Effective Estimation Research Articles

Population genomics of eastern oysters, Crassostrea virginica, in a well-mixed estuarine system: advancement and implications for restoration strategies

Eastern oysters, Crassostrea virginica, are historically a keystone species in many of the estuaries in which they reside, providing critical ecosystem services. Because oyster populations have been on the decline for the past century, restoration initiatives currently are underway in many estuarine systems, including Great Bay Estuary (GBE), New Hampshire. Results of prior studies of eastern oyster population genomics cannot be applied directly to GBE, as it is a well-mixed estuarine system that is relatively contained, and the sources of recruits are split among cultivated and native. This study aimed to identify the population genomic structure of eastern oysters in GBE to facilitate determination of effective population size and estimation of genetic differentiation among subpopulations. Results showed moderate genomic differentiation among native, cultivated, and restoration C. virginica subpopulations in the Bay. A small number of breeders (Ne=163–276) was found in all subpopulations except the Lamprey River site (Ne=995). This research provides a contemporary snapshot of eastern oyster subpopulation structure at the genomic level in GBE that will facilitate restoration and enhanced management.

Evaluation of Environmental Radiation Exposure and Algorithms for Determining the Occupational Effective Dose During 99mTc-MDP Bone Scintigraphy: A Comprehensive Analysis

This study evaluated the occupational exposure of radiopharmacists, nurses, radiological technologists, and radiological technologist assistants involved in 99mTc-MDP bone scintigraphy procedures. Actual occupational effective doses for individual staff needed ascertaining. An environmental radiation exposure audit revealed all nuclear medicine areas were compliant with ICRP and IAEA guidelines. To ascertain individual doses, they were recorded by OSL badges. The highest exposure was to the radiopharmacists, Hp(0.07) 2.19 µSv during radiolabeling. The nurse administering 99mTc-MDP recorded a dose of 0.27 µSv at Hp(0.07), 26.01% of the total occupational effective dose. The radiological technologist and assistant receive 73.05% of the total effective dose. However, the highest effective dose was received by the technologist assistant positioning patients for SPECT/CT scans, with an effective dose of 32.03 µSv. Single and double dosimetry effective dose estimate algorithms were evaluated, resulting in the double dosimetry being more accurate. The Padovani et al. algorithm was found to most closely align with 99mTc-MDP actual effective dose values (p > 0.05), thereby validating the measurement methods used in this study. The research offers benchmark environmental exposure and effective doses applicable in audits and the continuous effort to enhance radiation safety for personnel during 99mTc-MDP bone scintigraphy.

Evaluating the Impact of Dermal Absorption on Internal Doses of Dechlorane Plus in Chinese E-waste Recycling Employees

E-waste recycling employees represent a specific population with a high potential for exposure to dechlorane plus (DP). However, the impact of skin-adherent DP on human exposure within this group has not been well characterized. This study aimed to address this gap by collecting handwipe and matched serum samples (n=86 pairs) of Chinese e-waste recycling employees. In vivo human dermal bioavailability of DP was also examined to achieve effective exposure estimation. As a result, DP was detected in all handwipe and serum samples, indicating the occurrence of widespread exposure in the study population. For all the participants, the median level of ∑DP (the sum of syn-DP and anti-DP) in the serum was 45.1 ng g-1 lipid weight (lw), while handwipe samples showed the loading of 10.8 ng per wipe. Notably, significant associations were found between DP handwipe loadings and matched serum levels (p<0.05). Next, the in vivo human dermal bioavailability was estimated to be 9.54% for ∑DP. Based on this value, the estimated average daily dose was 0.231 ng kg-1 d-1 (median values), contributing 10.9% to serum levels of ∑DP. The significant associations and 10.9% percentage contribution together underscore the non-negligible influence of dermal absorption on DP internal doses in the e-waste recycling employees. Moreover, the extremely high levels of DP (up to 3.64E+04 ng g-1 lw) detected in serum highlight the extent of DP accumulation in humans. Continuous monitoring and assessment are warranted among e-waste workers due to the persistent organic pollutant properties of DP.

An Evaluation of an Algorithm for the Selection of Flexible Survival Models for Cancer Immunotherapies: Pass or Fail?

Accurately extrapolating survival beyond trial follow-up is essential in a health technology assessment where model choice often substantially impacts estimates of clinical and cost effectiveness. Evidence suggests standard parametric models often provide poor fits to long-term data from immuno-oncology trials. Palmer et al. developed an algorithm to aid the selection of more flexible survival models for these interventions. We assess the usability of the algorithm, identify areas for improvement and evaluate whether it effectively identifies models capable of accurate extrapolation. We applied the Palmer algorithm to the CheckMate-649 trial, which investigated nivolumab plus chemotherapy versus chemotherapy alone in patients with gastroesophageal adenocarcinoma. We evaluated the algorithm's performance by comparing survival estimates from identified models using the 12-month data cut to survival observed in the 48-month data cut. The Palmer algorithm offers a systematic procedure for model selection, encouraging detailed analyses and ensuring that crucial stages in the selection process are not overlooked. In our study, a range of models were identified as potentially appropriate for extrapolating survival, but only flexible parametric non-mixture cure models provided extrapolations that were plausible and accurately predicted subsequently observed survival. The algorithm could be improved with minor additions around the specification of hazard plots and setting out plausibility criteria. The Palmer algorithm provides a systematic framework for identifying suitable survival models, and for defining plausibility criteria for extrapolation validity. Using the algorithm ensures that model selection is based on explicit justification and evidence, which could reduce discordance in health technology appraisals.

Home-Based Testing as an Approach to Estimate Influenza Vaccine Effectiveness in South Africa, 2021-2022-A Pilot Study.

Surveillance programmes for influenza and other respiratory pathogens are important to generate vaccine effectiveness (VE) estimates and to inform vaccine composition. We aimed to explore the feasibility and acceptability of home-based testing. In three out of nine provinces in South Africa, we established a self-referral system for individuals aged ≥ 18 years with respiratory symptoms of ≤ 10 days duration. Following consent, swab collection material was delivered to participants who also completed a questionnaire including self-reported vaccination status. Swabs were tested by PCR for influenza, respiratory syncytial virus (RSV) and SARS-CoV-2. A test-negative methodology was used to estimate influenza VE. Of 1456 samples collected between 19 November 2021 and 3 September 2022, 73 (5%) tested positive for influenza, 38 (3%) tested positive for RSV and 394 (27%) for SARS-CoV-2. We subtyped 55% (40/73) of the influenza positive specimens; 16/40 (40%) were influenza A(H1N1)pdm09; 10/40 (25%)A(H3N2)) and all 14/40(35%) influenza B were B/Victoria. Only 20% (279/1451) of participants reported influenza-like illness case definition symptoms of fever and cough. Influenza vaccine coverage was 11% (157/1454). The overall influenza VE was 26% (95% confidence interval: -73%, 69%). Of the completed acceptability questionnaires, 123/127 (97%) participants would make use of the service again; 90% (1306) were recruited via the COVID-19 testing centre (call in, social media, webpage), and 7% (99/1306) through CoughWatchSA. Home-based swabbing was feasible and acceptable. We were able to calculate an influenza VE, although a larger sample size and verification of vaccine status may improve the VE estimates in the future.

A Novel Fitting Polynomial Approach for An Accurate Soc Estimation In Li-Ion Batteries Considering Temperature Hysteresis

Lithium-ion batteries are essential to modern technology, requiring accurate estimation of the state of charge (SOC) for optimal performance. Traditional methods such as Coulomb Counting (CC) are ineffective in the face of temperature variations, leading to inaccuracies in SOC estimation, which in turn cause obvious deformation of hysteresis curves. To address this, this paper introduces a novel method called Polynomial Fit State of Charge (FPSOC), for effective SOC estimation. This method incorporates a fifth-degree polynomial fitting that accounts for a wide range of temperature variations (from -10°C to +80°C), a feature that, according to the authors, has not been offered by all previously published methods. A series of simulation tests using the MATLAB/Simulink tool are conducted under various temperature profiles to evaluate the effectiveness of the FPSOC method. The results demonstrate the notable superiority of the FPSOC model compared to the CC method, with a significantly reduced RMSE of only 0.93% compared to 6.77% of the CC model. Particularly effective at low SOC levels (30%), the FPSOC model demonstrates precision up to six times higher compared to the CC model. Additionally, when evaluated against other recent SOC estimation techniques such as CM, RLSF, EKF, DST, BBDST, ASMO, LPM_H, LSTM-SA Group A and B, and baseline ECM-ID, The FPSOC method proves extremely accurate, with the lowest average error under different temperature conditions.

Estimating occupational exposure in interventional radiology through Air Kerma Area Product - calculation of Effective Dose conversion factors

During interventional radiology procedures, interventionists are positioned close to the patient and directly exposed to ionizing radiation. This study presents a methodology to estimate the effective dose of interventionists through Air Kerma Area Product (PKA) in three interventional procedure modalities: coronary, cerebral, and peripheral. Optically stimulated luminescence dosimeters were used in several body regions (thyroid, eye lens, abdomen, feet, and hands) to assess equivalent doses and to convert external to internal doses – leading to effective doses related to each modality. These effective doses were then correlated to the total PKA given by the equipment. Thus, an effective dose estimate factor was calculated to represent the conversion between equipment parameters and the effective dose obtained from dosimeters, resulting in a novel methodology. The agreement between the methods was in the range of ± 0.13 μSv for angiographies and -8.3 to 5.3 μSv for angioplasties. These findings underscore the reliability of PKA as a predictor of effective dose, facilitating a rapid and precise assessment of occupational exposure in IR. This novel approach has the potential to enhance radiation safety protocols and optimize protection strategies for IR professionals, ensuring more accurate monitoring and control of radiation exposure in clinical settings.

Effective real-time transmission estimations incorporating population viral load distributions amid SARS-CoV-2 variants and pre-existing immunity.

Population-level viral load distribution, measured by cycle threshold (Ct), has been demonstrated to enable real-time estimation of Rt for SARS-CoV-2 ancestral strain. The generalizability of the framework under different circulating variants and pre-existing immunity remains unclear. We obtained the first Ct record of local COVID-19 cases from July 2020 to January 2023 in Hong Kong. The log-linear regression model, fitting on daily Ct mean and skewness to the Rt estimated by case count, was trained using data from wave 3 (i.e., ancestral strain with minimal population immunity), and we predicted Rt for wave 5, 6 and 7 (i.e., Omicron subvariants with > 70% vaccine coverage). Cross-validation was performed by training on the other 4 waves. Stratification analysis by disease severity was conducted to retrospectively evaluate the impact of the changing severity profiles. Trained with the ancestral dominated wave 3, our model can accurately estimate whether Rt was above 1, with the area under the receiver operating characteristic curve of 0.98 (95% confidence interval: 0.96, 1.00), 0.62 (95% CI: 0.53, 0.70) and 0.80 (95% CI: 0.73, 0.88) for three Omicron dominated waves 5 to 7, respectively. Models trained on the other four waves also had discriminative performance. Stratification analysis suggested the potential impact of case severity on model estimation, which coincided with the fluctuation of sampling delay. Our findings suggested that incorporating population viral shedding can provide timely and accurate transmission estimation with evolving variants and population immunity. Model application needs to account for sampling delay.

Intelligent mobility: Unveiling triboelectric nanogenerator-powered intelligent tire realization

Energy harvesting and sensing devices utilizing triboelectric nanogenerators (TENG) are advancing rapidly and offer significant potential for a wide range of future applications. Simultaneously, the integration of sensors into intelligent tires enables precise estimation of tire conditions, thereby enhancing vehicle intelligence. As the concept of intelligent tires has long intrigued researchers, the pursuit of a cost-effective, self-powered, and easily integrated sensor remained unexplored until the emergence of TENGs. The main research objectives of this paper are: (1) to test and optimize the structure of TENG and design a sandwich-type TENG (St-TENG), which can be used as a tire sensor; (2) to integrate St-TENG into the tire, establish the mapping relationship between St-TENG signal and tire load, and achieve effective estimation of tire load. The St-TENG was successfully prepared using several easily available and inexpensive materials, and it has robust performance in the confined and harsh environment inside the tire. The methodology proposed herein charts a novel path for TENG and intelligent tire development, paving the way for the evolution of intelligent tire systems in the foreseeable future.

Bias and negative values of COVID-19 vaccine effectiveness estimates from a test-negative design without controlling for prior SARS-CoV-2 infection

Test-negative designs (TNDs) are used to assess vaccine effectiveness (VE). Protection from infection-induced immunity may confound the association between case and vaccination status, but collecting reliable infection history can be challenging. If vaccinated individuals have less infection-induced protection than unvaccinated individuals, failure to account for infection history could underestimate VE, though the bias is not well understood. We simulated individual-level SARS-CoV-2 infection and COVID-19 vaccination histories and a TND. VE against symptomatic infection and VE against severe disease estimates unadjusted for infection history underestimated VE compared to estimates adjusted for infection history, and unadjusted estimates were more likely to be below 0%, which could lead to an incorrect interpretation that COVID-19 vaccines are harmful. TNDs assessing VE immediately following vaccine rollout introduced the largest bias and potential for negative VE against symptomatic infection. Despite the potential for bias, VE estimates from TNDs without prior infection information are useful because underestimation is rarely more than 8 percentage points.

Effectiveness of Cognitive Remediation Interventions for Schizophrenia in Asian Population: A Systematic Review and Meta-analysis of Randomized Controlled Trials

Background: Cognitive deficits are an integral part of schizophrenia, with negatively impacting functionality. Current treatments, primarily pharmacological, do not adequately address these cognitive impairments. Cognitive remediation (CR) interventions, designed to improve cognitive functions, have shown promising outcomes. However, most research has been conducted in western contexts, necessitating a review of CR effectiveness in the Asian populations. This systematic review aims to provide comprehensive update on the effectiveness of CR interventions in improving cognition among individuals with schizophrenia in Asian countries. The meta-analysis aims to synthesize and summarize the relevant CR studies to obtain a pooled estimate of effectiveness. Methods: A systematic search of three databases, PubMed, Ovid MEDLINE, and EMBASE, was conducted from the date of inception to March 19, 2024, for eligible records using prespecified search criteria. Primary studies with a randomized controlled trial (RCT) design, conducted on individuals aged 18–65 years, diagnosed with schizophrenia in Asian countries, comparing CR interventions with other non-pharmacological interventions or treatment as usual, reporting cognitive outcomes, using standardized tools, and published in English were included in the study. The review excluded unpublished RCTs, low-quality studies, open-label studies, cohort studies, studies focusing on noncognitive outcomes, and those lacking a clear comparison arm. The quality of the studies was assessed using the Cochrane risk of bias 2 (RoB2) tool by two independent reviewers. Meta-analyses were performed using R version 4.3.0 software using meta package to synthesize the overall impact of CR interventions on cognitive outcomes. Results and Discussion: This systematic review included 17 studies with 1272 participants, conducted in China, Japan, Hong Kong, Taiwan, Iran, Korea, and Singapore. The interventions varied in type and format. These were delivered through group and individual sessions, both in-person and computer-based, with varied duration. Quality assessment done with RoB2 tool indicated high-risk concerns in 14 of the studies. The meta-analysis of four studies on composite cognitive scores using the brief assessment of cognition in schizophrenia showed a significant positive effect of CR interventions (standardized mean difference [SMD] = 0.89, 95% confidence interval [CI]: 0.35 to 1.43). For executive functioning, the meta-analysis of three studies indicated no significant improvement (SMD = 0.75, 95% CI: −0.11 to 1.61). Conclusion: CR is an effective intervention for improving cognitive functions in individuals with schizophrenia in Asia based on the meta-analysis conducted, akin to evidence from western countries. However, quality concerns and high heterogeneity among the included studies underscore the need for standardized protocols and uniform outcome measures to enhance the reliability of future research.

Effective Market Power and Welfare Loss

ABSTRACTThis paper introduces a straightforward and effective estimation methodology for quantifying firm‐level welfare losses attributable to market power in monopolistic competition markets. Our analysis underscores that welfare losses are primarily contingent on effective market power, expressed as the product of the Lerner index and market share. Furthermore, our study demonstrates that welfare loss computed using the Hicksian measurement is smaller than that obtained through the Marshallian measurement. Finally, we apply this methodology to estimate firm‐level market power and welfare losses in the Chinese carbonated beverage industry.

Robust joint estimation of sideslip angle and tire lateral force for distributed drive electric vehicle

Accurate estimation of vehicle sideslip angle (SA) and tire lateral force (TLF) is essential for the effective functioning of vehicle active safety control systems. However, effective estimation of SA and TLF using low-cost on-board sensors is a major challenge. In this paper, a robust joint estimation method of SA and TLF for distributed drive electric vehicle (DDEV) is proposed. Adaptive sliding-mode observer (ASMO) is used to estimate the TLF and is used as the input to the subsequent filter. Maximum correntropy criterion (MCC) is introduced to improve the robustness of the unscented Kalman filter (UKF) under mixed Gaussian noise, and the maximum correntropy unscented Kalman filter (MCUKF) algorithm is composed to estimate SA. The effectiveness of the proposed ASMO and MCUKF joint estimation method is verified by Simulink/Carsim joint simulation experiments. The experimental results show that compared with the existing methods, the estimation method proposed in this paper effectively improves the estimation accuracy and robustness of SA and TLF under the mixed Gaussian environment, which is of great significance for the improvement of vehicle active safety.

A new outlier rejection approach for non-Lambertian photometric stereo

Photometric stereo (PS) has garnered increasing attention due to its adeptness in restoring local fine textures. However, non-Lambertian reflections present in almost all real-world objects limit the effectiveness of the Lambertian model for surface normal vector estimation. Although BRDF-based and deep learning-based methods have become mainstream, recent research shows that comparable accuracy can also be achieved through simple filtering of observed intensity values. Nevertheless, these methods only consider the relative bias of pixel values and require manual specification of the number of pixels to be culled and the number of iterations. To address these issues, this paper proposes corresponding improvement methods. Firstly, a weighted inter-relationship function (IRF) fused with Huber loss is introduced to robustly and effectively evaluate the abnormal degree of pixel value. Secondly, based on the IRF curve and histogram statistical analysis, the number of excluded pixels is adaptively calculated. Thirdly, linear equations are then constructed based on the photometric equations, and the maximum between-class variance method is employed to achieve a high degree of sparsity, enabling fast and effective normal vector estimation. Finally, to verify the effectiveness of the proposed algorithm for non-Lambertian PS vision, quantitative verification tests on the synthetic dataset and the open-source datasets “DiLiGenT” and “DiLiGenT-PI” in real-world scenarios, and qualitative assessment experiments on real metal roughness samples are conducted. The experimental results demonstrate that, compared with the position threshold and IRF methods, our algorithms not only significantly enhance the accuracy of normal vector solutions but also markedly improve the operational efficiency of the algorithm, laying a solid foundation for practical online applications. These results fully validate the correctness and effectiveness of the proposed algorithm and provide a reference for the further development of outlier removal algorithms.

Statistical and source characterization of the 2023 Kahramanmaraş Türkiye earthquake sequence

AbstractWe studied seismic features of the 2023 Kahramanmaraş Türkiye earthquake sequence by analyzing the spatiotemporal behavior of the b- and -p values and source characteristics of the mainshocks. We complemented our study by determining the regional stress field. The b-values in the region varied from 0.45 to 1.15. We observed a slight b-value decrease (Δb≈0.2) months before the two mainshocks. Our results exhibited complex b-value patterns on the fault planes and regular aftershock productivity rates (1.14 &lt; p &lt; 1.25). We compare static stress drop estimates derived from effective fault dimensions to those of finite-fault dimensions. Total effective stress drops (the sum of the stress drops of all fault segments) for the earthquake doublet were almost identical (~ 2.05 MPa), while those from finite-fault dimensions are somewhat lower (0.35 and 0.96 MPa). Based on a complete stress drop case, effective seismic efficiency was 0.65 and 0.43 for both mainshocks. The amount of partial stress drop was used to discriminate between different stress drop models. No clear model is discerned for the first mainshock, but a partial or even complete stress drop, assuming finite-fault dimensions, is supported by our measurements. Stress drop estimations derived from spectral analysis (25.46 and 34.39 MPa) agreed with global studies but larger than finite and effective fault estimates. Stress inversion results indicated that in the fault segments where the mainshocks occurred, the orientation of principal axes was consistent with a strike-slip regime. Conversely, the normal-faulting regime dominates adjacent areas of the main fault system.

A straightforward method for estimating evolutionary power spectral density of non-stationary typhoon wind speed

PurposeA precise estimation of the evolutionary power spectral density (EPSD) of typhoon wind speed is a difficult and significant undertaking in the analysis of turbulence effects on large-expansive structures. A majority of the prevailing EPSD estimation techniques rely on complex signal processing methodologies, such as wavelet decomposition, Hilbert–Huang transformation and time-varying autoregressive moving average (ARMA) model. However, these approaches often pose challenges in terms of comprehensibility and practical implementation for engineers. In light of this issue, the present study introduces a straightforward and effective EPSD estimation method tailored specifically for typhoon wind speed, aiming to facilitate its understanding and application in engineering contexts.Design/methodology/approachFirstly, the mathematical model of a uniformly modulated non-stationary process is employed to represent the typhoon wind speed. Secondly, the reverse arrangement test serves as an auxiliary tool in conjunction with wavelet transform or empirical mode decomposition, aiding in the determination of the optimal slowly varying mean wind speed. Thirdly, Kernel regression technique is utilized to discern the time-dependent standard deviation of wind speed fluctuations. Finally, the power spectral density (PSD) of wind speed residuals is computed to facilitate the estimation of the EPSD.FindingsFirstly, the reverse arrangement test-assisted approach enables the determination of an optimal time-dependent mean from the candidate results obtained through discrete wavelet transform (DWT) and empirical mode decomposition (EMD). Secondly, the application of the Kernel regression technique facilitates accurate identification of the time-dependent variance from the fluctuating wind speed data. Thirdly, due to the influence of the extreme weather, the Gaussianity of the reduced turbulent fluctuations in typhoon wind is easily disturbed, resulting in the obvious non-Gaussian features.Originality/valueThis paper employs the mathematical model of uniformly modulated non-stationary process to characterize typhoon wind speeds and then proposes a straightforward and efficient method for estimating the EPSD of typhoon wind. The accuracy and efficacy of the presented estimation method are verified using the field-measured wind speed data from Typhoon Rammasun. The proposed EPSD estimation method for typhoon wind exhibits suitability for engineering applications owing to its simplicity and computational efficiency.

Unraveling coupling delays through a transfer entropy analysis in stochastic processes and non-linear systems

Abstract In this work we propose a transfer entropy approach to discern time-delay couplings within non-linear and stochastic coupled systems. We introduce the concept of “time-wise transfer entropy”, which quantifies the reduction in future uncertainty for a process Y by considering the values of processes X and Y at a specific past moment. The key advantage of our approach is a reduction in the number of parameters required for estimation when compared to other transfer entropy methodologies. Our proposed time-wise transfer entropy not only lends itself to effective estimation from actual data but also enhances our understanding of the origins of seemingly “spurious” couplings observed in some transfer entropy approaches. To validate our method, we apply it to determine coupling delays in minimal stochastic models where the time-wise transfer entropy can be precisely derived in terms of the Shannon entropy. We further assess the technique performance in coupled non-linear systems with delays, demonstrating its capacity to accurately reproduce the corresponding coupling delays. The developed technique may be useful in the analysis of multifactor nonlinear physical systems where complex causal relationships may exist.

Assessing Methods for Adjusting Estimates of Treatment Effectiveness for Patient Nonadherence in the Context of Time-to-Event Outcomes and Health Technology Assessment: A Simulation Study.

We aim to assess the performance of methods for adjusting estimates of treatment effectiveness for patient nonadherence in the context of health technology assessment using simulation methods. We simulated trial datasets with nonadherence, prognostic characteristics, and a time-to-event outcome. The simulated scenarios were based on a trial investigating immunosuppressive treatments for improving graft survival in patients who had had a kidney transplant. The primary estimand was the difference in restricted mean survival times in all patients had there been no nonadherence. We compared generalized methods (g-methods; marginal structural model with inverse probability of censoring weighting [IPCW], structural nested failure time model [SNFTM] with g-estimation) and simple methods (intention-to-treat [ITT] analysis, per-protocol [PP] analysis) in 90 scenarios each with 1,900 simulations. The methods' performance was primarily assessed according to bias. In implementation nonadherence scenarios, the average percentage bias was 20% (ranging from 7% to 37%) for IPCW, 20% (8%-38%) for SNFTM, 20% (8%-38%) for PP, and 40% (20%-75%) for ITT. In persistence nonadherence scenarios, the average percentage bias was 26% (9%-36%) for IPCW, 26% (14%-39%) for SNFTM, 26% (14%-36%) for PP, and 47% (16%-72%) for ITT. In initiation nonadherence scenarios, the percentage bias ranged from -29% to 110% for IPCW, -34% to 108% for SNFTM, -32% to 102% for PP, and between -18% and 200% for ITT. In this study, g-methods and PP produced more accurate estimates of the treatment effect adjusted for nonadherence than the ITT analysis did. However, considerable bias remained in some scenarios. Randomized controlled trials are usually analyzed using the intention-to-treat (ITT) principle, which produces a valid estimate of effectiveness relating to the underlying trial, but when patient adherence to medications in the real world is known to differ from that observed in the trial, such estimates are likely to result in a biased representation of real-world effectiveness and cost-effectiveness.Our simulation study demonstrates that generalized methods (g-methods; IPCW, SNFTM) and per-protocol analysis provide more accurate estimates of the treatment effect than the ITT analysis does, when adjustment for nonadherence is required; however, even with these adjustment methods, considerable bias may remain in some scenarios.When real-world adherence is expected to differ from adherence observed in a trial, adjustment methods should be used to provide estimates of real-world effectiveness.

Effective count of square-tiled surfaces with prescribed real and imaginary foliations in connected components of strata

Abstract We prove an effective estimate with a power saving error term for the number of square-tiled surfaces in a connected component of a stratum of quadratic differentials whose vertical and horizontal foliations belong to prescribed mapping class group orbits and which have at most L squares. This result strengthens asymptotic counting formulas in the work of Delecroix, Goujard, Zograf, Zorich, and the author.

CGLight: An effective indoor illumination estimation method based on improved convmixer and GauGAN

Illumination consistency is a key factor for seamlessly integrating virtual objects with real scenes in augmented reality (AR) systems. High dynamic range (HDR) panoramic images are widely used to estimate scene lighting accurately. However, generating environment maps requires complex deep network architectures, which cannot operate on devices with limited memory space. To address this issue, this paper proposes CGLight, an effective illumination estimation method that predicts HDR panoramic environment maps from a single limited field-of-view (LFOV) image. We first design a CMAtten encoder to extract features from input images, which learns the spherical harmonic (SH) lighting representation with fewer model parameters. Guided by the lighting parameters, we train a generative adversarial network (GAN) to generate HDR environment maps. In addition, to enrich lighting details and reduce training time, we specifically introduce the color consistency loss and independent discriminator, considering the impact of color properties on the lighting estimation task while improving computational efficiency. Furthermore, the effectiveness of CGLight is verified by relighting virtual objects using the predicted environment maps, and the root mean square error and angular error are 0.0494 and 4.0607 in the gray diffuse sphere, respectively. Extensive experiments and analyses demonstrate that CGLight achieves a balance between indoor illumination estimation accuracy and resource efficiency, attaining higher accuracy with nearly 4 times fewer model parameters than the ViT-B16 model.