Calibration Tool Research Articles

In-Situ Form Metrology of Structured Composite Surfaces Using Hybrid Structured-Light Measurement with a Novel Calibration Method

Accurately measuring the form of structured composite surfaces in situ is critical for advanced manufacturing in various engineering fields. However, challenges persist in achieving precision, miniaturization, and calibration using current structured light techniques. In this work, a hybrid structured light with compact configuration is proposed for the in-situ and embedded form metrology of structured composite surfaces. The proposed technique contains three subsystems: phase-measuring deflectometry (PMD), fringe projection profilometry (FPP), and stereo vision. The PMD subsystem accurately reconstructs the form data of specular surfaces based on the principle of structured light reflection, and the FPP subsystem measures rough surfaces by projecting structured light onto them. Output data from these subsystems are then stitched to reconstruct a complete form of the measured composite surfaces. The compact configuration is explored to reduce the system volume to improve the technique’s portability and embedded measurement ability. With the stereo vision subsystem as an intermediary, a novel calibration method is applied for calculating the relations among the subsystems to improve the hybrid structured light system’s calibration and data stitching accuracy between PMD and FPP subsystems. Three calibration tools are designed and manufactured for the proposed calibration technique. A portable metrology prototype based on the proposed hybrid structured light technique’s principle and configuration is also developed and then calibrated using the novel calibration method. An embedded measurement experiment in a diamond turning machine demonstrates that the proposed techniques can achieve 400 nm form accuracy in specular surface measurement.

Calibration, Clinical Utility and Specificity of Clinical Decision Support Tools in Inflammatory Bowel Disease

Background and AimsClinical decision support tools (CDSTs) have been developed to predict response to vedolizumab (VDZ) and ustekinumab (UST) in Crohn’s disease (CD) and ulcerative colitis (UC). In addition to assessing their discrimination performance, our study aimed to evaluate their calibration, clinical utility and specificity. MethodsWe included 280 CD and 218 UC patients initiating VDZ, and 194 CD patients initiating UST. We assessed discrimination by comparing rates of effectiveness outcomes between response probability groups forecasted by CDSTs. Calibration curves and decision curve analysis evaluated the calibration and clinical utility of VDZ-CDSTs. Additionally, we examined the agreement between UST-CDST and VDZ-CDST in assigning response probability groups among CD patients starting UST. ResultsIn the overall cohort, CDSTs allocated 7.2%, 50.0% and 42.8% of the patients to the low, intermediate and high response probability groups, respectively. VDZ-CDSTs groups demonstrated significant differences in the rates of clinical and endoscopic response and remission, while UST-CDST groups showed significant discrimination only for clinical remission. Although VDZ-CDSTs overestimated clinical remission rates, they more accurately predicted rates of VDZ persistence without need for surgery or dose escalation. Compared to empirically treating all patients with VDZ, VDZ-CDSTs yielded higher net benefits in selecting patients who would continue VDZ without need for surgery or dose escalation. Finally, the agreement between UST-CDST and VDZ-CDST in predicting response was 73.7%. ConclusionVDZ-CDSTs significantly discriminated response to VDZ and were more beneficial in identifying patients who would continue therapy without requiring surgery or dose escalation, compared to treating all patients empirically.

Calibration of additional computational tools expands ClinGen recommendation options for variant classification with PP3/BP4 criteria.

We previously developed an approach to calibrate computational tools for clinical variant classification, updating recommendations for the reliable use of variant impact predictors to provide evidence strength up to Strong. A new generation of tools using distinctive approaches have since been released, and these methods must be independently calibrated for clinical application. Using our local posterior probability-based calibration and our established data set of ClinVar pathogenic and benign variants, we determined the strength of evidence provided by three new tools (AlphaMissense, ESM1b, VARITY) and calibrated scores meeting each evidence strength. All three tools reached the Strong level of evidence for variant pathogenicity and Moderate for benignity, though sometimes for few variants. Compared to previously recommended tools, these yielded at best only modest improvements in the tradeoffs of evidence strength and false positive predictions. At calibrated thresholds, three new computational predictors provided evidence for variant pathogenicity at similar strength to the four previously recommended predictors (and comparable with functional assays for some variants). This calibration broadens the scope of computational tools for application in clinical variant classification. Their new approaches offer promise for future advancement of the field.

161 Development, deployment, and calibration of genomic selection tools to improve performance traits for Canadian beef cattle

Abstract Genomic selection has the potential to accelerate the genetic improvement rate for difficult/expensive to measure traits such as feed efficiency and fertility traits in beef cattle. To develop genomic selection tools for Canadian beef cattle, we collected or sourced phenotypic data of feed efficiency along with other traits including carcass merit, female feed intake and fertility related traits, and their genotypes from both research and commercial herds. The phenotypic data were consolidated and recorded for contemporary groups, leading to the development of data sets including 11,292 beef cattle with residual feed intake, dry matter intake, average daily gain, metabolic body weight, 7,299 to 8,081 cattle with carcass merit traits, and 1,802 to 2,792 cows with feed intake and fertility traits after quality controls. Genotype data of the animals from various single nucleotide polymorphisms (SNP) panels were merged to the same allele format of 50K, and then imputed to 770K SNPs, and eventually to whole genome sequence variants. The refined Canadian beef cattle data sets have not only allowed us to investigate genetic architectures of the beef performance traits but also enabled the development of genomic selection tools including molecular breeding values and multiple trait selection indexes with a moderate to moderately high accuracy for the traits through a cross-validation of within reference data set. The genomic selection tools have been successfully deployed to over 10,415 breeding candidates submitted by Canadian beef producers through multiple demonstration projects. The average accuracy of the molecular breeding values and multiple trait selection index of the commercial beef cattle ranged from 0.38 to 0.49 for feed efficiency and carcass merit traits, and from 0.26 to 0.38 for female feed intake and fertility traits. Current research is focusing on calibrating the genomic selection tools with larger data sets and with more advanced genomic prediction methods.

Enhancing surgical navigation: a robust hand-eye calibration method for the Microsoft HoloLens 2.

Optical-see-through head-mounted displays have the ability to seamlessly integrate virtual content with the real world through a transparent lens and an optical combiner. Although their potential for use in surgical settings has been explored, their clinical translation is sparse in the current literature, largely due to their limited tracking capabilities and the need for manual alignment of virtual representations of objects with their real-world counterparts. We propose a simple and robust hand-eye calibration process for the depth camera of the Microsoft HoloLens2, utilizing a tracked surgical stylus fitted with infrared reflective spheres as the calibration tool. Using a Monte Carlo simulation and a paired-fiducial registration algorithm, we show that a calibration accuracy of 1.65mm can be achieved with as little as 6 fiducial points. We also present heuristics for optimizing the accuracy of the calibration. The ability to use our calibration method in a clinical setting is validated through a user study, with users achieving a mean calibration accuracy of 1.67mm in an average time of 42s. This work enables real-time hand-eye calibration for the Microsoft HoloLens2, without any need for a manual alignment process. Using this framework, existing surgical navigation systems employing optical or electromagnetic tracking can easily be incorporated into an augmented reality environment with a high degree of accuracy.

A data science pipeline applied to Australia's 2022 COVID-19 Omicron waves

The field of software engineering is advancing at astonishing speed, with packages now available to support many stages of data science pipelines. These packages can support infectious disease modelling to be more robust, efficient and transparent, which has been particularly important during the COVID-19 pandemic. We developed a package for the construction of infectious disease models, integrated it with several open-source libraries and applied this composite pipeline to multiple data sources that provided insights into Australia's 2022 COVID-19 epidemic. We aimed to identify the key processes relevant to COVID-19 transmission dynamics and thereby develop a model that could quantify relevant epidemiological parameters.The pipeline's advantages include markedly increased speed, an expressive application programming interface, the transparency of open-source development, easy access to a broad range of calibration and optimisation tools and consideration of the full workflow from input manipulation through to algorithmic generation of the publication materials. Extending the base model to include mobility effects slightly improved model fit to data, with this approach selected as the model configuration for further epidemiological inference. Under our assumption of widespread immunity against severe outcomes from recent vaccination, incorporating an additional effect of the main vaccination programs rolled out during 2022 on transmission did not further improve model fit. Our simulations suggested that one in every two to six COVID-19 episodes were detected, subsequently emerging Omicron subvariants escaped 30–60% of recently acquired natural immunity and that natural immunity lasted only one to eight months on average. We documented our analyses algorithmically and present our methods in conjunction with interactive online code notebooks and plots.We demonstrate the feasibility of integrating a flexible domain-specific syntax library with state-of-the-art packages in high performance computing, calibration, optimisation and visualisation to create an end-to-end pipeline for infectious disease modelling. We used the resulting platform to demonstrate key epidemiological characteristics of the transition from the emergency to the endemic phase of the COVID-19 pandemic.

ProDOL: a general method to determine the degree of labeling for staining optimization and molecular counting

Determining the label to target ratio, also known as the degree of labeling (DOL), is crucial for quantitative fluorescence microscopy and a high DOL with minimal unspecific labeling is beneficial for fluorescence microscopy in general. Yet robust, versatile and easy-to-use tools for measuring cell-specific labeling efficiencies are not available. Here we present a DOL determination technique named protein-tag DOL (ProDOL), which enables fast quantification and optimization of protein-tag labeling. With ProDOL various factors affecting labeling efficiency, including substrate type, incubation time and concentration, as well as sample fixation and cell type can be easily assessed. We applied ProDOL to investigate how human immunodeficiency virus-1 pathogenesis factor Nef modulates CD4 T cell activation measuring total and activated copy numbers of the adapter protein SLP-76 in signaling microclusters. ProDOL proved to be a versatile and robust tool for labeling calibration, enabling determination of labeling efficiencies, optimization of strategies and quantification of protein stoichiometry.

A mid-Cretaceous carbon isotope reference curve for the SE Neo-Tethys region (Zagros, W Iran)

Stable carbon isotope chemostratigraphy and the associated reference curves have proved to be valuable tools for stratigraphic calibration and long-distance correlations. Nonetheless, the reference curves covering the Cretaceous have mostly been produced from localities located within the W Neo-Tethys. In order to establish a reference curve for the SE Neo-Tethys margin, an Aptian–Albian hemipelagic carbonate-rich succession deposited along the Zagros Basin (SW Iran) was examined for planktic foraminifera biostratigraphy, carbon isotope stratigraphy, and sedimentological characteristics. Eight biozones including Globigerinelloides ferreolensis, Globigerinelloides algerianus, Hedbergella trocoidea, Paraticinella bejaouaensis, Muricohedbergella planispira, Ticinella (T.) primula, Biticinella breggiensis, and Pseudothalmanninella (P.)ticinensis as well as two subzones enclosing T. praeticinensis and P. subticinensis assign the strata to the early late Aptian–late Albian age. A carbon isotope record constrained by the planktic foraminifera zonation scheme shows good concordance with existing composite reference curves from the W Neo-Tethys region. Several carbon isotope excursions (CIEs) characterizing global marine events including Oceanic Anoxic Event (OAE) 1a, OAE1b, OAE1c and associated sub-events can be identified in the studied section. Deoxygenated bottom waters associated with the formation of OAE1a and OAE1b are indicated by distinct lithological signatures including enrichment in pyrite and glauconite, nodular and stratiform chert beds, as well as enhanced organic matter contents. We integrate the here-studied interval with a previously published upper Albian−Turonian succession from the same outcrop section, forming a composite, stratigraphically well-constrained section. This results in a high-resolution carbon isotope record for the SE Neo-Tethys margin, considered to represent an expedient reference Aptian–Turonian curve for this region. Correlation of this new curve with previously published records from the Middle East has helped to address ambiguities regarding the stratigraphic positions of the early/late Aptian and Aptian/Albian boundaries identified by previous studies.

A Novel Approach of Surface Texture Mapping for Cone-Beam Computed Tomography in Image-Guided Surgical Navigation.

The demand for cone-beam computed tomography (CBCT) imaging in clinics, particularly in dentistry, is rapidly increasing. Preoperative surgical planning is crucial to achieving desired treatment outcomes for imaging-guided surgical navigation. However, the lack of surface texture hinders effective communication between clinicians and patients, and the accuracy of superimposing a textured surface onto CBCT volume is limited by dissimilarity and registration based on facial features. To address these issues, this study presents a CBCT imaging system integrated with a monocular camera for reconstructing the texture surface by mapping it onto a 3D surface model created from CBCT images. The proposed method utilizes a geometric calibration tool for accurate mapping of the camera-visible surface with the mosaic texture. Additionally, a novel approach using 3D-2D feature mapping and surface parameterization technology is proposed for texture surface reconstruction. Experimental results, obtained from both real and simulation data, validate the effectiveness of the proposed approach with an error reduction to 0.32 mm and automated generation of integrated images. These findings demonstrate the robustness and high accuracy of our approach, improving the performance of texture mapping in CBCT imaging.

Enhanced SWAT calibration through intelligent range-based parameter optimization

Hydrological models are vital tools in environmental management. Weaknesses in model robustness for hydrological parameters transfer uncertainties to the model outputs. For streamflow, the optimized parameters are the primary source of uncertainty. A reliable calibration approach that reduces prediction uncertainty in model simulations is crucial for enhancing model robustness and reliability. The optimization of parameter ranges is a key aspect of parameter calibration, yet there is a lack of literature addressing the optimization of parameter ranges in hydrological models. In this paper, we introduce a parameter calibration strategy that applies a clustering technique, specifically the Self-Organizing Map (SM), to intelligently navigate the parameter space during the calibration of the Soil and Water Assessment Tool (SWAT) model for monthly streamflow simulation in the Baishan Basin, Jilin Province, China. We selected the representative algorithm, the Sequential Uncertainty Fitting version 2 (SUFI-2), from the commonly used SWAT Calibration and Uncertainty Programs for comparison. We developed three schemes: SUFI-2, SUFI-2-Narrowing Down (SUFI-2-ND), and SM. Multiple diagnostic error metrics were used to compare simulation accuracy and prediction uncertainty. Among all schemes, SM outperformed the others in describing watershed streamflow, particularly excelling in the simulation of spring snowmelt runoff (baseflow period). Additionally, the prediction uncertainty was effectively controlled, demonstrating the SM's adaptability and reliability in the interval optimization process. This provides managers with more credible prediction results, highlighting its potential as a valuable calibration tool in hydrological modeling.

Quantifying Lumbar Foraminal Volumetric Dimensions: Normative Data and Implications for Stenosis-Part 2 of a Comprehensive Series.

Lumbar foraminal stenosis (LFS) occurs primarily due to degenerative changes in older adults, affecting the spinal foramina and leading to nerve compression. Characterized by pain, numbness, and muscle weakness, LFS arises from structural changes in discs, joints, and ligaments, further complicated by factors like inflammation and spondylolisthesis. Diagnosis combines patient history, physical examination, and imaging, while management ranges from conservative treatment to surgical intervention, underscoring the need for a tailored approach. This multicenter study, conducted over six years at a tertiary hospital, analyzed the volumetric dimensions of lumbar foramina and their correlation with nerve structures in 500 patients without lumbar pathology. Utilizing high-resolution MRI with a standardized imaging protocol, eight experienced researchers independently reviewed the images for accurate measurements. The study emphasized quality control through the calibration of measurement tools, double data entry, validation checks, and comprehensive training for researchers. To ensure reliability, interobserver and intraobserver agreements were analyzed, with statistical significance determined by kappa statistics and the Student's t-test. Efforts to minimize bias included blinding observers to patient information and employing broad inclusion criteria to mitigate referral and selection biases. The methodology and findings aim to enhance the understanding of normal lumbar foramina anatomy and its implications for diagnosing and treating lumbar conditions. The study's volumetric analysis of lumbar foramina in 500 patients showed a progressive increase in foraminal volume from the L1/L2 to the L5/S1 levels, with significant enlargement at L5/S1 indicating anatomical and biomechanical complexity in the lumbar spine. Lateral asymmetry suggested further exploration. High interobserver and intraobserver agreement levels (ICC values of 0.91 and 0.95, respectively) demonstrated the reliability and reproducibility of measurements. The patient cohort comprised 58% males and 42% females, highlighting a balanced gender distribution. These findings underscore the importance of understanding foraminal volume variations for lumbar spinal health and pathology. Our study significantly advances spinal research by quantifying lumbar foraminal volumes, revealing a clear increase from the L1/L2 to the L5/S1 levels, indicative of the spine's adaptation to biomechanical stresses. This provides clinicians with a precise tool to differentiate between pathological narrowing and normal variations, enhancing the detection and treatment of lumbar foraminal stenosis. Despite limitations like its cross-sectional design, the strong agreement in measurements underscores the method's reliability, encouraging future research to further explore these findings' clinical implications.

Ecological control of the Pelsonian colonisation event (Anisian, Triassic) leading to the first settlement of Tetractinella-beds (Brachiopoda) in the Iberian palaeomargins. Palaeobiogeographical and biostratigraphical significance

Marine benthic biota recovery after the Permian/Triassic extinction is still a challenging topic in several fossil clades such as brachiopods. In fact, available data from the Early-Middle Triassic are scarce worldwide and their global record derives from limited localities throughout the Tethys Ocean. This work presents the first record of the Anisian Tetractinella-beds (Brachiopoda, Athyridida) in the peri-Iberian epicontinental platforms established in the westernmost Tethys margins. Their utility as a reliable biohorizon marker is emphasised as they provide an effective tool for age calibration and correlation in the middle Anisian (Pelsonian) successions, also supported by the co-occurring fossil assemblage. In the South-Iberian Palaeomargin, Tetractinella-beds occur in an intensively-burrowed shallow-marine succession. The rich ichnological content has been exhaustively evaluated in order to assess the ecological factors controlling the development of the brachiopod fauna. Three ichnoassemblages are documented dominated by Oravaichnium, Balanoglossites and Thalassinoides, respectively. The gathered faunal, ichnological, taphonomical, and sedimentological data reveal an alternation of sea-level changes influencing the composition of the Anisian marine communities. The first stage consists of near-shore restricted environments sporadically disturbed by short-term shallow-marine interludes, characterised by the presence of Oravaichnium and its associated fauna. Subsequently, the Pelsonian transgression event is recorded (MID-2), documented by a firm-ground surface pointed out by the development of a Balanoglossites surface, followed by the highstand, when shallow epicontinental environments developed in connection with open-marine areas. This phase involved the spreading of Tetractinella and deep-water bivalves. Finally, near-shore restricted conditions were re-established typified by Oravaichnium and the last occurrences of Tetractinella, with the disappearance of the open sea-water fauna. The maximum flooding reached after the Pelsonian transgression facilitated new palaeobiogeographical migration routes, allowing for an Alpine-Germanic dispersion of taxa throughout the North-South epicontinental Tethyan seaway as well as the earliest first faunal migration event from the Sephardic Bioprovince northwards to the South-Iberian palaeomargin.

ChatGPT as an item calibration tool: Psychometric insights in a high-stakes examination

Introduction:ChatGPT has attracted a lot of interest worldwide for its versatility in a range of natural language tasks, including in the education and evaluation industry. It can automate time- and labor-intensive tasks with clear economic and efficiency gains. Methods: This study evaluated the potential of ChatGPT to automate psychometric analysis of test questions from the 2020 Portuguese National Residency Selection Exam (PNA). ChatGPT was queried 100 times with the 150 MCQ from the exam. Using ChatGPT's responses, difficulty indices were calculated for each question based on the proportion of correct answers. The predicted difficulty levels were compared to the actual difficulty levels of the 2020 exam MCQ’s using methods from classical test theory. Results:ChatGPT's predicted item difficulty indices positively correlated with the actual item difficulties (r (148) = −0.372, p < .001), suggesting a general consistency between the real and the predicted values. There was also a moderate significant negative correlation between the difficulty index predicted by ChatGPT and the number of challenges (r (148) = −0.302, p < .001), highlighting ChatGPT′s potential for identifying less problematic questions. Conclusion: These findings unveiled ChatGPT’s potential as a tool for assessment development, proving its capability to predict the psychometric characteristics of high-stakes test items in automated item calibration without pre-testing in real-life scenarios.

EMBER creates a unified space for independent breast cancer transcriptomic datasets enabling precision oncology

Transcriptomics has revolutionized biomedical research and refined breast cancer subtyping and diagnostics. However, wider use in clinical practice is hampered for a number of reasons including the application of transcriptomic signatures as single sample predictors. Here, we present an embedding approach called EMBER that creates a unified space of 11,000 breast cancer transcriptomes and predicts phenotypes of transcriptomic profiles on a single sample basis. EMBER accurately captures the five molecular subtypes. Key biological pathways, such as estrogen receptor signaling, cell proliferation, DNA repair, and epithelial-mesenchymal transition determine sample position in the space. We validate EMBER in four independent patient cohorts and show with samples from the window trial, POETIC, that it captures clinical responses to endocrine therapy and identifies increased androgen receptor signaling and decreased TGFβ signaling as potential mechanisms underlying intrinsic therapy resistance. Of direct clinical importance, we show that the EMBER-based estrogen receptor (ER) signaling score is superior to the immunohistochemistry (IHC) based ER index used in current clinical practice to select patients for endocrine therapy. As such, EMBER provides a calibration and reference tool that paves the way for using RNA-seq as a standard diagnostic and predictive tool for ER+ breast cancer.

Bayes_Opt-SWMM: A Gaussian process-based Bayesian optimization tool for real-time flood modeling with SWMM

Real-time flood model plays a pivotal role in averting urban flood damage, particularly when there is minimal lead time for preparatory measures. However, urban flood modeling in real-time often contends with inherent uncertainties arising from input data uncertainty and parameter ambiguities. This study introduces a real-time calibration (RTC) tool called Bayes_Opt-SWMM , specifically tailored for real-time urban flood modeling and uncertainty optimization. This tool leverages the Gaussian process-based Bayesian optimization algorithm and interfaces seamlessly with the Stormwater Management Model (SWMM). It integrates real-time model forcing data and flood monitoring collected through sensors and gauges which are strategically placed within critical locations of urban drainage systems. Our approach hinges on the Surrogate Model based Uncertainty Optimization (SMUO) concept, providing an avenue for enhancing real-time flood modeling. Bayes_Opt-SWMM runs the optimization process using a surrogate model called Gaussian Process emulator with two inference methods: (1) the Gaussian Process (GP) model and (2) Markov Chain Monte Carlo (MCMC) algorithm in GP model (GP_MCMC). Furthermore, three acquisition functions, namely Expected Improvement (EI), Maximum Probability of Improvement (MPI), and Lower Confidence Bound (LCB), facilitate optimal parameter fitting within the surrogate models. The efficiency of GP-based surrogate models in learning SWMM model parameters, leads to an improved uncertainty quantification and accelerated real-time flood modeling in urban areas. Overall, Bayes_Opt-SWMM emerges as a cost-effective and valuable tool for real-time flood modeling and monitoring, with significant potential for managing intelligent storm water systems in urban environments.

Calibration of a Hybrid Machine Tool from the Point of View of Positioning Accuracy

The development of machine tools in the last twenty years includes, among other things, the application of mechanisms with a non-linear kinematic structure as the mechanical basis of machines. This results in significant improvements in kinematic characteristics and problems related to non-linear dependencies of the accuracy of the drive elements and the realization of movement in the machine’s external coordinates. The paper presents an approach to machine tool calibration based on the original O-X glide mechanism based on the ISO 230-4 standard with the mono- and bi-directional compensation of systematic errors and adaptation to the specifics of the mechanism’s kinematics. A machine tool prototype was designed and built for the research presented in the paper. The obtained results indicate the possibility of applying the existing recommendations and standards for testing the accuracy of machine tools with the need to correct the methodology by using linear and non-linear kinematic structures in machine tools.

Theoretical and Experimental Study on the Performance of Hermetic Diaphragm Squeeze Film Dampers for Gas-Lubricated Bearings

Low damping characteristics have always been a key sticking points in the development of gas bearings. The application of squeeze film dampers can significantly improve the damping performance of gas lubricated bearings. This paper proposed a novel hermetic diaphragm squeeze film damper (HDSFD) for oil-free turbomachinery supported by gas lubricated bearings. Several types of HDSFDs with symmetrical structure were proposed for good damping performance. By considering the compressibility of the damper fluid, based on hydraulic fluid mechanics theory, a dynamic model of HDSFDs under medium is proposed, which successfully reflects the frequency dependence of force coefficients. Based on the dynamic model, the effects of damper fluid viscosity, bulk modulus of damper fluid, thickness of damper fluid film and plunger thickness on the dynamic stiffness and damping of HDSFDs were analyzed. An experimental test rig was assembled and series of experimental studies on HDSFDs were conducted. The damper fluid transverse flow is added to the existing HDSFD concept, which aims to make the dynamic force coefficients independent of frequency. Although the force coefficient is still frequency dependent, the damping coefficient at high frequency excitation with damper fluid supply twice as that without damper fluid supply. The results serve as a benchmark for the calibration of analytical tools under development.

A UK MW catalogue derived from coda envelopes

Summary The United Kingdom (UK) experiences low-to-moderate levels of seismicity; only 12 onshore earthquakes with local magnitude (ML) ≥ 4.0 have been recorded in the past 20 years. It is therefore difficult to estimate moment magnitude (MW) using conventional moment tensor inversion for the majority of UK seismicity, resulting in limited reliable estimates of MW. To address this, we calibrated coda envelopes at 16 broadband seismic stations distributed across the UK, to produce a MW catalogue for 100 events with MW≥2.13 that occurred since 2006. This was achieved using the open-source Coda Calibration Tool, which requires independent source parameter estimates for calibration. For 13 UK events between 2006 and 2022, we used spectral modelling to estimate apparent stress (0.32 to 1.74 MPa), and moment tensor inversion to estimate MW (3.35 to 4.52). These independent source parameters formed a subset of the inputs into the final calibration, which used seismic data from 33 events with coda-derived values of 2.57$\le $Mw$\le $4.49. The resultant coda calibration parameters were applied to 67 further events (MW≥2.13). The coda envelopes exhibit slow seismic coda decay across the UK, with significant energy up to 20 Hz, consistent with other regions of low tectonic activity. This MW catalogue, and the application of the calibration to future UK seismic events, will be useful for both assessing seismic hazard and event characterisation.

A Rapid Construction Method for High-Throughput Wheat Grain Instance Segmentation Dataset Using High-Resolution Images

Deep learning models can enhance the detection efficiency and accuracy of rapid on-site screening for imported grains at customs, satisfying the need for high-throughput, efficient, and intelligent operations. However, the construction of datasets, which is crucial for deep learning models, often involves significant labor and time costs. Addressing the challenges associated with establishing high-resolution instance segmentation datasets for small objects, we integrate two zero-shot models, Grounding DINO and Segment Anything model, into a dataset annotation pipeline. Furthermore, we encapsulate this pipeline into a software tool for manual calibration of mislabeled, missing, and duplicated annotations made by the models. Additionally, we propose preprocessing and postprocessing methods to improve the detection accuracy of the model and reduce the cost of subsequent manual correction. This solution is not only applicable to rapid screening for quarantine weeds, seeds, and insects at customs but can also be extended to other fields where instance segmentation is required.

The Effects of Climate Change on Streamflow, Nitrogen Loads, and Crop Yields in the Gordes Dam Basin, Turkey

The Mediterranean region is highly vulnerable to climate change. Longer and more intense heatwaves and droughts are expected. The Gordes Dam in Turkey provides drinking water for Izmir city and irrigation water for a wide range of crops grown in the basin. Using the Soil and Water Assessment Tool (SWAT), this study examined the effects of projected climate change (RCP 4.5 and RCP 8.5) on the simulated streamflow, nitrogen loads, and crop yields in the basin for the period of 2031–2060. A hierarchical approach to define the hydrological response units (HRUs) of SWAT and the Fast Automatic Calibration Tool (FACT) were used to reduce computational time and improve model performance. The simulations showed that the average annual discharge into the reservoir is projected to increase by between 0.7 m3/s and 4 m3/s under RCP 4.5 and RCP 8.5 climate change scenarios. The steep slopes and changes in precipitation in the study area may lead to higher simulated streamflow. In addition, the rising temperatures predicted in the projections could lead to earlier spring snowmelt. This could also lead to increased streamflow. Projected nitrogen loads increased by between 8.8 and 25.1 t/year. The results for agricultural production were more variable. While the yields of poppy, tobacco, winter barley, and winter wheat will increase to some extent because of climate change, the yields of maize, cucumbers, and potatoes are all predicted to be negatively affected. Non-continuous and limited data on water quality and crop yields lead to uncertainties, so that the accuracy of the model is affected by these limitations and inconsistencies. However, the results of this study provide a basis for developing sustainable water and land management practices at the catchment scale in response to climate change. The changes in water quality and quantity and the ecological balance resulting from changes in land use and management patterns for economic benefit could not be fully demonstrated in this study. To explore the most appropriate management strategies for sustainable crop production, the SWAT model developed in this study should be further used in a multi-criteria land use optimization analysis that considers not only crop yields but also water quantity and quality targets.