Short-term Variability Research Articles

Time-Dependent Stress Drop Variations After Large Earthquakes Along the North Chilean Megathrust

ABSTRACT We observe a time dependence of the median stress drop in spatiotemporal proximity of large earthquakes. The median stress drop of the early aftershock seismicity is elevated for only a few days after the mainshocks and then rapidly falls back to the long-term average. This short-term variation has remained largely unnoticed by previous studies, presumably due to their usually low temporal resolution. Our study uses a recent extensive stress drop catalog, which contains more than 51,000 events from northern Chile. It includes observations from three Mw>7 megathrust earthquakes, namely the 2007 Mw 7.7 Tocopilla earthquake, the 2014 Mw 8.1 Iquique earthquake, and its largest Mw 7.6 aftershock, as well as another eleven Mw>6 earthquakes. A detailed analysis reveals that the elevated stress drop is not primarily linked to an increase of seismic moment during the early aftershock phase but is rather attributable to higher measured corner frequencies in the corresponding time interval. We propose two possible explanations: (1) The high stress changes induced by the mainshock allow failure of strong structures at or adjacent to the main rupture area, which produces higher stress drop events. After a few days, afterslip activity has reduced the in situ stresses and thereby the failure potential of the stronger regions. (2) The mainshock disturbs the fluid-sealing plate interface, allowing overpressured fluids to escape, exhibiting the so-called fault-valve behavior. The effect appears to persist only for several days until the sealing effect is restored and average stress drop levels are recovered.

Mapping soil moisture across the UK: assimilating cosmic-ray neutron sensors, remotely sensed indices, rainfall radar and catchment water balance data in a Bayesian hierarchical model

Abstract. Soil moisture is important in many hydrological and ecological processes. However, data sets which are currently available have issues with accuracy and resolution. To translate remotely sensed data to an absolute measure of soil moisture requires mapped estimates of soil hydrological properties and estimates of vegetation properties, and this introduces considerable uncertainty. We present an alternative methodology for producing daily maps of soil moisture over the UK at 2 km resolution (“SMUK”). The method is based on a simple linear statistical model, calibrated with 5 years of daily data from cosmic-ray neutron sensors at ∼ 40 sites across the country. The model is driven by precipitation, humidity, a remotely sensed “soil water index” satellite product and soil porosity. The spatial variation in the parameter describing the soil water retention (and thereby the response to precipitation) was estimated using daily water balance data from ∼ 1200 catchments with good coverage across the country. The model parameters were estimated by Bayesian calibration using a Markov chain–Monte Carlo method, so as to characterise the posterior uncertainty in the parameters and predictions. The approach reduces uncertainty by integrating multiple data sources, all of which have weaknesses but together act as a better constraint on the true soil moisture. The model explains around 70 % of the variance in the daily observations with a root-mean-square error of 0.05 m3 m−3, better than results from more complex process-based models. Given the high resolution of the inputs in time and space, the model can predict the very detailed variation in soil moisture which arises from the sporadic nature of precipitation events, including the small-scale and short-term variations associated with orographic and convective rainfall. Predictions over the period 2016 to 2023 demonstrated realistic patterns following the passage of weather fronts and prolonged droughts. The model has negligible computation time, and inputs and predictions are updated daily, lagging approximately 1 week behind real time.

Short-term variability in ocular biometry and the impact of preoperative dry eye.

This study aimed to investigate the short-term variability of ocular biometric measurements and to analyze the factors associated with high variability of biometric values in patients with cataract. This retrospective study included 60 eyes of 60 patients who underwent ocular biometry twice with the IOLMaster 700 (March to November 2023). Ocular surface parameters included tear film break-up time (TBUT), corneal and conjunctival staining scores (CSS/ConSS). Refractive surprise was defined as a mean absolute error exceeding 0.5 diopter (D). Participants included 22 men and 38 women with a mean age of 70.6 ± 8.4 years. The variability of mean keratometric (K) values and astigmatism showed notable variability compared to axial length (mean K: 0.18 ± 0.17D, astigmatism: 0.21 ± 0.26D, axial length: 0.01 ± 0.01mm). A decreased TBUT and increased CSS were significantly associated with high variability (≥ 0.25D) of mean K (p = 0.040/0.008). The high K variability group (≥ 0.25D) revealed a significantly higher incidence of refractive surprise (> 0.5D) compared with the low K variability group (< 0.25D) (45.5%/17.9%, p = 0.037). Preoperative lower TBUT and higher CSS influenced the variability of K values in ocular biometry and may be related with postoperative refractive surprise due to erroneous K values.

Burn Period: A use-inspired metric to track wildfire risk across Arizona and New Mexico in the southwest U.S.

Abstract Numerous tools and indices exist for wildland fire managers to anticipate and track changes in wildfire risk driven by variability in weather and climate conditions at hourly to seasonal scales. However, in working closely with southwest U.S. managers, we learned of a simple meteorological metric being informally used, but not widely accessible in existing tools or information products, to gauge short-term changes in wildfire risk. This metric, termed ‘Burn Period’ (BP), is the local count of hours per day with relative humidity values equal to or less than 20%. Our collaboration led to the development of an experimental tool called the ‘Burn Period Tracker’ to ease access and promote use of BP values for planning and response. This study is a climatological analysis of BP values at 124 fire weather stations across Arizona and New Mexico for the period 2000-2022 to aid in interpretation and understanding of this use-inspired metric. BP values reflect the strong seasonality in temperature and moisture deficit-driven wildfire risk across the southwest U.S., with risk climbing through the arid spring season, peaking in June, and then falling rapidly with the onset of the summer monsoon in July. Regression analyses show that short-term variability in BP values are driven by variability in low level atmospheric moisture in all months with strongest relationships during the summer after the onset of the monsoon. This study highlights the utility of BP as a short-term wildfire planning tool as well as an example of collaborative weather and climate services development.

Searching for Stellar Activity Cycles Using Flares: The Short- and Long-timescale Activity Variations of TIC-272272592

We examine 4 yr of Kepler 30 minutes data, and five sectors of Transiting Exoplanet Survey Satellite 2 minutes data for the dM3 star KIC-8507979/TIC-272272592. This rapidly rotating (P = 1.2 day) star has previously been identified as flare active, with a possible long-term decline in its flare output. Such slow changes in surface magnetic activity are potential indicators of solar-like activity cycles, which can yield important information about the structure of the stellar dynamo. We find that while TIC-272272592 shows evidence for both short- and long-timescale variations in its flare activity, it is unlikely physically motivated. Only a handful of stars have been subjected to such long-baseline point-in-time flare studies, and we urge caution in comparing results between telescopes due to differences in bandpass, signal-to-noise ratio, and cadence. In this work, we develop an approach to measure variations in the flare frequency distributions over time, which is quantified as a function of the observing baseline. For TIC-272272592, we find a 2.7σ detection of a sector which has a flare deficit, therefore indicating the short-term variation could be a result of sampling statistics. This quantifiable approach to describing flare-rate variation is a powerful new method for measuring the months-to-years changes in surface magnetic activity, and provides important constraints on activity cycles and dynamo models for low-mass stars.

Chloroplast redox state mediates the short-term regulation of leaf isoprene emission.

Isoprene emission from plants not only confers thermoprotection, but also has profound impacts on atmospheric chemistry and the climate. Leaf isoprene emission is dynamically regulated in response to various environmental cues, but the exact mechanism remains unclear. It has been proposed that chloroplast redox/energy state or cytosolic phosphoenolpyruvate carboxylation regulates isoprene biosynthesis and consequently emission, and the latter has been disproven by recent literature. However, the possible covariation of chloroplast redox/energy state and cytosolic PEP carboxylation in previous experiments impedes the independent examination of the former hypothesis. We developed an index of chloroplast redox state and showed its validity by examining the relationships between the index and the rates of certain processes which have been demonstrated to be affected or unaffected by chloroplast redox/energy state. According to the former hypothesis alone, we modelled how isoprene emission rate (IER) responded to different short-term environmental variations, and compared theoretical predictions with experimental data. We predicted that no matter which environmental factor was varied, IER would respond to the index of chloroplast redox state with similar velocities. We found that IER showed comparable increasing rates in response to the increase in the index of chloroplast redox state caused by different environmental variations (0.0479, 0.0439 or 0.0319 when ambient CO2 concentration, photosynthetic photon flux density or leaf temperature was varied, respectively). These results support that chloroplast redox/energy state regulates isoprene biosynthesis, leading to dynamic isoprene emission in nature.

Advancing complex urban traffic forecasting: A fully attentional spatial-temporal network enhanced by graph representation

Accurate urban traffic forecasting is essential for intelligent transportation systems (ITS). However, the majority of existing forecasting methodologies predominantly concentrate on point-based forecasts (e.g., traffic detector forecasts). A limited number of them pay attention to the urban bidirectional road segments and the complex road network topology. To advance accurate traffic forecasting in complex urban scenarios, this paper proposes a Graph Representation enhanced Fully Attentional Spatial-Temporal network (GR-FAST). First, we construct a refined bidirectional road network graph (BRG) to depict the urban road network topology more accurately, particularly focusing on the turning patterns at intersections. Then, we adopt the graph representation methodology and introduce spatial information encoding (SIE) to explicitly characterize the significance of roads and network structure from multiple perspectives. Enhanced by SIE, spatial attention can capture spatial dependencies from both road network topologies and traffic pattern similarities, thereby forming a unified urban spatial cognition. Finally, a multi-scale residual perception (MRP) module is designed to balance the interplay of short-term temporal variability and long-term periodicity. Experiments on a real-world urban dataset from Wuhan, China, demonstrate that GR-FAST outperforms the state-of-the-art deep learning methods, achieving an improvement of 9.19%. Furthermore, ablation studies suggest that the explicit incorporation of complex road spatial topologies can significantly enhance forecasting accuracy.

Collocating wind data: A case study on the verification of the CERRA dataset

Abstract Wind resource assessment often relies on reanalysis data or meso-scale models due to the challenges and costs associated with on-site measurement campaigns, especially in offshore locations. While these datasets offer extensive spatial coverage, they often provide the desired parameters on a coarse grid only. The spatial averaging over the grid and the low frequency of the outputs hinders the model’s ability to capture the short-term wind speed variability and events with a time scale of a few minutes to a few dozen of minutes. In this paper we investigate the temporal scales of the wind events resolved by the Copernicus European Regional Re-Analysis (CERRA) dataset, ERA5, and an in-house regional down-scaling of ERA5 using the WRF model by comparing them against measurements. We propose a method to collocate the two sources of wind data (measured and modeled), focusing on the verification of CERRA with respect to first and second-order statistics in the North Sea. The results highlight the superiority of CERRA compared to ERA5 in estimating the wind speed distribution, improving the 95th percentile error from -0.85 to -0.19 ms−1 and the average bias from -0.286 to -0.004 m s−1 across all locations. The wind speed change over one hour is underestimated by both CERRA and ERA5 when collocated with 10-minute measurements at the full hour. However, hourly averaged measurements align well with CERRA’s wind speed variation predictions, while an averaging window of 3.5 hours is needed to align with ERA5’s hourly wind speed changes. The results showed that the synthetic wind speeds exhibit optimal correlation with the measurements when the measurements are averaged over 4.5 to 7 hours, depending on the modeled dataset. This time scale corresponds to a length scale of 160-250 km, assuming a mean wind speed of 10 m s−1, which falls within the meso-scale range. The study contributes to the understanding of model validation in offshore wind energy studies, with the potential to enhance wind resource assessment calculations and improve the results of long-term extrapolations.

Intraoperative short-term blood pressure variability and postoperative acute kidney injury: a single-center retrospective cohort study using sample entropy analysis

BackgroundTo investigate if intraoperative very short-term variability in blood pressure measured by sample entropy improves discrimination of postoperative acute kidney injury after noncardiac surgery.MethodsAdult surgical patients undergoing general, thoracic, urological, or gynecological surgery between August 2016 to June 2017 at Seoul National University Hospital were included. The primary outcome was acute kidney injury stage 1, defined by the Kidney Disease: Improving Global Outcomes guidelines. Exploratory and explanatory variables included sample entropy of the mean arterial pressure and standard demographic, surgical, anesthesia and hypotension over time indices known to be associated with acute kidney injury respectively. Random forest classification and L1 logistic regression were used to assess four models for discriminating acute kidney injury: (1) Standard risk factors which included demographic, anesthetic, and surgical variables (2) Standard risk factors and cumulative hypotension over time (3) Standard risk factors and sample entropy (4) Standard risk factors, cumulative hypotension over time and sample entropy.ResultsTwo hundred and thirteen (7.4%) cases developed postoperative acute kidney injury. The median and interquartile range for sample entropy of mean arterial pressure was 0.34 and [0.26, 0.42] respectively. C-statistics were identical between the random forest and L1 logistic regression models. Results demonstrated no improvement in discrimination of postoperative acute kidney injury with the addition of the sample entropy of mean arterial pressure: Standard risk factors: 0.81 [0.76, 0.85], Standard risk factors and hypotension over time indices: 0.80 [0.75, 0.85], Standard risk factors and sample entropy of mean arterial pressure: 0.81 [0.76, 0.85] and Standard risk factors, sample entropy of mean arterial pressure and hypotension over time indices: 0.81 [0.76, 0.86].ConclusionAssessment of very short-term blood pressure variability does not improve the discrimination of postoperative acute kidney injury in patients undergoing non-cardiac surgery in this sample.

A Cross-Sectional Study on the Effect of Stress on Short-Term Heart Rate Variability and Muscle Strength Among Construction Site Workers

This cross-sectional study investigates the immediate impact of occupational stress on short-term heart rate variability (HRV) and muscle strength among 200 construction workers in Chennai. The Perceived Stress Scale (PSS) was employed to assess stress levels, while HRV was measured using a portable ECG device, focusing on time-domain (RMSSD) and frequency-domain (HF Power) parameters. Muscle strength was evaluated using a MicroFET 2 dynamometer. Pearson correlation and multivariate regression analyses were applied to determine the associations between stress, HRV, and muscle strength, adjusting for confounding factors such as age, body mass index (BMI), and work duration. Results revealed a significant negative correlation between perceived stress and both HRV indices (RMSSD: r=-0.45, p&lt;0.01; HF Power: r=-0.50, p&lt;0.01) as well as muscle strength (r=-0.40, p&lt;0.01). Higher stress levels were associated with reduced HRV and diminished muscle strength. Regression analyses confirmed that stress independently predicted lower HRV (RMSSD: ?=-0.35, p&lt;0.01; HF Power: ?=-0.42, p&lt;0.01) and decreased muscle strength (?=-0.32, p&lt;0.01). These findings suggest that elevated stress negatively impacts both cardiovascular and muscular functioning, potentially increasing the health risks among construction workers. The study highlights the importance of implementing stress management interventions to improve both the physical well-being and productivity of workers in the construction industry.

Simulations and machine learning models for cosmic-ray short-term variations and test-mass charging on board LISA

Simulations and machine learning models for cosmic-ray short-term variations and test-mass charging on board LISA

Beat-to-beat analysis of hemodynamic response to mental and psychological stress in sickle cell anemia

Abstract Vaso-occlusive crises are a hallmark symptom of sickle cell disease. Physical stressors can trigger decreased microvascular blood flow and increase the risk for vaso-occlusive crises. However, the effect of mental and psychological stressors on vascular physiology in sickle cell disease is not well-established. We hereby examined fluctuations in continuous blood pressure to evaluate hemodynamic changes in sickle cell disease patients during mental and psychological stress. Thirteen sickle cell disease subjects from the Children’s Hospital Los Angeles and 11 healthy volunteers were recruited. Continuous blood pressure was recorded during two mental tasks and one psychological stress task. Systolic beat-to-beat blood pressure variability measurements were calculated for each subject. Three very short-term blood pressure variability metrics served as outcome measures: standard deviation, coefficient of variation, and average real variability. Peripheral augmentation index was calculated from arterial waveforms. Linear mixed effects models evaluated associations between patient factors and outcome measures. Sickle cell disease patients exhibit increased systolic blood pressure variability in response to psychological stress. All subjects exhibited a decrease in systolic blood pressure variability in response to mental stress tasks. During mental stress, both groups displayed increased augmentation index, reflective of stress-induced vasoconstriction, while psychological stress in sickle cell disease patients led to both decreased mean arterial pressure and increased augementation index, suggestive of uncompensated vasoconstriction. These findings emphasize the impact of mental and psychological stressors on vascular function in sickle cell disease and the potential for monitoring physiological signals to predict vaso-occlusive crisis events.

Serial troponin sampling over 60-minutes allows early distinction between myocardial infarction and chronic myocardial injury in patients presenting to emergency departments

Abstract Background High-sensitivity cardiac troponins (hs-cTn) are key clinical biomarkers in the assessment and diagnosis of patients with suspected acute coronary syndromes presenting to Emergency Departments (ED). These tests facilitate rapid triage through 0h/1h blood sampling regimens. Most patients will not have myocardial infarction (MI) diagnosed, having either hs-cTn levels below the upper reference limit (URL) or stable elevations. The latter group of complex, often multimorbid, patients is termed chronic myocardial injury (CMI) according to the 4th Universal Definition of MI (4thUDMI). These patients have long-term morbidity and mortality rates higher than those with type 1 MI. Early distinction of CMI from MI remains challenging with debate regarding optimal hs-cTnT delta cut-offs; both 3ng/L and 5ng/L over one-hour have been proposed. Methods and Results We prospectively collected 0-, 30- and 60-minutes (+/- 5 min) hs-cTnT samples from 185 patients presenting to Liverpool Hospital ED (Sydney, Australia) between March and August 2023. Diagnoses were adjudicated according to the 4thUDMI as follows: 16 (8.6%) MI ruled-in, 66 (35.7%) CMI and 103 (55.7%) MI ruled-out. Linear regression was used to calculate individual hs-cTnT slopes for each patient. Between CMI and MI, each 1 ng/L/hour increase in the slope of hs-cTnT resulted in a two-fold (95% CI: 1.5 to 2.9) greater likelihood of MI being diagnosed (P &amp;lt; 0.001). A stable hs-cTnT trajectory was observed in CMI, which ranged between 0 and 3 ng/L/hour in all patients with an initial hs-cTnT level ≤ 50 ng/L. Chronic myocardial injury patients with an initial hs-cTnT level &amp;gt; 50ng/L (n=12), exhibited more kinetic slopes ranging up to 8ng/L/hour (P &amp;lt;0.001). Unexpectedly, three MI patients had a non-linear slope, of whom two patients would not have displayed a significant rise had the additional 30-minute sample not been collected. These outlying patients presented at 9-12 hours and over 12 hours from symptom onset. Conclusion This study offers a novel characterisation of CMI, which has not previously been investigated in an unselected ED cohort using three serial samples over one hour. It validates a hs-cTnT delta of 3ng/L/hour in the recent ESC guidelines. However, there is a need for greater characterisation of short-term hs-cTn variability for more personalised MI risk stratification and to guide more targeted observation and further investigation of CMI patients.

High serum levels of CXCL13 predict lower response to csDMARDs in both ACPA-positive and ACPA-negative early rheumatoid arthritis.

Increased circulating levels of CXCL13 reflect synovial production and indicate immune dysregulation in patients with rheumatoid arthritis (RA). Here we tested whether CXCL13 predicts response to first-line treatment with methotrexate (MTX) in patients with early RA, independently and in association with anti-citrullinated protein antibodies (ACPA) and IgM-rheumatoid factor (RF). A prospective cohort of 243 early RA patients undergoing treat-to-target with MTX was evaluated. CXCL13, ACPA and IgM-RF were determined on baseline sera. Short-term variations of CXCL13 were measured after 2 months. The association of high CXCL13 (≥100 pg/ml) with disease remission after 6 months and escalation to second-line therapies within year 2 was evaluated in the total population and in ACPA-subgroups separately. High levels of CXCL13 were found in 53.6% of ACPA-positive and 31.5% of ACPA-negative patients, with minimal association with disease activity and RF. Serum CXCL13 remained stable after 2 months. High baseline CXCL13 independently predicted failure to achieve remission and more frequent requirement of second-line treatment in ACPA-positive patients, with adjusted ORs in the range of 0.17-0.49 for remission and 6.75 for second-line treatment. In ACPA-negative patients with high CXCL13, remission occurred at the expense of higher doses of MTX, and levels of CXCL13 predicted MTX escalations with an adjusted OR (95% CI) of 2.69 (1.35-5.34). High serum levels of CXCL13 identify a subgroup of RA patients who are more refractory to first-line treatment with MTX. CXCL13 appears a promising biomarker of response to MTX in both ACPA-positive and -negative early RA.

Quantitative analysis of high-frequency radiometric databases. Application to the case of Seville

AbstractThe short-term variability of solar resource is one of the main challenges faced by the large-scale implementation of photovoltaic (PV) systems today; this will increase as the installed power of solar PV increases. To evaluate its influence on electrical grid operation, it is essential to analyse the variability of solar resource based on high-frequency data, 30 s and shorter. In this study, the high-frequency measurements of Global Horizontal Irradiation (GHI) and Direct Normal Irradiation (DNI) recorded every 5 s at the radiometric station of the University of Seville for 20 years are analysed and characterised. For this purpose, a corrected database for GHI and DNI was obtained from the application of a correction methodology. To understand the reliability of this database it is required to know its uncertainty. This work proposes a methodology to quantify the uncertainty of high-frequency radiometric databases whose wrong data have been corrected. Applying it, the daily uncertainty is 2.69% for GHI and 4.07% for DNI. To characterize the distribution of the database, the distribution of high frequency kt and kb indices are modelled providing the fitting parameters. To characterize the variability of high-frequency measurements, transition matrices are proposed, which allow identifying both the magnitude and frequency of jumps. The results obtained show that jumps of up to 800 W/m2 occur in both GHI and DNI. In the case of GHI, the percentage of jumps equal to or greater than 500 W/m2 is 1.36%, about 43,000 jumps in a year in measurements every 5 s.

Effects of six antipsychotic drug treatment regimens on short-term heart rate variability in patients with schizophrenia.

Many antipsychotic drugs have cardiac side effects due to their pharmacological actions. Heart rate variability (HRV) analysis can be used as a potential indicator of cardiotoxicity in cases where a decrease in HRV occurs after the administration of antipsychotics such as clozapine. The purpose of this study was to explore the effects of 6 antipsychotic drug regimens on short-term HRV in patients with schizophrenia. Data from 164 patients with schizophrenia between January 2018 and June 2023 were retrospectively analysed. Based on the drug used for treatment, the patients were categorised into clozapine combination (clozapine combined with aripiprazole, risperidone or ziprasidone), clozapine alone, olanzapine, aripiprazole, ziprasidone or risperidone groups. Heart rate variability indices were calculated using time domain analysis, including the standard deviation of the RR intervals (SDNN), the root mean square of successive RR interval differences (RMSSD) and the percentage of successive RR intervals over 50ms (PNN50). Compared with the pretreatment period, the SDNN, RMSSD and PNN50 were significantly lower in the clozapine combination, clozapine, olanzapine and aripiprazole groups at the end of weeks 2 and 4 of treatment (P < 0.05). However, these indicators in ziprasidone and risperidone groups did not show this significant decrease (P > 0.05). The effects of clozapine combination and clozapine on HRV were greater than for olanzapine, aripiprazole, ziprasidone or risperidone. Attention should be paid to controlling the dosage of clozapine combination and clozapine and monitoring the patient's electrocardiogram during administration.

Long- and short-term variability of the possible nascent planetary nebula IRAS 22568+6141: A late thermal pulse?

Long- and short-term variability of the possible nascent planetary nebula IRAS 22568+6141: A late thermal pulse?

Detecting and sizing the Earth with PLATO: A feasibility study based on solar data

The PLAnetary Transits and Oscillations of stars (PLATO) mission will observe the same area of the sky continuously for at least two years in an effort to detect transit signals of an Earth-like planet orbiting a solar-like star. We aim to study how short-term solar-like variability caused by oscillations and granulation would affect PLATO's ability to detect and size Earth if PLATO were to observe the Solar System itself. We also compare different approaches to mitigate noise caused by short-term solar-like variability and perform realistic transit fitting of transit signals in PLATO-like light curves. We injected Earth-like transit signals onto real solar data taken by the Helioseismic and Magnetic Imager (HMI) instrument on board the Solar Dynamics Observatory (SDO). We isolated short-term stellar variability in the HMI observations by removing any variability with characteristic timescales longer than five hours using a smooth Savitzky-Golay filter. We then added a noise model for a variety of different stellar magnitudes computed by assuming an observation by all 24 normal cameras. We first compared four different commonly used treatments of correlated noise in the time domain by employing them in a transit fitting scheme. We then tried to recover pairs of transit signals using an algorithm similar to the transit least squares algorithm. Finally, we performed transit fits using realistic priors on planetary and stellar parameters and assessed how accurately the pair of two injected transits was recovered. We find that short-term solar-like variability affects the correct retrieval of Earth-like transit signals in PLATO data. Variability models accounting for variations with typical timescales at the order of one hour are sufficient to mitigate these effects. We find that when the limb-darkening coefficients of the host star are properly constrained, the impact parameter does not negatively affect the detectability of a transit signal or the retrieved transit parameters, except for high values ($b &gt; 0.8$). For bright targets (8.5 - 10.5 mag), the transit signal of an Earth analogue can reliably be detected in PLATO data. For faint targets a detection is still likely, though the results of transit search algorithms have to be verified by transit-fitting algorithms to avoid false positive detections being flagged. For bright targets (V-mag leq 9.5), the radius of an Earth-like planet orbiting a solar-like star can be correctly determined at a precision of 3&lt;!PCT!&gt; or less, assuming that at least two transit events are observed and the characteristics of the host star are well understood.

Association of holidays and the day of the week with suicide risk: multicounty, two stage, time series study

ObjectivesTo assess the short term temporal variations in suicide risk related to the day of the week and national holidays in multiple countries.DesignMulticountry, two stage, time series design.SettingData from 740...

Seasonal variation of coastal currents and residual currents in the CAT BA – HA long coastal area (VIET NAM): Results of coherens model

The COHERENS model is used to investigate the temporal and spatial variation of the coastal currents in 2021 at the Cat Ba - Ha Long, northern Vietnam. The findings indicate that tidal oscillation has a notable impact on the current fields in short-term variations (hours to days). Meanwhile, the wind field and river discharge are the decisive factors affecting the seasonal variation of the current fields in Cat Ba - Ha Long coastal area. Furthermore, the characteristics of residual currents are significantly affected by river discharges and wind patterns, which vary across different months and seasons. During the southwest monsoon season (May to August), the residual currents have a prevailing direction towards the sea, from the west and south-southwest towards the east and north-northeast, reaching maximum speeds of approximately 0.1–0.15 m/s. Conversely, in the transitional and northeast monsoon seasons, the directions of residual currents are from the east-northeast to the west-southwest, with peak speed up to 0.2–0.25 m/s. Notably, the residual currents in the bottom and surface layers in the eastern-southwestern area of Cat Ba Island and the north of Ha Long Bay are in opposite directions.