Equivalent Estimates Research Articles

Finite element model-informed deep learning for equivalent force estimation and full-field response calculation

The measured data in structural health monitoring are discrete in space, which means that the full-field load distribution or structural responses cannot be captured. Conventional deep-learning-based data expansion methods require a huge volume of training datasets, and they tend to be overfitted because constraints are insufficient. To address these issues, a physics-informed neural network (PINN) is developed in this study by estimating an arbitrarily distributed load to an equivalent load. Subsequently, a finite element model is used to reconstruct structural responses. The proposed PINN is divided into connected trainable data model and untrainable physics-driven model. The data-driven model in the proposed PINN is a convolutional autoencoder architecture, while the physics-informed model incorporates the impulse response matrix derived from the time-invariant system. The autoencoder is fed with the monitored data, and an equivalent load is outputted. Consecutively, the impulse response function in the physics-informed model is used to calculate the responses at the sensor positions to the equivalent load. The difference between the measured and predicted responses is formulated as the loss function to train the PINN. In this way, the physics-driven model is integrated into the data model to guide its training direction in a novel self-supervised manner, thereby avoiding the overfitting effects in network training. In addition, the unmeasured and randomly-distributed load can be estimated automatically. The proposed PINN is applied to a numerical continuous beam and a laboratory-tested three-storey frame, where only a few sensors are required for response reconstruction. Results suggest that the PINN can accurately reconstruct the full-field structural responses via the estimated equivalent load and applies to complex conditions with random and unknown load distributions.

Breast radiation dose equivalent and cancer risk estimation during PET/CT examinations

Mammary glands or breasts are radiosensitive organs that makeup 12% of the body's overall radiation sensitivity. According to estimates, Saudi Arabia has a 27.3% overall cancer incidence rate for breast cancer. Recent studies reported that breasts get significant organ equivalent doses during diagnostic procedures with ionizing radiation, even though the breast is not the target organ. The objective of this work is to assess the effective and breast equivalent doses during positron emission tomography (PET)/Computed tomography (CT) imaging examinations. The effective and breast equivalent doses per PET/CT (GE PET/CT 710) examination were quantified using the amount of the injected radiopharmaceutical (MBq) (18F- fluorodeoxyglucose (FDG)), and external doses from CT examinations. The average, standard deviation (SD) and range of examined patients' age (years) and body mass index ((BMI), kg/m2) were 58 ± 11 (39–76) and 30 ± 5 (20.7–43.1), respectively. Constant administered activity (AA, 185 MBq) and tube potential (120 kVp) were used for all patients. The average, SD, and range of the patients effective dose (mSv) from PET/CT (18F- FDG) examination was 8.0 ± 0.93 (7.0–10.0) and 2.075 ± 0.11 (1.91–2.29) in that order. Breast doses are comparable with previous studies during the PET/CT and mammographic examination. Proper justification criteria and dose optimization are essential to lowering excessive exposure while preserving image quality, especially for young females because they have a higher risk of radiation-induced cancer.

Second International Consensus Study of Antipsychotic Dosing (ICSAD-2).

Expert consensus-based clinically equivalent dose estimates and dosing recommendations can provide valuable support for the use of drugs for psychosis in clinical practice and research. This second International Consensus Study of Antipsychotic Dosing provides dosing equivalencies and recommendations for newer drugs for psychosis and previously reported drugs with low consensus. We used a two-step Delphi survey process to establish and update consensus with a broad, international sample of clinical and research experts regarding 26 drug formulations to obtain dosing recommendations (start, target range, and maximum) and estimates of clinically equivalent doses for the treatment of schizophrenia. Reference agents for equivalent dose estimates were oral olanzapine 20 mg/day for 15 oral and 7 long-acting injectable (LAI) agents and intramuscular haloperidol 5 mg for 4 short-acting injectable (SAI) agents. We also provide a contemporary list of equivalency estimates and dosing recommendations for a total of 44 oral, 16 LAI, and 14 SAI drugs for psychosis. Survey participants (N = 72) from 24 countries provided equivalency estimates and dosing recommendations for oral, LAI, and SAI formulations. Consensus improved from survey stages I to II. The final consensus was highest for LAI formulations, intermediate for oral agents, and lowest for SAI formulations of drugs for psychosis. As randomized, controlled, fixed, multiple-dose trials to optimize the dosing of drugs for psychosis remain rare, expert consensus remains a useful alternative for estimating clinical dosing equivalents. The present findings can support clinical practice, guideline development, and research design and interpretation involving drugs for psychosis.

Equivalent Dose Estimation in FLASH Irradiation with a Deep Learning Approach

Ultra-high dose rate (FLASH) irradiation has been reported to provide decreased normal tissue toxicity without compromising tumor control compared with conventional (CONV) irradiation. However, a comprehensive understanding of the FLASH biological effect requires precise quantification of radiobiology. The study is to explore whether deep learning (DL) can tackle the task. As a proof of concept, we investigate a DL model for estimating FLASH dose to its equivalent CONV dose. Healthy C57Bl/6 female mice underwent FLASH (200Gy/s; n = 43) or CONV (0.12Gy/s; n = 41) whole abdominal irradiation using ∼16 MeV electron beams with a dose escalation scheme of 5 groups (n = 8 or 9) at 1Gy increments: 12-16Gy FLASH, 11-15Gy CONV. 4 days post-irradiation, 9 jejunum cross-sections per mouse were H&E stained for histological analysis. Each cross-section image was processed to remove lumen background and oversampled into multiple large-scale and small-scale patches along jejunal circumference. In CONV dataset, we randomly selected the data of 32 mice (80%) for model training and the rest (20%) for model validation. A ResNet101-based DL model, pre-trained with an unsupervised contrastive learning scheme, was retrained with only CONV training set to estimate corresponding CONV dose. For comparison, a crypt counting (CC) approach was implemented by manually counting the number of regenerating crypts on each cross-section image. An exponential function of dose vs crypt number was fitted with the CONV training set and used for dose estimation on the testing set. Mean squared error (MSE) was used to assess the accuracy of DL and CC approaches in estimating dose levels in CONV irradiation. The validated DL model was applied to the FLASH set to project FLASH dose into corresponding CONV dose that results in equivalent biological response. The CONV dose estimated by DL and CC approaches and DL-estimated FLASH equivalent dose were summarized in Table 1. The DL model achieved an MSE of 0.21 Gy2 on CONV testing set compared with 0.32 Gy2 of the CC approach. FLASH equivalent dose estimated by DL model for 12, 13, 14, 15 and 16Gy were 12.16±0.40, 12.53±0.32, 12.72±0.24, 12.85±0.20 and 13.04±0.27 Sv, respectively. Our proposed DL model can accurately estimate the CONV dose based on histological images. The DL predictions of FLASH dataset demonstrate that FLASH may reduce normal tissue toxicity with a lower equivalent dose, especially at high irradiated dose levels. Our study indicates that deep learning can be potentially used to assess the equivalent dose of FLASH irradiation to normal tissue.

Use of peak over threshold data for flood frequency estimation: An application at the UK national scale

This study investigates choices of statistical distributions to represent the threshold exceedance frequency and magnitude of peaks-over-threshold (POT) series from a national dataset of extreme hydrological events from 842 gauging stations in the UK. From the initial POT series, two new series were created, POT1 and POT3, representing POT series with an average of, respectively, one and three exceedances per year. Using a χ2 goodness-of-fit test, the choice of distributions for both the annual exceedance counts and the magnitude of threshold exceedances were explored for both the POT1 and POT3 datasets. The results show that the negative binomial and geometrical distributions provide a better fit to the annual exceedance count than the Poisson distribution typically assumed. These results are particularly pronounced when considering the POT3 dataset and in the South-East of the UK where many river flow series are dominated by slow responding groundwater dominated catchments. Finally, estimates of design floods with a return period of 2 and 100 years were obtained for each POT series and compared to the equivalent estimates obtained from direct at-site analysis of the annual maximum series. The results show a good alignment between the magnitude of the design floods estimated by the two methods, but a generally lower standard deviation of estimates obtained from the POT data, as quantified using a bootstrap procedure. The results presented here show that the POT series could beneficially replace the current operational guidelines based on annual maximum series for design flood estimation in the UK.

CATNet: Convolutional attention and transformer for monocular depth estimation

Monocular depth estimation has received more and more attention due to its wide range of application scenarios. In this paper, we propose a novel simple framework, called CATNet, which treats monocular depth estimation as an ordinal regression problem. At present, in order to obtain higher performance, the research on monocular depth estimation is achieved by increasing the amount of calculation and parameters of the model. Based on this, we propose a novel simple encoder–decoder architecture that aims to reduce the SOTA model parameters and complexity while keeping the depth estimation accuracy as high as possible rather than aiming for extremely lightweight. Meanwhile, in order to further refine the multi-scale information extracted by the encoder, we propose a Multi-dimensional Convolutional Attention (MCA) module. To enhance the extraction of global information for accurate pixel classification, we propose a Dual Attention Transformer (DAT) module to extract global features of images. Furthermore, experimental results on the KITTI and NYU datasets demonstrate that the advantage of our proposed framework is that it achieves almost equivalent depth estimation performance to the current SOTA with fewer parameters and lower complexity. To the best of our knowledge, CATNet is the first work that achieves nearly the same depth estimation accuracy as Transformer-based large model encoders with so few parameters.

Non-Intrusive Efficiency Estimation of Inverter-Fed Induction Motors

The use of inverter supplies over sinusoidal supplies for induction motors is growing considerably. Inverter supplies enable effective and robust control of induction motors in many automated systems. However, this benefit features at the expense of additional harmonic motor losses due to pulse-width modulated (PWM) voltages and currents produced by inverter supplies. The quantification of these additional harmonic losses and the estimation of efficiency in a non-intrusive manner can be quite challenging. This paper proposes a non-intrusive efficiency estimation technique for inverter–fed induction motors that is based on harmonic regression analysis, harmonic equivalent circuit parameter estimation and harmonic loss analysis using limited measured data. The efficiency results from the proposed technique are comparable to those obtained using the IEC 60034-2-3 Standard up to 1% accuracy when tested on three different inverter-fed motors at different loads.

Equivalent estimation method (EEM) for quasi-distributed bridge-deflection measurement using only strain data

Deflection is a crucial parameter for bridge health monitoring procedures. In this paper, an equivalent estimation method (EEM) is proposed for quasi-distributed deflection estimation using onboard strain data. Leveraging the installation of the self-designed sensing bar on the bridge girder, an equivalent analysis model is established. An innovative inverse finite element method (iFEM) is employed to discretize the model and to establish a strain-deflection transformation matrix. Then, quasi-distributed bridge deflections are estimated by reconstructing the deformed shape of the equivalent analysis model. The present model is simple and independent of the typology of bridge structures, making the EEM sufficiently general to analyse complex bridges. The EEM framework is computationally fast, powerful, and robust for real-time applications since iFEM enables deformation predictions by simple matrix–vector multiplication without a priori loading and material information. Experimental validation cases have been performed, and they demonstrate the excellent quasi-distributed sensing capability of the EEM.

The oncologic risk of magnetic resonance imaging-targeted and systematic cores in patients treated with radical prostatectomy.

Magnetic resonance imaging (MRI)-targeted prostate biopsy (MRI-biopsy) detects high-Grade Group (GG) prostate cancers not identified by systematic biopsy (S-biopsy). However, questions have been raised whether cancers detected by MRI-biopsy and S-biopsy, grade-for-grade, are of equivalent oncologic risk. The authors evaluated the relative oncologic risk of GG diagnosed by S-biopsy and MRI-biopsy. This was a retrospective analysis of all patients who had both MRI-biopsy and S-biopsy and underwent with prostatectomy (2014-2022) at Memorial Sloan Kettering Cancer Center. Three logistic regression models were used with adverse pathology as the primary outcome (primary pattern 4, any pattern 5, seminal vesicle invasion, or lymph node involvement). The first model included the presurgery prostate-specific antigen level, the number of positive and negative S-biopsy cores, S-biopsy GG, and MRI-biopsy GG. The second model excluded MRI-biopsy GG to obtain the average risk based on S-biopsy GG. The third model excluded S-biopsy GG to obtain the risk based on MRI-biopsy GG. A secondary analysis using Cox regression evaluated the 12-month risk of biochemical recurrence. In total, 991 patients were identified, including 359 (36%) who had adverse pathology. MRI-biopsy GG influenced oncologic risk compared with S-biopsy GG alone (p<.001). However, if grade was discordant between biopsies, then the risk was intermediate between grades. For example, the average risk of advanced pathology for patients who had GG2 and GG3 on S-biopsy was 19% and 66%, respectively, but the average risk was 47% for patients who had GG2 on S-biopsy and patients who had GG3 on MRI-biopsy. The equivalent estimates for 12-month biochemical recurrence were 5.8%, 15%, and 10%, respectively. The current findings cast doubt on the practice of defining risk group based on the highest GG. Because treatment algorithms depend fundamentally on GG, further research is urgently required to assess the oncologic risk of prostate tumors depending on detection technique. Using magnetic resonance imaging (MRI) to help diagnose prostate cancer can help identify more high-grade cancers than using a systematic template biopsy alone. However, we do not know if high-grade cancers diagnosed with the help of an MRI are as dangerous to the patient as high-grade cancers diagnosed with a systematic biopsy. We examined all of our patients who had an MRI biopsy and a systematic biopsy and then had their prostates removed to find out if these patients had risk factors and signs of aggressive cancer (cancer that spread outside the prostate or was very high grade). We found that, if there was a difference in grade between the systematic biopsy and the MRI-targeted biopsy, the risk of aggressive cancer was between the two grades.

Principal force pattern and impulse response mode for structural equivalent force estimation and full-field response reconstruction

An impulse response mode-based approach is proposed in this study to estimate the equivalent force and achieve full-field monitoring under complex loading conditions. The structural impulse responses to quasi-static and dynamic forces are decomposed as the superposition of a set of predefined representative basis to provide the low-rank property of responses. The principal impulse response modes are calculated from the correlation matrix of the impulse responses in a time step using the principal component analysis. The principal relative force pattern is the eigenvector of the correlation matrix, and the truncation number is determined by the distribution of the eigenvalues. The equivalent force is subsequently defined and solved by the dynamic stiffness method. The full-field responses are then reconstructed by calculating the model responses to the estimated equivalent force. The systematic uncertainty is also quantified by an error propagation method for sensor placement optimization. Numerical examples and experimental tests demonstrate that the proposed methodology can estimate the equivalent force and reconstruct the structural responses accurately under various load scenarios.

Best Integer Equivariant Estimation With Quality Control in GNSS RTK for Canyon Environments

Global Navigation Satellite System (GNSS) has been widely used for navigation and positioning in many areas, from professional applications to the mass market. However, in the real-time kinematic (RTK) positioning, the ambiguities usually cannot be fixed in natural and urban canyon environments due to the high occlusion, strong reflection, and frequent maneuvering. In this paper, the best integer equivariant (BIE) estimation with quality control is proposed. Specifically, the quality control issues based on the observation and state domains are added to the BIE estimator. First, an improved multipath processing approach is used to mitigate the effects of multipath and diffraction, and a modified detection, identification, and adaptation procedure is adopted for processing the outliers and non-line-of-sight reception. For state-domain quality control, a two-step selection and segment estimation are used to refine the ambiguity candidates. Finally, the BIE estimator is obtained. To validate the effectiveness of the proposed method, two experiments, including natural and urban canyon environments, are conducted. Compared with the float, fixed, and traditional BIE solutions, the BIE solution with quality control has the best positioning performance, including precision and reliability. Moreover, the problem of ambiguity resolution is also alleviated to a great extent. Specifically, in real-time monitoring, the real-time single-epoch positioning accuracy in east, north, and up directions are approximately 0.023, 0.014, and 0.044 m, respectively. For the low-cost urban vehicle data, the proposed method performs best regarding availability, precision, and reliability.

A Multifactor Eigenvector Spatial Filtering-Based Method for Resolution-Enhanced Snow Water Equivalent Estimation in the Western United States

Accurate snow water equivalent (SWE) products are vital for monitoring hydrological processes and managing water resources effectively. However, the coarse spatial resolution (typically at 25 km from passive microwave remote sensing images) of the existing SWE products cannot meet the needs of explicit hydrological modeling. Linear regression ignores the spatial autocorrelation (SA) in the variables, and the measure of SA in the data assimilation algorithm is not explicit. This study develops a Resolution-enhanced Multifactor Eigenvector Spatial Filtering (RM-ESF) method to estimate daily SWE in the western United States based on a 6.25 km enhanced-resolution passive microwave record. The RM-ESF method is based on a brightness temperature gradience algorithm, incorporating not only factors including geolocation, environmental, topographical, and snow features but also eigenvectors generated from a spatial weights matrix to take SA into account. The results indicate that the SWE estimation from the RM-ESF method obviously outperforms other SWE products given its overall highest correlation coefficient (0.72) and lowest RMSE (56.70 mm) and MAE (43.88 mm), compared with the AMSR2 (0.33, 131.38 mm, and 115.45 mm), GlobSnow3 (0.50, 100.03 mm, and 83.58 mm), NCA-LDAS (0.48, 98.80 mm, and 81.94 mm), and ERA5 (0.65, 67.33 mm, and 51.82 mm), respectively. The RM-ESF model considers SA effectively and estimates SWE at a resolution of 6.25 km, which provides a feasible and efficient approach for SWE estimation with higher precision and finer spatial resolution.

Deterministic sampling methods coupled with dynamic coverage factors for uncertainty analyses in MELCOR simulation

This study proposed a modified uncertainty quantification (UQ) approach which couples deterministic sampling (DS) methods with dynamic coverage factors, to overcome the disadvantage of the previous UQ approach (Wang and Ma, 2023b) in obtaining numerically equivalent estimates of 95/95 tolerance limits for MELCOR simulations of postulated severe accidents in a Swedish pressurized water reactor (PWR). In the modified UQ approach two deterministic sampling (DS) methods were used to obtain the first two statistical moments, and dynamic coverage factors calculated from fitted beta distributions are employed to extend the moment information to the 95th percentile. The modified UQ approach was compared with the previous UQ approach (coupling DS methods with a fixed coverage factor) and conventional UQ approach of the first order Wilks’ method. The comparative results showed that the modified UQ approach not only enabled eliminating unrealistic estimates in the previous UQ approach, but also offered estimates that are covered by boxplots from the first order Wilks’ method with a significant reduction of computational cost. In the modified UQ approach 10–18 samples were sufficient, while at least 59 samples were required for the first order Wilks’ method. In the scope of the present study, DS-Standard method was recommended in the modified UQ approach in view of conservatism, while DS-Simplex method was preferred in view of accuracy and computational cost. Since the behavior of a DS method is dependent of time and outputs, in practice different DS methods should be integrated to the modified UQ approach for numerically equivalent estimates of 95/95 tolerance limits in MELCOR severe accident simulations.

Disturbance rejection‐based cluster synchronization of nonlinear complex dynamical networks with hybrid‐driven mechanism

AbstractThe issue of cluster synchronization and disturbance rejection of multi‐weighted complex dynamical networks subject to nonlinearities and external disturbances are studied in this article. In particular, a hybrid‐triggering control scheme is implemented to mitigate the burden of the network transmission according to the prespecified triggering condition, under which each node will transmit its signal independently to the controller. To be more specific, a Bernoulli distributed stochastic variable is introduced to describe the switching law of the communication scheme. Also, the disturbance rejection performance is carried out with the aid of an improved equivalent input disturbance estimator approach. The sufficient criterion in the form of linear matrix inequalities is derived by constructing a suitable Lyapunov–Krasovskii functional and employing stochastic theory. Based on these settings, the feedback gain matrices and the trigger parameters affirming the desired cluster synchronization performance of the considered networks are established. Eventually, two illustrative examples including Lur'e networks with simulations are presented, which evidently proves the effectiveness of the obtained results.

Stratified Simple Random Sampling Versus Volunteer Community-Wide Sampling for Estimates of COVID-19 Prevalence.

Objectives. To evaluate community-wide prevalence of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infection using stratified simple random sampling. Methods. We obtained data for the prevalence of SARS-CoV-2 in Jefferson County, Kentucky, from adult random (n = 7296) and volunteer (n = 7919) sampling over 8 waves from June 2020 through August 2021. We compared results with administratively reported rates of COVID-19. Results. Randomized and volunteer samples produced equivalent prevalence estimates (P < .001), which exceeded the administratively reported rates of prevalence. Differences between them decreased as time passed, likely because of seroprevalence temporal detection limitations. Conclusions. Structured targeted sampling for seropositivity against SARS-CoV-2, randomized or voluntary, provided better estimates of prevalence than administrative estimates based on incident disease. A low response rate to stratified simple random sampling may produce quantified disease prevalence estimates similar to a volunteer sample. Public Health Implications. Randomized targeted and invited sampling approaches provided better estimates of disease prevalence than administratively reported data. Cost and time permitting, targeted sampling is a superior modality for estimating community-wide prevalence of infectious disease, especially among Black individuals and those living in disadvantaged neighborhoods. (Am J Public Health. 2023;113(7):768-777. https://doi.org/10.2105/AJPH.2023.307303).

Impact of supporting substrate on the measured neutron yields from natLi (p, n) system: A case study with natC and natTa stopping targets

Abstract The high energy neutron generation facilities worldwide uses the Li(p,n) system for production of the quasi mono-energetic neutrons from epithermal to few hundreds of MeV. Among the facilities either self-standing thick Li target or Li foil supported on a target assembly is used. In case of a supported Li target, either low or high mass supporting substrates like Al, C, Au, Ta etc. can be used. However, the neutron emission from the supporting substrate can interfere with the Li(p,n) reaction neutrons. In the present study neutron yields from a thin natural Li target supported on the thick natural Ta (high Z) and C (low Z) targets were measured at incident proton energies between 8-20 MeV. The proton irradiation of targets were performed at the Pelletron accelerator Facility, Mumbai and the emitted neutrons were measures at both forward (0°) and lateral (90°) directions with respect to the incident beam. The neutron yield was estimated using the CR-39 detectors and dual scintillator based active neutron spectrometer. A neutron dose equivalent (NDE) meter was also used to measure the neutron ambient dose equivalent at the measurement point. Experimentally measured neutron yields and ambient neutron dose equivalent estimates indicated higher Li(p,n) neutron yield with carbon as the supporting substrate. This is due to fewer self-emissions from natC compared to natTa target. Carbon supporting substrate also ensures improved spectral features of the Li(p,n) emission neutrons and lesser neutron fluence around the source location. This ensures efficient implementation of radiation protection practices with carbon target due to lower neutron fluence around the source compared to tantalum target. Carbon is a favorable supporting substrate compared to high Z- metal targets like Ta for emission neutron studies on Li target at proton energies between 8-20 MeV.

The effect of reference sample composition and size on dental age interval estimates.

Validation studies in juvenile dental age estimation primarily focus on point estimates while interval performance for reference samples of different ancestry group compositions has received minimal attention. We tested the effect of reference sample size and composition by sex and ancestry group on age interval estimates. The dataset consisted of Moorrees et al. dental scores from panoramic radiographs of 3334 London children of Bangladeshi and European ancestry and 2-23 years of age. Model stability was assessed using standard error of mean age-at-transition for univariate cumulative probit and sample size, group mixing (sex or ancestry), and staging system as factors. Age estimation performance was tested using molar reference samples of four sizes, stratified by year of age, sex, and ancestry. Age estimates were performed using Bayesian multivariate cumulative probit with 5-fold cross-validation. Standard error increased with decreasing sample size but showed no effect from mixing by sex or ancestry. Estimating ages using a reference and target sample of different sex reduced success rate significantly. The same test by ancestry groups had a lesser effect. Small sample size (n < 20/year of age) negatively affected most performance metrics. We found that reference sample size, followed by sex, primarily drove age estimation performance. Combining reference samples by ancestry produced equivalent or better estimates of age by all metrics than using a single-demographic reference of smaller size. We further proposed that population specificity is an alternative hypothesis of intergroup difference that has been erroneously treated as a null.

Recurrent headache, stomachache, and backpain among adolescents: association with exposure to bullying and parents' socioeconomic status.

Recurrent pain is a prevalent and severe public health problem among adolescents and is associated with several negative health outcomes. In a representative sample of adolescents this study examined 1) whether exposure to bullying and low socioeconomic status (SES) were associated with recurrent headache, stomachache and backpain, 2) the combined effect of exposure to bullying and low SES on recurrent pain and 3) whether SES modified the association between bullying and recurrent pain. Data derived from the Danish contribution to the international collaborative study Health Behaviour in School-aged Children (HBSC). The study population was students in three age groups, 11-, 13- and 15-year-olds from nationally representative samples of schools. We pooled participants from the surveys in 2010, 2014 and 2018, n=10,738. The prevalence of recurrent pain defined as pain 'more than once a week' was high: 11.7 % reported recurrent headache, 6.1 % stomachache, and 12.1 % backpain. The proportion who reported at least one of these pains 'almost every day' was 9.8 %. Pain was significantly associated with exposure to bullying at school and low parental SES. The adjusted odds ratio (AOR, 95 % CI) for recurrent headache when exposed to both bullying and low SES was 2.69 (1.75-4.10). Equivalent estimates for recurrent stomachache were 5.80 (3.69-9.12), for backpain 3.79 (2.58-5.55), and for any recurrent pain 4.81 (3.25-7.11). Recurrent pain increased with exposure to bullying in all socioeconomic strata. Students with double exposure, i.e.,to bullying and low SES, had the highest OR for recurrent pain. SES did not modify the association between bullying and recurrent pain.

Neutron spectrometry and dosimetry using a multi-shell Single Sphere Neutron Spectrometer with thermo-luminescent and optically stimulated luminescent detectors

A portable neutron spectrometer with concentric multi-shell moderating assembly and TL, OSL detectors has been validated with standard neutron reference fields to generate the neutron spectra and estimate the ambient dose equivalents, H*(10). Further, the system has been used in the mixed neutron–gamma radiation fields at accelerator facility. The neutron fluence and the ambient dose equivalent estimates were verified using the standard reference neutron fields, viz. 241Am-Be, 252Cf, D-T neutron sources. The system was then employed in the accelerator radiation environment to measure the neutron spectral yield from the interactions of 15 and 20 MeV protons with a thick natural Ta target. The results were compared with those obtained from FLUKA simulations. A conventional neutron dose equivalent meter was also used to verify the ambient dose equivalent estimated from the spectrometric measurements. The reproducibility of the neutron spectra and dose values were found to be satisfactory, thereby encouraging the implementation of the portable single sphere neutron spectrometer in complex radiation environment around particle accelerators. Present studies demonstrate the measurements with the in-house built spectrometer for neutron energies up to 20 MeV.

Economic and environmental impacts of agricultural non-tariff measures: evidence based on ad valorem equivalent estimates

Non-tariff measures as hidden barriers to agricultural trade would not only result in production and welfare distortions due to the international relocation of activities along the agricultural value chain, but also yield subsequent consequences to both the scale and distribution of carbon emissions from the agri-food system. This paper estimates ad valorem equivalents of non-tariff measures using a gravity model in combination with detailed bilateral trade data of 2001-2019, and incorporates the estimations in the Global Trade Analysis Project model and a multi-regional input-output table of Eora26 to quantify economic and environmental impacts of non-tariff measures. We show that while tariff equivalents are on average positive for all types of non-tariff measures, there are substantial heterogeneities across countries and products. The extra trade barriers imposed by these measures would increase the scale of domestic agriculture-related sectors for most agriculture importing countries, and vice versa for major exporters. Meanwhile, they would reduce the global welfare at amount of 16 millions US dollars on average and in particular, the welfare of key imposers and targeting markets of non-tariff measures. Carbon emissions from the agri-food system tend to increase about 1% around the world, especially due to the larger food processing industry in developed countries. Our paper confirms that non-tariff measures lead to both welfare distortions and carbon emissions in the agri-food system. It thus calls for urgent needs to promote further reforms of the agricultural trade regime and the policy coordination across countries to facilitate agri-food system transformation with more integration and sustainability.