Precise Measurements Research Articles

Augmented Reality-Based Chronic Wound Healing Assessment Using 3D Stereoscopic Imaging and Self-Organizing Maps

Background: Chronic wounds (CW) present significant public health challenges, impacting individuals' well-being and daily activities. Traditional wound measurement methods, such as the ruler technique, are time-consuming, imprecise, and pose infection risks. Advances in wound care technology, including artificial intelligence (AI) and augmented reality (AR), have improved wound healing (WH) assessment accuracy and enhanced clinician-patient communication. Methods: This study developed a novel CW evaluation device combining AR and a stereoscopic camera system. The device captures images using two Leopard Imaging LI-OV580 stereo cameras and a pico-projector to create a 3D wound model. The system calculates the wound area by processing stereoscopic images, utilizing structured-from-motion techniques and convolutional neural networks (CNNs). Additionally, the device measures epipolar and projection errors (PE) during AR-based wound evaluation. A Self-Organizing Map (SOM) was employed to refine the 3D reconstruction of the wound. Results: The AR-PE analysis demonstrated the device's accuracy, with the lowest PE recorded at 20 cm from the wound area. The projection error increased as the measurement distance moved toward the edges of the wound. The average epipolar error (EE) was 0.46 pixels, indicating high precision in stereoscopic measurements. Conclusion: The study introduced a non-invasive CW evaluation device that improves wound assessment accuracy and clinician-patient interaction. By utilizing 3D wound modeling and AR, the system enables more effective communication and progress tracking during WH. The results suggest this technology could enhance CW management, offering a reliable alternative to traditional methods.

A novel testing method for ultra-low-frequency vibration signal based on passive radio frequency tag sensing.

Ultra-low-frequency vibration is prevalent in many critical research fields. Nevertheless, for ultra-low-frequency vibration signals below 1Hz, there is currently a lack of a cost-effective and efficient measurement method. A new ultra-low-frequency vibration signal testing method based on the passive radio frequency tag phase is proposed using the Radio Frequency Identification (RFID) sensing method. By employing vibration detection on ultra-low-frequency vibration signals, the effectiveness of the proposed approach across different frequencies is validated while thoroughly considering factors such as measurement range, precision, distance, and occlusion effects. The results indicate that this method can accurately measure ultra-low frequency vibration signals as low as 0.01Hz, with an average relative error of only less than 1.5% for all measurement results, and the error decreases with increasing detection frequency. For the measurement of a 1Hz vibration signal, the average relative error is less than 1%. In addition, the measurement accuracy remains unaffected by distance or occlusion. Sensitivity and stability tests are also conducted. Continuous monitoring for 8 hours demonstrates the excellent measurement stability of the proposed method. Finally, a performance comparison has been made with laser displacement sensors commonly used in non-contact ultra-low-frequency measurement methods. The results show that the RFID sensing method can detect lower vibration frequencies and has a larger amplitude measurement range and better environmental adaptability. Overall, for ultra-low-frequency vibration, this method offers advantages such as high precision, passive non-contact operation, non-line-of-sight path monitoring, affordability, and convenience. These attributes render it suitable for extensive application in various engineering scenarios requiring ultra-low-frequency vibration testing.

GGR Handbook of Rock and Mineral Analysis Chapter 1 (Part 1) Geoanalytical Metrology

This chapter (Geoanalytical Metrology) is a contribution to the Geostandards and Geoanalytical Research Handbook of Rock and Mineral Analysis – an online textbook that is a fully revised and updated edition of Handbook of Silicate Rock Analysis, which was written by Philip J. Potts and published in 1987 by Blackie and Son (Glasgow). This second edition comprises chapters, written by prominent research scientists, designed to provide comprehensive overviews of the relevant techniques for the elemental characterisation of rocks and minerals. Chapters are designed to allow new practitioners to the field (including research students) to attain a comprehensive understanding of the theory, practice and capabilities of each technique, as well as being of benefit to established research geoanalysts. In addition to the content, chapter titles have been revised where appropriate to reflect progress in this field.Chapter 1, Part 1 (from Section 1 of the handbook dealing with fundamentals of measurement and instrument design) first sets out the overarching conventions that operate in analytical chemistry, including a description of the international organisations and systems that regulate the standards governing the discipline. This is followed by coverage of the statistical basis on which geoanalytical data sets are treated, analysed and interpreted, which summarises most of the relevant tests and terminology employed in this field. The methods by which the calibration of measured signals from instrumental techniques is tackled, followed by method validation, which covers aspects including measurement uncertainty, bias, precision and trueness. Sections detailing metrological traceability and quality management conclude this chapter.

Demodulation algorithm of low coherent heterodyne interference signal for axial clearance measurement of rotating machinery

The time-domain low coherent heterodyne interference axial clearance measurement method has the challenges of small range and low precision in the clearance measurement of rotating machinery. An axial clearance demodulation algorithm based on the windowed power spectrum density transform (WPSD) is proposed. Based on the characteristics of low coherent heterodyne interference signal, the relationship model between window width and rotating structure motion parameters (amplitude of axial clearance change, rotor speed) is constructed, and the optimal window width mathematical model is obtained. The axial clearance measurement experiments are completed under different axial clearance measurement distances based on an experimental clearance platform, different rotor speeds and different axial clearance variation amplitudes. The experimental results show that the absolute error of the axial clearance reconstruction signal is less than 17 μm and the relative error is less than 4.8 % when the axial clearance measurement distance is 2.1 mm ∼ 11.3 mm, the rotor speed is in the range of 6000 rad/min ∼ 12000 rad/min, and the variation of axial clearance is in the range of 150 μm ∼ 350 μm. The experimental and simulation results are basically consistent.

Unidimensional or multidimensional? Revisiting the psychometrics of PHQ-9 and EPDS using bifactor model and item response theory in 2939 Chinese perinatal women

BackgroundPerinatal depression often goes undetected and untreated in low- and middle-income countries like China. Reliable screening tools can improve this situation. The Patient Health Questionnaire-9 (PHQ-9) and the Edinburgh Postnatal Depression Scale (EPDS), two widely used tools, often exhibit inconsistent factor structures, leading to debates regarding their unidimensionality versus multidimensionality and casting doubts on their psychometric properties. MethodsOur study aimed to assess the utility of PHQ-9 and EPDS in Chinese perinatal women and to address the debate by employing the bifactor model and item response theory (IRT). We enrolled 2939 perinatal women from a maternity and infant health hospital serving all 16 districts of Shanghai. The bifactor model was used to examine the factor structure of PHQ-9 and EPDS, while IRT analysis evaluated the psychometric properties. ResultsThe indices derived from the bifactor model indicated that both PHQ-9 and EPDS should be used as unidimensional measurements. All items in PHQ-9 and EPDS showed adequate discriminative ability and difficulty, but certain items require further refinement. PHQ-9 demonstrated better measurement precision at high levels of latent depression than EPDS. LimitationsThese findings might not generalize to perinatal women in impoverished areas. The absence of clinical diagnoses limited the exploration of sensitivity and specificity. ConclusionsPHQ-9 and EPDS are effective tools for detecting depression in Chinese perinatal women and should be used as unidimensional tools. Our study expands upon existing psychometric findings related to PHQ-9 and EPDS, offering valuable insights for their application in research and clinical settings.

Diffusion Correction in Fricke Hydrogel Dosimeters: A Deep Learning Approach with 2D and 3D Physics-Informed Neural Network Models.

In this work an innovative approach was developed to address a significant challenge in the field of radiation dosimetry: the accurate measurement of spatial dose distributions using Fricke gel dosimeters. Hydrogels are widely used in radiation dosimetry due to their ability to simulate the tissue-equivalent properties of human tissue, making them ideal for measuring and mapping radiation dose distributions. Among the various gel dosimeters, Fricke gels exploit the radiation-induced oxidation of ferrous ions to ferric ions and are particularly notable due to their sensitivity. The concentration of ferric ions can be measured using various techniques, including magnetic resonance imaging (MRI) or spectrophotometry. While Fricke gels offer several advantages, a significant hurdle to their widespread application is the diffusion of ferric ions within the gel matrix. This phenomenon leads to a blurring of the dose distribution over time, compromising the accuracy of dose measurements. To mitigate the issue of ferric ion diffusion, researchers have explored various strategies such as the incorporation of additives or modification of the gel composition to either reduce the mobility of ferric ions or stabilize the gel matrix. The computational method proposed leverages the power of artificial intelligence, particularly deep learning, to mitigate the effects of ferric ion diffusion that can compromise measurement precision. By employing Physics Informed Neural Networks (PINNs), the method introduces a novel way to apply physical laws directly within the learning process, optimizing the network to adhere to the principles governing ion diffusion. This is particularly advantageous for solving the partial differential equations that describe the diffusion process in 2D and 3D. By inputting the spatial distribution of ferric ions at a given time, along with boundary conditions and the diffusion coefficient, the model can backtrack to accurately reconstruct the original ion distribution. This capability is crucial for enhancing the fidelity of 3D spatial dose measurements, ensuring that the data reflect the true dose distribution without the artifacts introduced by ion migration. Here, multidimensional models able to handle 2D and 3D data were developed and tested against dose distributions numerically evolved in time from 20 to 100 h. The results in terms of various metrics show a significant agreement in both 2D and 3D dose distributions. In particular, the mean square error of the prediction spans the range 1×10-6-1×10-4, while the gamma analysis results in a 90-100% passing rate with 3%/2 mm, depending on the elapsed time, the type of distribution modeled and the dimensionality. This method could expand the applicability of Fricke gel dosimeters to a wider range of measurement tasks, from simple planar dose assessments to intricate volumetric analyses. The proposed technique holds great promise for overcoming the limitations imposed by ion diffusion in Fricke gel dosimeters.

Large-Scale Item-Level Analysis of the Figural Matrices Test in the Norwegian Armed Forces: Examining Measurement Precision and Sex Bias.

Figural matrices tests are common in intelligence research and have been used to draw conclusions regarding secular changes in intelligence. However, their measurement properties have seldom been evaluated with large samples that include both sexes. Using data from the Norwegian Armed Forces, we study the measurement properties of a test used for selection in military recruitment. Item-level data were available from 113,671 Norwegian adolescents (32% female) tested between the years 2011 and 2017. Utilizing item response theory (IRT), we characterize the measurement properties of the test in terms of difficulty, discrimination, precision, and measurement invariance between males and females. We estimate sex differences in the mean and variance of the latent variable and evaluate the impact of violations to measurement invariance on the estimated distribution parameters. The results show that unidimensional IRT models fit well in all groups and years. There is little difference in precision and test difficulty between males and females, with precision that is generally poor on the upper part of the scale. In the sample, male latent proficiency is estimated to be slightly higher on average, with higher variance. Adjusting for measurement invariance generally reduces the sex differences but does not eliminate them. We conclude that previous studies using the Norwegian GMA data must be interpreted with more caution but that the test should measure males and females equally fairly.

Genetically Encoded, Noise-Tolerant, Auxin Biosensors in Yeast.

Auxins are crucial signaling molecules that regulate the growth, metabolism, and behavior of various organisms, most notably plants but also bacteria, fungi, and animals. Many microbes synthesize and perceive auxins, primarily indole-3-acetic acid (IAA, referred to as auxin herein), the most prevalent natural auxin, which influences their ability to colonize plants and animals. Understanding auxin biosynthesis and signaling in fungi may allow us to better control interkingdom relationships and microbiomes from agricultural soils to the human gut. Despite this importance, a biological tool for measuring auxin with high spatial and temporal resolution has not been engineered in fungi. In this study, we present a suite of genetically encoded, ratiometric, protein-based auxin biosensors designed for the model yeast Saccharomyces cerevisiae. Inspired by auxin signaling in plants, the ratiometric nature of these biosensors enhances the precision of auxin concentration measurements by minimizing clonal and growth phase variation. We used these biosensors to measure auxin production across diverse growth conditions and phases in yeast cultures and calibrated their responses to physiologically relevant levels of auxin. Future work will aim to improve the fold change and reversibility of these biosensors. These genetically encoded auxin biosensors are valuable tools for investigating auxin biosynthesis and signaling in S. cerevisiae and potentially other yeast and fungi and will also advance quantitative functional studies of the plant auxin perception machinery, from which they are built.

Collision cross-section measurements of small molecules via transient decay profiles observed in Orbitrap mass analyzers.

Collision cross section (CCS) of organic compounds can be measured via Fourier transform-based mass spectrometry (MS) by modeling the decay rate of transient signals in the analyzer. Deriving CCS values of low-mass molecules (mass < 2000 Da and CCS < 500 Å2) with Orbitrap MS is challenging due to their high axial frequencies and small absolute variances in cross-sectional profiles. Here, we acquired mass spectra of progressively more complex low-mass analytes using commercial Orbitrap mass spectrometers. The transient signals were processed using Fast Fourier transform (FFT) and short-time Fourier transform (StFFT) to derive decay constants of multiple select ionic species from a single MS full-scan experiment. Decay constants were translated into CCS values using at least two internal standards in the same mass spectrum. Our results suggest target ionic species should have high S/N in order to derive CCS values with ≤0.5% uncertainty. Limitations in the precision of CCS measurements reflect local space charge effects that disturb ion motion in the analyzer. The derived CCS values of polymer like fragments of Ultramark 1621 and small molecules such as individual protonated amino acids can achieve average ±1% error with selection of internal standards across a wide mass range. Future studies need to optimize the strategy to select internal standards in order to improve the precision and accuracy of CCS measurements for small molecules via Orbitrap MS.

Optimizing a national examination for medical undergraduates via modern automated test assembly approaches

BackgroundAutomated test assembly (ATA) represents a modern methodology that employs data science optimization on computer platforms to automatically create test form, thereby significantly improving the efficiency and accuracy of test assembly procedures. In the realm of medical education, large-scale high-stakes assessments often necessitate lengthy tests, leading to elevated costs in various dimensions (such as examinee fatigue and expenses associated with item development). This study aims to augment the design of the medical education assessments by leveraging modern ATA approaches.MethodsTo achieve the objective, a four-step process employing psychometric methodologies was used to calibrate and analyze the item pool of the Standardized Competence Test for Clinical Medicine Undergraduates (SCTCMU), a nationwide summative test comprising 300 multiple-choice questions (MCQ) in China. Subsequently, two modern ATA approaches were employed to determine the optimal item combination, accounting for both statistical and content requirements specified in the test blueprint. The qualities of the assembled test form, generated using modern ATA approaches, underwent meticulous evaluation.ResultsThrough an exploration of the psychometric properties of the SCTCMU as a foundational step, the evaluation revealed commendable quality in the item properties. Furthermore, the evaluation of the quality of assembled test form using modern ATA approaches indicated the ability to ascertain the optimal test length within the predefined measurement precision. Specifically, this investigation demonstrates that the application of modern ATA approaches can substantially reduce the test length of assembled test form, while simultaneously maintaining the required statistical and content standards specified in the test blueprint.ConclusionsThis study harnessed modern ATA approaches to facilitate the automatic construction of test form, thereby significantly enhancing the efficiency and precision of test assembly procedures. The utilization of modern ATA approaches offers medical educators a valuable tool to enhance the efficiency and cost-effectiveness of medical education assessment.

Precise management and control around the landfill integrating artificial intelligence and groundwater pollution risks

The Landfill plays an important role in urban development and waste disposal. However, landfill leachate may also bring more serious pollution and health risks to the surrounding groundwater environment. Compared with other areas, the area around the landfill needs more precise management. To solve this problem, based on the “pressure-state-response” framework, a method for the identification and evaluation of groundwater pollution around the landfill was constructed. The LPI method was used to assess the contamination potential of the leachate. The comprehensive quality of groundwater was evaluated by the entropy-AHP water quality assessment method, sodium adsorption ratio and sodium percentage. The probabilistic health risks of groundwater were assessed based on a Monte Carlo algorithm. The sources of pollutants were identified by comprehensively using the PCA-APCS-MLR model and the PMF model. Finally, the self-organizing map algorithm and the Kmeans algorithm were integrated to enhance the precision of groundwater management and control measures. The results showed that the leachate of the landfill was in the mature stage, and the concentration of inorganic substances was relatively high. Leachate had the potential to contaminate surrounding groundwater. The groundwater quality of 68.14% of the study area was in the poor or lower level. The groundwater near the landfill was unsuitable not only for drinking but also for irrigation purposes. Cl− was the main non-carcinogenic risk factor. Reducing pollutant concentration and controlling exposure time are effective strategies for mitigating health risks caused by high-concentration pollutants (Cl−, NO3−) and low-concentration pollutants (F−), respectively. The groundwater around the landfill was jointly affected by six pollution sources. The PMF model has better analytical ability in mixed pollution areas. The groundwater in the study area was divided into five clusters, of which cluster Ⅰ was significantly affected by leachate, and cluster Ⅴ had the lowest pollution and health risk.

Investigating the impact of OCT imaging of the crystalline lens on the accuracy and precision of cataract assessment.

To determine if supplementing standard clinical assessments with Optical Coherence Tomography (OCT) imaging of the crystalline lens improves the accuracy and precision of lens opacity assessment and associated clinical management decisions by optometrists. Fifty optometrists registered in the UK or Éire undertook a clinical vignette study where participants graded lens opacities and made associated clinical management decisions based on the image(s)/information displayed. Three forms of vignettes were presented: (1) Slit-lamp (SL) images of the lens, (2) SL and OCT images and (3) SL, OCT and visual function measures. Vignettes were constructed using anonymised data from 50 patients with varying cataract severity, each vignette being presented twice in a randomised order (total vignette presentations = 300). The accuracy of opacity and management decisions were evaluated using descriptive statistics and non-parametric Bland-Altman analysis where assessments from experienced clinicians were the reference. The precision of assessments was examined for each vignette form using non-parametric Bland-Altman analysis. All (n = 50) participants completed the study, with 36 working in primary eyecare (primary eyecare) settings and 14 in hospital eyecare services (HES). Agreement was highest where vignettes contained all clinical data (i.e., SL, OCT and visual function data-grading: 51.0%, management: 50.5%), and systematically reduced with decreasing vignette content (p < 0.001). A larger number of vignettes containing imaging and visual function measures exhibited below reference (i.e., less conservative) grading compared with vignettes containing imaging data alone (all p < 0.05). HES-based optometrists were more likely to grade lens opacities lower than clinicians working in primary eyecare (p < 0.001). Good measurement precision was evident for all vignettes, with a mean bias close to zero and limits of agreement below one grading step for all conditions. The addition of anterior segment OCT to SL images improved the accuracy of lens opacity grading. Structural assessment alone yielded more conservative decision making, which reversed once visual functional data was available.

Development and validation of two shortened anxiety sensitive index-3 scales based on item response theory

Anxiety sensitivity refers to an individual’s belief that anxiety symptoms adversely affect physical, cognitive, and social appraisals, thereby exacerbating the fear of these symptoms. The Anxiety Sensitivity Index-3 (ASI-3) has been widely used to measure anxiety sensitivity. To provide researchers with more flexibility in selecting scale lengths, this study developed two shortened versions of the ASI-3 via item response theory analysis: one containing 12 items and the other containing six items. Given the overall good quality of the original scale, this study primarily achieved scale shortening by retaining items that could provide a substantial amount of item information, namely, items of high measurement precision. Compared to the original scale, the two shortened versions demonstrate good reliability while maintaining the same three-dimensional latent structure, robust inter-construct relationships, and highly correlated latent traits.

Rapid Analysis of Soil Organic Carbon in Agricultural Lands: Potential of Integrated Image Processing and Infrared Spectroscopy

The significance of soil in the agricultural industry is profound, with healthy soil representing an important role in ensuring food security. In addition, soil is the largest terrestrial carbon sink on earth. The soil carbon pool is composed of both inorganic and organic forms. The equilibrium of the soil carbon pool directly impacts the carbon cycle via all of the other processes on the planet. With the development of agricultural systems from traditional to conventional ones, and with the current era of precision agriculture, which involves making decisions based on information, the importance of understanding soil is becoming increasingly clear. The control of microenvironment conditions and soil fertility represents a key factor in achieving higher productivity in these systems. Furthermore, agriculture represents a significant contributor to carbon emissions, a topic that has become timely given the necessity for carbon neutrality. In addition to these concerns, updating soil-related data, including information on macro and micronutrient conditions, is important. Carbon represents one of the major nutrients for crops and plays a key role in the retention and release of other nutrients and the management of soil physical properties. Despite the importance of carbon, existing analytical methods are complex and expensive. This discourages frequent analyses, which results in a lack of soil carbon-related data for agricultural fields. From this perspective, in situ soil organic carbon (SOC) analysis can provide timely management information for calibrating fertilizer applications based on the soil–carbon relationship to increase soil productivity. In addition, the available data need frequent updates due to rapid changes in ecosystem services and the use of extensive fertilizers and pesticides. Despite the importance of this topic, few studies have investigated the potential of image analysis based on image processing and spectral data recording. The use of spectroscopy and visual color matching to develop SOC predictions has been considered, and the use of spectroscopic instruments has led to increased precision. Our extensive literature review shows that color models, especially Munsell color charts, are better for qualitative purposes and that Cartesian-type color models are appropriate for quantification. Even for the color model, spectroscopy data could be used, and these data have the potential to improve the precision of measurements. On the other hand, mid-infrared radiation (MIR) and near-infrared radiation (NIR) diffuse reflection has been reported to have a greater ability to predict SOC. Finally, this article reports the availability of inexpensive portable instruments that can enable the development of in situ SOC analysis from reflection and emission information with the integration of images and spectroscopy. This integration refers to machine learning algorithms with a reflection-oriented spectrophotometer and emission-based thermal images which have the potential to predict SOC without the need for expensive instruments and are easy to use in farm applications.

Measurement precision bounds on aberrated single-molecule emission patterns

Single-molecule localization microscopy (SMLM) has revolutionized the study of biological phenomena by providing exquisite nanoscale spatial resolution. However, optical aberrations induced by sample and system imperfections distort the single-molecule emission patterns (i.e. PSFs), leading to reduced precision and resolution of SMLM, particularly in three-dimensional (3D) applications. While various methods, both analytical and instrumental, have been employed to mitigate these aberrations, a comprehensive analysis of how different types of commonly encountered aberrations affect single-molecule experiments and their image formation remains missing. In this study, we addressed this gap by conducting a quantitative study of the theoretical precision limit for position and wavefront distortion measurements in the presence of aberrations. Leveraging Fisher information and Cramér-Rao lower bound (CRLB), we quantitively analyzed and compared the effects of different aberration types, including index mismatch aberrations, on localization precision in both biplane and astigmatism 3D modalities as well as 2D SMLM imaging. Furthermore, we studied the achievable wavefront estimation precision from aberrated single-molecule emission patterns, a pivot step for successful adaptive optics in SMLM through thick specimens. This analysis lays a quantitative foundation for the development and application of SMLM in whole-cells, tissues and with a large field of view, providing in-depth insights into the behavior of different aberration types in single-molecule imaging and thus generating theoretical guidelines for developing highly efficient aberration correction strategies and enhancing the precision and reliability of 3D SMLM.

Research on high-precision angular measurement based on machine learning and optical vortex interference technology

Abstract The paper innovatively constructs a regression prediction model based on the Stacking ensemble learning algorithm by utilizing the distortion degree of vortex optical interference patterns, achieving high-precision measurement of small angles. It constructs a regression prediction model based on the Stacking ensemble learning algorithm. Initially, in the spiral optical conjugate interference system, minute variations in the optical axis yield corresponding interference patterns, within an angle range of 0.0006° to 0.3°. The angle formed between the centroids of the upper two petals in the deformed interference patterns and the center is extracted as a feature for feature extraction. A dataset is established and randomly divided into training, validation, and testing sets in a 6:2:2 ratio. Subsequently, four models—support vector regression, particle swarm optimization back propagation, Gaussian process regression, and the stacking ensemble algorithm—are optimized for hyperparameters, trained, and evaluated based on coefficients of determination, root mean square error, and mean absolute error to compare their predictive performance. Through multiple rounds of training and prediction on randomly partitioned datasets, it is evident that the ensemble model exhibits a reduction in relative error compared to single learners, demonstrating that the Stacking-based ensemble algorithm can combine the strengths of base learners, showcasing superior predictive performance and enhanced stability. Moreover, the Stacking ensemble model achieves a measurement precision of 0.0006°, with a relative error maintained within 0.6%, indicating the feasibility of achieving high-precision measurement of tiny angles in the optical axis using machine learning and spiral optical conjugate interference systems.

Utility of an Instantaneous Salt Dilution Method for Measuring Streamflow in Headwater Streams.

Streamflow records are biased toward large streams and rivers, yet small headwater streams are often the focus of ecological research in response to climate change. Conventional flow measurement instruments such as acoustic Doppler velocimeters (ADVs) do not perform well during low-flow conditions in small streams, truncating the development of rating curves during critical baseflow conditions dominated by groundwater inflow. We revisited an instantaneous solute tracer injection method as an alternative to ADVs based on paired measurements to compare their precision, efficiency, and feasibility within headwater streams across a range of flow conditions. We show that the precision of discharge measurements using salt dilution by slug injection and ADV methods were comparable overall, but salt dilution was more precise during the lowest flows and required less time to implement. Often, headwater streams were at or below the depth threshold where ADV measurements could even be attempted and transects were complicated by coarse bed material and cobbles. We discuss the methodological benefits and limitations of salt dilution by slug injection and conclude that the method could facilitate a proliferation of streamflow observation across headwater stream networks that are highly undersampled compared to larger streams.

Bone Mineral Density Monitoring in Contiguous versus Non-Contiguous Lumbar Vertebrae: The Manitoba BMD Registry

Introduction: Only change in bone mineral density (BMD) on repeat DXA that exceeds the 95% least significant change (LSC) should be considered clinically meaningful. Frequently lumbar spine DXA must be reported after omitting vertebrae with localized structural artifact, which reduces measurement precision. Previous reports have raised concerns of higher least significant change (LSC) when spine BMD is based on non-contiguous rather than contiguous vertebrae. The current study was performed to compare lumbar spine LSC and BMD response to intervening anti-osteoporosis medication use from non-contiguous versus contiguous vertebrae.Methodology: LSCs for lumbar spine DXA based on L1-L4 and all combinations of non-contiguous and contiguous vertebrae were calculated using 879 scan-pairs from the Manitoba BMD Program. We compared BMD change from these regions, overall and in relation to intervening anti-osteoporosis medication use, in 11,722 patients who had 2 DXA examinations.Results: LSCs were slightly greater when calculated from combinations of fewer than 4 vertebrae, but there was no meaningful difference between contiguous versus non-contiguous vertebrae. There were consistently high correlations between lumbar spine BMD change from L1-L4 and all combinations of continuous and non-contiguous vertebrae (all Pearson r≥ 0.9, p<0.001). Percentage changes in spine BMD and the fraction with treatment-concordant change exceeding the LSC were similar using contiguous or non-contiguous vertebrae.Conclusions: Lumbar spine BMD change can be assessed from 2 or 3 non-contiguous vertebrae when clinically necessary, and precision in such cases is similar to using contiguous vertebrae. Non-contiguous vertebrae can detect treatment-concordant changes similar in spine BMD to contiguous vertebrae.

Probing CPV mixing in the Higgs sector in vector boson fusion at a 1 TeV ILC

With the current precision of measurements by the ATLAS and CMS experiments, it cannot be excluded that a standard model (SM)-like Higgs boson is a CP violating mixture of CP-even and CP-odd states. We explore this possibility here, assuming Higgs boson production in ZZ-fusion, at 1 TeV ILC, with unpolarized beams. The full simulation of SM background and fast simulation of the signal is performed, simulating 8 ab−1 of data collected with the ILD detector. We demonstrate that the CP mixing angle ΨCP between scalar and pseudoscalar states can be measured with the statistical uncertainty of 3.8 mrad at 68% CL, corresponding to 1.44×10−5 for the CP parameter fCP, for the pure scalar state. This is the first result on sensitivity of an e+e− collider to measure fCP in the Higgs production vertex in vector boson fusion. Published by the American Physical Society 2024

Computerized Adaptive Testing for the Berg Balance Scale Improves Measurement Efficiency Without Compromising Precision in People With Stroke.

Objectives of this study were to confirm the Berg Balance Scale's (BBS) measurement properties and unidimensionality with an item response theory analysis in persons with subacute and chronic stroke and examine the precision and efficiency of computerized adaptive testing (CAT). Data were obtained from 519 ambulatory persons with subacute and chronic stroke in 2 retrospective databases. A principal component analysis (PCA) of residuals was used to evaluate unidimensionality. BBS fit to a rating scale model versus a partial credit model was examined and item parameters were generated for CAT calibration. Person measures from all 14 items were defined as actual balance ability. BBS CAT simulations were used to examine changes in measurement precision with increasing number of items administered and a precision-based stopping rule (0.5 logit standard error (SE) threshold). A PCA of residuals supports the BBS's unidimensionality and Rasch analysis supports using the rating scale model for measurement. Maximum precision for BBS CAT was SE = 0.40 logits when administering all items. BBS CAT estimated balance ability was highly correlated with actual ability when 4 or more items were administered (r > 0.9). Precision was within 0.5 logits when 5 or more items were administered (SE < 0.48 logits). BBS CAT estimated balance ability was highly correlated with actual ability (r = 0.952) using a precision-based stopping rule. The average number of items administered with the precision-based stopping rule was 5.43. The BBS is sufficiently unidimensional and the rating scale model can be used for measurement. BBS CAT is efficient and replicates the full instrument's reliability when measuring balance ability in ambulatory persons with subacute and chronic stroke. Future work should aim to enhance the interpretability of measures to facilitate clinical decision making. BBS CAT provides an efficient way of measuring balance ability for individuals in stroke rehabilitation giving clinicians more time with patients.