Reconstruction Process Research Articles

Full-angle single-shot quantitative phase imaging based on Kramers-Kronig relations.

As a non-interference and non-iterative method, annular-illumination quantitative phase imaging based on Kramers-Kronig relations (AIKK) can realize phase measurement with full-angle resolution enhancement under multiple exposures. In order to completely record the object spectrum with a single shot, we proposed a colorful complementary illumination method in the recording process. The angle of this illumination mode is not symmetrical with each other, so the spectrum between the three channels can complement each other to avoid spectrum loss caused by spectrum conjugation. Meanwhile, the three spectral segments of full-angle information spectrum respectively carried by three wavelengths can be recorded. Additionally, the numerical filter is applied to correct the overlapped spectrum in the reconstruction process. Simulation and experimental results show that this method can achieve high spatiotemporal resolution quantitative phase measurement.

Automated geometric reconstruction and cable force inference for cable-net structures using 3D point clouds

Laser scanning provides an efficient solution to digital twin construction in civil engineering. The complexity and redundancy of large-scale point clouds substantially prolong the labor-intensive model reconstruction process. This paper presents an automated and high-precision geometric reconstruction approach for cable-net structures with a complete workflow from raw points to the extraction of cable shapes and forces. The strategy involves the extraction of coordinates of key cable regions through the characterization of cable geometry and local registration methods, followed by the computation of cable forces and shapes using segmented catenary theory and nonlinear optimization. The approach is validated with a single-curvature cable-net structure, with the accuracy of cable shapes within ±5 mm and errors in cable forces <5%. The method contributes to health monitoring and rapid reconstruction of cable-net structures. The precise geometry and forces will facilitate the creation of the mechanical models reflecting the physical authenticity of structures.

Improving diagnostic precision in amyloid brain PET imaging through data-driven motion correction

BackgroundHead motion during brain positron emission tomography (PET)/computed tomography (CT) imaging degrades image quality, resulting in reduced reading accuracy. We evaluated the performance of a head motion correction algorithm using 18F-flutemetamol (FMM) brain PET/CT images.MethodsFMM brain PET/CT images were retrospectively included, and PET images were reconstructed using a motion correction algorithm: (1) motion estimation through 3D time-domain signal analysis, signal smoothing, and calculation of motion-free intervals using a Merging Adjacent Clustering method; (2) estimation of 3D motion transformations using the Summing Tree Structural algorithm; and (3) calculation of the final motion-corrected images using the 3D motion transformations during the iterative reconstruction process. All conventional and motion-corrected PET images were visually reviewed by two readers. Image quality was evaluated using a 3-point scale, and the presence of amyloid deposition was interpreted as negative, positive, or equivocal. For quantitative analysis, we calculated the uptake ratio (UR) of 5 specific brain regions, with the cerebellar cortex as a reference region. The results of the conventional and motion-corrected PET images were statistically compared.ResultsIn total, 108 sets of FMM brain PET images from 108 patients (34 men and 74 women; median age, 78 years) were included. After motion correction, image quality significantly improved (p < 0.001), and there were no images of poor quality. In the visual analysis of amyloid deposition, higher interobserver agreements were observed in motion-corrected PET images for all specific regions. In the quantitative analysis, the UR difference between the conventional and motion-corrected PET images was significantly higher in the group with head motion than in the group without head motion (p = 0.016).ConclusionsThe motion correction algorithm provided better image quality and higher interobserver agreement. Therefore, we suggest that this algorithm be adopted as a routine post-processing protocol in amyloid brain PET/CT imaging and applied to brain PET scans with other radiotracers.

Research on Unsupervised Low-Light Railway Fastener Image Enhancement Method Based on Contrastive Learning GAN

The railway fastener, as a crucial component of railway tracks, directly influences the safety and stability of a railway system. However, in practical operation, fasteners are often in low-light conditions, such as at nighttime or within tunnels, posing significant challenges to defect detection equipment and limiting its effectiveness in real-world scenarios. To address this issue, this study proposes an unsupervised low-light image enhancement algorithm, CES-GAN, which achieves the model’s generalization and adaptability under different environmental conditions. The CES-GAN network architecture adopts a U-Net model with five layers of downsampling and upsampling structures as the generator, incorporating both global and local discriminators to help the generator to preserve image details and textures during the reconstruction process, thus enhancing the realism and intricacy of the enhanced images. The combination of the feature-consistency loss, contrastive learning loss, and illumination loss functions in the generator structure, along with the discriminator loss function in the discriminator structure, collectively promotes the clarity, realism, and illumination consistency of the images, thereby improving the quality and usability of low-light images. Through the CES-GAN algorithm, this study provides reliable visual support for railway construction sites and ensures the stable operation and accurate operation of fastener identification equipment in complex environments.

Robin coefficient determination problem in a fractional parabolic differential equation

This article discusses the identification of an unknown Robin coefficient in a fractional parabolic equation using a noisy measurement of the ultimate solution over time. The problem is quite complex, involving a nonlocal, nonlinear, and ill‐posed operator. To solve this problem, the article proposes a regularized optimization approach that minimizes a least‐squares cost function. The article also examines the various conceptual and real‐world challenges associated with this problem. The article demonstrates the presence of a singular and stable solution for the optimization problem. It utilizes the Morozov discrepancy principle and the conjugate gradient method to streamline the iterative reconstruction process. The proposed method is accurate and efficient, as several numerical examples demonstrate.

Assessing the accountability mechanisms in the 2015 Nepal earthquakes housing reconstruction: a case study of Bungamati, Lalitpur Metropolitan city, Nepal

PurposeThis research examined the accountability measures put into practice during the housing reconstruction following the 2015 Nepal earthquakes. Additionally, it aimed to identify gaps in accountability practices and suggest approaches for improving their efficacy.Design/methodology/approachThe research methodology encompassed a qualitative approach. Around 30 research participants were selected for in-depth interviews using a purposive sampling strategy to ensure data richness and relevance. The interviews were recorded, transcribed and then iteratively analysed using thematic framework analysis. This analytical technique involved identifying recurrent themes and patterns within the transcriptions of the participants' articulation.FindingsThe study showed the pivotal role of meetings and interactive forums in promoting transparency and disseminating vital information during housing reconstruction. Additionally, it underscored the contributions of nongovernmental organizations (NGOs) and community-based organizations (CBOs) in information dissemination within affected communities. Policy challenges centre on identifying vulnerable populations, local support mechanisms and contextualizing reconstruction policies, specifically focusing on urban heritage settlements. Strengthening local service delivery and complaint mechanisms, capacity building and defining the roles and responsibilities of actors and beneficiaries are suggestive measures for a participatory and inclusive reconstruction process.Originality/valueResearch holds distinctive value due to its recent inception and its focus on both the subject matter and the specific geographical context. It contributes novel insights into the principles and practices of accountability mechanisms during the housing reconstruction process following a major disaster.

National trends in revision procedures in post-mastectomy breast reconstruction: Autologous vs implant-based approaches

BackgroundBreast reconstruction involves collaborative decision-making between patients and surgeons, but the need for multiple revisions after the initial reconstructive surgery process can burden patients and the healthcare system. This study explored how the type of breast reconstruction (autologous [ABR], immediate implant-based reconstruction [IBR], or two-stage IBR) impacts postreconstruction revision rates. MethodUsing MarketScan Databases, a retrospective database study (2007–2021) was conducted, identifying revision procedures through Current Procedural Terminology codes. Statistical analysis with linear models, adjusted for patient characteristics and surgical factors, used a significance threshold of p < 0.05. ResultsAmong 58,264 patients, 6.2% of ABR patients, 3.8% of immediate IBR patients, and 3.6% of two-stage IBR patients underwent future revisions. IBR had a 51% lower incidence rate of revision operations than ABR (incidence rate ratio = 0.49, p < 0.001). Within IBR, there was no significant difference in the number of operations between immediate IBR (0.06 ± 0.32) and two-stage IBR (0.05 ± 0.32, p = 0.95). Immediate IBR demonstrated 12% (OR = 0.88, p = 0.0022) and 70% (OR = 0.30, p < 0.001) lower odds of requiring breast revision and fat grafting compared to ABR, respectively. Two-stage reconstruction had 66 % lower odds of requiring only fat grafting than ABR (OR = 0.34, p < 0.001). ConclusionABR necessitated a higher number of total revision procedures after completion of the initial reconstruction. These findings will better equip providers and patients to counsel patients in understanding their reconstructive journey, planning their reconstructions and timing, and provide more accurate estimates of the number of procedures that will be required to reach their aesthetic goals and final outcome.

Modeling the macroeconomic consequences of natural disasters: Capital stock, recovery dynamics, and monetary policy

Natural disasters can cause substantial damage to public and private sector infrastructure capital, generating macroeconomic losses through complex channels. These shocks must be managed and accounted for with macrofiscal and monetary policy to minimize their welfare impacts. To support this process, we adapt the World Bank Macrostructural Model to capture key transmission channels of natural (geophysical or climate-related) disasters and their immediate aftermath. The macroeconomic model is extended on several fronts: (1) a distinction is made between infrastructure and noninfrastructure capital; (2) the production function is adjusted to account for short-term complementarity across capital assets; (3) the reconstruction process is modeled to consider postdisaster constraints, with distinct processes for the reconstruction of public and private assets. Destroyed infrastructure capital makes the remaining noninfrastructure capital less productive, which means that disasters reduce the total stock of capital and its marginal productivity. Upon applying the model to Türkiye data, the welfare impact of a disaster — proxied by the discounted consumption loss — is found to increase nonlinearly with direct asset losses. Macroeconomic responses reduce the welfare impact of minor disasters but magnify it when direct asset losses exceed the economy’s absorption capacity. The welfare impact also depends on the preexisting economic situation, the ability of the economy to reallocate resources toward reconstruction, and the response of monetary policy. Appropriate macrofiscal and monetary policies offer cost-effective opportunities to mitigate the welfare impact of major disasters.

AN INPUT-OUTPUT MODEL OF THE ECONOMIC STRUCTURE OF THE EARTHQUAKE AFFECTED REGION IN TÜRKİYE FOR EFFECTIVE AND EFFICIENT RECOVERY

Türkiye is frequently exposed to earthquakes due to its location in one of the most active earthquake zones in the world. Following the recent earthquakes in the southeastern part of the country, the need to quickly recover from the losses in the local and regional economies of the affected areas makes it extremely important to analyze economic structure to ensure a planned economic development. In view of this, this study aims to investigate the key economic sectors that should be prioritized for investment in the reconstruction process of these places for an effective and efficient economic recovery. In doing so, a static explicit Input-Output (I-O) Model based on Leontief's work is adopted. The key sectors that require investment priority for the recovery and development of local economies in the shortest possible time have been identified using a categorical classification methodology based on the unbalanced growth model proposed by Hirschman.

Spectral Reconstruction from RGB Imagery: A Potential Option for Infinite Spectral Data?

Spectral imaging has revolutionisedvarious fields by capturing detailed spatial and spectral information. However, its high cost and complexity limit the acquisition of a large amount of data to generalise processes and methods, thus limiting widespread adoption. To overcome this issue, a body of the literature investigates how to reconstruct spectral information from RGB images, with recent methods reaching a fairly low error of reconstruction, as demonstrated in the recent literature. This article explores the modification of information in the case of RGB-to-spectral reconstruction beyond reconstruction metrics, with a focus on assessing the accuracy of the reconstruction process and its ability to replicate full spectral information. In addition to this, we conduct a colorimetric relighting analysis based on the reconstructed spectra. We investigate the information representation by principal component analysis and demonstrate that, while the reconstruction error of the state-of-the-art reconstruction method is low, the nature of the reconstructed information is different. While it appears that the use in colour imaging comes with very good performance to handle illumination, the distribution of information difference between the measured and estimated spectra suggests that caution should be exercised before generalising the use of this approach.

Surface Reconstruction of La2CuO4 during the Electrochemical Reduction of Carbon Dioxide to Ethylene and Its Benefits for Enhanced Performance.

Electrochemical reduction (ECR) of CO2 to C2H4 has a potential key role in realizing the carbon neutral future, which ultimately relies on the availability of an efficient electrocatalyst that can exhibit a high Faradaic efficiency (FE) for C2H4 production and robust, long-term operational stability. Here, for the first time, we report that upon applying reductive potential and electrolyte to the benchmark La2CuO4 catalyst, surface reconstruction occurred, i.e., the appearance of a distinctive phase evolution process over time, which was successfully monitored using ex situ powder XRD and operando Mott-Schottky (M-S) measurements of La2CuO4 samples that were soaked into the electrolyte and subjected to CO2-ECR for different durations. At the end of such a reconstruction process, an outermost layer consisting of lanthanum carbonate, a thin outer layer made of an amorphous Cu+ material formed over the core bulk La2CuO4, as confirmed by various characterization techniques, which resulted in the redistribution of interfacial electrons and subsequent formation of electron-rich and electron-deficient interfaces. This contributed to the enhancement in FE for C2H4, reaching as much as 58.7%. Such surface reconstruction-induced electronic structure tuning gives new explanations for the superior catalytic performance of La2CuO4 perovskite and also provides a new pathway to advance CO2-ECR technology.

Manipulating the electron redistribution of Fe3O4 for anion exchange membrane based alkaline seawater electrolysis

Seawater electrolysis is a green and sustainable method of producing clean hydrogen. However, the presence of chloride anion (Cl-, ∼0.5 M) usually leads to competitive chloride oxidation reaction (ClOR). In this paper, the redistribution of electrons in Fe3O4 was manipulated using a fast melting salt combined with gas phase phosphating strategy. The Co/P dual-doped bifunctional catalyst (Co/P-Fe3O4@IF) was constructed on iron foam, which own excellent resistance to the adsorption of chloride ions by experimental and theoretical calculations. In addition, phosphating promotes the conversion of Fe2O3 to inverse spinel Fe3O4 phase, accelerating the dissociation of H2O. At the same time, Co doping manipulates the electron distribution and regulates the d-band center in Fe3O4, which is conducive to optimizing the ΔGH* and promoting the reconstruction process of OER, thus improving the catalytic activity in seawater. In alkaline seawater AEM electrolyzer, the catalyst can achieve a current density of 500 mA cm−2 at a voltage of 1.83 V without ClOR, and only 4.38 KWh can be consumed to produce hydrogen per cubic meter. This work opens up the possibility that transition metal oxides can become an important catalyst for hydrogen production from seawater.

ASSESSMENT OF THE POSSIBILITY OF USING RECYCLED COARSE AGGREGATE IN A WOOD-SOIL CONCRETE SLAB

Problem statement. During the process of post-war reconstruction in Ukraine, there will be a need for a large number of affordable and ecological building materials, especially in the part of restoration and construction of low-rise buildings. Such building materials can be soil-concrete and aggregates from crushed concrete waste. The proposed constructive solution of the ribbed floor, in which the ribs are made of pine beams and OSB sheets, and the slab part is made of soil concrete on fixed formwork. The purpose of the article is to determine the optimal composition of the soil-concrete mixture using crushed stone obtained after crushing concrete scrap, taking into account the configuration of the wood-soil concrete slab, to determine the strength of soil-concrete of this composition, to determine the possibility of using recycled coarse aggregates. Conclusions. Based on the results of determining the optimal composition of soil-concrete, it was established that the maximum compressive strength is achieved when the mass content of the binder in the cement-soil mixture is at the level of 20 %. Taking into account the configuration of the slab part of the floor, a fraction of 5...7.5 mm is accepted for coarse aggregate. During the test of soil-concrete samples with recycled aggregate with a mass content of 10 %, 20 % and 30 %, the compressive strength of soil concrete decreased. Obviously, the presence of a clay component (loam) in the mixture reduces the adhesion in the contact zone between the aggregate and the soil-concrete, which increases the heterogeneity of the entire structure of the soil-concrete. Thus, the use of natural or recycled coarse aggregates in soil-concrete is impractical. Taking into account the determined strength of soil-concrete without aggregate, a scheme of combined concreting of the soil-concrete slab is proposed – heavy concrete above the rib and soil-concrete in the overhangs. In this case, there is a need for research on the possibility of using recycled coarse aggregate of the required size in heavy concrete.

Machine learning driven instance segmentation providing new porosity insights into wire arc directed energy deposited Ti-22V-4Al

Non-destructive x-ray methods such as micro-computed tomography (micro-CT) are useful for investigating porosity defects in additively manufactured products. Understanding the porosity knowledge accessible to micro-CT technologies relies on quantifying the spatial and morphological characteristics of porosity instances. So far, machine learning-based semantic segmentation techniques and threshold-based image analysis methods have been employed for this purpose. However, the reliability of these methods is compromised by their limitations in segmentation accuracy and delineating connected pores. This work proposed a porosity investigation strategy involving a deep learning-based instance segmentation process on micro-CT slices followed by a 3D reconstruction process to reliably examine the spatial and morphological data of 3D porosities, cased studied by a wire arc directed energy deposited Ti-22 V-4Al alloy. Systematic data analysis captured new insights of pore defects in terms of pore formation tendency, the relationship between pore size and pore sphericity, pore evolution, and the discovery of pore-free zones. The proposed workflows are transferrable for the porosity investigation of other additively manufactured components. The new insights may also guide future advancements in porosity elimination and manipulation techniques to fabricate mechanically robust complex structured components.

Exit wave reconstruction of a focal series of images with structural changes in high-resolution transmission electron microscopy.

High-resolution transmission electron microscopy (HRTEM) images can capture the atomic-resolution details of the dynamically changing structure of nanomaterials. Here, we propose a new scheme and an improved reconstruction algorithm to reconstruct the exit wave function for each image in a focal series of HRTEM images to reveal structural changes. In this scheme, the wave reconstructed from the focal series of images is treated as the initial wave in the reconstruction process for each HRTEM image. Additionally, to suppress noise at the frequencies where the signal is weak due to the modulation of the lens transfer function, a weight factor is introduced in the improved reconstruction algorithm. The advantages of the new scheme and algorithms are validated by using the HRTEM images of a natural specimen and a single-layer molybdenum disulphide. This algorithm enables image resolution enhancement and lens aberration removal, while potentially allowing the visualisation of the structural evolution of nanostructures.

Problems and related results in algebraic vision and multiview geometry

This article is a survey of results in algebraic vision and multiview geometry. The starting point is the study of projective algebraic varieties which are critical for scene reconstruction. Initially studied for reconstructing static scenes in three-dimensional spaces, these critical loci are later investigated for dynamic and segmented scenes in higher-dimensional projective spaces. The formal analysis of the ideals of critical loci employs Grassmann tensors, introduced as crucial tools for determining these ideals and aiding the reconstruction process away from critical loci. A long-term goal of the authors with other co-authors involves two main aspects: firstly studying properties of Grassmann tensors, as rank, multi-rank and core, along with the varieties that parameterize these tensors; concurrently conducting an analysis of families of critical loci in various scenarios.

Liver resection with two-step vascular exclusion, in situ hypothermic portal perfusion for the treatment of end-stage hepatic alveolar echinococcosis.

To evaluate the safety and efficacy of two-step vascular exclusion and in situ hypothermic portal perfusion in patients with end-stage hepatic hydatidosis. This study involved patients with advanced hepatic hydatid disease undergoing surgical treatment between 2022 and 2023, which included resection and reconstruction of the hepatic veins, inferior vena cava (IVC), and portal vein (PV). We described the technical details of liver resection and vascular reconstruction, as well as the use of two-step vascular exclusion and in situ hypothermic portal perfusion techniques during the vascular reconstruction process. We included 7 patients with advanced hepatic hydatid disease who underwent surgical resection using two-step vascular exclusion and in situ hypothermic portal perfusion. The mean duration of surgery was 12.5h (range, 7.5-15.0h). The average hepatic ischemia time was 45min (range, 25-77min), while the occlusion time of the IVC was 87min (range, 72-105min). The total blood loss was 1000 milliliters (range, 500-1250 milliliters). Postoperatively, patients exhibited good recovery of liver and renal function. The mean ICU stay was 2 days (range, 1-3 days), and the mean postoperative hospital stay was 13 days (range, 9-16 days), with no Grade III or above complications observed during a mean follow-up period of 15 months (range, 9-24 months), CONCLUSION: two-step vascular exclusion and in situ hypothermic portal perfusion for surgical resection of end-stage hepatic hydatid disease is safe and effective. This significantly reduces the anhepatic time.

ESatSR: Enhancing Super-Resolution for Satellite Remote Sensing Images with State Space Model and Spatial Context

Super-resolution (SR) for satellite remote sensing images has been recognized as crucial and has found widespread applications across various scenarios. Previous SR methods were usually built upon Convolutional Neural Networks and Transformers, which suffer from either limited receptive fields or a lack of prior assumptions. To address these issues, we propose ESatSR, a novel SR method based on state space models. We utilize the 2D Selective Scan to obtain an enhanced capability in modeling long-range dependencies, which contributes to a wide receptive field. A Spatial Context Interaction Module (SCIM) and an Enhanced Image Reconstruction Module (EIRM) are introduced to combine image-related prior knowledge into our model, therefore guiding the process of feature extraction and reconstruction. Tailored for remote sensing images, the interaction of multi-scale spatial context and image features is leveraged to enhance the network’s capability in capturing features of small targets. Comprehensive experiments show that ESatSR demonstrates state-of-the-art performance on both OLI2MSI and RSSCN7 datasets, with the highest PSNRs of 42.11 dB and 31.42 dB, respectively. Extensive ablation studies illustrate the effectiveness of our module design.

Implicit 3D Human Reconstruction Guided by Parametric Models and Normal Maps.

Accurate and robust 3D human modeling from a single image presents significant challenges. Existing methods have shown potential, but they often fail to generate reconstructions that match the level of detail in the input image. These methods particularly struggle with loose clothing. They typically employ parameterized human models to constrain the reconstruction process, ensuring the results do not deviate too far from the model and produce anomalies. However, this also limits the recovery of loose clothing. To address this issue, we propose an end-to-end method called IHRPN for reconstructing clothed humans from a single 2D human image. This method includes a feature extraction module for semantic extraction of image features. We propose an image semantic feature extraction aimed at achieving pixel model space consistency and enhancing the robustness of loose clothing. We extract features from the input image to infer and recover the SMPL-X mesh, and then combine it with a normal map to guide the implicit function to reconstruct the complete clothed human. Unlike traditional methods, we use local features for implicit surface regression. Our experimental results show that our IHRPN method performs excellently on the CAPE and AGORA datasets, achieving good performance, and the reconstruction of loose clothing is noticeably more accurate and robust.

Enhancing data sparsity in spectral signals using wavelet decomposition for improved compression and storage efficiency

Wavelet decomposition (WD) is integrated into the Compressive Sensing (CS) model to enhance data sparsity. This approach aims to ensure efficiency and quality in the received data after compression and reconstruction processes. The proposed WD-CS model is developed for reflected spectra signals from Fiber Bragg Gratings-based extensometers, which were subjected to induced deflections simulating the underground soil movement. WD decomposes the input signal before compressive measurement, followed by transferring and storing the compressed data in the cloud. The spectral data were reconstructed using Orthogonal Matching Pursuit (OMP) followed by wavelet reconstruction. The impact of wavelet decomposition on the quality of compression and reconstruction is assessed and compared against that of the standard CS model. The findings indicate that the WD-CS model can improve data sparsity by a factor of three and compressibility by a factor of ten without degenerating the reconstructed data quality. The error in the Bragg wavelength shift of the reconstructed spectra is minimal extracted using Pseudo-high Resolution Interrogation (PHRI) method. The proposed WD-CS model is useful in improving data compression for FBG spectra as well as storage efficiency, which make it potentially applicable in FBG-based sensor networks for long-term structural health monitoring applications.