Magnitude Of Variation Research Articles

Magnitude and Temporal Variations of Socioeconomic Inequalities in the Quality of Life After Early Breast Cancer: Results From the Multicentric French CANTO Cohort.

Socioeconomic status (SES) influences the survival outcomes of patients with early breast cancer (EBC). However, limited research investigates social inequalities in their quality of life (QoL). This study examines the socioeconomic inequalities in QoL after an EBC diagnosis and their time trends. We used data from the French prospective multicentric CANTO cohort (ClinicalTrials.gov identifier: NCT01993498), including women with EBC enrolled between 2012 and 2018. QoL was assessed using the European Organisation for Research and Treatment of Cancer QoL Core 30 questionnaire (QLQ-C30). summary score at diagnosis and 1 and 2 years postdiagnosis. We considered three indicators of SES separately: self-reported financial difficulties, household income, and educational level. We first analyzed the trajectories of the QLQ-C30 summary score by SES group. Then, social inequalities in QLQ-C30 summary score and their time trends were quantified using the regression-based slope index of inequality (SII), representing the absolute change in the outcome along socioeconomic gradient extremes. The analyses were adjusted for age at diagnosis, Charlson Comorbidity Index, disease stage, and type of local and systemic treatment. Among the 5,915 included patients with data on QoL at diagnosis and at the 2-year follow-up, social inequalities in QLQ-C30 summary score at baseline were statistically significant for all SES indicators (SIIfinancial difficulties = -7.6 [-8.9; -6.2], SIIincome = -4.0 [-5.2; -2.8]), SIIeducation = -1.9 [-3.1; -0.7]). These inequalities significantly increased (interaction P < .05) in year 1 and year 2 postdiagnosis, irrespective of prediagnosis health, tumor characteristics, and treatment. Similar results were observed in subgroups defined by menopausal status and type of adjuvant systemic treatment. The magnitude of preexisting inequalities in QoL increased over time after EBC diagnosis, emphasizing the importance of considering social determinants of health during comprehensive cancer care planning.

3-generation family histories of mental, neurologic, cardiometabolic, birth defect, asthma, allergy, and autoimmune conditions associated with autism: an open-source catalogue of findings.

The relatively few conditions and family members investigated in autism family health history limits etiologic understanding. For more comprehensive understanding and hypothesis-generation we produced an open-source catalogue of autism associations with family histories of mental, neurologic, cardiometabolic, birth defect, asthma, allergy, and autoimmune conditions. All live births in Denmark, 1980-2012, of Denmark-born parents (1,697,231 births), and their 3-generation family members were followed through April 10, 2017 for each of 90 diagnoses (including autism), emigration or death. Adjusted hazard ratios (aHR) were estimated via Cox regression for each diagnosis-family member type combination, adjusting for birth year, sex, birth weight, gestational age, parental ages at birth, and number of family member types of index person; aHRs also calculated for sex-specific co-occurrence of each disorder. We obtained 6,462 individual family history aHRS across autism overall (26,840 autistic persons; 1.6% of births), by sex, and considering intellectual disability (ID); and 350 individual co-occurrence aHRS. Results are catalogued in interactive heat maps and down-loadable data files: https://ncrr-au.shinyapps.io/asd-riskatlas/ and interactive graphic summaries: https://public.tableau.com/views/ASDPlots_16918786403110/e-Figure5. While primarily for reference material or use in other studies (e.g., meta-analyses), results revealed considerable breadth and variation in magnitude of familial health history associations with autism by type of condition, family member type, sex of the family member, side of the family, sex of the index person, and ID status, indicative of diverse genetic, familial, and non-genetic autism etiologic pathways. Careful attention to sources of autism likelihood in family health history, aided by our open data resource, may accelerate understanding of factors underlying neurodiversity.

Experimental and theoretical studies to investigate molecular interactions between ethylene glycol oligomers and 1-butyl-3-methylimidazolium bromide

The thermophysical characteristics of pure liquids and their mixes are substantial in applied as well as theoretical research. This paper presents the measured data on: density (ρ), viscosity (η), refractive index (nD) and consequent calculated parameters: {excess molar volume (VmE), partial molar volume (V¯m), excess partial molar volume (V¯mE), deviation in viscosity (Δη), excess Gibbs energy of activation of viscous flow (ΔG∗E), deviation in refractive index (ΔϕnD) and deviation in molar refraction (ΔxRM)} of binaries containing 1-butyl-3-methylimidazolium bromide ([BMIM]Br) with glycol oligomers (mono/di/tri/tetra-ethylene glycol) at T/K = 298.15–313.15 and pressure p/MPa = 0.1. The Redlich-Kister equation efficiently estimates the standard deviation between the experimental and calculated excess properties. The interactions between constituents were considered while interpreting the obtained results. The values of VmE, V¯mE, Δη, ΔϕnD and ΔxRM exhibited negative trends throughout the range, whereas ΔG∗E values displayed a sigmoidal shift (negative to positive) with increasing [BMIM]Br concentration. The VmE data has been explored in terms of Prigogine-Flory-Patterson (PFP) and Graph theories to elucidate the deviations in thermophysical properties of the mixtures caused by variations in strength and magnitude of interactions. Additionally, the η values were correlated using ten models and their correlation ability has been perceived via the average standard deviation percentage. The pure and binary mixtures were also spectroscopically examined using Fourier Transform InfraRed (FT-IR) and Raman spectroscopy to elucidate the structural variations.

Diagnosing uncertainties in global biomass burning emission inventories and their impact on modeled air pollutants

Abstract. Large uncertainties persist within current biomass burning (BB) inventories, and the choice of these inventories can substantially impact model results when assessing the influence of BB aerosols on weather and climate. We evaluated discrepancies among BB emission inventories by comparing carbon monoxide (CO) and organic carbon (OC) emissions from seven major BB regions globally between 2013 and 2016. Mainstream bottom-up inventories, including the Fire INventory from NCAR 1.5 (FINN1.5) and Global Fire Emissions Database version 4s (GFED4s), along with the top-down inventories Quick Fire Emissions Dataset 2.5 (QFED2.5) and Visible Infrared Imaging Radiometer (VIIRS-)based Fire Emission Inventory version 0 (VFEI0), were selected for this study. Global CO emissions range from 252 to 336 Tg, with regional disparities reaching up to a 6-fold difference. Dry matter is the primary contributor to the regional variation in CO emissions (50 %–80 %), with emission factors accounting for the remaining 20 %–50 %. Uncertainties in dry matter often arise from biases in calculating bottom fuel consumption and burned area, influenced by vegetation classification methods and fire detection products. In the tropics, peatlands contribute more fuel loads and higher emission factors than grasslands. At high latitudes, increased cloud fraction amplifies the discrepancy in estimated burned area (or fire radiative power) by 20 %. The global OC emissions range from 14.9 to 42.9 Tg, exhibiting higher variability than CO emissions due to the corrected emission factors in QFED2.5, with regional disparities reaching a factor of 8.7. Additionally, we applied these BB emission inventories to the Community Atmosphere Model version 6 (CAM6) and assessed the model performance against observations. Our results suggest that the simulations based on the GFED4s agree best with the Measurements of Pollution in the Troposphere (MOPITT-)retrieved CO. While comparing the simulation with the Moderate Resolution Imaging Spectroradiometer (MODIS) and AErosol RObotic NETwork (AERONET) aerosol optical depth (AOD), our results reveal that there is no global optimal choice for BB inventories. In the high latitudes of the Northern Hemisphere, using GFED4s and QFED2.5 can better capture the AOD magnitude and diurnal variation. In equatorial Asia, GFED4s outperforms other models in representing day-to-day changes, particularly during intense burning. In Southeast Asia, we recommend using the OC emission magnitude from FINN1.5 combined with daily variability from QFED2.5. In the Southern Hemisphere, the latest VFEI0 has performed relatively well. This study has implications for reducing the uncertainties in emissions and improving BB emission inventories in further studies.

A size‐dependent energy‐based strain burst criterion

AbstractThis paper presents a size‐dependent energy‐based strain burst criterion linking strength, elasticity, fracture energies and specimen size effect with stress state due to changes in boundary conditions. It proposes the concept of a ‘Burst Envelope’, a surface in three‐dimensional principal stress space derived based on energy storing and dissipation characteristics of a rock sample, taking into account the size of the specimen and potential localised failure pattern. A scalar burst index is also proposed to quantify the bursting scale. To illustrate and verify its functioning, a numerical modelling framework based on the distinct element method and employing a new cohesive‐frictional contact model is used to perform virtual strain burst experiments under different polyaxial loading‐unloading scenarios, mimicking various underground excavation scenarios. The obtained results are in good agreement with the theoretical prediction of burst occurrence. On that basis, the variation of burst possibility and magnitude are investigated with key factors, including confinement level and the material's elastic, strength and fracture properties. The effect of the specimen's aspect ratio and size on the rock burst potential is elaborated and verified using virtual strain burst experiments, facilitating the linking of the proposed theoretical framework with the evaluation of in‐situ strain bursts in rock masses around underground openings.

Observations of enhanced rainfall variability in Kenya, East Africa

Understanding local patterns of rainfall variability is of great concern in East Africa, where agricultural productivity is dominantly rainfall dependent. However, East African rainfall climatology is influenced by numerous drivers operating at multiple scales, and local patterns of variability are not adequately understood. Here, we show evidence of substantial variability of local rainfall patterns between 1981 and 2021 at the national and county level in Kenya, East Africa. Results show anomalous patterns of both wetting and drying in both the long and short rainy seasons, with evidence of increased frequency of extreme wet and dry events through time. Observations also indicate that seasonal and intraseasonal variability increased significantly after 2013, coincident with diminished coherence between ENSO (El Nino Southern Oscillation) and rainfall. Increasing frequency and magnitude of rainfall variability suggests increasing need for local-level climate change adaptation strategies.

Evaluation of walking activity and gait to identify physical and mental fatigue in neurodegenerative and immune disorders: preliminary insights from the IDEA-FAST feasibility study

BackgroundMany individuals with neurodegenerative (NDD) and immune-mediated inflammatory disorders (IMID) experience debilitating fatigue. Currently, assessments of fatigue rely on patient reported outcomes (PROs), which are subjective and prone to recall biases. Wearable devices, however, provide objective and reliable estimates of gait, an essential component of health, and may present objective evidence of fatigue. This study explored the relationships between gait characteristics derived from an inertial measurement unit (IMU) and patient-reported fatigue in the IDEA-FAST feasibility study.MethodsParticipants with IMIDs and NDDs (Parkinson's disease (PD), Huntington's disease (HD), rheumatoid arthritis (RA), systemic lupus erythematosus (SLE), primary Sjogren’s syndrome (PSS), and inflammatory bowel disease (IBD)) wore a lower-back IMU continuously for up to 10 days at home. Concurrently, participants completed PROs (physical fatigue (PF) and mental fatigue (MF)) up to four times a day. Macro (volume, variability, pattern, and acceleration vector magnitude) and micro (pace, rhythm, variability, asymmetry, and postural control) gait characteristics were extracted from the accelerometer data. The associations of these measures with the PROs were evaluated using a generalised linear mixed-effects model (GLMM) and binary classification with machine learning.ResultsData were recorded from 72 participants: PD = 13, HD = 9, RA = 12, SLE = 9, PSS = 14, IBD = 15. For the GLMM, the variability of the non-walking bouts length (in seconds) with PF returned the highest conditional R2, 0.165, and with MF the highest marginal R2, 0.0018. For the machine learning classifiers, the highest accuracy of the current analysis was returned by the micro gait characteristics with an intrasubject cross validation method and MF as 56.90% (precision = 43.9%, recall = 51.4%). Overall, the acceleration vector magnitude, bout length variation, postural control, and gait rhythm were the most interesting characteristics for future analysis.ConclusionsCounterintuitively, the outcomes indicate that there is a weak relationship between typical gait measures and abnormal fatigue. However, factors such as the COVID-19 pandemic may have impacted gait behaviours. Therefore, further investigations with a larger cohort are required to fully understand the relationship between gait and abnormal fatigue.

Microlensing signatures of extended dark objects using machine learning

This paper presents a machine learning-based method for the detection of the unique gravitational microlensing signatures of extended dark objects, such as boson stars, axion miniclusters and subhalos. We adapt MicroLIA, a machine learning-based package tailored to handle the challenges posed by low-cadence data in microlensing surveys. Using realistic observational time stamps, our models are trained on simulated light curves to distinguish between microlensing by pointlike and extended lenses, as well as from other object classes which give a variable magnitude. We focus on boson stars and Navarro-Frenk-White (NFW) subhalos and show that the former, which are examples of objects with a relatively flat mass distribution, can be confidently identified for 0.8≲r/rE≲3. Intriguingly, we also find that more sharply peaked structures, such as NFW subhalos, can be distinctly recognized from point lenses under regular observation cadence. Our findings significantly advance the potential of microlensing data in uncovering the elusive nature of extended dark objects. The code and dataset used are also provided. Published by the American Physical Society 2024

Multipopulation Evolution-Based Dynamic Constrained Multiobjective Optimization Under Diverse Changing Environments

Dynamic constrained multiobjective optimization involves irregular changes in the distribution of the true Pareto-optimal fronts, drastic changes in the feasible region caused by constraints, and the movement directions and magnitudes of the optimal distance variables due to diverse changing environments. To solve these problems, we propose a multi-population evolution based dynamic constrained multiobjective optimization algorithm. In this algorithm, we design a tribe classification operator to divide the population into different tribes according to a feasibility check and the objective values, which is beneficial for driving the population toward the feasible region and Pareto-optimal fronts. Meanwhile, a population selection strategy is proposed to identify promising solutions from tribes and exploit them to update the population. The optimal values of the distance variables vary differently with dynamic environments, thus, we design a dynamic response strategy for solutions in different tribes that estimates their distances to approach the Pareto-optimal fronts and regenerates a promising population when detecting environmental changes. In addition, a scalable generator is designed to simulate diverse movement directions and magnitudes of the optimal distance variables in real-world problems under dynamic environments, obtaining a set of improved test problems. Experimental results show the effectiveness of test problems, and the proposed algorithm is impressively competitive with several chosen state-of-the-art competitors.

Detection of a cyclotron line in the Be X-ray pulsar IGR J06074+2205

Context. IGR J0607.4+2205 is a transient Be X-ray binary discovered two decades ago. IGR J0607.4+2205 underwent an outburst in 2023 during which it was observed twice with NuSTAR. Aims. The main goal of this work is to model the broadband X-ray spectrum of IGR J0607.4+2205 during the outburst and to study the variations of the spectral and timing features at different intensities. Methods. We extracted the light curve and spectrum of the source from the two NuSTAR observations carried out during the recent outburst in the energy range of 3−78 keV. We used the epoch folding technique to find pulsation from the source and to study the changes in emission characteristics from the source with energy across an order of magnitude variation in source luminosity. Results. IGR J0607.4+2205 shows pulsations with a period of ∼347.6 s during both the observations, with a pulse fraction of ≥50%. The broadband spectrum of the source was modelled using a power-law continuum with a high-energy cutoff. During the first observation, a cyclotron absorption line at ∼51 keV was also present in the source with an optical depth of ∼1.3. However, no cyclotron line feature was detected in the second observation when the source was an order of magnitude fainter. Additionally, soft excess was detected in the second observation, which was modelled with a black body component emerging from close to the neutron star (NS). Conclusions. We report the first ever detection of a cyclotron line in the broadband spectrum of IGR J0607.4+2205 centred at 51 ± 1 keV. The magnetic field strength of the NS is estimated to be ∼4 × 1012 G from the centroid energy of the absorption line. A significant change is observed in the pulse profile with luminosity during the decay of the outburst, indicating an associated change in the beaming pattern.

The Recovery and Re-Calibration of a 13-Month Aerosol Extinction Profiles Dataset from Searchlight Observations from New Mexico, after the 1963 Agung Eruption

The recovery and re-calibration of a dataset of vertical aerosol extinction profiles of the 1963/64 stratospheric aerosol layer measured by a searchlight at 32° N in New Mexico, US, is reported. The recovered dataset consists of 105 aerosol extinction profiles at 550 nm that cover the period from December 1963 to December 1964. It is a unique record of the portion of the aerosol cloud from the March 1963 Agung volcanic eruption that was transported into the Northern Hemisphere subtropics. The data-recovery methodology involved re-digitizing the 105 original aerosol extinction profiles from individual Figures within a research report, followed by the re-calibration. It involves inverting the original equation used to compute the aerosol extinction profile to retrieve the corresponding normalized detector response profile. The re-calibration of the original aerosol extinction profiles used Rayleigh extinction profiles calculated from local soundings. Rayleigh and aerosol slant transmission corrections are applied using the MODTRAN code in transmission mode. Also, a best-estimate aerosol phase function was calculated from observations and applied to the entire column. The tropospheric aerosol phase function from an AERONET station in the vicinity of the searchlight location was applied between 2.76 to 11.7 km. The stratospheric phase function, applied for a 12.2 to 35.2 km altitude range, is calculated from particle-size distributions measured by a high-altitude aircraft in the vicinity of the searchlight in early 1964. The original error estimate was updated considering unaccounted errors. Both the re-calibrated aerosol extinction profiles and the re-calibrated stratospheric aerosol optical depth magnitudes showed higher magnitudes than the original aerosol extinction profiles and the original stratospheric aerosol optical depth, respectively. However, the magnitudes of the re-calibrated variables show a reasonable agreement with other contemporary observations. The re-calibrated stratospheric aerosol optical depth demonstrated its consistency with the tropics-to-pole decreasing trend, associated with the major volcanic eruption stratospheric aerosol pattern when compared to the time-coincident stratospheric aerosol optical depth lidar observations at Lexington at 42° N.

Reproductive Tract Infections among Women in Pakistan: An Urban Case Study

Reproductive tract infections (RTIs) among women—despite being common and having grave consequences—are not given much attention by policy-makers and health planners. The asymptomatic nature of most infections makes their detection and diagnosis difficult, making laboratory testing the most accurate method of bio-medical diagnosis. The present paper assesses the magnitude and nature of infections as diagnosed through laboratory testing and looks into the variation in magnitude and the nature of RTIs among women with different socio-economic and demographic characteristics. The aetiological rate of infection among women is found to be 24 percent, with the majority of these women testing positive for endogenous infections. Factors significantly increasing the likelihood of having an infection include intrauterine device use or getting a tubectomy, short inter-pregnancy intervals, and lower economic status of women.

Cut-off point (COP) for early gear fault diagnosis via Meshing impact modulation (MIM) analysis

Comparing the number and magnitude differences and variations of the fault-induced features in frequency domain, such as the sidebands of meshing frequency in the raw spectrum, or the rotation frequency of faulty gear in the envelope spectrum between healthy and faulty conditions are the underlying principle of most of classical fault diagnostic methods for gearbox. However, several inevitable factors, for instance, circumferential stiffness anisotropy of gear shaft (including coupling mechanisms like keyways) and gear meshing backlash, can also lead to similar features, which may challenge these classical methods, especially when the fault is in its early stage. In specific, the initial early gear fault may neither lead to the significant increment of sideband or rotation frequency nor stimulate the high amplitude resonance, more than the inevitable factors mentioned above. As such, a meshing impact modulation frequency band is constructed to create a unique feature which does not exist in healthy condition. Considering both the impact nature and the modulation mechanism of gear meshing process, an MIM (Meshing Impact Modulation) index is designed, with which can effectively identify the optimal meshing impact modulation response frequency band with high stability and robustness across entire lifespan of gears. Within the demodulation spectrum of this band, the presence or absence of distinct gear rotation frequency spectral lines and their mirage as sidebands around the meshing frequency can serve as the effective and robust indicators to discern whether the gear is in a healthy "before cut-off point (pre-COP) state " or in an early faulty "after cut-off point (post-COP) state ". The effectiveness and the superiority of the proposed algorithm is testified by the life-test data from a test rig using industrial gearbox.

Comparative analysis of near-surface and surface urban heat islands in the Yangtze River Delta region

Compared with surface temperature, the near-surface temperature is more related with human health. However, extensive researches have been conducted on the UHI effect globally using surface temperature considering its accessibility. In this study, a comparative analysis of near-surface and surface urban heat islands in the Yangtze River Delta Region is investigated. This study first proposed a spatialization method suitable for air temperature in highly urbanized areas with complex land cover. Based on this method, a dataset of 1-km gridded air temperature is developed, and an in-depth analysis of the changes of near-surface and surface heat island is further carried out. Results show that both the near-surface urban heat island intensity (NSUHII) and surface urban heat island intensity (SUHII) are rather strong over the past 20 years, presenting similar spatial distributions as well. However, in the rapidly expanding urban areas especially during summer and winter seasons, the difference in magnitude and time variations (R) between NSUHII and SUHII are pronounced. Hence, adaptions and mitigation strategies on NSUHI and SUHI should be developed and implemented separately in such occasions, which is especially important for developed areas such as Yangtze River Delta Region.

A framework for multivariate analysis of compound extremes based on correlated hydrologic time series

While hydrologic design has primarily relied on use of annual maxima values, many events of hydrologic interest such as active and break spells in monsoonal rains, heat waves, flash flooding from snowmelt, etc. manifest at time scales of days to weeks, and require daily (or finer resolution) data for proper characterization. Often combinations of several hydrologic variables herald highly impactful events and are labelled as compound extremes. Using a time-varying interval-censored estimation method of copula models, a novel multivariate approach for dealing with compound extremes under temporal dependence amongst variables and when data contain significant ties is developed here to enable the determination of design magnitudes and associated risk. The efficacy of this method is demonstrated over the Godavari River Basin, India, using daily precipitation and temperature data from a recent period (1977 to 2020) during the monsoon season. A conservative approach is proposed for estimating the design magnitudes of hydrologic variables in multivariate settings. The significance of ties and temporal dependence amongst precipitation and temperature data in estimation of design magnitudes of cold-wet compound extremes at specified probabilities of exceedance is explored at various spatial scales. Ties and temporal dependence are both shown to have a profound influence on design estimates. Since ties and temporal variation in dependence amongst hydrologic variables are ubiquitous features of most hydrologic data, this framework would be applicable for characterization of other compound extremes in hydrology.

Exploring the yield and yield contributing traits variability in dolichos bean (Lablab purpureus): A character association and path analysis approach

An experiment was conducted during 2019–20 and 2020–21 at ICAR-Central Institute for Subtropical Horticulture, Lucknow, Uttar Pradesh to evaluate the magnitude of variability in dolichos bean [Lablab purpureus (L.) Sweet] genotypes for important horticultural traits and their associations. The observed range and mean variations of the studied traits revealed a significant genetic diversity among the genotypes. The narrow gap in the genotypic (GCV) and the phenotypic coefficient of variation (PCV) for delineated characters indicated that they are governed by genetic factors, and environmental factors had little or no impact on the phenotypic expression of these traits. The traits including pod yield, days taken to first flowering, weight of pod, plant height, and pod width, showed positive GCV, indicating the presence of additive genes action. Traits such as pod weight, days taken to first flowering, and pod width displayed high heritability and moderate genetic advance, suggesting that the high heritability of these traits is due to the effects of additive genes and that genetic gain can be expected through the phenotypic selection. The higher genotypic correlations revealed an inherent association between the traits. Therefore, the selection criterion for high yield was proposed based on the strong character association between plant height, number of pods/cluster, pod width, and pod weight. The number of pods/plant, followed by the number of clusters/plant had the highest direct positive effect on yield. Consequently, pod yield may be increased through selection of heavier pods, more clusters/ plant, and more pods/cluster.

Development of a fully implicit ODE-solver for containment analysis code

The thermal–hydraulic dynamics in containment are governed by a system of stiff ordinary differential equations (ODEs). A fully implicit discretization scheme is adopted to discretize these ODEs in order to mitigate the effects of stiffness. In comparison with explicit or semi-implicit discretization schemes that are subject to Courant limits on time steps, the fully implicit discretization scheme is more suitable for a containment analysis code that focuses on predicting both short-term and long-term thermal–hydraulic parameters after an accident. This study introduces a general-purpose ODE solver for the containment analysis code. The outline of the solver is as follows: The fully implicit discrete equations lead to a large set of nonlinear equations that need to be solved using Newton’s iterative method. The partial derivative components in the Jacobi matrix are calculated by the perturbation method using finite difference approximation, which avoids the complicated derivation of partial derivatives. The scaling modification technique is incorporated into this ODE solver to deal with significant differences in unknown variable magnitudes, and the line search method is introduced to address the difficulty of obtaining an accurate root estimate with Newton’s method when the initial guess is far from the actual root. This proposed ODE solver was applied to two typical stiff ODE problems to test its stiffness-suppressed ability and to demonstrate that this proposed solver can perform calculations with a very large time step. Then, the CASSIA code, a containment analysis code developed by China Nuclear Power Technology Research Institute Co., Ltd (CNPRI), equipped with this ODE solver, was applied to the CSNI (Committee on the Safety of Nuclear Installations) benchmark problem and the Carolinas Virginia Tube Reactor (CVTR) test 3 problem to preliminarily demonstrate that the proposed ODE solver can perform containment thermal–hydraulic analysis correctly. This study could provide references for the development of a home-made containment analysis code.

Glory interference spectroscopy in Sr atom

Slow (meV) photoelectron imaging spectroscopy is employed in the experimental study of near-threshold photoionization of strontium atoms in the presence of an external static electric field. Specifically, the study is devoted to the glory effect, that is, the appearance of an intense peak at the center of the recorded photoelectron images, when dealing with m= 0 final ionized Stark states (m denoting the magnetic quantum number). This critical effect is formally identical to that encountered in classical scattering theory, where, for a nonzero value of the impact parameter, the zero-crossing of the deflection function leads to a divergent classical differential cross section. By recording the magnitude variation of this glory peak as a function of electron excitation energy, we observe that, besides the traces of classical origin, it also exhibits intense quantum interference and beating phenomena, above and below the zero-static-field ionization threshold. We study both, single- and two-photon ionization of Sr, thus enabling a comparison not only between the different excitation schemes, but also with an earlier work devoted to two-photon ionization of Mg atom by Kalaitzis et al (2020 Phys. Rev. A 102 033101). Our recordings are analyzed within the framework of the Harmin–Fano frame transformation Stark effect theory that is applied to both the hydrogen atom and a non-hydrogenic one simulating Sr. We discuss the various aspects of the recorded and calculated glory interference and beating structures and their ‘short time Fourier transforms’ and classify them as either atom-specific or atom independent. In particular, we verify the ‘universal’ connection between the glory oscillations above the zero-field threshold and the differences between the origin-to-detector times of flight corresponding to pairs of classical electron trajectories that end up to the image center.

The role of beat-by-beat cardiac features in machine learning classification of ischemic heart disease (IHD) in magnetocardiogram (MCG)

Cardiac electrical changes associated with ischemic heart disease (IHD) are subtle and could be detected even in rest condition in magnetocardiography (MCG) which measures weak cardiac magnetic fields. Cardiac features that are derived from MCG recorded from multiple locations on the chest of subjects and some conventional time domain indices are widely used in Machine learning (ML) classifiers to objectively distinguish IHD and control subjects. Most of the earlier studies have employed features that are derived from signal-averaged cardiac beats and have ignored inter-beat information. The present study demonstrates the utility of beat-by-beat features to be useful in classifying IHD subjects (n = 23) and healthy controls (n = 75) in 37-channel MCG data taken under rest condition of subjects. The study reveals the importance of three features (out of eight measured features) namely, the field map angle (FMA) computed from magnetic field map, beat-by-beat variations of alpha angle in the ST-T region and T wave magnitude variations in yielding a better classification accuracy (92.7 %) against that achieved by conventional features (81 %). Further, beat-by-beat features are also found to augment the accuracy in classifying myocardial infarction (MI) Versus control subjects in two public ECG databases (92 % from 88 % and 94 % from 77 %). These demonstrations summarily suggest the importance of beat-by-beat features in clinical diagnosis of ischemia.

Terahertz time-domain spectroscopy for the inspection of dry fibre preforms

Liquid moulded polymer matrix composites (LM-PMCs) are increasingly used in aerospace, automotive and other industrial applications. Liquid moulding processes featuring dry fibre preforms provide flexibility and enable cost reductions for secondary load-bearing structures. However, preform variability and manufacturing reproducibility remain major obstacles to wider use in primary structures. The open literature records only marginal use of non-destructive inspection (NDI) methods for dry multilayer preforms due to technological limitations and cost of NDI methods. In this work, terahertz time-domain spectroscopy (THz-TDS) is used for inspecting three dry multilayer glass fibre preforms featuring different defects, for the first time. A novel time-domain enhancement method is compared with classical image processing methods, aiming at improving image contrast and detecting potential defects. Furthermore, THz B-Scan is used for verifying the accuracy of interply defect detection. Finite difference time domain is simulated for analyzing THz magnitude variation in time-domain. Finally, quantitative evaluation is applied to further illustrate the significant potential of THz-TDS for the inspection of dry fibre preforms. The results show that the errors on defects lengths, widths, angles, and diameters for THz-TDS are in the ranges 8.2 %–34.3 %, 18.67 %–75 %, 0.29 %–6.67 %, and 1.33 %–10 % respectively.