Spatial Resolution Research Articles

A machine learning approach to estimate mid-infrared fluxes from WISE data

While the Wide-field Infrared Survey Explorer (WISE) is the largest, best quality infrared all-sky survey to date, a smaller coverage mission Spitzer was designed to have better sensitivity and spatial resolution at similar wavelengths. Confusion and contamination in WISE data result in discrepancies between them. We aim to present a novel approach to work with WISE measurements with the goal of maintaining both its high coverage and vast amount of data while, at the same time, taking full advantage of the higher sensitivity and spatial resolution of Spitzer We have applied machine learning (ML) techniques to a complete WISE data sample of open cluster members, using a training set of paired data from high-quality Spitzer Enhanced Imaging Products (SEIP), MIPS and IRAC, and allWISE catalogs, W1 (3.4 mu m) to W4 (22 mu m) bands. We have tested several ML regression models with the aim of predicting mid-infrared fluxes at MIPS1 (24 mu m) and IRAC4 (8 mu m) bands from WISE variables (fluxes and quality flags). In addition, to improve the prediction quality, we have implemented feature selection techniques to remove irrelevant WISE variables. We have notably enhanced WISE detection capabilities, mostly for the targets with the lowest magnitudes, which previously showed the largest discrepancies with Spitzer . In our particular case, extremely randomized trees was found to be the best algorithm to predict mid-infrared fluxes from WISE variables, attaining coefficients of determination $R^2 and $R^2 for 24 mu m (MIPS1) and 8 mu m (IRAC4), respectively. We have tested our results in members of IC 348 and compared their observed fluxes with the predicted ones in their spectral energy distributions. We show discrepancies in the measurements of Spitzer and WISE and demonstrate the good concordance of our predicted mid-infared fluxes with the real ones. Machine learning is a fast and powerful tool that can be used to find hidden relationships between datasets, as the ones we have shown to exist between WISE and Spitzer fluxes. We believe this approach could be employed for other samples from the allWISE catalog with SEIP positional counterparts, and in other astrophysical studies in which analogous discrepancies might arise when using datasets from different instruments.

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.

Gap-filling of land surface temperature in arid regions by combining Landsat 8 and 9 imageries

Abstract Land surface temperature (LST) is an important factor in land monitoring studies, but due to the presence of clouds, dust and sensor issues, there are missing values. The aims of this research are to determine the optimal parameters for the reconstruction of Landsat-LST images, required in many applications, by the harmonic analysis of time series algorithm (HANTS) and to investigate the possibility of improving LST reconstruction accuracy using Landsat 8 and 9 images simultaneously. For these aims, 91 Landsat 8 and 9 images with 100 m spatial resolution in 2022 and 2023 are employed, covering Yazd-Ardakan plain in Iran. Three methods are used for evaluation. In method one, a part of LST image is considered as a gap and is compared with the initial value after reconstruction. In method two, on a cloudy day and a cloudless day, surface temperature values are measured using thermometers at fifty points in plain lands, and the difference between gap-filled satellite measurements and ground measurements is calculated. In method three, all the reconstructed LST images are compared with the original images. In method one, the root mean square error (RMSE) of reconstructed LST reduces by 1.3°C when using the combined Landsat 8 and 9 images. In method two, RMSEs of reconstructed LST images are 6.1°C when using Landsat 8 and 5.4°C when using the combined Landsat 8 and 9. Method three shows that 41% of the study region has RMSE of less than 2°C when using only Landsat 8, while this value becomes 72% when combining Landsat 8 and 9. In general, the combined use of Landsat 8 and 9 LST images improves the accuracy of reconstruction using HANTS. The findings of this research are crucial for regional applications and remote monitoring of surface temperature in areas with limited weather stations.

Beyond traditional methods: Innovative integration of LISS IV and Sentinel 2A imagery for unparalleled insight into Himalayan ibex habitat suitability.

The utilization of satellite images in conservation research is becoming more prevalent due to advancements in remote sensing technologies. To achieve accurate classification of wildlife habitats, it is important to consider the different capabilities of spectral and spatial resolution. Our study aimed to develop a method for accurately classifying habitat types of the Himalayan ibex (Capra sibirica) using satellite data. We used LISS IV and Sentinel 2A data to address both spectral and spatial issues. Furthermore, we integrated the LISS IV data with the Sentinel 2A data, considering their individual geometric information. The Random Forest approach outperformed other algorithms in supervised classification techniques. The integrated image had the highest level of accuracy, with an overall accuracy of 86.17% and a Kappa coefficient of 0.84. Furthermore, to delineate the suitable habitat for the Himalayan ibex, we employed ensemble modelling techniques that incorporated Land Cover Land Use data from LISS IV, Sentinel 2A, and Integrated image, separately. Additionally, we incorporated other predictors including topographical features, soil and water radiometric indices. The integrated image demonstrated superior accuracy in predicting the suitable habitat for the species. The identification of suitable habitats was found to be contingent upon the consideration of two key factors: the Soil Adjusted Vegetation Index and elevation. The study findings are important for advancing conservation measures. Using accurate classification methods helps identify important landscape components. This study offers a novel and important approach to conservation planning by accurately categorising Land Cover Land Use and identifying critical habitats for the species.

A Modified Method for Reducing the Scale Effect in Land Surface Temperature Downscaling at 10 m Resolution

Satellite-derived Land Surface Temperature (LST) plays an important role in research on natural energy balance and water cycle. Considering the tradeoff between spatial and temporal resolutions, accurate fine-resolution LST must be obtained through the use of LST downscaling (DLST) technology. Various methods have been proposed for DLST at fine resolutions (e.g., 10 m) and small scales. However, the scale effect of these methods, which is inherent to DLST processes at different extents, has rarely been addressed, thus limiting their application. In this study, a modified daily 10 m resolution DLST method based on Google Earth Engine, called mDTSG, is proposed in order to reduce the scale effect at fine spatial resolutions. The proposed method introduces a convolution-based moving window into the DLST process for the fusion of different remote sensing data. The performance of the modified method is compared with the original method in six regions characterized by various extents and landscape heterogeneity. The results show that the scale effect is significant in the DLST process at fine resolutions across extents ranging from 100 km2 to 22,500 km2. Compared with the original method, mDTSG can effectively reduce the LST value differences between tile edges, especially when considering large extents (>22,500 km2) with an average R2 improvement of 33.75%. The average MAE is 1.63 °C, and the average RMSE is 2.3 °C in the mDTSG results, when compared with independent remote sensing products across the six regions. A comparison with in situ observations also shows promising results, with an MAE of 2.03 °C and an RMSE of 2.63 °C. These findings highlight the robustness and scalability of the mDTSG method, making it a valuable tool for fine-resolution LST applications in diverse and extensive landscapes.

Long-Range Three-Dimensional Tracking of Nanoparticles Using Interferometric Scattering Microscopy.

Tracking nanoparticle movement is highly desirable in many scientific areas, and various imaging methods have been employed to achieve this goal. Interferometric scattering (iSCAT) microscopy has been particularly successful in combining very high spatial and temporal resolution for tracking small nanoparticles in all three dimensions. However, previous works have been limited to an axial range of only a few hundred nanometers. Here, we present a robust and efficient measurement and analysis strategy for three-dimensional tracking of nanoparticles at high speed and with nanometer precision. After discussing the principle of our approach using synthetic data, we showcase the performance of the method by tracking gold nanoparticles with diameters ranging from 10 to 80 nm in water, demonstrating an axial tracking range from 4 μm for the smallest particles up to over 30 μm for the larger ones. We point out the limitations and robustness of our system across various noise levels and discuss its promise for applications in cell biology and material science, where the three-dimensional motion of nanoparticles in complex media is of interest.

Comparison of On-Sky Wavelength Calibration Methods for Integral Field Spectrograph

With advancements in technology, scientists are delving deeper in their explorations of the universe. Integral field spectrograph (IFS) play a significant role in investigating the physical properties of supermassive black holes at the centers of galaxies, the nuclei of galaxies, and the star formation processes within galaxies, including under extreme conditions such as those present in galaxy mergers, ultra-low-metallicity galaxies, and star-forming galaxies with strong feedback. IFS transform the spatial field into a linear field using an image slicer and obtain the spectra of targets in each spatial resolution element through a grating. Through scientific processing, two-dimensional images for each target band can be obtained. IFS use concave gratings as dispersion systems to decompose the polychromatic light emitted by celestial bodies into monochromatic light, arranged linearly according to wavelength. In this experiment, the working environment of a star was simulated in the laboratory to facilitate the wavelength calibration of the space integral field spectrometer. Tools necessary for the calibration process were also explored. A mercury–argon lamp was employed as the light source to extract characteristic information from each pixel in the detector, facilitating the wavelength calibration of the spatial IFS. The optimal peak-finding method was selected by contrasting the center of weight, polynomial fitting, and Gaussian fitting methods. Ultimately, employing the 4FFT-LMG algorithm to fit Gaussian curves enabled the determination of the spectral peak positions, yielding wavelength calibration coefficients for a spatial IFS within the range of 360 nm to 600 nm. The correlation of the fitting results between the detector pixel positions and corresponding wavelengths was >99.99%. The calibration accuracy during wavelength calibration was 0.0067 nm, reaching a very high level.

Enhancing low-energy X-ray excited afterglow in lanthanide-doped fluoride core@shell nanoparticles for autofluorescence-free imaging

Lanthanide-doped fluoride nanoparticles (NPs) exhibit tunable X-ray excited afterglow (XEA), holding great promise for autofluorescence-free flexible X-ray imaging. However, materials with low atomic numbers, while sensitive to low-energy X-ray photons, suffer from weak X-ray absorption coefficients. This poses a significant challenge in achieving high-performance XEA with minimized radiation risk. In this study, we enhance the low-energy XEA of lanthanide activators by engineering the interfacial defect formation energy (Ef) in CaF2-based heterogeneous core/shell nanoarchitectures. Mechanistic investigations demonstrate that a large lattice misfit between the core and shell reduces the interfacial defect Ef, facilitating the formation of Frenkel defects and significantly increasing XEA intensity after low-energy X-ray irradiation. Specifically, the heterogeneous CaF2:Tb@CaLuF5 core/shell NPs, with a misfit of 3.382 %, exhibit approximately ∼ 8.9 times higher XEA intensity compared to the CaF2:Tb@CaF2 homogeneous core/shell NPs at 10 kV. Furthermore, integrating the CaF2:Tb@CaLuF5 NPs into a flexible scintillation screen enables XEA-based delayed imaging with high spatial resolution, up to approximately 14.2 lp mm−1, and an autofluorescence-free background. These findings advance the development of superior low-energy XEA materials and pave the way for autofluorescence-free three-dimensional X-ray imaging.

Perspectives on non-genetic optoelectronic modulation biointerfaces for advancing healthcare

AbstractAdvancements in optoelectronic biointerfaces have revolutionized healthcare by enabling targeted stimulation and monitoring of cells, tissues, and organs. Photostimulation, a key application, offers precise control over biological processes, surpassing traditional modulation methods with increased spatial resolution and reduced invasiveness. This perspective highlights three approaches in non-genetic optoelectronic photostimulation: nanostructured phototransducers for cellular stimulation, micropatterned photoelectrode arrays for tissue stimulation, and thin-film flexible photoelectrodes for multiscale stimulation. Nanostructured phototransducers provide localized stimulation at the cellular or subcellular level, facilitating cellular therapy and regenerative medicine. Micropatterned photoelectrode arrays offer precise tissue stimulation, critical for targeted therapeutic interventions. Thin-film flexible photoelectrodes combine flexibility and biocompatibility for scalable medical applications. Beyond neuromodulation, optoelectronic biointerfaces hold promise in cardiology, oncology, wound healing, and endocrine and respiratory therapies. Future directions include integrating these devices with advanced imaging and feedback systems, developing wireless and biocompatible devices for long-term use, and creating multifunctional devices that combine photostimulation with other therapies. The integration of light and electronics through these biointerfaces paves the way for innovative, less invasive, and more accurate medical treatments, promising a transformative impact on patient care across various medical fields.

The Role of 70-MHz Ultrahigh-Frequency Ultrasound in the Peripheral Nerve Injury.

High-frequency ultrasound with an 18-MHz probe (18 MHz-HFUS) plays a relevant role in the evaluation of peripheral nerve injury (PNI). Ultrahigh-frequency ultrasound with a 70-MHz probe (70 MHz-UHFUS) offers higher spatial resolution and could allow a better detection of PNI. This study aimed to compare the diagnostic performance of HFUS and UHFUS in PNI detection. In this retrospective study, were selected, between July 2022 and April 2024, 61 patients underwent HFUS, UHFUS, and nerve conduction study (NCS) for clinical suspicion of traumatic forearm PNI. Sensitivity, specificity, positive predictive value (PPV), negative predictive value (NPV), and diagnostic accuracy of HFUS and UHFUS in PNI detection were calculated and compared. NCS was used as the reference standard. Nonparametric statistical tests were used. A p value of < 0.05 was considered statistically significant. Comparing the diagnostic performance in PNI detection, the 70 MHz-UHFUS showed a sensitivity and diagnostic accuracy significantly higher than 18 MHz-HFUS, respectively, 98.0% versus 82.4% (p = 0.0205) and 95.1% versus 82.0% (p = 0.0468). Otherwise, not significantly difference were in specificity, PPV, and NPV. UHFUS compared to HFUS demonstrated a higher sensitivity and diagnostic accuracy in PNI detection.

Performance evaluation of a small-animal PET scanner with 213 mm axis using NEMA NU 4-2008.

Long-axis positron emission tomography (PET) has emerged as one of the recent research directions in PET due to its ability to significantly enhance sensitivity and counting performance for low-dose imaging, rapid imaging, and whole-body dynamicimaging. The PET system presented in this study is a long-axis animal PET based on lutetium-yttrium orthosilicate and silicon photomultiplier, designed for whole-body imaging in rats. It features a diameter of 143 mm and an axial length of 213.3 mm. This study evaluated the performance of this PET system in accordance with the National Electrical Manufacturers Association (NEMA) NU 4-2008standards. The performance evaluation was conducted according to the NEMA NU 4-2008 standards in terms of spatial resolution, sensitivity, counting rate performance, scatter fraction (SF) and image quality. In addition, a rat imaging study was conducted to assess the imaging capability of this PETsystem. The average energy resolution of the PET system was 12.87%, the average coincidence timing resolution was 751 ps. The FWHM of spatial resolution reconstructed by filtered back projection and 3D-OSEM-PSF algorithm at 5 mm radial offset from the axial center were 1.65 and 0.88 mm. The peak absolute sensitivity measured by a point source at the center of the field of view was evaluated as 6.71% (361-661keV) and 10.31% (250-750 keV). For the mouse-like phantom, the SF was 11.0% and the peak noise equivalent counting rate (NECR) was 1193 kcps at 94.2 MBq (2.54 mCi). For the rat-like phantom, the SF was 26.8% and the NECR was 682.5 kcps at 78.6 MBq (2.12 mCi). The performance measurement results demonstrate that this PET system exhibits high sensitivity and count rate performance, making it potential for high-quality whole-body dynamic imaging ofrats.

Prediction of Commercial Street Location Based on Point of Interest (POI) Big Data and Machine Learning

Identifying optimal locations for sustainable commercial street development is crucial for driving economic growth and enhancing social vitality in cities. This study proposes a data-driven approach to predict potential sites for commercial streets in Foshan City, China, utilizing Points of Interest (POI) big data and machine learning techniques. Decision tree algorithms are employed to quantitatively assess and predict optimal locations at a fine-grained spatial resolution, dividing the study area into 9808 grid cells. The analysis identifies 2157 grid cells as potential sites for commercial street development, highlighting the significant influence of Medical Care, Shopping, and Recreation and Entertainment POIs on site selection. The study underscores the importance of considering population base, human activity patterns, and cultural elements in sustainable urban development. The main contributions include providing a novel decision-support method for data-driven and sustainable commercial street site selection and offering insights into the complex interplay between urban land use, human activities, and commercial development. The findings have important implications for urban planning and policy-making, showcasing the potential of data-driven approaches in guiding sustainable urban development and fostering vibrant commercial areas.

Highly Amplified Broadband Ultrasound in Antiresonant Hollow Core Fibers

High‐frequency broadband ultrasound in nested antiresonant hollow core fibers (NANFs) is investigated for the first time. NANFs have remarkable features enabling high‐resolution microscale optoacoustic imaging sensors and neurostimulators. Solid optical fibers have been successfully employed to measure and generate ultrasonic signals, however, they face issues concerning attenuation, limited frequency range, bandwidth, and spatial resolution. Herein, highly efficient ultrasonic propagation in NANFs from 10 to 100 MHz is numerically demonstrated. The induced pressures and sensing responsivity are evaluated in detail, and important parameters for the development of ultrasonic devices are reviewed. High pressures (up to 234 MPa) and sensing responsivities (up to −207 dB) are tuned over 90 MHz range by changing the diameters of two distinct NANF geometries. To the best of knowledge, this is the widest bandwidth reported using similar diameter fibers. The results are a significant advance for fiber‐based ultrasonic sensors and transmitters, contributing to improve their efficiency and microscale spatial resolution for the detection, diagnosis, and treatment of diseases in biomedical applications.

Forgotten treasures in the HST/FOC UV imaging polarimetric archives of active galactic nuclei. III. Five years monitoring of M87

The active galactic nucleus within M87, a giant elliptical galaxy, is responsible for one of the closest kiloparsec-scale relativistic jets to Earth. It is thus a perfect target for spatially resolved observations. This one-sided jet has been extensively observed at almost all wavelengths, with almost all techniques. Among many other discoveries, it was found that the optical emission is more concentrated in the knots and along the center line of the jet, in comparison to, for example, the radio emission. A remaining question relates to what we can learn from its polarized counterpart. We unearthed unpublished polarization maps taken with the Faint Object Camera (FOC) aboard the Hubble Space Telescope (HST), obtained between 1995 and 1999. At a rate of one observation per year, we can follow the evolution of the polarized flux knots in the jet. We can thus constrain the timescale of variation in the magnetic field up to a spatial resolution of one tenth of an arcsecond (sim 11.5 pc). After coherently reducing the five observations using the same methodology presented in the first paper of this series, the analysis of polarized maps from POS 1 (base of the jet) and POS 3 (end of the jet) reveals significant temporal and spatial dynamics in the jet's magnetic field morphology. Despite minimal changes in the overall intensity structure, notable fluctuations in polarization degrees and angles are detected across various knots and inter-knot regions. In addition, the emission and polarization characteristics of M87's jet differ significantly between POS1 and POS3. POS1 shows a more collimated jet with strong variability in polarization, while POS3 reveals a thicker structure, a quasi-absence of variability, and complex magnetic field interactions. This suggests that the jet may have coaxial structures with distinct kinetic properties. Theoretical models like the jet-in-jet scenario, featuring double-helical magnetic flux ropes, help to explain these observations and indicate a strong density contrast and higher speeds in the inner jet. Our temporal analysis demonstrates the importance of high-spatial-resolution polarization mapping in understanding jets' polarization properties and overall dynamics, especially if such maps are taken at different wavelengths (ultraviolet and radio).

Atomic-scale visualization of defect-induced localized vibrations in GaN.

Phonon engineering is crucial for thermal management in GaN-based power devices, where phonon-defect interactions limit performance. However, detecting nanoscale phonon transport constrained by III-nitride defects is challenging due to limited spatial resolution. Here, we used advanced scanning transmission electron microscopy and electron energy loss spectroscopy to examine vibrational modes in a prismatic stacking fault in GaN. By comparing experimental results with ab initio calculations, we identified three types of defect-derived modes: localized defect modes, a confined bulk mode, and a fully extended mode. Additionally, the PSF exhibits a smaller phonon energy gap and lower acoustic sound speeds than defect-free GaN, suggesting reduced thermal conductivity. Our study elucidates the vibrational behavior of a GaN defect via advanced characterization methods and highlights properties that may affect thermal behavior.

High-resolution population mapping based on SDGSAT-1 glimmer imagery and deep learning: a case study of the Guangdong-Hong Kong-Marco Greater Bay Area

ABSTRACT Accurate population mapping is crucial for disaster management, urban planning, etc. However, current methods using nighttime light (NTL) and gridded population datasets are limited by low spatial resolution and insufficient training data for complex models such as deep learning. These models do not adequately utilize spatial information in population mapping. To address these limitations, this study proposes a high-resolution population mapping method using the Sustainable Development Goals Science Satellite 1 (SDGSAT-1) glimmer imager data and deep learning. The method includes a sample generation strategy with multiple regression and multilevel screening to provide sufficient, high-quality samples for deep learning. A Fine Population mapping network (FinePop-net) is also developed to train regression models using image samples, capturing multi-scale features for model training. When applied to the Guangdong-Hong Kong-Macao Greater Bay Area with 40-meter resolution SDGSAT 1 glimmer imagery, the method significantly reduced the average absolute error and root-mean-square error by 9.35% and 11.44%, respectively, compared with those of the pixel-level learning methods. It also outperformed other population spatialization datasets and NTL data by over 30% and 10%, respectively, in terms of error reduction. The results highlight the method’s effectiveness and the value of SDGSAT-1 glimmer imagery for fine population spatialization.

Monitoring Coastal Evolution and Geomorphological Processes Using Time-Series Remote Sensing and Geospatial Analysis: Application Between Cape Serrat and Kef Abbed, Northern Tunisia

The monitoring of coastal evolution (coastline and associated geomorphological features) caused by episodic and persistent processes associated with climatic and anthropic activities is required for coastal management decisions. The availability of open access, remotely sensed data with increasing spatial, temporal, and spectral resolutions, is promising in this context. The coastline of Northern Tunisia is currently showing geomorphic process, such as increasing erosion associated with lateral sedimentation. This study aims to investigate the potential of time-series optical data, namely Landsat (from 1985–2019) and Google Earth® satellite imagery (from 2007 to 2023), to analyze shoreline changes and morphosedimentary and geomorphological processes between Cape Serrat and Kef Abbed, Northern Tunisia. The Digital Shoreline Analysis System (DSAS) was used to quantify the multitemporal rates of shoreline using two metrics: the net shoreline movement (NSM) and the end-point rate (EPR). Erosion was observed around the tombolo and near river mouths, exacerbated by the presence of surrounding dams, where the NSM is up to −8.31 m/year. Despite a total NSM of −15 m, seasonal dynamics revealed a maximum erosion in winter (71% negative NSM) and accretion in spring (57% positive NSM). The effects of currents, winds, and dams on dune dynamics were studied using historical images of Google Earth®. In the period from 1994 to 2023, the area is marked by dune face retreat and removal in more than 40% of the site, showing the increasing erosion. At finer spatial resolution and according to the synergy of field observations and photointerpretation, four key geomorphic processes shaping the coastline were identified: wave/tide action, wind transport, pedogenesis, and deposition. Given the frequent changes in coastal areas, this method facilitates the maintenance and updating of coastline databases, which are essential for analyzing the impacts of the sea level rise in the southern Mediterranean region. Furthermore, the developed approach could be implemented with a range of forecast scenarios to simulate the impacts of a higher future sea-level enhanced climate change.

Modeling Civil Aviation Emissions with Actual Flight Trajectories and Enhanced Aircraft Performance Model

Aviation emissions are continuously increasing along with the rapid development of air transportation, and results in the deterioration in regional air quality and the global climate. Accurate emission estimation is of great importance for relevant policies promotion and the sustainable development of the environment. Previous studies focused on the total emissions of a flight and lacked high precision in both spatial and temporal resolutions, especially aviation activities near ground. In this research, we propose an open-sourced emission calculation framework based on actual flight trajectories (TrajEmission), which calculates both the ground and airborne emissions simultaneously according to the configuration parameters, trajectory characteristics, and ambient conditions. We compare the emission results with five emission inventory methods. The results indicate that pollutant (nitrogen oxides, carbon monoxide, and unburned hydrocarbons) emissions in the landing and takeoff (LTO) cycle might usually be underestimated due to a lack of trajectory-based methods. In addition, in the overall results, the method based on the great circle route leads to an overestimation of 56.8% of pollutant emissions compared to the method based on actual routes. We also investigate the extent to which other factors could influence the emission results. To summarize, the TrajEmission framework can build inventories for the whole process of flight movements with high spatial–temporal resolutions and provide solid data support for environmental science and other related fields.

Forecasting to support EMS tactical planning: what is important and what is not.

Forecasting emergency medical service (EMS) call volumes is critical for resource allocation and planning. The development of many commercial and free software packages has made a variety of forecasting methods accessible. Practitioners, however, are left with little guidance on selecting the most appropriate method for their needs. Using 5 years of data from 3 cities in Alberta, we compute exponential smoothing and benchmark forecasts for 8-hour periods for each ambulance station catchment area and with a forecast horizon of two weeks-a spatio-temporal resolution appropriate for tactical planning. The methods that we consider differ on three spectra: the number and type of time-series components, whether forecasts are computed individually or jointly, and the way in which forecasts at a specific resolution are converted to forecasts at the resolution of interest. We find that it is important to include a weekly seasonal component when forecasting EMS demand. Multiplicative seasonality, however, shows no benefit over additive seasonality. Adding other time-series components (e.g., trend, ARMA errors, Box-Cox transformation) does not improve performance. Spatial resolutions of station catchment area and lower, and temporal resolution of 4-24 hours perform similarly. We adapt an existing hierarchical forecasting framework to a two-dimensional spatio-temporal hierarchy, but find that hierarchical reconciliation of forecasts does not improve performance at the forecast resolution of interest for tactical planning. Neither does jointly forecasting time series. We show that added complexity does not materially improve forecasting performance. The simple methods that we find perform well are easy to implement and interpret, making implementation in practice more likely. In a simulation study we alter the empirical weekly patterns and demonstrate how extreme differences between the weekly seasonality patterns of different regions cause hierarchically-reconciled bottom-up approaches to outperform top-down approaches.

Intraoperative visualization of cerebral aneurysms using navigated 3D-ultrasound power-Doppler angiography.

The questions of whether the spatial resolution of navigated 3D-ultrasound (3D-US) power-Doppler angiography imaging rendered by existing 3D-US systems is sufficient for the intraoperative visualization of cerebral aneurysms, and in what percentage of cases, are largely unanswered. A study on this topic is lacking in the literature. From 2015 to 2022, we performed 86 surgeries on 83 aneurysm patients. Navigated 3D-US was used at the discretion of the operating neurosurgeons when available (i.e., not being used during parallel tumor surgeries). Twenty-five aneurysms (15 ruptured) were operated on using 3D-US; 22 aneurysms were located at the middle cerebral artery (MCA). Patient 3D-US power-Doppler angiography images and surgical reports were retrospectively reviewed to assess the intraoperative ultrasound visibility of aneurysms. In 20 patients (80%) the aneurysms were successfully visualized. In five patients (20%), the aneurysms visualization was insufficient or absent. Nineteen of 22 aneurysms (86.4%) were visualized in the MCA aneurysm subgroup. We observed no association between aneurysm visibility and aneurysm size or the presence of subarachnoid hemorrhage. In the subgroup of MCA aneurysms, no association between aneurysm visibility and the presence of subarachnoid hemorrhage was found; a trend toward poor sonographic visibility of smaller aneurysms was observed (p = 0.09). Our initial data show that intraoperative 3D-US power-Doppler angiography, rendered by current navigated 3D-US systems, clearly depicts the majority of aneurysms in the MCA aneurysm subgroup. However, future prospective studies performed on a higher number of aneurysms localized at various anatomic sites are needed to confirm our initial findings and determine their potential clinical relevance.