor matching and random sample consensus (RANSAC), refined feature point matching and 3D spatial representation are achieved via antipodal geometry. Then, the Poisson surface reconstruction algorithm converts the point cloud into a mesh model. Additionally, texture images are enhanced by leveraging a visual geometry group (VGG) network-based deep learning approach. Content images from a dataset provide geometric contours via higher-level VGG layers, while textures from style images are extracted using the lower-level layers. These are fused to create texture-transferred images, where the image quality assessment (IQA) metrics SSIM and PSNR are used to evaluate texture-enhanced images. Finally, texture mapping integrates the enhanced textures with the mesh model, improving the scene representation with enhanced texture. The method presented in this paper surpassed a LiDAR-based reconstruction approach by 20% in terms of point cloud density and number of model facets, while the hardware cost was only 1% of that associated with LiDAR.

Gaussian Opacity Fields: Efficient Adaptive Surface Reconstruction in Unbounded Scenes

Recently, 3D Gaussian Splatting (3DGS) has demonstrated impressive novel view synthesis results, while allowing the rendering of high-resolution images in real-time. However, leveraging 3D Gaussians for surface reconstruction poses significant challenges due to the explicit and disconnected nature of 3D Gaussians. In this work, we present Gaussian Opacity Fields (GOF), a novel approach for efficient, high-quality, and adaptive surface reconstruction in unbounded scenes. Our GOF is derived from ray-tracing-based volume rendering of 3D Gaussians, enabling direct geometry extraction from 3D Gaussians by identifying its levelset, without resorting to Poisson reconstruction or TSDF fusion as in previous work. We approximate the surface normal of Gaussians as the normal of the ray-Gaussian intersection plane, enabling the application of regularization that significantly enhances geometry. Furthermore, we develop an efficient geometry extraction method utilizing Marching Tetrahedra, where the tetrahedral grids are induced from 3D Gaussians and thus adapt to the scene's complexity. Our evaluations reveal that GOF surpasses existing 3DGS-based methods in surface reconstruction and novel view synthesis. Further, it compares favorably to or even outperforms, neural implicit methods in both quality and speed.

A new method based on physical patterns to impute aerobiological datasets.

Limited research has assessed the accuracy of imputation methods in aerobiological datasets. We conducted a simulation study to evaluate, for the first time, the effectiveness of Gappy Singular Value Decomposition (GSVD), a data-driven approach, comparing it with the moving mean interpolation, a statistical approach. Utilizing complete pollen data from two monitoring stations in northeastern Italy for 2022, we randomly generated missing data considering the combination of various proportions (5%, 10%, 25%) and gap lengths (3, 5, 7, 10 days). We imputed 4800 time series using the GSVD algorithm, specifically implemented for this study, and the moving mean algorithm of the "AeRobiology" R package. We assessed imputation accuracy by calculating the Root Mean Square Error and employed multiple linear regression models to identify factors independently affecting the error (e.g. pollen variability, simulation settings). The results showed that the GSVD was as good as the well-established moving mean method and demonstrated its strong generalization capabilities across different data types. However, the imputation error was primarily influenced by pollen characteristics and location, regardless of the imputation method used. High variability in pollen concentrations and the distribution of missing data negatively affected imputation accuracy. In conclusion, we introduced and tested a novel imputation method, demonstrating comparable performance to the statistical approach in aerobiological data reconstruction. These findings contribute to advancing aerobiological data analysis, highlighting the need for improving imputation methods.

On the origin of counter-gradient transport in turbulent scalar flux: physics interpretation and adjoint data assimilation

The present study offers a twofold contribution on counter-gradient transport (CGT) of turbulent scalar flux. First, by examining turbulent scalar mixing through synchronized particle image velocimetry and planar laser-induced fluorescence on an inclined jet in cross-flow, we clarify the previously unexplained phenomenon of CGT, revealing key flow structures, their spatial distribution and modelling implications. Statistical analysis identifies two distinct CGT regions: local cross-gradient transport in the windward shear layer and non-local effects near the wall after injection. These behaviours are driven by specific flow structures, namely Kelvin–Helmholtz vortices (local) and wake vortices (non-local), suggesting that scalar flux can be decomposed into a gradient-type term for gradient diffusion and a term for large-eddy stirring. Second, we propose a new approach for reconstruction of turbulent mean flow and scalar fields using continuous adjoint data assimilation (DA). By rectifying model-form errors through anisotropic correction under observational constraints, our DA model minimizes discrepancies between experimental measurements and numerical predictions. As expected, the introduced forcing term effectively identifies regions where traditional models fall short, particularly in the jet centreline and near-wall regions, thereby enhancing the accuracy of the mean scalar field. These enhancements occur not only within the observation region but also in unseen regions, underscoring present DA approach's reliability and practicality for reproducing mean flow behaviours from limited data. These findings lay a solid foundation for adjoint-based model-consistent data-driven methods, offering promising potential for accurately predicting complex flow scenarios like film cooling.

Characterization of a radiation detector based on opaque water-based liquid scintillator

We present the characterization of a novel radiation detector based on an opaque water-based liquid scintillator. Opaque scintillators, also known as LiquidO, are made to be highly scattering, such that the scintillation light is effectively confined, and read out through wavelength-shifting fibers. The 1-liter, 32-channel prototype demonstrates the capability for both spectroscopy and topological reconstruction of point-like events. The design, construction, and evaluation of the detector are described, including modeling of the scintillation liquid optical properties and the detector’s response to gamma rays of several energies. A mean position reconstruction error of 4.4 mm for 1.6 MeV-equivalent events and 7.4 mm for 0.8 MeV-equivalent events is demonstrated using a simple reconstruction approach analogous to center-of-mass.

How I Do It: Madrid posterior component separation in 10 steps, with special consideration of its anatomy.

The Madrid Posterior Component Separation technique (Madrid PCS) is an approach for abdominal wall reconstruction that leverages anatomical landmarks to optimize outcomes. This manuscript describes ten essential steps for performing the Madrid PCS, focusing on technique, safety, and efficiency. A comprehensive step-by-step approach is outlined, beginning with the initial incision and adhesiolysis, progressing through retromuscular dissection, and concluding with lateral release and subxiphoid dissection. Emphasis is placed on key anatomical landmarks, tissue planes, and surgical maneuvers critical for each phase. Detailed tips and tricks, challenging scenarios, and potential risks are provided to enhance the reproducibility and safety of the procedure. Applying these ten steps provides an organized and methodical approach to the Madrid PCS. The focus on anatomical landmarks minimizes the risk of complications such as nerve injury, peritoneal tears, and inadvertent organ damage, while ensuring for the optimal placement of prosthetic materials. Adherence to these ten steps, based on anatomical landmarks, significantly improves the safety and effectiveness of the Madrid PCS technique. By understanding the nuances of the procedure, surgeons can achieve consistent and reproducible results in complex abdominal wall reconstructions.

Multi-Layered Implant Approach for Hemilaryngectomy Reconstruction in a Porcine Model.

Partial laryngectomies result in voice, swallowing, and airway impairment for thousands of patients in the United States each year. Treatment options for dynamic restoration of laryngeal function are limited. Thus, there is a need for new reconstructive approaches. Here, we evaluated early (4 week) outcomes of multi-layered mucosal-myochondral (MMC) implants when used to restore laryngeal form and function after hemilaryngectomy in a porcine model. Six Yucatan minipigs underwent transmural hemilaryngectomies followed by reconstruction with customized MMC implants aiming to provide site-appropriate localization of regenerated laryngeal tissues, while supporting laryngeal function. All implants were fabricated from polymeric collagen, with a subset of muscle and cartilage implants containing motor endplate-expressing muscle progenitor cells or cartilage-like cells differentiated from adipose stem cells, respectively. Vocalization and laryngeal electromyography (L-EMG) measurements with nerve conduction studies were performed post-operatively and compared with baseline along with gross and histological analyses of the healing response. All animals (n = 6) survived and maintained airway patency, safe swallowing, and phonation, without the use of tracheostomy and/or gastrostomy tubes. Histological evaluation indicated no adverse tissue reaction or implant degradation, showing progressive regenerative remodeling with mucosa reformation and ingrowth of new muscle and cartilage. Preliminary L-EMG suggested weak but detectable motor unit action potentials. Although vocalization duration, frequency, and intensity decreased post-operatively, all animals retained vocal capacity and parameter recovery was evident over the study duration. Engineered collagen polymeric implants in the presence or absence of autologous cell populations may serve as a feasible reconstructive option to restore dynamic function after hemilaryngectomy. Long-term follow-up is needed to further assess functional outcomes. NA Laryngoscope, 2024.

The historic city of Benghazi in Libya: Towards a holistic post-conflict reconstruction

ABSTRACT In the aftermath of armed conflicts, societies grapple with profound impacts on historic city centres, with cultural heritage damage intensifying crises in environments, identities and sense of place. This paper addresses the under-documented post-conflict journey of the Libyan city of Benghazi, focusing on the sudden demolition of its built heritage in March 2023. Grounded in a mixed-methods approach, this study explores reconstruction complexities in post-conflict emerging states through empirical research and a humanistic lens. In addition, the authors investigate aspects, such as the stakeholder’s opinion and the community’s reaction and expectations. The findings reveal that while post-conflict situations globally are inherently complex between balancing change and continuity, the case of Benghazi is exacerbated by political fragmentation, top-down reconstruction approaches, and lack of coordination between decision-makers and the community. The results of the survey indicate that most respondents support the preservation of their cultural heritage. This research concludes that stakeholders in Benghazi should adopt a nuanced approach that aligns with sustainable aspirations for a better future while retaining historical continuity. This paper contributes to the broader post-conflict reconstruction literature by offering insights for cities facing similar challenges and emphasizing the imperatives of inclusion, transparency, justice, and equity during the reconstruction process.

Reconstructing Richtmyer–Meshkov instabilities from noisy radiographs using low dimensional features and attention-based neural networks

We develop an ML-based approach for density reconstruction based on transformer neural networks. This approach is demonstrated in the setting of ICF-like double shell hydrodynamic simulations wherein the parameters related to material properties and initial conditions are varied. The new method can robustly recover the complex topologies given by the Richtmyer-Meshkoff instability (RMI) from a sequence of hydrodynamic features derived from radiographic images corrupted with blur, scatter, and noise. A noise model is developed to characterize errors in extracting features from synthetic radiographs of the simulated density field. The key component of the network is a transformer encoder that acts on a sequence of features extracted from noisy radiographs. This encoder includes numerous self-attention layers that act to learn temporal dependencies in the input sequences and increase the expressiveness of the model. This approach is shown to exhibit an excellent ability to accurately recover the RMI growth rates, despite the gas-metal interface being greatly obscured by radiographic noise. Our approach can be applied in a broad array of fields involving shock physics and material science.

All-optical image transmission through a dynamically perturbed multimode fiber and a ring-core fiber using diffractive deep neural networks.

In this study, we present an all-optical image reconstruction technique leveraging a diffractive deep neural network (D2NN) within a ring-core fiber (RCF) architecture. Orbital angular momentum (OAM) modes are employed to facilitate imaging transmission. We experimentally validate the efficacy of our approach for complex field diffractive image reconstruction through a multimode fiber (MMF) and RCF at a 1550 nm operating wavelength. The experimental results demonstrate the superior performance of the proposed method in mitigating RCF scattering-to-restoration transformation issues, significantly outperforming traditional MMF-based imaging correction techniques.

Predicting plant water availability from phytolith assemblages: an experimental approach for archaeological reconstructions in drylands

Predicting plant water availability from phytolith assemblages: an experimental approach for archaeological reconstructions in drylands

Spatio-temporal reconstruction of droplet impingement dynamics by means of color-coded glare points and deep learning

Abstract The present work introduces a deep learning approach for the three-dimensional reconstruction of the spatio-temporal dynamics of the gas–liquid interface on the basis of monocular images obtained via optical measurement techniques. The method is tested and evaluated at the example of liquid droplets impacting on structured solid substrates. The droplet dynamics are captured through high-speed imaging in an extended shadowgraphy setup with additional glare points from lateral light sources that encode further three-dimensional information of the gas–liquid interface in the images. A neural network is trained for the physically correct reconstruction of the droplet dynamics on a labeled dataset generated by synthetic image rendering on the basis of gas–liquid interface shapes obtained from direct numerical simulation. The employment of synthetic image rendering allows for the efficient generation of training data and circumvents the introduction of errors resulting from the inherent discrepancy of the droplet shapes between experiment and simulation. The accurate reconstruction of the three-dimensional shape of the gas–liquid interface during droplet impingement on the basis of images obtained in the experiment demonstrates the practicality of the presented approach based on neural networks and synthetic training data generation. The introduction of glare points from lateral light sources in the experiments is shown to improve the reconstruction accuracy, which indicates that the neural network learns to leverage the additional three-dimensional information encoded in the images for a more accurate depth estimation. By the successful reconstruction of obscured areas in the input images, it is demonstrated that the neural network has the capability to learn a physically correct interpolation of missing data from the numerical simulation. Furthermore, the physically reasonable reconstruction of unknown gas–liquid interface shapes for drop impact regimes that were not contained in the training dataset indicates that the neural network learned a versatile model of the involved two-phase flow phenomena during droplet impingement.