Effective Estimation Research Articles

The EBLM Project—From False Positives to Benchmark Stars and Circumbinary Exoplanets

The EBLM project aims to characterise very-low-mass stars that are companions to solar-type stars in eclipsing binaries. We describe the history and motivation for this project, the methodology we use to obtain the precise mass, radius, and effective temperature estimates for very-low-mass M dwarfs, and review the results of the EBLM study and those from related projects. We show that radius inflation in fully convective stars is a more subtle effect than what was previously thought based on less precise measurements, i.e., the mass–radius–effective temperature relations we observe for fully convective stars in single-line eclipsing binaries show reasonable agreement with the theoretical models, particularly if we account for the M-dwarf metallicity, as inferred from the analysis of the primary star spectrum.

Effectiveness of Influenza Vaccination During Pregnancy Against Laboratory-Confirmed Seasonal Influenza Among Infants Under 6 Months of Age in Ontario, Canada.

Randomized trials conducted in low- and middle-income settings demonstrated efficacy of influenza vaccination during pregnancy against influenza infection among infants <6 months of age. However, vaccine effectiveness (VE) estimates from settings with different population characteristics and influenza seasonality remain limited. We conducted a test-negative study in Ontario, Canada. All influenza virus tests among infants <6 months from 2010 to 2019 were identified and linked with health databases to ascertain information on maternal-infant dyads. VE was estimated from the odds ratio for influenza vaccination during pregnancy among cases versus controls, computed using logistic regression with adjustment for potential confounders. Among 23 806 infants tested for influenza, 1783 (7.5%) were positive and 1708 (7.2%) were born to mothers vaccinated against influenza during pregnancy. VE against laboratory-confirmed infant influenza infection was 64% (95% confidence interval [CI], 50%-74%). VE was similar by trimester of vaccination (first/second, 66% [95% CI, 40%-80%]; third, 63% [95% CI, 46%-74%]), infant age at testing (0 to <2 months, 63% [95% CI, 46%-75%]; 2 to <6 months, 64% [95% CI, 36%-79%]), and gestational age at birth (≥37 weeks, 64% [95% CI, 50%-75%]; < 37 weeks, 61% [95% CI, 4%-86%]). VE against influenza hospitalization was 67% (95% CI, 50%-78%). Influenza vaccination during pregnancy offers effective protection to infants <6 months, for whom vaccines are not currently available.

Real-World Effectiveness of Asciminib in Patients with Chronic Myeloid Leukemia (CML) Harboring the T315I Mutation: A Global Chart Review Study of Patients Treated in the Asciminib Managed Access Program (MAP)

Background: In patients (pts) with CML, the BCR::ABL1 T315I gatekeeper mutation confers treatment resistance to all approved ATP-competitive tyrosine kinase inhibitors (TKIs) except ponatinib and olverembatinib; thus pts harboring this mutation have limited treatment options. Asciminib, a first-in-class TKI that works by specifically targeting the ABL myristoyl pocket (STAMP), has a distinct mechanism of action and no known overlapping resistance profile with ATP-competitive TKIs. In clinical trials, asciminib has demonstrated efficacy and acceptable safety in adult pts with Ph+ CML in chronic phase and the T315I mutation. The asciminib MAP (NCT04360005) enables access to asciminib treatment for pts with CML who are resistant or intolerant to available treatments or for whom available treatments are contraindicated. This global chart review study aimed to better understand the effectiveness and safety of asciminib in pts with CML and the T315I mutation treated as part of the MAP. Methods: This non-interventional chart review analyzed data from existing routine medical charts of pts enrolled in the MAP who received at least one dose of asciminib. Pts with CML aged ≥18 years old were included in the study if they had confirmed presence of the T315I mutation detected at any point prior to commencement of asciminib and had received their first dose of asciminib between 01 November 2018 and 30 April 2022. Data was abstracted from CML diagnosis date to the first dose of asciminib (index date) and up until 12 months after the start of asciminib. Treatment effectiveness was assessed in terms of major molecular response (MMR) in pts in MMR at index date or not, at and by 3, 6, 9, and 12 months. Other variables included demographic and disease characteristics, treatment patterns, response to prior TKIs and safety. All analyses were descriptive. Results: 31 pts were included across 8 countries (Netherlands, UK, Italy, Hong Kong, Pakistan, Spain, Australia, and US). Median age at CML diagnosis was 47 (range 18‒74) years and most pts were in chronic phase ( Table 1). Pts were heavily pretreated, with the majority having received ≥2 prior TKIs. Nearly all pts (28/31; 90.3%) had received prior ponatinib treatment at any time before the switch to asciminib. Median prior TKI treatment duration was longest for ponatinib (43.4 months, range 0.3‒117.3) vs other TKIs. The most common reason for discontinuation of all previous TKIs at any time before asciminib was lack of response to treatment, with the exception of ponatinib, where the most common reason was due to adverse events (AEs). Ponatinib was the last TKI prior to commencement of asciminib in 27/31 (87.1%) pts. Overall, 22/31 pts (71.0%) experienced AEs that led to a switch to another TKI at any time prior to asciminib, whereas nearly half of pts discontinued the last TKI before starting asciminib due to intolerance (14 pts, 45.2%). A total of 12/31 patients (38.7%) were already in MMR at index date ( Table 2), confirming that intolerance was an important reason for switching to asciminib. Of the pts not in MMR at the start of asciminib, 4/10 (40.0%) and 5/12 (41.7%) evaluated pts achieved MMR by 6 and 12 months, respectively. Of the 12 pts in MMR at the start of asciminib, 6/12 (50.0%) maintained MMR by 6 months. Overall, 10/31 pts (32.3%) discontinued asciminib before completion of the 12-month follow-up (death, 7 [4 serious AEs, SAEs; 2 CML; 1 unknown]; physician decision, 3). SAEs were reported in 9 pts (29.0%) that received asciminib; 8 SAEs led to hospitalization, 4 to death (1 cerebrovascular accident, 1 CML, 1 pneumonia, 1 fungal infection), 2 to asciminib discontinuation, and 2 were considered life-threatening. SAEs reported for 1 pt (hematotoxicity and procedural hemorrhage) were assessed as asciminib-related. Conclusions: Asciminib demonstrated effectiveness in pts with CML and the T315I mutation in routine medical practice, including ponatinib pre-treated pts, a population with limited available treatment options. Effectiveness estimates were consistent with those observed in clinical trials. Although the number of pts was small, this global study reports on one of the largest cohorts of pts with CML and the T315I mutation.

Evaluation of Horizontal Single‐Axis Solar Tracker Algorithms in Terms of Energy Production and Operational Performance

Horizontal single‐axis solar tracking systems with Astronomical tracking algorithm are commonly used in photovoltaic (PV) installations. However, different algorithms can increase the PV installation's performance without implementing new equipment or technologies. In this article, the performance of three tracking algorithms is compared to the Astronomical one. Two algorithms aim at optimizing the received irradiance focusing on the diffuse component. The third one, called Analytical, is presented as a new development in this study, defining the optimal inclination angle depending on all radiation components. The comparison focuses on different parameters like the in‐plane irradiance, DC power output from a monofacial PV system and operational aspects like the performed number of movements. High‐time‐resolution (1 min) simulations are performed with proprietary software taking into account several effects on the effective irradiance and power output estimation, such as shading effects or PV configuration. Eight locations with different climatic conditions are analyzed. All three algorithms derive in higher in‐plane irradiance and power output values than the Astronomical algorithm, although the gain depends on the climatic conditions. At locations with high diffuse fraction, the power output from the Analytical algorithm, which outperforms the others, can be up to 3% higher than the Astronomical one.

1718. Effectiveness of 13-valent pneumococcal conjugate vaccine for prevention of invasive pneumococcal disease among children in the United States between 2010 and 2019: an indirect cohort study

Abstract Background The 13-valent pneumococcal conjugate vaccine (PCV13) was introduced for infant use in the United States in 2010, replacing PCV7. A U.S. case-control study (2010–2014) demonstrated vaccine effectiveness (VE) for ≥1 dose of PCV13 at 86%; however, this study lacked statistical power to examine VE by number of doses and against individual serotypes. Methods We used the indirect cohort method to estimate VE of PCV13 against vaccine-type invasive pneumococcal disease among children &amp;lt; 5 years in the U.S. from May 1, 2010 to December 31, 2019. We included IPD cases identified through CDC’s Active Bacterial Core surveillance in 10 U.S. states; during 2010 – 2014, we additionally included cases enrolled in a post-licensure matched case-control study from expanded sites. Cases and controls were defined as individuals with PCV13-type-IPD (VT-IPD) and non-PCV13-type-IPD (NVT), respectively; serotype 6C was categorized as VT. We used logistic regression to estimate the adjusted odds of prior PCV13 receipt, controlling for confounders identified a priori including age category, race/ethnicity, sex, state, year, and any immunocompromising and/or chronic conditions. Results A total of 1,161 IPD cases were identified; 223 (19.2%) were VT cases and 938 (80.8%) were non-VT controls. Of those, 108 cases (48.4%; 108/223) and 600 controls (64.0%; 600/938) had received &amp;gt;3 PCV13 doses; 47 cases (21.1%) and 53 controls (5.7%) had received no PCV doses. Serotypes 19A (N=96), 3 (N=60), and 19F (N=45) caused 90.1% (201/223) of VT-IPD. VE of &amp;gt;1 or ≥3 PCV13 doses against VT-IPD was 81.7% (95% Confidence Interval: 69.1–89.1%) and 87.8% (75.2–94.0%), respectively. VE of ≥3 PCV13 doses was 87.0% (75.8–93.0%), 54.6% (-8.8–81.0%), and 92.9% (74.4–98.0%) against serotypes 19A, 3, and 19F, respectively. VE was 87.6% (67.9-95.2%) for three primary doses before 12 months of age and 92.4% (78.2–97.2%) for three primary doses and a booster at 12 months of age or older. Vaccine effectiveness estimates against PCV13-type IPD among US children under five years of age, 2010 - 2019 Conclusion VE of ≥1 PCV13 dose against IPD was consistent with the previous estimate from the case-control study, and ≥3 doses appear to provide substantial protection. Among the most commonly circulating VT-IPD, PCV13 was protective against serotypes 19A and 19F IPD; protection against serotype 3 IPD did not reach statistical significance. Disclosures Lee Harrison, MD, GSK: Advisor/Consultant|Merck: Advisor/Consultant|Pfizer: Advisor/Consultant|Sanofi: Advisor/Consultant Tamara Pilishvili, PhD MPH, GSK: Employed by GSK since February 2023. At the time of data collection and analysis for this work was employed by the CDC

1737. Cost-Effectiveness Analysis of Catch-Up Vaccination with the 20-Valent Pneumococcal Conjugate Vaccine

Abstract Background A 20-valent pneumococcal vaccine (PCV20) was approved in April 2023 by the US FDA for use in children &amp;lt; 18 years of age. Routine use in infants consists of a 3-dose schedule followed by a booster dose between 12 and 15 months of age. In addition, a catch-up program for children previously vaccinated with PCV13 who remain at risk of disease associated with the 7 new covered serotypes in PCV20 can potentially improve direct protection and accelerate the onset of indirect effects in the population. This study evaluated the cost-effectiveness of a PCV20 catch-up program in US children aged 14– 59 months who previously completed the recommended PCV13 series. Methods A population-based, multi-cohort, decision-analytic Markov model was developed to estimate the public health impact and cost-effectiveness of a PCV20 catch-up program from both a societal (base-case) and health-care perspective over a 10-year time horizon. In the model a single supplemental dose was assumed to be given to 63% of children aged 14-59 months who had received a complete schedule of PCV13. Input parameters for costs, disutilities, and epidemiology of pneumonia and otitis media (OM) were extracted from published data sources. Data on invasive pneumococcal disease (IPD) were based on published and unpublished (ABC) data. PCV20 effectiveness estimates were based on PCV7/PCV13 studies. Results The direct and indirect effects of a PCV20 catch-up program was estimated to prevent an additional 5,500 invasive IPD cases, 270,000 pneumonia cases, 0.6 million OM cases, and 1800 deaths compared to a 3+1 PCV20 program without a PCV20 catch-up program. This corresponds to a net gain of 30,000 life years or 55,600 QALYs. Most health gains were achieved due to the assumed accelerated indirect effects because of the catch-up program. Acquisition costs for the PCV20 catch-up program were offset by monetary savings from averted disease cases resulting in $800 million net saving. The catch-up program was also dominant from the health-care perspective. Conclusion Implementation of a PCV20 catch-up program is expected to increase direct impact and accelerated indirect effects, more rapidly reducing pneumococcal disease burden and saving substantial health-care costs across the US population. Disclosures Mark Rozenbaum, Ph.D., M.B.A., Pfizer: Stocks/Bonds Liping Huang, MD, MA, MS, Pfizer Inc.: Stocks/Bonds Johnna Perdrizet, MPH, Pfizer Inc: Stocks/Bonds Alejandro D. Cane, MD, PhD, Pfizer: Stocks/Bonds Adriano Arguedas, Medical director, Pfizer: Emplyee|Pfizer: Stocks/Bonds Kyla hayford, PhD, Pfizer: Employee|Pfizer: Ownership Interest|Pfizer: Stocks/Bonds Maria J Tort, PhD, Pfizer Inc: Stocks/Bonds Ruth Chapman, MSc, PhD, Pfizer: Advisor/Consultant Vincenza Snow, MD, Pfizer Vaccines: employee|Pfizer Vaccines: Stocks/Bonds Erica Chilson, PharmD, Pfizer, Inc: Ownership Interest|Pfizer, Inc: Stocks/Bonds Ray Farkouh, PhD, Pfizer: Stocks/Bonds Desmond Dillon-Murphy, MSc, PhD, Pfizer: Advisor/Consultant

Estimation of the Degradation of Epoxy Resin due to Void Discharge using Weighted SVM and Partial Discharge Characteristics

The performance and reliability of power apparatus are affected by the degradation of electrical insulation, often caused by voids in resin‐molded components. To address this issue, we investigated the use of machine learning to estimate the degree of degradation due to void discharges in epoxy resin. We prepared samples with artificial void defects and collected partial discharge data up to the dielectric breakdown, applying long‐term AC voltage. Utilizing this data, a machine learning model was developed to estimate degradation levels. The results showed that the accuracy was 55% and the false estimation rate which underestimates the degradation level was as high as 31%, which was a significant problem for practical use. Therefore, for regions of data that were difficult to classify, we constructed a weighted SVM model that predicted higher levels of degradation by weighting the data according to their degradation labels. With this approach, the accuracy remained at 57%, but the rate of underestimation of the degree of degradation was reduced to only 4%. Consequently, we were able to develop an effective estimation model that is practical for the maintenance of power apparatus. © 2023 Institute of Electrical Engineer of Japan and Wiley Periodicals LLC.

Performance Analysis of Sensor less Induction Motor Drive using Improved Control Techniques

AC Drives demand robust motor design with rugged construction, low cost, high reliability in service, and simple maintenance. In modern power drives, Sensorless Induction motor drives are more popular than other drives. A speed sensor/encoder-based drive is costlier and requires more space in case more parallel units are coupled together in drive operation. To address these difficulties, speed sensorless drives are introduced without loss of efficiency and reliability. However, Sensorless speed drive requires advanced control techniques in which complex calculations are there due to the nonlinearity of IM. In the recent past, Machine model-based methods i.e., MRAS (Model reference adaptive systems) and ELO (Extended Luenberger observer) have been popularized due to ease of implementation compared to other techniques. In this work, improved MRAS and ELO models are implemented and verified using Simulink software. Then, the same is validated with the help of an FPGA (field programmable gate array) based Snetly real-time controller. The key improvements are achieved as follows: effective speed estimation, robust speed tracking, reduced speed estimation error, and robust stability which helps to enhance the transient and steady-state performance of the drive.

Combined intra symbol frequency domain averaging channel estimation for polarization division multiplexed coherent optical OFDM/OQAM systems

Intrinsic imaginary interference (IMI) induced by chromatic dispersion (CD) and polarization mode dispersion (PMD) seriously degrades the performance of optical orthogonal frequency division multiplexing offset-quadrature amplitude modulation (O-OFDM/OQAM) systems. Therefore, effective channel estimations are required for such systems to maintain good transmission performance. In the previous publications, both the full loaded (FL) and the half loaded (HL) frequency-domain channel estimation methods for polarization-division-multiplexed (PDM) coherent optical OFDM/OQAM (CO-OFDM/OQAM) systems have been proposed. However, for these frequency-domain methods, the channel frequency-domain responses caused by the correlation of the amplified spontaneous emission (ASE) noise are not considered. In this paper, we systematically discuss the frequency-domain channel transmission model for PDM CO-OFDM/OQAM systems. With the analysis of the correlation of the ASE noise, we propose a combined intra symbol frequency-domain averaging (CISFDA) channel estimation method for PDM OFDM/OQAM systems. Compared with the full-loaded frequency-domain channel estimation (FL-FD) method, the CISFDA method promotes the system robustness against the ASE noise and fiber nonlinear effect significantly. The proposed method is validated by numerical Monte Carlo simulations of the PDM CO-OFDM/OQAM system. Our new algorithm outperforms the FL-FD method in the 36 Gb/s PDM OFDM CO-OFDM/OQAM system, and the transmission distance is improved by 500 km with the bit-error-rate (BER) target at 3.8×10-3.

Enhanced Estimators and Effective Estimation Procedures for Population Variance under Missing at Random Data in Successive Sampling

This paper explores some potent estimation procedures of population variance under missing at random situations in two-occasion successive sampling. Information on auxiliary variables has been involved in supporting for effective estimation of some chain-type exponential and regression estimators under the assumption of sampling units on which information of study variables cannot be obtained due to missing at random. The non-respondents follow the Binomial type of distribution in the estimation. The proposed estimators are compared with the competent estimators under a complete response of sample units. The Empirical studies support theoretical results in the present probability of non-respondent. Results have been interpreted and suitable recommendations are made for data practitioners.

Collection and Analysis of Adherence Information for Software as a Medical Device Clinical Trials: Systematic Review.

The rapid growth of digital health apps has necessitated new regulatory approaches to ensure compliance with safety and effectiveness standards. Nonadherence and heterogeneous user engagement with digital health apps can lead to trial estimates that overestimate or underestimate an app's effectiveness. However, there are no current standards for how researchers should measure adherence or address the risk of bias imposed by nonadherence through efficacy analyses. This systematic review aims to address 2 critical questions regarding clinical trials of software as a medical device (SaMD) apps: How well do researchers report adherence and engagement metrics for studies of effectiveness and efficacy? and What efficacy analyses do researchers use to account for nonadherence and how appropriate are their methods? We searched the Food and Drug Administration's registration database for registrations of repeated-use, patient-facing SaMD therapeutics. For each such registration, we searched ClinicalTrials.gov, company websites, and MEDLINE for the corresponding clinical trial and study articles through March 2022. Adherence and engagement data were summarized for each of the 24 identified articles, corresponding to 10 SaMD therapeutics. Each article was analyzed with a framework developed using the Cochrane risk-of-bias questions to estimate the potential effects of imperfect adherence on SaMD effectiveness. This review, funded by the Richard King Mellon Foundation, is registered on the Open Science Framework. We found that although most articles (23/24, 96%) reported collecting information about SaMD therapeutic engagement, of the 20 articles for apps with prescribed use, only 9 (45%) reported adherence information across all aspects of prescribed use: 15 (75%) reported metrics for the initiation of therapeutic use, 16 (80%) reported metrics reporting adherence between the initiation and discontinuation of the therapeutic (implementation), and 4 (20%) reported the discontinuation of the therapeutic (persistence). The articles varied in the reported metrics. For trials that reported adherence or engagement, there were 4 definitions of initiation, 8 definitions of implementation, and 4 definitions of persistence. All articles studying a therapeutic with a prescribed use reported effectiveness estimates that might have been affected by nonadherence; only a few (2/20, 10%) used methods appropriate to evaluate efficacy. This review identifies 5 areas for improving future SaMD trials and studies: use consistent metrics for reporting adherence, use reliable adherence metrics, preregister analyses for observational studies, use less biased efficacy analysis methods, and fully report statistical methods and assumptions.

Comparison of MIRACLE2 and downtime as predictors of outcome following out-of-hospital cardiac arrest and evidence for the potential of MIRACLE2 as a selection tool for mechanical circulatory support

Abstract Background Out-of-hospital cardiac arrest (OOHCA) is associated with high morbidity and mortality, driven primarily by poor neurological outcome. Appropriate application of invasive therapies, such as mechanical circulatory support (MCS), currently poses significant challenges. To guide patient selection, effective estimation of patients at high risk of neurological injury, particularly early on arrival to a heart attack centre, is essential. Purpose We compared the discrimination performance of MIRACLE2 score with downtime, and its components, low-flow and zero flow, as predictors of poor neurological outcome following OOHCA. Furthermore, we modelled the use of MIRACLE2 score as a selection criterion for MCS. Methods The European Cardiac Arrest Registry (EUCAR), a retrospective cohort of patients with OOHCA with likely cardiac aetiology, from 6 European centres (May 2012-September 2022), was utilised. The primary end point was poor neurological outcome on hospital discharge (Cerebral Performance Category 3-5). Results 1259 patient were included at the point of analysis. The median downtime was 25 minutes (IQR 15-36), with an average age of 62.9 ± 14.5 years. Baseline demographics (age, gender, co-morbidities) were similar across groups. In patients with a total downtime of &amp;lt;10 minutes and MIRACLE2 score &amp;gt;4, poor outcome occurred in 76.5%, whilst those with a downtime &amp;gt;30 minutes and MIRACLE2 score 0-2 had poor outcome in 24.0%. In a multivariable logistic regression analysis, MIRACLE2 had a stronger association with poor neurological outcome (OR 2.06 [CI 1.83–2.33], p&amp;lt;0.0001) than zero-flow (OR 1.08 [CI 1.03–1.14], p=0.005), low-flow (OR 1.04 [CI 1.00–1.00], p=0.004) and total downtime (OR 0.99 [CI 0.95–1.03]. MIRACLE2 had superior discrimination for poor neurological outcome with AUC of 0.870 (95% CI 0.846 to 0.891) than zero-flow [AUC of 0.616 (95% CI 0.583-0.648)], low-flow [AUC of 0.716 (95% CI 0.685-0.745)] and downtime [AUC of 0.726 (95% CI 0.695-0.754)]. When modelled for inclusion into landmark cardiogenic shock RCTs, based on study criteria (which all incorporate downtime, zero-flow or low-flow times), those modelled for exclusion from study recruitment, the positive predictive value of MIRACLE2 ≥5 for poor outcome was significantly higher (0.91) than CULPRIT-SHOCK (0.62), EUROSHOCK (0.53) and ECLS-SHOCK criteria (0.80) (p&amp;lt;0.001). Discussion: MIRACLE2 demonstrates superiority over downtime in predicting neurological outcome following out-of-hospital cardiac arrest in this large, multicentre, retrospective cohort study and has potential to improve patient selection for mechanical circulatory support in future cardiogenic shock trials.MIRACLE2 vs Downtime

A Gridless DOA Estimation Method for Sparse Sensor Array

Direction-of-arrival (DOA) estimation is still a pivotal research direction in array signal processing. Traditional algorithms based on the signal subspace and compressed sensing theory usually suffer from off-grid and computational complexity. Deep-learning-based methods usually face difficulty in obtaining labeled datasets. With the development of array technology, sparse sensor arrays can effectively reduce the number of sensors, which in turn reduces the complexity of the hardware. Therefore, effective DOA estimation algorithms for sparse sensor arrays need to be further investigated. An unsupervised deep learning method is proposed here to address the above issues. A training model was built based on the residual network structure. The DOA estimation was implemented using Vandermonde decomposition. Finally, the experimental findings confirmed the efficacy of the proposed algorithms presented in this article.

Error bound of Gaussian quadrature rules for certain Gegenbauer weight functions

In this paper we present an extension of our previous research, focusing on a method to numerically evaluate the error term in the Gaussian quadrature formula with the Legendre weight function, as discussed by Jandrlic et al. (2022). For an analytic integrand, the error term in Gaussian quadrature can be expressed as a contour integral. Consequently, determining the upper bound of the error term involves identifying the maximum value of the modulus of the kernel within the subintegral expression for the error along this contour. In our previous study, we investigated the position of this maximum point on the ellipse for Legendre polynomials. In this paper, we establish sufficient conditions for the maximum of the modulus of the kernel, which we derived analytically, to occur at one of the semi-axes for Gegenbauer polynomials. This result extends to a significantly broader case. We present an effective error estimation that we compare with the actual one. Some numerical results are presented.

Dual-level Deep Evidential Fusion: Integrating multimodal information for enhanced reliable decision-making in deep learning

Multimodal learning has gained significant attention in recent years for combining information from different modalities using Deep Neural Networks (DNNs). However, existing approaches often overlook the varying importance of modalities and neglect uncertainty estimation, leading to limited generalization and unreliable predictions. In this paper, we propose a novel algorithm, Dual-level Deep Evidential Fusion (DDEF), to address these challenges by integrating multimodal information at both the Basic Belief Assignment (BBA) level and multimodal level, for enhancing accuracy, robustness, and reliability. The proposed DDEF approach utilizes the Dirichlet framework and BBA methods to connect neural network outputs with Dirichlet distribution parameters, enabling effective uncertainty estimation, and the Dempster-Shafer Theory (DST) is used for dual-level fusion, facilitating the fusion of evidence from two BBA methods and multiple modalities. It has been validated by two experiments on synthetic digit classification, and real-world medical prognosis after brain–computer interface (BCI) treatment, and by demonstrating superior performance compared to existing methods. Our findings emphasize the importance of considering multimodal integration and uncertainty estimation for reliable decision-making in deep learning.

Interpreting effective hydrologic depth estimates derived from soil moisture remote sensing: A Bayesian non-linear modeling approach

The water balance equation (WBE) describes how net water inflows into a system relate to storage changes over a time span (dt). This equation is fundamental in hydrologic studies, helping to determine water supply and elucidate the terrestrial water cycle. For land surface, the WBE links water fluxes like precipitation (P), evapotranspiration, drainage and surface runoff to soil moisture (SM) changes within a discrete soil layer (dSM). While traditional focus has been on the precision of the WBE-estimated fluxes, there has been increased recent interest in fitting the WBE using remotely-sensed P and SM data to infer water balance parameters, particularly the ΔZ that associates net fluxes and dSM within dt. However, obtaining physically interpretable WBE parameters like ΔZ is a potentially ill-posed problem due to simplifying assumptions imbedded in a WBE implementation, missing hydrologic constraints, and accuracy and revisit limitations in available remotely-sensed data. In addition, WBE parameters obtained from classical maximum likelihood estimation approaches can vary significantly. Here, using a Bayesian non-linear modeling approach, we demonstrate how these factors can impede the identification of useful ΔZ estimates. Specifically, we find that ΔZ estimates are likely to be spuriously biased due to limitations in the temporal resolution and accuracy of satellite-based dSM estimates – as well as the neglect of other hydrological components in the WBE. Maps showcasing the lower and upper bounds of the 94 % HDI from our Bayesian model further elucidate these biases. Results suggests that estimates of ΔZ are effective in nature and great care should be exercised when attempting to interpret them physically. Instead, they should be defined as effective parameters reflecting the characteristics of their fitted remotely-sensed data sets. Drawing from current progress, accurate ΔZ estimates, informed by more advanced remote sensing techniques and precise WBE, will enhance our understanding of terrestrial water cycle.

Annual effective dose estimation of radon in drinking water sources of Nizampur basin, North Western Pakistan

ABSTRACT This study assessed radon (222Rn) levels in drinking water sources in the Nizampur basin and their potential health risks for the local community. We analyzed 48 water samples on-site using RAD7. Additionally, we measured pH, temperature (T), total dissolved solids (TDS), redox potential (ORP), and electrical conductivity (EC) with a multiparameter analyzer. Results showed pH, T, TDS, ORP, and EC ranging from 7.2 to 8, 17 to 26 °C, 333 to 1130 mg/l, –56 to 284 mV, and 469 to 2370 µS/cm. 222Rn levels varied significantly (0.7–107 Bq/l, mean 23 ± 21, median = 17 Bq/l), with about 65 % exceeding the EPA's limit of 11.1 Bq/l, indicating health risks likely due to local geological conditions. The annual effective doses for ingestion (EwIng) were 0.87 ± 0.01, 0.35 ± 0.006, and 0.13 ± 0.002 µSv/a for adults, infants, and children, respectively. Exposure risk via the inhalation (EwInh) route ranged from 1.75 to 270 µSv/a, with the highest risk in infants, followed by children and adults. Inhalation was the primary exposure route for all age groups. Further, spatial distribution maps and hotspot analysis suggested that the central region characterized by high structural deformation and favorable geology for radon emanation was the area of concern in terms of health risks.

Parametric estimation of photovoltaic systems using a new multi-hybrid evolutionary algorithm

Integrating solar photovoltaic (PV) systems into the modern power grid introduces a variety of new problems. The accurate modelling of PV is required to strengthen the system characteristics in simulation environments. Modelling such PV systems is reflected by a nonlinear I–V characteristic curve behaviour with numerous unknown parameters because there is insufficient data in the cells’ datasheet. As a result, it is always a priority to identify these unknown parameters. To extract features of solar modules and build high-accuracy models for modelling, control, and optimization of PV systems, current–voltage data is required. A hybrid evolutionary algorithm is proposed in this paper for precise and effective parameter estimation from experimental data of various PV models. The proposed algorithm is named as hybrid flower grey differential (HFGD) algorithm and is based on the hybridization of flower pollination algorithm (FPA), grey wolf optimizer (GWO), and differential evolution (DE) algorithm. For performance evaluation, CEC 2019 benchmark data set is used. To increase the accuracy of the output solutions, we also combined the Newton–Raphson approach with the proposed algorithm. Four PV cells/modules with diverse characteristics, including RTC France Single Diode Model (SDM), RTC France Double DM (DDM), Amorphous Silicon aSi:H, and PVM 752 GaAs Thin-Film, are used to validate the effectiveness as well as the feasibility of the proposed algorithm. The parameter results obtained through the utilization of HFGD algorithm have been compared with other evolutionary algorithms through aspects of precision, reliability, and convergence. Based on the outcomes of the comparison, it has been seen that the HFGD algorithm obtained the lowest root-mean-square error (RMSE) value. Friedman’s rank and Wilcoxon test are carried out for the statistical analysis of the proposed work. The I–V and P–V characteristics are drawn along with the box plot for different PV cells/modules. Statistical and experimental results show the superiority of the proposed algorithm with respect to its counterpart.

Evaluation of municipal solid waste transportation and cost analysis: case study in Banyuwangi Regency

Population growth in Banyuwangi Regency reached 0.61% BPS Banyuwangi Regency, 2022) with waste generation reaching 1,963,033.98 m3/year. Only 13% of the waste that is transported to the Temporary Disposal Site, TDS (RPJMD, 2021) is generated in Banyuwangi Regency in 2022; its waste generation in 2022 is 5378,2 m3/day with existing transportation using 17 units of dump trucks and 5 units of arm-roll trucks. The aim of this paper is to evaluate the most effective and optimal waste transportation system and estimate vehicle operating costs. The research methods were field studies and interviews with staff, drivers, and waste workers. The main information needed is the distance between the TDS and landfill sites locations, the time between locations, and the operating time of the waste trucks. The results showed that the amount of waste transported daily was 211.89 m3/day with a proportion of 4%. Optimization is carried out by setting aside distances on eight dump truck routes using the nearest-neighbor method with the R algorithm. Based on optimization, the distance reduction achieved by sorting eight dump truck routes reaches 102.95 km. In Banyuwangi Regency, the average daily vehicle operating costs for waste transportation are IDR 625,600 for arm roll trucks and IDR 3,325,200 for dump trucks. The average vehicle operating cost per year for 5 arm-roll trucks and 17 dump trucks reaches IDR 4,277,304,940.

Stiff, Smooth, and Solid? Complex Fracture Hydraulics' Imprint on Oscillatory Hydraulic Testing

AbstractFractured bedrock aquifers, especially deep aquifers, represent increasingly common targets for waste storage and alternative energy development, necessitating detailed quantitative descriptions of fracture hydraulic properties, geometry, and connectivity. Yet, multi‐scale characterization of the physical properties that govern fluid flow through and storage in fractured bedrock remains a fundamental hydrogeologic challenge. Oscillatory hydraulic testing, a novel hydraulic characterization technique, has been showing promise in field experiments to characterize the effective hydraulic properties of bedrock fractures. To date, these characterization efforts utilize simplified diffusive analytical models that conceptualize a non‐deforming, parallel‐plate fracture embedded within impermeable host rock, and have found that the returned fracture hydraulic parameter estimates exhibit an apparent period‐dependence. We conduct synthetic experiments using three different numerical models to examine proposed mechanisms that might contribute to the observed period‐dependence including heterogeneous flow and storage within the fracture (i.e., aperture heterogeneity), fracture‐host rock fluid exchange, and fracture hydromechanics. This work represents the first systematic analysis that seeks to understand the process(es) occurring within a bedrock fracture that might be contributing to this apparent period‐dependence. Our analysis demonstrates that all investigated mechanisms generate period‐dependent effective hydraulic parameter estimates, each with their own potentially diagnostic trends; however, fracture hydromechanics is the only explored mechanism that consistently reproduces period‐dependent trends in parameter estimates that are consistent with existing field investigations. These results highlight the need to develop more complex numerical modeling approaches that account for this hydromechanical behavior when characterizing fractured bedrock aquifers.