62880 research outputs found
Sort by
Machine learning for improvement of upper-tropospheric relative humidity in ERA5 weather model data
Get PDFKnowledge of humidity in the upper troposphere and lower stratosphere (UTLS) is of special interest
due to its importance for cirrus cloud formation and its climate impact. However, the UTLS water vapor
distribution in current weather models is subject to large uncertainties. Here, we develop a dynamic-based humidity
correction method using an artificial neural network (ANN) to improve the relative humidity over ice
(RHi) in ECMWF numerical weather predictions. The model is trained with time-dependent thermodynamic
and dynamical variables from ECMWF ERA5 and humidity measurements from the In-service Aircraft for a
Global Observing System (IAGOS). Previous and current atmospheric variables within 2 ERA5 pressure layers
around the IAGOS flight altitude are used for ANN training. RHi, temperature, and geopotential exhibit the
highest impact on ANN results, while other dynamical variables are of low to moderate or high importance.
The ANN shows excellent performance, and the predicted RHi in the UT has a mean absolute error (MAE) of
5.7% and a coefficient of determination (R^2) of 0.95, which is significantly improved compared to ERA5 RHi
(MAE of 15.8 %; R^2 of 0.66). The ANN model also improves the prediction skill for all-sky UT/LS and cloudy
UTLS and removes the peak at RHiD100 %. The contrail predictions are in better agreement with Meteosat
Second Generation (MSG) observations of ice optical thickness than the results without humidity correction for
a contrail cirrus scene over the Atlantic. The ANN method can be applied to other weather models to improve
humidity predictions and to support aviation and climate research applications
Personalized uncertainty quantification in artificial intelligence
Get PDFArtificial intelligence (AI) tools are increasingly being used to help make consequential decisions about individuals. While AI models may be accurate on average, they can simultaneously be highly uncertain about outcomes associated with specific individuals or groups of individuals. For high-stakes applications (such as healthcare and medicine, defence and security, banking and finance), AI decision-support systems must be able to make personalized assessments of uncertainty in a rigorous manner. However, the statistical frameworks needed to do so are currently incomplete. Here, we outline current approaches to personalized uncertainty quantification(PUQ) and define a set of grand challenges associated with the development and use of PUQ in a range of areas, including multimodal AI, explainable AI, generative AI and AI fairness
Networks of knowledge, materials, and practice in the Neolithic Zagros
Get PDFAcross a fragmented landscape, the Neolithic communities of the Zagros Mountains (in modern Iraq and Iran) maintained complex networks of material exchange and knowledge transfer. From the early Holocene, small groups of people explored new ways of doing and being in the world, sharing innovative ideas with one another through tangible material media. Drawing on research at sites in the Central Zagros,
case studies illustrate differing approaches to the curation of networks amongst the inhabitants of highland and lowland landscapes from the Epipalaeolithic to the Chalcolithic. Through key material strands and shared networks of practice, we can identify catalysing factors behind the growth of communication networks in the Early Neolithic and consider the implications of intensified connections. The research addresses transects through time and landscapes through inter-disciplinary research at PPNA Sheikh-e Abad and Jani in the high Zagros of Iran and in the Zagros foothills at Epipalaeolithic Zarzi Cave, a PPNA open-air site Zawi Chemi Rezan, PPNB Bestansur, and Shimshara in the Kurdish Region of Iraq. This article examines material case studies from these sites and considers how engagement with networks was selective and contingent for individual communities
The papacy and crusaders: from the Saracens to Stalin
No full textExploration of the papacy's call for crusades from the eleventh to the twenty-first centuries
Image clustering and classification using content features
No full textThe ability to automatically recognise, classify, and cluster images is important
in many applied fields, including medical healthcare, astronomy, entertainment,
sports, defense, etc. Thus, it is important to develop efficient algorithms that can be
used to recognise images in different scenarios. In this thesis, three novel image-processing techniques based on content features are proposed.
First, modified probabilistic neural networks (PNNs) are introduced for image
classification based on various distance measures in probability space, in which the
input to the model is the local binary pattern (LBP) histogram of images. Conventional PNNs have an input layer that computes the Euclidean distance of pairwise
input features. The proposed modified PNN considers various probability distance
measures for computing the distances of LBP histograms between images. It is
shown that a PNN based on the Bhattacharyya distance measure is superior to other
studied measures. Moreover, using the subset of uniform LBP features is generally
better than using full LBP features.
Secondly, a novel self-tuning spectral clustering technique was proposed for
image classification application. Uniform and full-histogram LBP were used as features, while modified affinity matrices based on four distance measures were tested.
The experimental results indicate that uniform LBP features generally achieved
higher accuracy when compared to full-histogram LBP features. On the distance
measure side, Bhattacharyya distance-based affinity matrices achieved higher accuracy than other distance measures, especially in terms of large image data sets.
The disadvantage of supervised learning techniques is that they are limited to
learning from labelled data sets, which are often expensive to obtain. A novel de-
cision fusion framework is proposed by combining semi-supervised clustering with
well-known image feature analysis methods in computer vision. Initially, image
features are generated by applying the Gray level co-occurrence matrix analysis to
the processed data sets transformed by Gabor, Laplacian, or Gaussian filters. Then,
an on-line spherical k-means clustering technique guided by a minimum number of
labelled data sets is used to train the base classifiers. The final decision of classification is produced by selecting the classifier that produces the max-cosine value
among the baseline classifiers. Comparative experiments have been conducted to
demonstrate that the proposed approaches are suitable for automatic recognition
Dynamic optimal estimation with atmospheric correction smoothing for sea surface skin temperature retrieval from infrared satellite imagery
Get PDFThis study offers an in-depth exploration into Sea
Surface Skin Temperature (SSTskin) from the Haiyang-1D (HY-1D)
Chinese Ocean Color and Temperature Scanner (COCTS). The
main components include inter-calibration, cloud detection, and
SSTskin retrieval. First, we conduct the inter-calibration of COCTS
infrared channels utilizing Visible Infrared Imaging Radiometer
Suite (VIIRS) as the reference instrument. A double-differencing
methodology is employed to evaluate and correct the COCTS
calibration. Next, we introduce a physically based deep learning
algorithm for cloud detection, designed to interpret complex
textures in satellite imagery. The algorithm demonstrates the
superior performance across diverse conditions and geographical
areas, especially reducing false flagging of ocean fronts. Lastly, we
propose an Optimal Estimation (OE) methodology for COCTS
SSTskin retrieval. One focus is on estimating appropriate
covariance matrices within the OE algorithm, including an
innovative method for dynamically setting the prior SST
uncertainty appropriate to local spatial variability. A second focus
is to employ atmospheric correction smoothing algorithm of OE.
Both these measures combine to suppress noise and enhance
sensitivity of SSTskin. We assign quality levels to the retrieved
SSTskin data. The high-quality COCTS SSTskin is validated using
iQuam in-situ data. Our results indicate the bias of -0.20 °C and
the robust standard deviation of 0.27 °C between COCTS and insitu SST, with an average sensitivity of 0.87. These findings affirm
that the successful implementation of these methodologies
significantly enhances the accuracy and reliability of SSTskin data
from HY-1D COCTS. This advancement provides substantial
benefits to expand the global high precision SSTskin dataset
Evaluating the effectiveness of sodium hypochlorite for genomic DNA decontamination
Get PDFEnvironmental DNA (eDNA) is an increasingly popular, sensitive, and cost-efficient method for studying biodiversity and detecting species. This non-invasive approach involves collecting environmental samples that contain genetic material shed by organisms into their surroundings. Due to the method’s sensitivity, robust decontamination strategies are crucial, with sodium hypochlorite, commonly known as bleach, frequently employed. Despite its widespread use, there is no consensus on the most effective bleach concentration, leading to inconsistencies in how the chemical is used in research. This study aimed to determine the minimum concentration of bleach needed for effective decontamination. Genomic DNA of signal crayfish was treated with various concentrations of bleach, ranging from 0.01% to 5% (w/w). Results were observed using Qubit High Sensitivity reagents, quantitative PCR, agarose gel electrophoresis and the Agilent TapeStation. Our results indicate that a minimum concentration of 0.5% (w/w) bleach is sufficient to prevent the detection of genomic DNA by the techniques tested. These results provide important insights into the use of bleach for decontamination in eDNA research. Establishing a standard bleach concentration for decontamination protocols will help to reduce inconsistencies and enhance the reliability of eDNA studies
Earth's energy imbalance more than doubled in recent decades
Get PDFGlobal warming results from anthropogenic greenhouse gas emissions which upset the delicate balance between the incoming sunlight, and the reflected and emitted radiation from Earth. The imbalance leads to energy accumulation in the atmosphere, oceans and land, and melting of the cryosphere, resulting in increasing temperatures, rising sea levels, and more extreme weather around the globe. Despite the fundamental role of the energy imbalance in regulating the climate system, as known to humanity for more than two centuries, our capacity to observe it is rapidly deteriorating as satellites are being decommissioned