Mean Absolute Percentage Difference Research Articles

Performance of two semi-analytical algorithms in deriving water inherent optical properties in the Southern Ocean

Remotely sensed inherent optical properties (IOPs) are key proxies for synoptic mapping of primary production and carbon export in the global ocean. However, the IOPs inversion algorithms are scarcely evaluated in the Southern Ocean (SO) because of limited field observations. In this study, the performance of two widely used semi-analytical algorithms (SAAs), i.e., the quasi-analytical algorithm (QAA) and the generalized IOP model (GIOP), were evaluated using a compiled in situ bio-optical dataset in SO, as well as measurements from the Visible Infrared Imaging Radiometer Suite (VIIRS). Evaluations with in situ data show that QAA and GIOP have comparable performance in retrieving the total absorption coefficient (a(λ)), absorption coefficients of phytoplankton (aph(λ)), and that of detritus and colored dissolved organic matter (adg(λ)). Overall, it was found that remotely sensed a(λ) and aph(λ) by both SAAs agreed well with field measurements, with the mean absolute percentage difference (MAPD) of derived a(λ) and aph(λ) in the blue-green bands being ∼20% and ∼40%, respectively. However, derived adg(λ) by both SAAs were higher than the measured values at the lower end (adg(443) < ∼0.01 m-1), but lower at the higher end (adg(443) > ∼0.02 m-1), with MAPD of ∼60%. Results of this effort suggest confident products of a(λ) and aph(λ) from VIIRS in SO, but more dedicated efforts on the measurements and evaluation of adg(λ) in SO would be desired.

First clinical experience following the consensus guide for calibrating a proton stopping power ratio curve in a new proton centre

Background and PurposeThis work introduces the first assessment of CT calibration following the ESTRO’s consensus guidelines and validating the HLUT through the irradiation of biological material. MethodsTwo electron density phantoms were scanned with two CT scanners using two CT scan energies. The stopping power ratio (SPR) and mass density (MD) HLUTs for different CT scan energies were derived using Schneider’s and ESTRO’s methods. The comparison metric in this work is based on the Water-Equivalent Thickness (WET) difference between the treatment planning system and biological irradiation measurement. The SPR HLUTs were compared between the two calibration methods. To assess the accuracy of using MD HLUT for dose calculation in the treatment planning system, MD vs SPR HLUT was compared. Lastly, the feasibility of using a single SPR HLUT to replace two different energy CT scans was explored. ResultsThe results show a WET difference of less than 3.5% except for the result in the Bone region between Schneider’s and ESTRO’s methods. Comparing MD and SPR HLUT, the results from MD HLUT show less than a 3.5% difference except for the Bone region. However, the SPR HLUT shows a lower mean absolute percentage difference as compared to MD HLUT between the measured and calculated WET difference. Lastly, it is possible to use a single SPR HLUT for two different CT scan energies since both WET differences are within 3.5%. ConclusionThis is the first report on calibrating an HLUT following the ESTRO’s guidelines. While our result shows incremental improvement in range uncertainty using the ESTRO’s guideline, the prescriptional approach of the guideline does promote harmonization of CT calibration protocols between different centres.

Predicting energy consumption of zero emission buses using route feature selection methods

This paper reports new insights into how the selection of route based characteristic parameters can influence the predicted energy consumption for next generation battery electric buses. 24 characteristic parameters have been studied to understand their relative importance on vehicle energy consumption and to develop new data-driven prediction models. The parameters are grouped into two scenarios, representative of the varying levels of route information available to a typical bus operator. A combination of feature selection methods was used to determine which characteristic parameters had the greatest influence on energy consumption. Regression based prediction models were developed, and models were then validated using standard and real vehicle drive cycles. The prediction models had a mean absolute percentage difference of 2.10–10.67%. This paper presents a novel methodology to estimate energy consumption of operating zero emission vehicles, which will support public transport operators, policy makers and energy suppliers in the decarbonisation of public transport.

Impacts of Hydrology and Extreme Events on Dissolved Organic Carbon Dynamics in a Heavily Urbanized Estuary and Its Major Tributaries: A View From Space

AbstractDissolved organic matter and its colored component, Colored Dissolved Organic Matter (CDOM), play a major role in global carbon budgets, and their fluxes provide an essential link between terrestrial and aquatic biogeochemical cycles. Satellite observations can uniquely capture the hydro‐biogeochemical connectivity of terrestrial and aquatic landscapes, across scales. Yet, accurate satellite retrievals of CDOM and dissolved organic carbon (DOC) dynamics remain challenging in urbanized estuaries and coasts. Here, we present an advanced unified algorithm for space‐based retrieval of coastal CDOM and DOC dynamics and its application in Long Island Sound—one of the world's most heavily urbanized estuaries that is becoming increasingly vulnerable to climate change stressors. A rich bio‐optical data set, encompassing a wide range of environmental conditions, was integrated into the algorithm training to retrieve DOC concentrations and CDOM spectral shape (i.e., spectral slope S275–295)—a proxy for DOC quality. The new algorithms were applied to full‐resolution satellite imagery from the Sentinel‐3 Ocean and Land Color Instrument (OLCI) after thoroughly evaluating the performance of six ocean color atmospheric correction approaches (ACOLITE, BAC, C2RCC, MUMM, l2gen, and Polymer). Evaluation of the algorithms yielded mean absolute percent differences of 28%, 12%, and 10% for aCDOM(300), S275–295, and DOC, respectively. Application of the algorithms to multi‐year satellite OLCI imagery captured, for the first time, the coupled impact of seasonal transitions, wind regimes, freshwater inputs, anthropogenic disturbances, and hydrological extremes (both intense precipitation and droughts) on DOC fluxes and CDOM quality at the ecosystem scale. Results have important implications for improved predictions of coastal biogeochemical fluxes in complex urban−estuary systems.

Robustness of magnetic resonance imaging and positron emission tomography radiomic features in prostate cancer: Impact on recurrence prediction after radiation therapy

Background and purposeRadiomic features from MRI and PET are an emerging tool with potential to improve prostate cancer outcomes. However, feature robustness due to image segmentation variations is currently unknown. Therefore, this study aimed to evaluate the robustness of radiomic features with segmentation variations and their impact on predicting biochemical recurrence (BCR). Materials and methodsMulti-scanner, pre-radiation therapy imaging from 142 patients with localised prostate cancer was used. Imaging included T2-weighted (T2), apparent diffusion coefficient (ADC) MRI, and prostate-specific membrane antigen (PSMA)-PET. The prostate gland and intraprostatic tumours were manually and automatically segmented, and differences were quantified using Dice Coefficient (DC). Radiomic features including shape, first-order, and texture features were extracted for each segmentation from original and filtered images. Intraclass Correlation Coefficient (ICC) and Mean Absolute Percentage Difference (MAPD) were used to assess feature robustness. Random forest (RF) models were developed for each segmentation using robust features to predict BCR. ResultsProstate gland segmentations were more consistent (mean DC = 0.78) than tumour segmentations (mean DC = 0.46). 112 (3.6 %) radiomic features demonstrated ‘excellent’ robustness (ICC > 0.9 and MAPD < 1 %), and 480 features (15.4 %) demonstrated ‘good’ robustness (ICC > 0.75 and MAPD < 5 %). PET imaging provided more features with excellent robustness than T2 and ADC. RF models showed strong predictive power for BCR with a mean area under the receiver-operator-characteristics curve (AUC) of 0.89 (range 0.85–0.93). ConclusionWhen using radiomic features for predictive modelling, segmentation variability should be considered. To develop BCR predictive models, radiomic features from the entire prostate gland are preferable over tumour segmentation-based features.

Mapping particulate organic carbon in lakes across China using OLCI/Sentinel-3 imagery

Remote sensing monitoring of particulate organic carbon (POC) concentration is essential for understanding phytoplankton productivity, carbon storage, and water quality in global lakes. Some algorithms have been proposed, but only for regional eutrophic lakes. Based on in-situ data (N = 1269) in 49 lakes across China, we developed a blended POC algorithm by distinguishing Type-I and Type-II waters. Compared to Type-I, Type-II waters had higher reflectance peak around 560 nm (>0.0125 sr−1) and mean POC (4.65 ± 4.11 vs. 2.66 ± 3.37 mg/L). Furthermore, because POC was highly related to algal production (r = 0.85), a three-band index (R2 = 0.65) and the phytoplankton fluorescence peak height (R2 = 0.63) were adopted to estimate POC in Type-I and Type-II waters, respectively. The novel algorithm got a mean absolute percent difference (MAPD) of 35.93 % and outperformed three state-of-the-art formulas with MAPD values of 40.56–76.42 %. Then, the novel algorithm was applied to OLCI/Sentinel-3 imagery, and we first obtained a national map of POC in 450 Chinese lakes (> 20 km2), which presented an apparent spatial pattern of “low in the west and high in the east”. In brief, water classification should be considered when remotely monitoring lake POC concentration over a large area. Moreover, a process-oriented method is required when calculating water column POC storage from satellite-derived POC concentrations in type-II waters. Our results contribute substantially to advancing the dynamic observation of the lake carbon cycle using satellite data.

Spherical Cap Studs: A novel speed bump alternative to reduce discomfort with effective speed reduction

Objective The aim of this study is to assess the potential of Spherical Cap Studs (SCS) as a substitute for conventional speed bumps, with a focus on limiting two wheeler speed while minimizing discomfort to riders by comparing the speed reduction capabilities and discomfort levels associated with SCS and speed bumps. Methods The study uses experimental approach to compare the speed limiting ability and discomfort caused to rider by the proposed SCS and a standard speed bump. Speed profiles were developed for two wheelers passing over both SCS and speed bump. The parameter employed to compare speed profiles is the Mean Absolute Percentage Difference (MAPD), offering valuable insights into how effectively the two traffic calming measures reduce two wheeler speeds. To compare discomfort, the study calculates the ‘Vibration Dose Value’ (VDV) experienced by riders when traversing both speed bump and SCS. Additionally, ‘Static Compressive Stress’ (Se) applied to the spinal cord is also calculated in both scenarios. Results The analysis of speed profiles reveals an MAPD value of 13.70% indicating that SCS exhibits speed reduction capabilities comparable to traditional speed bump. In terms of discomfort, the VDV for two wheelers passing over a speed bump is measured at 5.92 m/s1.75, whereas the VDV for SCS is found to be 5.16 m/s1.75. Similarly, the Static Compressive Stress (Se) experienced at a speed bump is 0.60 MPa, in contrast to the 0.33 MPa recorded for SCS. This data underscores a noteworthy 12.8% reduction in VDV and a substantial 45.57% reduction in Se. Conclusion The study’s findings support the potential adoption of SCS as an effective alternative to conventional speed bumps for controlling two wheeler speeds. SCS demonstrate a speed reduction capability similar to that of traditional speed bumps while significantly alleviating discomfort for riders. SCS is expected to be a promising solution for traffic calming purposes in various settings, such as markets, residential areas, institutional campuses, and parking lots.

Prevalence of Cognitive Domain Impairments in Mild Cognitive Impairment Subtypes in the NACC Database

AbstractBackgroundMild cognitive impairment (MCI) reflects cognitive deficits without significant functional decline in activities of daily living. MCI subtypes correspond to phenotypic cognitive impairment patterns in memory, attention, language, executive, and visuospatial compared to age‐adjusted norms: Amnestic single domain (ASD) or multi‐domain (AMD), Nonamnestic single domain (NASD), or Nonamnestic multi‐domain (NAMD. In this study, we explore frequencies of domain impairment by MCI subtype, and frequency of each combination of (memory ±) all combinations of Language, Attention, Executive and Visuospatial impairment in the NACC database. This is of practical importance for both localization of amyloid or tau deposition as well as relative localization of areas of deficit.MethodUsing the National Alzheimer’s Coordinating Center (NACC) database (grant U24 AG072122), we selected all initial Uniform Data Set (UDS) assessments diagnosed as MCI with sub‐typing. The NACC data was collected from 37 participating Alzheimer’s disease Research Centers. 9852 individuals with MCI on baseline UDS visits were analyzed: 45.4% AMD, 33.7% ASD, 12.6% NASD, 8.2% NAMD.ResultFigure one shows the distribution overall of domain specific impairment by MCI subtype. Except for ASD, Executive function was the most common domain affected, and visuospatial was the least common. NAMD had higher percentages affected for all domains compared to AMD. In Figure 2, all combinations of language, executive, attention and visuospatial are compared. The most frequent combination was Language‐Executive, and the least common combination was Language‐Attention‐Visuospatial. The mean absolute percentage difference between AMD and NAMD was 2.2%. The Spearman’s rho of AMD vs. NAMD was 0.845.ConclusionWithin an MCI subtype, there is substantial variability in the frequency of individual domains. However, comparison of AMD and NAMD shows striking uniformity in frequency of combinations of individual domains with very correlation. The latter findings suggests that Amnestic and Nonamnestic multidomain subtypes may be closely related in pathophysiology but show greater heterogeneity within a MCI subtype.

A semi-analytical model to estimate Chlorophyll-a spatial-temporal patterns from Orbita Hyperspectral image in inland eutrophic waters

It can be challenging to accurately estimate the Chlorophyll-a (Chl-a) concentration in inland eutrophic lakes due to lakes' extremely complex optical properties. The Orbita Hyperspectral (OHS) satellite, with its high spatial resolution (10 m), high spectral resolution (2.5 nm), and high temporal resolution (2.5 d), has great potential for estimating the Chl-a concentration in inland eutrophic waters. However, the estimation capability and radiometric performance of OHS have received limited examination. In this study, we developed a new quasi-analytical algorithm (QAA716) for estimating Chl-a using OHS images. Based on the optical properties in Dianchi Lake, the ability of OHS to remotely estimate Chl-a was evaluated by comparing the signal-to-noise ratio (SNR) and the noise equivalent of Chl-a (NEChl-a). The main findings are as follows: (1) QAA716 achieved significantly better results than those of the other three QAA models, and the Chl-a estimation model, using QAA716, produced robust results with a mean absolute percentage difference (MAPD) of 11.54 %, which was better than existing Chl-a estimation models; (2) The FLAASH (Fast Line-of-sight Atmospheric Analysis of Spectral Hypercubes) atmospheric correction model (MAPD = 22.22 %) was more suitable for OHS image compared to the other three atmospheric correction models we tested; (3) OHS had relatively moderate SNR and NEChl-a, improving its ability to accurately detect Chl-a concentration and resulting in an average SNR of 59.47 and average NEChl-a of 72.86 μg/L; (4) The increased Chl-a concentration in Dianchi Lake was primarily related to the nutrients input, and this had a significant positive correlation with total nitrogen. These findings expand existing knowledge of the capabilities and limitations of OHS in remotely estimating Chl-a, thereby facilitating effective water quality management in eutrophic lake environments.

Assessment of satellite-based Net Primary Productivity models in different biogeochemical provinces over the northern Indian Ocean

ABSTRACT The biological productivity in the oceanic ecosystem plays a vital role in the global carbon cycle and energy flow in the oceanic food chain. Therefore, proper monitoring of phytoplankton primary production in the ocean is essential to understand the role and responses of aquatic ecosystems to global climate change. Its quantification is also important for higher trophic-level studies and sustainable ecosystem management. The advent of satellite technology has provided an opportunity to retrieve Net Primary Productivity (NPP) at high spatial and temporal resolution. However, for efficient utilization of satellite NPP it is imperative to assess the models for their accuracy and variability. The present study described NPP estimated from MODIS-Aqua satellite data (2003–2021) using five models (VGPM, Eppley-VGPM, CbPM, CAFE, and SABPM) and their variability in the four biogeochemical provinces (ARAB, MONS, INDW and INDE) in the northern Indian Ocean. The temporal variability indicated all models exhibit seasonal variability in ARAB and INDW with two prominent peaks during peak southwest monsoon (June-August) and winter monsoon (December-January). However, there was significant difference in the magnitude. The seasonal peak was primarily due to increase in NPP due to summer upwelling and winter mixing. The spatially averaged diversity index indicated maximum variability in INDW (74.6–77.1%), followed by INDE (77.2–78.0%), ARAB (77.2–78.4%), and MONS (78.6–78.9%). The satellite retrieved NPP by various models showed maximum diversity in the region above 15°N and west of 60°E in AS and 20°N and east 90°E in the BoB indicating a significant influence of discharge from perennial rivers resulting in poor performance of the models in these regions. The variability of other models was then assessed with respect CbPM as the model was known to perform better. The Eppley-VGPM showed a maximum (55%) and SABPM showed a minimum (11%) Mean Absolute Percentage Difference with respect to CbPM. The long-term spatio-temporal and quantifiable variability between the NPP models, and biogeochemical provinces, indicated that the study provided useful information on the selection and utilization of an appropriate model for future sustainability and climate change studies.

Retrieval of Chla Concentrations in Lake Xingkai Using OLCI Images

Lake Xingkai is a large turbid lake composed of two parts, Small Lake Xingkai and Big Lake Xingkai, on the border between Russia and China, where it represents a vital source of water, fishing, water transport, recreation, and tourism. Chlorophyll-a (Chla) is a prominent phytoplankton pigment and a proxy for phytoplankton biomass, reflecting the trophic status of waters. Regularly monitoring Chla concentrations is vital for issuing timely warnings of this lake’s eutrophication. Owing to its higher spatial and temporal coverages, remote sensing can provide a synoptic complement to traditional measurement methods by targeting the optical Chla absorption signals, especially for the lakes that lack regular in situ sampling cruises, like Lake Xingkai. This study calibrated and validated several commonly used remote sensing Chla retrieval algorithms (including the two-band ratio, three-band method, four-band method, and baseline methods) by applying them to Sentinel-3 Ocean and Land Colour Instrument (OLCI) images in Lake Xingkai. Among these algorithms, the four-band model (FBA), which removes the absorption signal of detritus and colored dissolved organic matter, was the best-performing model with an R2 of 0.64 and a mean absolute percentage difference of 38.26%. With the FBA model applied to OLCI images, the monthly and spatial distributions of Chla in Lake Xingkai were studied from 2016 to 2022. The results showed that over the seven years, the Chla concentrations in Small Lake Xingkai were higher than in Big Lake Xingkai. Unlike other eutrophic lakes in China (e.g., Lake Taihu and Lake Chaohu), Lake Xingkai did not display a stable seasonal Chla variation pattern. We also found uncertainties and limitations of the Chla algorithm models when using a larger satellite zenith angle or applying it to an algal bloom area. Recent increases in anthropogenic nutrient loading, water clarity, and warming temperatures may lead to rising phytoplankton biomass in Lake Xingkai, and the results of this study can be applied for the satellite-based monitoring of its water quality.

Evaluation of the Health Status of Indonesian Watersheds Using Impervious Surface Area as an Indicator.

Impervious surfaces affect the ecosystem function of watersheds. Therefore, the impervious surface area percentage (ISA%) in watersheds has been regarded as an important indicator for assessing the health status of watersheds. However, accurate and frequent estimation of ISA% from satellite data remains a challenge, especially at large scales (national, regional, or global). In this study, we first developed a method to estimate ISA% by combining daytime and nighttime satellite data. We then used the developed method to generate an annual ISA% distribution map from 2003 to 2021 for Indonesia. Third, we used these ISA% distribution maps to assess the health status of Indonesian watersheds according to Schueler's criteria. Accuracy assessment results show that the developed method performed well from low ISA% (rural) to high ISA% (urban) values, with a root mean square difference value of 0.52 km2, a mean absolute percentage difference value of 16.2%, and a bias of -0.08 km2. In addition, since the developed method uses only satellite data as input, it can be easily implemented in other regions with some modifications according to differences in light use efficiency and economic development in each region. We also found that 88% of Indonesian watersheds remain without impact in 2021, indicating that the health status of Indonesian watersheds is not a serious problem. Nevertheless, Indonesia's total ISA increased significantly from 3687.4 km2 in 2003 to 10,505.5 km2 in 2021, and most of the increased ISA was in rural areas. These results indicate that negative trends in health status in Indonesian watersheds may emerge in the future without proper watershed management.

High-quality annotations for deep learning enabled plaque analysis in SCAPIS cardiac computed tomography angiography

BackgroundPlaque analysis with coronary computed tomography angiography (CCTA) is a promising tool to identify high risk of future coronary events. The analysis process is time-consuming, and requires highly trained readers. Deep learning models have proved to excel at similar tasks, however, training these models requires large sets of expert-annotated training data. The aims of this study were to generate a large, high-quality annotated CCTA dataset derived from Swedish CArdioPulmonary BioImage Study (SCAPIS), report the reproducibility of the annotation core lab and describe the plaque characteristics and their association with established risk factors. Methods and resultsThe coronary artery tree was manually segmented using semi-automatic software by four primary and one senior secondary reader. A randomly selected sample of 469 subjects, all with coronary plaques and stratified for cardiovascular risk using the Systematic Coronary Risk Evaluation (SCORE), were analyzed. The reproducibility study (n = 78) showed an agreement for plaque detection of 0.91 (0.84–0.97). The mean percentage difference for plaque volumes was −0.6% the mean absolute percentage difference 19.4% (CV 13.7%, ICC 0.94). There was a positive correlation between SCORE and total plaque volume (rho = 0.30, p < 0.001) and total low attenuation plaque volume (rho = 0.29, p < 0.001). ConclusionsWe have generated a CCTA dataset with high-quality plaque annotations showing good reproducibility and an expected correlation between plaque features and cardiovascular risk. The stratified data sampling has enriched high-risk plaques making the data well suited as training, validation and test data for a fully automatic analysis tool based on deep learning.

Field Radiometric Calibration of a Micro-Spectrometer Based on Remote Sensing of Plateau Inland Water Colors

Remote sensing reflectance (Rrs), which is currently measured mainly using the above-water approach, is the most crucial parameter in the remote sensing inversion of plateau inland water colors. It is very difficult to measure the Rrs of plateau inland unmanned areas; thus, we provide a measurement solution using a micro-spectrometer. Currently, commercial micro-spectrometers are not factory calibrated for radiation, and thus, a radiometric calibration of the micro-spectrometer is an essential step. This article uses an Ocean Optics micro-spectrometer (STS-VIS) and a traditional water spectrometer (Trios) to simultaneously measure the irradiance and radiance of diffuse reflectance plates with different reflectance values for field calibration. The results show the following: (1) different fiber types have different calibration coefficients, and the integration time is determined according to the diameter of the fiber and the type of fiber, and (2) by comparing the simultaneous measurement results of STS-VIS with Trios, the mean absolute percentage difference (MAPD) of both reached 18.64% and 5.11% for Qinghai Lake and Golmud River, respectively, which are accurate Rrs measurements of water bodies. The Rrs of the Hoh Xil and Qarhan Salt Lake water bodies in unmanned areas of China was measured, and this was the first collection of in situ spectral information with a micro-spectrometer. This article shows that the micro-spectrometer can perform the in situ measurement of water Rrs in unmanned inland areas. With this breakthrough in the radiometric performance of the micro-spectrometer, we are able to obtain more accurate remote sensing reflectance results of unmanned water bodies.

Reconstruction of 3-D Ocean Chlorophyll a Structure in the Northern Indian Ocean Using Satellite and BGC-Argo Data

We present a novel method using satellite and biogeochemical Argo (BGC-Argo) data to retrieve the 3-D structure of chlorophyll <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$a$ </tex-math></inline-formula> (Chla) in the northern Indian Ocean (NIO). The random forest (RF)-based method infers the vertical distribution of Chla using the near-surface and vertical features. The input variables can be divided into three categories: 1) near-surface features acquired by satellite products; 2) vertical physical properties obtained from temperature and salinity profiles collected by BGC-Argo floats; and 3) the temporal and spatial features, i.e., day of the year, longitude, and latitude. The RF-model is trained and evaluated using a large database including 9738 profiles of Chla and temperature-salinity properties measured by BGC-Argo floats from 2011 to 2021, with synchronous satellite-derived products. The retrieved Chla values and the validation dataset (including 1948 Chla profiles) agree fairly well, with <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$R^{2} = 0.962$ </tex-math></inline-formula> , root-mean-square error (RMSE) = 0.012, and mean absolute percent difference (MAPD) = 11.31%. The vertical Chla profile in the NIO retrieved from the RF-model is more accurate and robust compared to the operational Chla profile datasets derived from the neural network and numerical modeling. A major application of the RF-retrieved Chla profiles is to obtain the 3-D Chla structure with high vertical resolution. This will help to quantify phytoplankton productivity and carbon fluxes in the NIO more accurately. We expect that RF-model can be used to develop long-time series products to understand the variability of 3-D Chla in future climate change scenarios.

A novel remote sensing index for brine shrimp (Artemia) slick detection in salt lakes

Artemia (brine shrimp) are a widely distributed zooplankton in inland salt lakes and artificial ponds throughout the tropical, subtropical, and temperate zones. Notably, they are of great importance to fishery production and salt lake ecosystem health. Recently, Artemia resources have been threatened by habitat deterioration and overfishing, which mandate adequate monitoring; however, advanced methods for detecting Artemia from remote sensing images have rarely been discussed. Accordingly, this study explored the possibility of detecting the adult Artemia (Artemia) slicks using multispectral optical sensors by analyzing their unique aggregation patterns and consistent optical properties. After measuring Artemia spectra in the field, reflectance troughs were observed at ∼431 nm and ∼ 560 nm, which were deemed likely related to the absorption of β-carotene and heme, respectively, resulting in their reddish color. These characteristics could also be used to differentiate Artemia from other confounding features, including clean water and chlorophyll-a (Chl-a)- or suspended particulate matter (SPM)- dominated water. Based on these spectral signatures, a brine shrimp index (BSI) was developed using the difference between the reflectance at NIR band and a linear baseline between green and SWIR1 bands of Landsat-8 Operational Land Imager (OLI) imagery. Further, a BSI-based Artemia slick pixel extraction method was proposed. Using observation geometry information in conjunction with the BSI value of entire lakes, the BSI thresholds for the Artemia-containing pixels were automatically determined, and > 80% overall accuracy was achieved. The index performance was affected by the presence of residual cloud shadows, high water turbidity, and mixed pixels with low Artemia density. Moreover, BSI values were found to be relatively insensitive to atmospheric signals. It was shown here that the BSI detection limit was >0.53% Artemia subpixel coverage under clean water conditions; whereas a higher coverage was required to separate Artemia from Chl-a- or SPM- dominated water. The mean absolute percentage difference of BSI values calculated from Rayleigh-corrected and top-of-atmosphere (TOA) reflectance was <4% compared with surface reflectance derived BSI, and the relative difference in coverage area was < ±10%. Lastly, time series analyses of Lake Aibi in northwest China and the Great Salt Lake in North America revealed that the annual areal coverage estimation of Artemia slicks based on BSI was a positive indicator of Artemia harvesting, which can facilitate the Artemia resource management.

A revision of NASA SeaDAS atmospheric correction algorithm over turbid waters with artificial Neural Networks estimated remote-sensing reflectance in the near-infrared

For atmospheric correction over turbid waters, due to non-negligible water-leaving radiance (Lw) in the near-infrared (NIR), measurements in the short-wave infrared (SWIR) are usually required to achieve reliable remote-sensing reflectance (Rrs). But several ocean color satellite sensors, such as the Sea-viewing Wide Field-of-view Sensor (SeaWiFS) and other small satellites, have no bands in the SWIR domain. We here present an atmospheric correction algorithm (termed as ACANIR-NN) based on NASA SeaDAS (version 7.5.3), which can achieve atmospheric correction seamlessly over clear and turbid waters, even for sensors having no spectral bands in SWIR. Specifically, ACANIR-NN uses estimated Rrs(NIR) from available Rrs in the visible bands with a specifically designed artificial Neural Networks to carry out atmospheric correction, and the performance of ACANIR-NN is evaluated over eight coastal locations having ground measurements by the Aerosol Robotic Network-Ocean Color (AERONET-OC) system. It is found that the Mean Absolute Percent Difference (MAPD) of Rrs retrievals by ACANIR-NN for this dataset is smaller by a factor of two or more than that by the standard SeaDAS algorithm (termed as ACANIR-bio) for each band, especially for Rrs(412) and Rrs(443), which is 7.5% and 7.7%, respectively, from ACANIR-NN, but they are 44.0% and 27.5% from ACANIR-bio. We further demonstrated the applicability of ACANIR-NN to SeaWiFS measurements over turbid waters, where consistent Rrs products were also obtained compared to that generated from the same-day MODerate resolution Imaging Spectrometer (MODIS) measurements using SWIR bands. These results indicate that ACANIR-NN can generate reliable Rrs over turbid coastal areas, as well as clear ocean waters, for sensors having no SWIR bands.

Predicting mechanically ventilated patients future respiratory system elastance – A stochastic modelling approach

Background and objectiveRespiratory mechanics of mechanically ventilated patients evolve significantly with time, disease state and mechanical ventilation (MV) treatment. Existing deterministic data prediction methods fail to comprehensively describe the multiple sources of heterogeneity of biological systems. This research presents two respiratory mechanics stochastic models with increased prediction accuracy and range, offering improved clinical utility in MV treatment. MethodsTwo stochastic models (SM2 and SM3) were developed using retrospective patient respiratory elastance (Ers) from two clinical cohorts which were averaged over time intervals of 10 and 30 min respectively. A stochastic model from a previous study (SM1) was used to benchmark performance. The stochastic models were clinically validated on an independent retrospective clinical cohort of 14 patients. Differences in predictive ability were evaluated using the difference in percentile lines and cumulative distribution density (CDD) curves. ResultsClinical validation shows all three models captured more than 98% (median) of future Ers data within the 5th – 95th percentile range. Comparisons of stochastic model percentile lines reported a maximum mean absolute percentage difference of 5.2%. The absolute differences of CDD curves were less than 0.25 in the ranges of 5 < Ers (cmH2O/L) < 85, suggesting similar predictive capabilities within this clinically relevant Ers range. ConclusionThe new stochastic models significantly improve prediction, clinical utility, and thus feasibility for synchronisation with clinical interventions. Paired with other MV protocols, the stochastic models developed can potentially form part of decision support systems, providing guided, personalised, and safe MV treatment.

Carbon fibre lattice strain mapping via microfocus synchrotron X-ray diffraction of a reinforced composite

Synchrotron X-ray diffraction (SXRD) strain analysis is well established for high crystalline materials such as metals and ceramics, however, previously it has not been used in Carbon Fibre Reinforced Polymer (CFRP) composites due to their complex turbostratic atomic structure. This paper will present the feasibility of using SXRD for fibre orientation and lattice strain mapping inside CFRPs. In particular, it is the first time that the radial {002} and axial {100} strains of carbon fibre crystal planes have been analysed and cross-validated via numerical multi-scale simulation in a two-scale manner. In order to simplify the analysis and provide comparable estimates, an UniDirectional (UD) CFRP formed into a well-established humpback bridge shape was used. The lattice strain estimates obtained from SXRD showed localised stress concentrations and effectively matched the numerical results obtained by modelling. The mean absolute percentage differences between the two were 25.8% and 28.5% in the radial and axial directions, respectively. Differences between the two measurements are believed to originate from the non-uniform thermal history, forming geometry and tool-part interaction which leads to localised residual strains in the laminate which are unable to be fully captured by the numerical simulation performed. The carbon fibre microstructures of the inner plies adjacent to the tool were found to be significantly influenced by these factors and therefore the largest errors were observed at these locations. The approach presented has significant promise and implications for research into the micromechanics of composite materials and areas for future improvement have been outlined. • First quantification of micro-scale lattice strain in Carbon Fibre Reinforced Polymers (CFRPs). • Microfocus Synchrotron X-ray diffraction used to quantify microstructural changes. • Effective match between experimental lattice strain and simulations. • Errors/uncertainties and improvements identified to overcome challenges associated with the turbostratic structure of CFRPs.

Assessment of PRISMA water reflectance using autonomous hyperspectral radiometry

Hyperspectral remote sensing reflectance (Rrs) derived from PRISMA in the visible and infrared range was evaluated for two inland and coastal water sites using above-water in situ reflectance measurements from autonomous hyper- and multispectral radiometer systems. We compared the Level 2D (L2D) surface reflectance, a standard product distributed by the Italian Space Agency (ASI), as well as outputs from ACOLITE/DSF, now adapted for processing of PRISMA imagery. Near-coincident Sentinel-3 OLCI (S3/OLCI) observations were also compared as it is a frequent data source for inland and coastal water remote sensing applications, with a strong calibration and validation record. In situ measurements from two optically diverse sites in Italy, equipped with fixed autonomous hyperspectral radiometer systems, were used: the REmote Sensing for Trasimeno lake Observatory (RESTO), positioned in a shallow and turbid lake in Central Italy, and the Acqua Alta Oceanographic Tower (AAOT), located 15 km offshore from the lagoon of Venice in the Adriatic Sea, which is characterised by clear to moderately turbid waters. 20 PRISMA images were available for the match-up analysis across both sites. Good performance of L2D was found for RESTO, with the lowest relative (Mean Absolute Percentage Difference, MAPD < 25%) and absolute errors (Bias < 0.002) in the bands between 500 and 680 nm, with similar performance for ACOLITE. The lowest median and interquartile ranges of spectral angle (SA < 8°) denoted a more similar shape to the RESTO in situ data, indicating pigment absorption retrievals should be possible. ACOLITE showed better statistical performance at AAOT compared to L2D, providing R2 > 0.5, Bias < 0.0015 and MAPD < 35%, in the range between 470 and 580 nm, i.e. in the spectral range with highest reflectances. The addition of a SWIR based sun-glint correction to the default atmospheric correction implemented in ACOLITE further improved performance at AAOT, with lower uncertainties and closer spectral similarity to the in situ measurements, suggesting that ACOLITE with glint correction was able to best reproduce the spectral shape of in situ data at AAOT. We found good results for PRISMA Rrs retrieval in our study sites, and hence demonstrated the use of PRISMA for aquatic ecosystem mapping. Further studies are needed to analyse performance in other water bodies, over a wider range of optical properties.