High-resolution Projection Research Articles

High-resolution modeling and projection of heat-related mortality in Germany under climate change

Abstract Heat is a leading cause of premature deaths during summer months. Understanding the relationship between high temperatures and excess mortality is crucial for designing effective prevention and adaptation plans. Traditional statistical methods have been repeatedly applied to analyze heat-related mortality. However, spatially and temporally high-resolution analyses are challenging due to fragmented data archives across different agglomeration levels, especially for mortality data. We propose a neural network-based model to estimate heat-related mortality. This approach allows for high temporal and spatial resolution estimations, such as regional heat risk during specific heatwaves. Using Germany as a case study, we calculated heat-related excess mortality rates at the district level and visualized the dynamics of local health risks within a heatwave. Overall, we estimated a total of 48,000 heat-related deaths in Germany over the last decade (2014-2023), with most occurring during specific heatwave events. For example, in 2023, the heatwave from July 7-14 contributed approximately 28% (1100 deaths) to the total of about 3,900 heat-related deaths for the entire year. This estimation is consistent with previously published reports from the Robert Koch Institute (RKI), considering the resolution differences in the input data. In addition, we combined our model with shared socio-economic pathways (SSPs) of future climate change, assuming a static population and baseline mortality rate. The results suggest that heat-related risk in Germany could further increase by a factor of 2.5 (SSP245) to 9 (SSP370) without adaptation to extreme heat. Our approach is a valuable tool for the monitoring of regional heat-related risks as well as scenario-based risks projections for the future. The results of the model can help develop climate-driven public health strategies, aiding in the identification of local risks during heatwaves and in long-term resilience planning. Key messages • High-resolution modeling of heat-related mortality helps us to develop targeted adaptation and prevention plans. • The majority of heat-related mortality occurs during heatwaves in summer.

High-resolution modeling and projection of heat-related mortality in Germany under climate change.

Heat has become a leading cause of preventable deaths during summer. Understanding the link between high temperatures and excess mortality is crucial for designing effective prevention and adaptation plans. Yet, data analyses are challenging due to often fragmented data archives over different agglomeration levels. Using Germany as a case study, we develop a multi-scale machine learning model to estimate heat-related mortality with variable temporal and spatial resolution. This approach allows us to estimate heat-related mortality at different scales, such as regional heat risk during a specific heatwave, annual and nationwide heat risk, or future heat risk under climate changescenarios. We estimate a total of 48,000 heat-related deaths in Germany during the last decade (2014-2023), and the majority of heat-related deaths occur during specific heatwave events. Aggregating our results over larger regions, we reach good agreement with previously published reports from Robert Koch Institute (RKI). In 2023, the heatwave of July 7-14 contributes approximately 1100 cases (28%) to a total of approximately 3900 heat-related deaths for the whole year. Combining our model with shared socio-economic pathways (SSPs) of future climate change provides evidence that heat-related mortality in Germany could further increase by a factor of 2.5 (SSP245) to 9 (SSP370) without adaptation to extreme heat under static sociodemographic developments assumptions. Our approach is a valuable tool for climate-driven public health strategies, aiding in the identification of local risks during heatwaves and long-term resilience planning.

Change of global land extreme temperature in the future

Understanding future temperature extremes is pivotal to preparing for and mitigating the impacts of climate change. This study proposed machine learning techniques to develop a multi-model ensemble model for high-resolution projection of global land temperature extremes under different emission scenarios, hence providing enhanced precision over previous climate model projections. By utilizing the NEX-GDDP-CMIP6 dataset with bias adjustment and the Gradient Booster algorithm, we reduced the biases that existed in Global Climate Models. The model significantly reduces the root mean square errors (RMSEs) for both the daily maximum and daily minimum temperature extremes. A future scenario analysis revealed that global temperature extremes would substantially increase under high-emission scenarios, highlighting the urgency for stringent emission reduction commitments. This study also identified regions like Greenland, the Tibetan Plateau, and the regional Arctic Archipelago as potential hotspots of temperature extremes under these scenarios. The multi-model ensemble approach, tuned with machine learning and driven by high-resolution data, contributes to climate science by providing refined insights into future temperature extremes, thereby offering direction to climate change mitigation and adaptation strategies.

Projection superimposition for the generation of high-resolution digital grating.

Fringe projection profilometry based on MEMS micro-vibration mirrors is very promising due to its rapid projection, large depth of field, compact size, and low cost. Although high-frequency fringes can achieve accurate reconstruction, the projector must offer sufficient pixel resolution. In this paper, we proposed a high-resolution projection technique called the delay superposition method. During a single exposure time of the camera, the projector projects a group of low-resolution fringe patterns, which are delayed according to the movement characteristics of the vibration mirror. Then, the camera exposure superimposes these low-resolution images to form a high-resolution image. These two steps effectively subdivide the angle intervals, thereby achieving a pixel interpolation. Finally, experimental results show that the proposed method can significantly improve the projector's pixel resolution and reconstruction accuracy. The proposed method allows the MEMS projector's pixel resolution (along one direction) to far exceed that of common DLP projectors. It holds great application potential for high-frequency fringe projection.

Efficient high-resolution fluorescence projection imaging over an extended depth of field through optical hardware and deep learning optimizations.

Optical microscopy has witnessed notable advancements but has also become more costly and complex. Conventional wide field microscopy (WFM) has low resolution and shallow depth-of-field (DOF), which limits its applications in practical biological experiments. Recently, confocal and light sheet microscopy become major workhorses for biology that incorporate high-precision scanning to perform imaging within an extended DOF but at the sacrifice of expense, complexity, and imaging speed. Here, we propose deep focus microscopy, an efficient framework optimized both in hardware and algorithm to address the tradeoff between resolution and DOF. Our deep focus microscopy achieves large-DOF and high-resolution projection imaging by integrating a deep focus network (DFnet) into light field microscopy (LFM) setups. Based on our constructed dataset, deep focus microscopy features a significantly enhanced spatial resolution of ∼260 nm, an extended DOF of over 30 µm, and broad generalization across diverse sample structures. It also reduces the computational costs by four orders of magnitude compared to conventional LFM technologies. We demonstrate the excellent performance of deep focus microscopy in vivo, including long-term observations of cell division and migrasome formation in zebrafish embryos and mouse livers at high resolution without background contamination.

The future of X-ray polarimetry towards the 3-dimensional photoelectron track reconstruction

The development of the first X-ray polarimeter, based on the photoelectric effect 20 years ago and implemented thanks to advances in gas amplification structures and readout techniques, had a significant impact in opening a new window for X-ray polarimetry. This system measures the X-ray polarization by reconstructing the initial direction of the photoelectron, emitted by the interaction of an incident photon with an atomic electron, in a gas mixture from an ionization track collected on a two-dimensional plane. However, actual X-ray polarimeters, are still requiring relatively long exposure time and cannot coupled with high effective area mirrors or concentrators. In this context, the high yield polarimetry experiment in X-rays (Hype-X) project is currently underway, aiming to improve the sensitivity of the next generation X-ray polarimetry detectors taking advantage of the recent advancements in imaging techniques for high-resolution time projection chambers. In particular, we are evaluating the use of TIMEPIX3 to be applied for the read-out of a gas detector, which will allow us to obtain a three-dimensional image of the photoelectron track. To evaluate the improvement achievable by using a 3D track reconstruction, in this paper, we have reproduced a three-dimensional photoelectron track from a `Geant4' Monte Carlo simulation and examined the sensitivity of X-ray polarimetry using a new three-dimensional track reconstruction algorithm. We report the improvement of the modulation factor with three-dimensional track reconstruction as ∼5% (relative) in the 2–8 keV range and ∼17% (relative) in the 2–4 keV range compared to the current two-dimensional polarimetry system. This is equivalent to add a further telescope to the three-telescope systems now employed in space on board the IXPE mission.

Focus stacking single-event particle radiography for high spatial resolution images and 3D feature localization

Objective. We demonstrate a novel focus stacking technique to improve spatial resolution of single-event particle radiography (pRad), and exploit its potential for 3D feature detection. Approach. Focus stacking, used typically in optical photography and microscopy, is a technique to combine multiple images with different focal depths into a single super-resolution image. Each pixel in the final image is chosen from the image with the largest gradient at that pixel's position. pRad data can be reconstructed at different depths in the patient based on an estimate of each particle's trajectory (called distance-driven binning; DDB). For a given feature, there is a depth of reconstruction for which the spatial resolution of DDB is maximal. Focus stacking can hence be applied to a series of DDB images reconstructed from a single pRad acquisition for different depths, yielding both a high-resolution projection and information on the features’ radiological depth at the same time. We demonstrate this technique with Geant4 simulated pRads of a water phantom (20 cm thick) with five bone cube inserts at different depths (1 × 1 × 1 cm3) and a lung cancer patient. Main results. For proton radiography of the cube phantom, focus stacking achieved a median resolution improvement of 136% compared to a state-of-the-art maximum likelihood pRad reconstruction algorithm and a median of 28% compared to DDB where the reconstruction depth was the center of each cube. For the lung patient, resolution was visually improved, without loss in accuracy. The focus stacking method also enabled to estimate the depth of the cubes within few millimeters accuracy, except for one shallow cube, where the depth was underestimated by 2.5 cm. Significance. Focus stacking utilizes the inherent 3D information encoded in pRad by the particle's scattering, overcoming current spatial resolution limits. It further opens possibilities for 3D feature localization. Therefore, focus stacking holds great potential for future pRad applications.

High-resolution climate projection dataset based on CMIP6 for Peru and Ecuador: BASD-CMIP6-PE

Here, we present BASD-CMIP6-PE, a high-resolution (1d, 10 km) climate dataset for Peru and Ecuador based on the bias-adjusted and statistically downscaled CMIP6 climate projections of 10 GCMs. This dataset includes both historical simulations (1850–2014) and future projections (2015–2100) for precipitation and minimum, mean, and maximum temperature under three Shared Socioeconomic Pathways (SSP1-2.6, SSP3-7.0, and SSP5-8.5). The BASD-CMIP6-PE climate data were generated using the trend-preserving Bias Adjustment and Statistical Downscaling (BASD) method. The BASD performance was evaluated using observational data and through hydrological modeling across Peruvian and Ecuadorian river basins in the historical period. Results demonstrated that BASD significantly reduced biases between CMIP6-GCM simulations and observational data, enhancing long-term statistical representations, including mean and extreme values, and seasonal patterns. Furthermore, the hydrological evaluation highlighted the appropriateness of adjusted GCM simulations for simulating streamflow, including mean, low, and high flows. These findings underscore the reliability of BASD-CMIP6-PE in assessing regional climate change impacts on agriculture, water resources, and hydrological extremes.

Capillary Pressure Controlled Water Pathways in Gas Diffusion Layers for Polymer Electrolyte Fuel Cells Designed By Additive Manufacturing

Polymer electrolyte fuel cells (PEFC) are considered to make a significant contribution to the transition towards carbon free energy conversion, as they produce electric power from hydrogen and oxygen with water as the only by-product [1]. On its way to widespread commercialization, in particular the water management in PEFC is challenging. On one hand, the electrolyte membrane needs to be humidified to be highly conductive for the hydrogen protons. On the other hand, accumulation of the product water in the gas diffusion layer (GDL) on the cathode side mitigates the oxygen transport towards the catalytic surface, which leads to severe performance limitations [2-3]. Previous work has shown that structured GDLs can enhance PEFC performance [5] and additive manufacturing (AM) has been identified as a viable method to fabricate just such [4,6].Currently available AM technologies are struggling to fulfill the needs in terms of minimal feature size (~8 um fiber diameter) and sample size (at least some cm2). Initial AM prepared GDLs were coarse with finest structures of about 120 um [6] which is in the range of typical GDL thickness. Here, we present AM prepared GDL like structures with 20 um printed features that can reach sizes of some tens of mm2.The 3D printed structures are designed to guide the product water through the GDL by adjusting the throat size [7] and therefore the capillary pressure, which is required in the water phase to percolate through the throats to the neighboring pore as described by Young-Laplace law. Figure 1 shows tomographic data of a test lattice structure manufactured by high-resolution projection micro stereolithography (PµSL). The concept is validated by measuring the pressure in the water phase as a function of the throat size under ex-situ conditions. The desired percolation path of the water is monitored by 2D as well as 3D image data acquired by X-ray radiography and X-ray tomographic microscopy (XTM), respectively. Figure caption: XTM scan of a lattice structure manufactured by P µ SL: a) 3D rendering, b) x-z-plane cross section, c) x-y-plane cross section. Slight deformation in the base layers of the lattice structure is observed.

Toward high-resolution projection of electricity prices: A machine learning approach to quantifying the effects of high fuel and CO2 prices

The growing share of renewables, the retirement of coal generation, and the increasing significance (and price) of carbon emissions continue to reshape electricity market dynamics. Ongoing power sector transformations, along with recurrent energy price shocks, bring new challenges to electricity price prediction and projection. Adept at capturing nonlinear and time-varying dynamics of the power market, machine learning (ML) methods can significantly enhance modeling and projection capabilities. This paper leverages ML modeling to (1) analyze the day-ahead electricity price dynamics and (2) develop future price projections under varying market conditions. First, we offer a methodology for identifying and ranking the factors determining day-ahead power price behavior. Guided by the understanding of power price drivers, we then develop an ensemble ML model for high-resolution (i.e., for each day of the next year) electricity price projection. Hence, the first part of our analysis helps relate fundamental modeling approaches to ML studies, and the second part extends the existing literature, which is limited to high aggregation level (e.g., yearly basis) projections. High-resolution projections provide valuable insights into within-year price dynamics and help answer highly debated questions raised by the growing share of renewables and the current energy market turmoil. We demonstrate our methodology on the German power market, using data on 80 explanatory variables from 2015 to 2021. We identify the most critical power price drivers and develop an ML model to study the within-year impact of high fuel and carbon prices. Our findings indicate that the often-neglected interaction variables may significantly impact price projections and should be included in day-ahead electricity price modeling. We confirm the efficacy of our approach with a forecasting exercise based on day-ahead electricity prices in the first two quarters of 2022.

Microscopic Projection Lithography for Integrating Metallic Microstructures on Silk Protein for Biodevice Applications.

Precise patterning of metallic micro/nanostructures enables application of silk protein in biomedical devices with a seamless human-machine interface. However, high-quality, expensive equipment and facilities involved in micro/nanofabrication hinder rapid prototyping for explorative laboratory-based research. Here, we report cost-effective and high-resolution light-emitting diode-based projection lithography methods for fabricating a Cr photomask and metallic microstructures on a silk protein layer. After two-step photolithography performed using a commercial camera and microscopic objective lens, inkjet-printed patterns are successfully projected on the silk layers with 100× and 500× demagnification ratios. A lift-off process is conducted to integrate Au patterns on the lithographic-patterned resist/silk layer, and various Au microstructures with sizes <2 μm are generated. In all the processes, the silk protein exhibits a high resistance to chemicals for resist solvent, development, resist strip, and lift-off, as well as a strong adhesion to gold, along with low cytotoxicity. Dopamine sensing and transistor operating capabilities are proved by measuring the changes in the electrical signals through the Au patterns. The proposed method is a cost-effective and simple approach for rapid prototyping of silk-based biomedical devices.

Frequency dominates intensity of future heat waves over India

Future changes in heat wave characteristics over India have been analyzed using Coordinated Regional Climate Downscaling Experiments (CORDEX) for South Asia (SA) regional climate model simulations for mid-term (2041-2060) and long-term (2081-2099) future under the representative concentration pathway (RCP) 4.5 and RCP 8.5 emission scenarios, respectively. SMHI_CSIRO-MK3.6 was found to be the best model in simulating heat wave trend over India for historical period. Future projections show a four-to-seven-fold increase in heat wave frequency for mid-term and long-term future under RCP 4.5 scenario, and five-to-ten-fold increase under RCP 8.5 scenario with increase in frequency dominating intensity in both the scenarios. Northwestern, Central, and South-central India emerged as future heat wave hotspots with largest increase in the south-central region. This high-resolution regional future projection of heat wave occurrence will serve as a baseline for developing transformational heat-resilient policies and adaptation measures to reduce potential impact on human health, agriculture, and infrastructure.

PyClim-SDM: Service for generation of statistically downscaled climate change projections supporting national adaptation strategies

The climate change impact and adaptation communities need future scenarios with sufficient high resolution, which are frequently achieved by applying Statistical Downscaling Models (SDMs) over global climate models. A large variety of SDMs exists, and some can be more suitable than others for each specific purpose. For this reason, it is important to develop tools to facilitate the evaluation and generation of downscaled scenarios following different approaches. In this paper we present a service, ‘pyClim-SDM’, which allows users to generate and evaluate their own downscaled scenarios with a very simple and user-friendly graphical interface. This tool includes a large collection of state-of-the-art methods belonging to different families to downscale daily data of the following surface variables: temperature, precipitation, wind, relative humidity and cloud coverage. Additionally, the software is prepared to be applied over any other user-defined target variable. Thus, multivariable indexes can be tackled as target variables themselves, instead of being calculated from the downscaled primary variables. With this possibility, potential intervariableinconsistencies are avoided. An application example for a Fire Weather Index, dependent on temperature, wind, humidity and precipitation, is shown. The service here presented -mainly based on a new downscaling software and a user-friendly graphical interface- is an essential piece for evaluating and generating high-resolution projection data within the Spanish national climate change adaptation strategy which includes, among other elements, a common database for all sectors, viewer and data distribution portal, etc.

SSP기반 남한상세 기후변화 시나리오를 활용한 온난화 수준(1.5, 2.0, 3.0℃)별 우리나라 기후변화 전망

SSP기반 남한상세 기후변화 시나리오를 활용한 온난화 수준(1.5, 2.0, 3.0℃)별 우리나라 기후변화 전망

Rapid increase of the nighttime electricity demand in Beijing due to compound heatwaves

Under the carbon-neutral development pathway in China, >96% of the total power generation will be provided by clean energy sources by 2060. However, since solar power generation is unavailable at night, it is crucial to investigate the changes in nighttime electricity demand in response to increasing occurrences of tropical nights. This study reveals that nighttime electricity consumption is more sensitive to temperature changes compared to daytime consumption, which has already been extensively studied. The correlation coefficient between nighttime consumption and daily minimum temperature (Tmin) is found to be 0.7. Furthermore, the elasticity of nighttime electricity consumption increases even more when Tmin exceeds 24 °C. The combined effects of heatwaves occurring during both daytime and nighttime can further increase electricity consumption by 40%. By utilizing a high-resolution hybrid downscaling climate projection dataset, we estimate the electricity consumption at the end of the 21st century. The frequency of compound daytime/nighttime heat extremes is projected to increase to 4.2 times the current level under RCP4.5. Consequently, the frequency of large nighttime electricity consumption over 6 kWh per capita will rise by 109%. Our quantitative analysis sheds light on the necessity of energy storage planning to ensure energy security under climate change.

Constrained high-resolution projection of hot extremes in the Beijing–Tianjin–Hebei region of China

The Beijing–Tianjin–Hebei (BTH) region is the political center and one of the largest and most dynamic economic centers in China. Relative to other regions of China, it faces greater challenges from increasing hot extremes. However, future projections of regional changes in hot extremes based on multi-model ensembles carry huge uncertainty. To improve the reliability of regional projections, we used the latest high-resolution outputs from nine GCMs in CMIP6-HighResMIP and constrained the projection of hot extremes in this region. In the BTH, the historical scaling of the annual maximum temperature with the mean summer (June–August) temperatures (TXx scaling) showed a significant linear relationship with the future TXx scaling in the multi-model ensemble. By comparing the observed and simulated historical TXx scaling we identified an observational constraint that could reduce the uncertainty of TXx scaling. We believed that the simulations of EC-Earth3P, EC-Earth3P-HR, MPI-ESM1-2-HR and MPI-ESM1-2-XR show advantages in simulating surface air temperature and related hot extremes in this region. Verification demonstrated that surface air temperature projection in the BTH region constrained by observational constraint is more reliable. In constrained projections, by the 2040s, the increase in summer surface air temperatures is projected to exceed 1 °C compared to 2010s. Also, the estimated number of days of compound (sequential hot day-night) hot extremes (HND), independent hot nights (HNi) and independent hot days (HDi) was found to increase by 0.3, 0.3, and −0.03 d per year during 2015–2049, respectively. The growth in numbers of days of HND, HNi and HDi was slower after observational constraint. Furthermore, the estimated increase in surface air temperature variables after constraint also reduced. This study provides support for adaptation policy-making and as a reference for the use of observational constraint in other regions of China.

AsiaPEX: Challenges and Prospects in Asian Precipitation Research

Abstract The Asian Precipitation Experiment (AsiaPEX) was initiated in 2019 to understand terrestrial precipitation over diverse hydroclimatological conditions for improved predictions, disaster reduction, and sustainable development across Asia under the framework of the Global Hydroclimatology Panel (GHP)/Global Energy and Water Exchanges (GEWEX). AsiaPEX is the successor to GEWEX Asian Monsoon Experiment (GAME; 1995–2005) and Monsoon Asian Hydro-Atmosphere Scientific Research and Prediction Initiative (MAHASRI; 2006–16). While retaining the key objectives of the aforementioned projects, the scientific targets of AsiaPEX focus on land–atmosphere coupling and improvements to the predictability of the Asian hydroclimatological system. AsiaPEX was designed for both fine-scale hydroclimatological processes occurring at the land surface and the integrated Asian hydroclimatological system characterized by multiscale interactions. We adopt six approaches including observation, process studies, scale interactions, high-resolution hydrological modeling, field campaigns, and climate projection, which bridge gaps in research activities conducted in different regions. Collaboration with mesoscale and global modeling researchers is one of the core methods in AsiaPEX. We review these strategies based on the literature and our initial outcomes. These include the estimation and validation of high-resolution satellite precipitation, investigations of extreme rainfall mechanisms, field campaigns over the Maritime Continent and Tibetan Plateau, areas of significant impact on the entire AsiaPEX region, process studies on diurnal- to interdecadal-scale interactions, and evaluation of the predictabilities of climate models for long-term variabilities. We will conduct integrated observational and modeling initiative, the Asian Monsoon Year (AMY)-II around 2025–28, whose strategies are the subregional observation platforms and integrated global analysis.

High-resolution climate projection over the Tibetan Plateau using WRF forced by bias-corrected CESM

The Tibetan Plateau (TP) has undergone significant climate warming with a stronger amplitude than that experienced elsewhere in the Northern Hemisphere during the past years, but it is still challenging for most regional climate models to realistically simulate the present-day climate and promisingly project the future climate over the TP. In this study, high-resolution simulation using the Weather Research and Forecasting model (WRF) driven by bias-corrected CESM is conducted from 1979 to 2100, with the period from 2006 to 2100 under RCP4.5 and RCP8.5 (Representative Concentration Pathways) scenarios. The simulated present-day climate is evaluated firstly and then the future climate is studied secondly. The results show that compared with station observation, WRF successfully captures the spatial pattern of annual mean surface air temperature (T2m) and precipitation over the TP, with the spatial correlation coefficients larger than 0.95 for T2m and larger than 0.70 for precipitation. However, great underestimation of T2m over the southeastern TP is found in the cold season which is related to the underestimation of snow there, and the snow-temperature positive feedback develops. WRF shows limited ability in reducing the dry bias in summer, which is related to the simulated weaker water vapor transport over the southern and eastern TP. For the future changes, substantial warming, general increase in precipitation and decrease in snow are projected under RCP8.5. The warming magnitude is greater over the western TP where the more significant decrease of snow will occur by the end of 21st century. Projected precipitation tends to consistently decrease over the western TP and along the south flank of TP which is related to the low-level circulation change. The occurring frequency of light precipitation will decrease while that of non-precipitation and extreme precipitation will increase especially in the far future, with the more obvious change occurring under RCP8.5. Overall, WRF shows high ability in simulating the present-day climate and thus reliable performance in future climate projection.

DLP fabrication of customized porous bioceramics with osteoinduction ability for remote isolation bone regeneration

Currently, various bioceramics have been widely used in bone regeneration. However, it remains a huge challenge to remote isolation bone regeneration, such as severed finger regeneration. The remote isolation bone tissue has a poor regenerative microenvironment that lacks enough blood and nutrition supply. It is very difficult to repair and regenerate. In this study, well-controlled multi-level porous 3D-printed calcium phosphate (CaP) bioceramic scaffolds with precision customized structures were fabricated by high-resolution digital light projection (DLP) printing technology for remote isolation bone regeneration. In vitro results demonstrated that optimizing material processing procedures could achieve multi-level control of 3D-printed CaP bioceramic scaffolds and enhance the osteoinduction ability of bioceramics effectively. In vivo results indicated that 3D-printed CaP bioceramic scaffolds constructed by optimized processing procedure exhibited a promising ability of bone regeneration and osteoinduction in ectopic osteogenesis and in situ caudal vertebrae regeneration in beagles. This study provided a promising strategy based on 3D-printed CaP bioceramic scaffolds constructed by optimized processing procedures for remote isolation bone regeneration, such as severed finger regeneration.

CuPARE: parametric reconstruction of curved fibres from glass fibre-reinforced composites

ABSTRACT A new framework for the parametric reconstruction of curved fibres from glass fibre-reinforced composite X-ray computed tomography data is proposed. It allows us to detect fibres in a fibre-reinforced polymer sample from a low-dose, low resolution computed tomography scan. An efficient curve representation is then used for each detected fibre, of which the parameters are estimated directly from few 2D high-resolution projection images. The framework is validated on both simulated and real data of glass fibre-reinforced polymers. The generated results demonstrate that it is robust to noise and requires less than 10 high-resolution projections to obtain reasonable fibre estimates. The method can also improve upon existing estimation frameworks relying on full 3D scans.