Fast and accurate recurrent event analysis for genome-wide association studies.

Many diseases recur after recovery, for example, recurrences in cancer and infections. However, research is often focused on analysing only time-to-first recurrence, thereby ignoring any subsequent recurrences that may occur after the first. Statistical models for the analysis of recurrent events are available, of which the extended Cox proportional hazards frailty model is the current state-of-the-art. However, this model is too statistically complex for computationally efficient application in high-dimensional data sets, including genome-wide association studies (GWAS). Here, we develop an application for fast and accurate recurrent event analysis in GWAS, called SPARE (SaddlePoint Approximation for Recurrent Event analysis). In SPARE, every DNA variant is tested for association with recurrence risk using a modified score statistic. A saddlepoint approximation is implemented to achieve statistical accuracy. SPARE controls the Type I error, and its statistical power is similar to existing recurrent event models, yet SPARE is significantly faster. An application of SPARE in a recurrent event GWAS on bladder cancer for 6.2 million DNA variants in 1,443 individuals required less than 15 min, whereas existing recurrent event methods would require several weeks.

Martingale-residual-based greedy model averaging for high-dimensional current status data.

Current status data are a type of failure time data that arise when the failure time of study subject cannot be determined precisely but is known only to occur before or after a random monitoring time. Variable selection methods for the failure time data have been discussed extensively in the literature. However, the statistical inference of the model selected based on the variable selection method ignores the uncertainty caused by model selection. To enhance the prediction accuracy for risk quantities such as survival probability, we propose two optimal model averaging methods under semiparametric additive hazards models. Specifically, based on martingale residuals processes, a delete-one cross-validation (CV) process is defined, and two new CV functional criteria are derived for choosing model weights. Furthermore, we present a greedy algorithm for the implementation of the techniques, and the asymptotic optimality of the proposed model averaging approaches is established, along with the convergence of the greedy averaging algorithms. A series of simulation experiments demonstrate the effectiveness and superiority of the proposed methods. Finally, a real-data example is provided as an illustration.

Alkali Extraction of Arsenic from Groundwater Treatment Sludge: An Essential Initial Step for Arsenic Recovery.

Arsenic (As)-bearing Fe(III) precipitate groundwater treatment sludge has traditionally been viewed by the water sector as a disposal issue rather than a resource opportunity, partly due to assumptions of the low value of As. However, As has now been classified as a Critical Raw Material (CRM) in many regions, providing new incentives to recover As and other useful components of the sludge, such as phosphate (P) and the reactive hydrous ferric oxide (HFO) sorbent. Here, we investigate alkali extraction to separate As from a variety of field and synthetic As-bearing HFO sludges, which is a critical first step to enable sludge upcycling. We found that As extraction was most effective using NaOH, with the As extraction efficiency increasing up to >99% with increasing NaOH concentrations (0.01, 0.1, and 1 M). Extraction with Na2CO3 and Ca(OH)2 was ineffective (<5%). Extraction time (hour, day, week) played a secondary role in As release but tended to be important at lower NaOH concentrations. Little difference in As extraction efficiency was observed for several key variables, including sludge aging time (50 days) and cosorbed oxyanions (e.g., Si, P). However, the presence of ∼10 mass% calcite decreased As release from field and synthetic sludges considerably (<70% As extracted). Concomitant with As release, alkali extraction promoted crystallization of poorly ordered HFO and decreased particle specific surface area, with structural modifications increasing with NaOH concentration and extraction time. Taken together, these results provide essential information to inform and optimize the design of resource recovery methods for As-bearing treatment sludge.

Incidence of and Risk Factors for Post-Operative Urinary Retention Following Surgery for Perineal Tears Among Ugandan Women: A Prospective Cohort Study.

INTRODUCTION AND HYPOTHESIS: We aimed to determine the incidence and risk factors for post-operative urinary retention (POUR) following surgery for perineal tears, and to determine the time to normal voiding after POUR. METHODS: This was a prospective cohort study of women who underwent surgery for old (≥ 3 months) obstetric perineal tears from January 2022 to December 2023. The diagnosis of POUR was made in a woman who completely failed to void despite a full bladder or, one who had post-void residual (PVR) > 150 ml within 10 min of voiding. Return to normal voiding was considered if a patient with POUR had two consecutive PVRs of ≤ 150 ml. Descriptive analyses and multivariable logistic regression were performed to determine risk factors for POUR. RESULTS: A total of 153 participants were enrolled in this study with a mean age of 35.9 (SD ± 10.8) years. The incidence of POUR was 19.6% (30/153, 95% CI 14.02-26.7), and the median time to normal voiding for these patients was 42.4 h (range 24-72). Risk factors for POUR included repeat perineal tear surgery (RR = 4.24; 95% CI 1.16-15.52; p = 0.029) and early urinary catheter removal (RR = 2.89; 95% CI 1.09-7.67; p = 0.033). CONCLUSION: Post-operative urinary retention following surgery for perineal tears is common. The time to return to normal voiding in patients with POUR is short. Women having repeat perineal tear surgery and those in whom the urinary catheter is removed early were more likely to experience POUR. Delayed urinary catheter removal could be considered, especially in patients undergoing repeat perineal tear surgery.

A Novel Underwater Acoustic Target Recognition Method Based on MFCC and RACNN.

In ocean remote sensing missions, recognizing an underwater acoustic target is a crucial technology for conducting marine biological surveys, ocean explorations, and other scientific activities that take place in water. The complex acoustic propagation characteristics present significant challenges for the recognition of underwater acoustic targets (UATR). Methods such as extracting the DEMON spectrum of a signal and inputting it into an artificial neural network for recognition, and fusing the multidimensional features of a signal for recognition, have been proposed. However, there is still room for improvement in terms of noise immunity, improved computational performance, and reduced reliance on specialized knowledge. In this article, we propose the Residual Attentional Convolutional Neural Network (RACNN), a convolutional neural network that quickly and accurately recognize the type of ship-radiated noise. This network is capable of extracting internal features of Mel Frequency Cepstral Coefficients (MFCC) of the underwater ship-radiated noise. Experimental results demonstrate that the proposed model achieves an overall accuracy of 99.34% on the ShipsEar dataset, surpassing conventional recognition methods and other deep learning models.

Multiscale Bayes Adaptive Threshold Wavelet Transform Geomagnetic Basemap Denoising Taking Residual Constraints into Account.

To achieve high-precision geomagnetic matching navigation, a reliable geomagnetic anomaly basemap is essential. However, the accuracy of the geomagnetic anomaly basemap is often compromised by noise data that are inherent in the process of data acquisition and integration of multiple data sources. In order to address this challenge, a denoising approach utilizing an improved multiscale wavelet transform is proposed. The denoising process involves the iterative multiscale wavelet transform, which leverages the structural characteristics of the geomagnetic anomaly basemap to extract statistical information on model residuals. This information serves as the a priori knowledge for determining the Bayes estimation threshold necessary for obtaining an optimal wavelet threshold. Additionally, the entropy method is employed to integrate three commonly used evaluation indexes-the signal-to-noise ratio, root mean square (RMS), and smoothing degree. A fusion model of soft and hard threshold functions is devised to mitigate the inherent drawbacks of a single threshold function. During denoising, the Elastic Net regular term is introduced to enhance the accuracy and stability of the denoising results. To validate the proposed method, denoising experiments are conducted using simulation data from a sphere magnetic anomaly model and measured data from a Pacific Ocean sea area. The denoising performance of the proposed method is compared with Gaussian filter, mean filter, and soft and hard threshold wavelet transform algorithms. The experimental results, both for the simulated and measured data, demonstrate that the proposed method excels in denoising effectiveness; maintaining high accuracy; preserving image details while effectively removing noise; and optimizing the signal-to-noise ratio, structural similarity, root mean square error, and smoothing degree of the denoised image.

Degradation of levofloxacin using electro coagulation residuals-alginate beads as a novel heterogeneous electro-fenton composite.

The presence of levofloxacin (LEV) in aqueous solutions can pose health risks to humans, have adverse effects on aquatic organisms and ecosystems, and contribute to the development of antibiotic-resistant bacteria. This study aims to investigate the feasibility of using electrocoagulation residuals (ECRs) as a heterogeneous catalyst in the electro-Fenton process for degrading LEV. By combining electrocoagulation residuals with sodium alginate, ECRs-alginate beads were synthesized as a heterogeneous electro-Fenton composite. The response surface method was employed to investigate the optimization and influence of various operating parameters such as the initial concentration of LEV (10-50 mg/L), voltage (15-35 V), pH (3-9), and catalyst dose (1-9 g/L). The successful incorporation of iron and other metals into the ECRs-alginate beads was confirmed by characterization tests such as EDX and FTIR. By conducting a batch reaction under optimal conditions (initial LEV concentration = 20 mg/L, pH = 4.5, voltage = 30V, and catalyst dose = 7 g/L), a remarkable degradation of 99% for LEV was achieved. Additionally, under these optimal conditions, a high removal efficiency of 92.3% for total organic carbon (TOC) could be attained within 120 min and these findings are remarkable compared to previous studies. The results further indicated that the degradation of levofloxacin (LEV) could be accurately quantified by utilizing the first-order kinetic reaction with a 0.03 min-1 rate constant. The synthesized beads offered notable advantages in terms of being eco-friendly, simple to use, highly efficient, and easily recoverable from the liquid medium after use.

Nursing practice of routine gastric aspiration in preterm infants and its link to necrotizing enterocolitis: is the practice still clinically relevant?

The practice of routine gastric residual aspiration in preterm infants remains controversial, with conflicting evidence regarding its impact on necrotizing enterocolitis (NEC). As front-line caregivers, nurses play a vital role in gastric aspiration procedures and must be informed by evidence. This quasi-experimental nursing study aimed to assess whether gastric aspiration is clinically relevant in reducing the risk of NEC in preterm infants.A total of 250 preterm infants from two NICUs in Egypt were allocated to the gastric aspiration (n = 125) and non-aspiration (n = 125) groups. Feeding practices, gastric residuals, and incidence/severity of NEC were compared between groups according to modified Bell's criteria. Risk factors were analyzed using multivariate regression. There were no significant baseline differences between the groups. The gastric residual attributes and feeding outcomes did not differ substantially from aspiration. The overall incidence of NEC was 14-15%, with no significant differences in the odds of onset or progression of NEC by stage between the groups. Lower gestational age and birth weight emerged as stronger predictors of NEC. Routine gastric aspiration does not appear to directly prevent or reduce the severity of NEC in this population. Although gastric residuals retain clinical importance, study findings question assumptions that aspiration protects against NEC and informs nursing practice. Evidence-based feeding protocols must continually evolve through ongoing research on modifiable risk factors for this devastating intestinal disease in preterm infants.

Negative Emotional Reactivity and Somatic Symptoms during Adolescence Predict Adult Health and Wellbeing in Early and Middle Adulthood.

Longitudinal research is lacking with respect to how negative emotional reactivity and somatic symptoms during adolescence set the stage for later health. The aim of this longitudinal study was to examine within-person associations between negative emotional reactivity and somatic symptoms during adolescence and their effects on health and wellbeing in adulthood. Participants (N = 1527; 48.3% female) were assessed annually at the age of 12 to 16 years and at the age of 35 and 45 years. Adolescents with frequent somatic symptoms reported higher reactivity. Individual differences in levels and changes of somatic symptoms and reactivity were independently associated with adult health and wellbeing decades later. The findings underscore the importance of considering how individual differences change during adolescent development.

Estimating Waning of Vaccine Effectiveness: A Simulation Study.

BACKGROUND: Developing accurate and reliable methods to estimate vaccine protection is a key goal in immunology and public health. While several statistical methods have been proposed, their potential inaccuracy in capturing fast intraseasonal waning of vaccine-induced protection needs to be rigorously investigated. METHODS: To compare statistical methods for estimating vaccine effectiveness (VE), we generated simulated data using a multiscale, agent-based model of an epidemic with an acute viral infection and differing extents of VE waning. We apply a previously proposed framework for VE measures based on the observational data richness to assess changes of vaccine-induced protection over time. RESULTS: While VE measures based on hard-to-collect information (eg, the exact timing of exposures) were accurate, usually VE studies rely on time-to-infection data and the Cox proportional hazards model. We found that its extension using scaled Schoenfeld residuals, previously proposed for capturing VE waning, was unreliable in capturing both the degree of waning and its functional form and identified the mathematical factors contributing to this unreliability. We showed that partitioning time and including a time-vaccine interaction term in the Cox model significantly improved estimation of VE waning, even in the case of dramatic, rapid waning. We also proposed how to optimize the partitioning scheme. CONCLUSIONS: While appropriate for rejecting the null hypothesis of no waning, scaled Schoenfeld residuals are unreliable for estimating the degree of waning. We propose a Cox-model-based method with a time-vaccine interaction term and further optimization of partitioning time. These findings may guide future analysis of VE waning data.

Phytodiversity is associated with habitat heterogeneity from Eurasia to the Hengduan Mountains.

The geographic distribution of plant diversity matches the gradient of habitat heterogeneity from lowlands to mountain regions. However, little is known about how much this relationship is conserved across scales. Using the World Checklist of Vascular Plants and high-resolution biodiversity maps developed by species distribution models, we investigated the associations between species richness and habitat heterogeneity at the scales of Eurasia and the Hengduan Mountains (HDM) in China. Habitat heterogeneity explains seed plant species richness across Eurasia, but the plant species richness of 41/97 HDM families is even higher than expected from fitted statistical relationships. A habitat heterogeneity index combining growing degree days, site water balance, and bedrock type performs better than heterogeneity based on single variables in explaining species richness. In the HDM, the association between heterogeneity and species richness is stronger at larger scales. Our findings suggest that high environmental heterogeneity provides suitable conditions for the diversification of lineages in the HDM. Nevertheless, habitat heterogeneity alone cannot fully explain the distribution of species richness in the HDM, especially in the western HDM, and complementary mechanisms, such as the complex geological history of the region, may have contributed to shaping this exceptional biodiversity hotspot.

Evaluating the Impact of Intracranial Volume Correction Approaches on the Quantification of Intracranial Structures in MRI: A Systematic Analysis.

BACKGROUND: In neuroscience, accurately quantifying individual brain regions in large cohorts is a challenge. Differences in intracranial structures can suggest functional differences, but they also reflect the effects of other factors. However, there is currently no standardized method for the correction of intracranial structure measurements. PURPOSE: To identify the optimal method to counteract the influence of total intracranial volume (TIV) and gender on the measurement of intracranial structures. STUDY TYPE: Prospective. POPULATION/SUBJECTS: One hundred forty-one healthy adult volunteers (70 male, mean age 21.8 ± 1.7 years). FIELD STRENGTH/SEQUENCE: T1-weighted 3D gradient-echo sequence at 3.0 T. ASSESSMENT: A radiologist with 5 years of work experience screened the raw images to exclude poor-quality images. Freesurfer then performed automated segmentation to obtain measurements of intracranial structures. Male-only, female-only, and TIV-matched sub-samples were created separately. Comparisons between the original data and these sub-samples were used to assess the effects of gender and TIV. Comparison the consistency between TIV-matched sample and corrected data that corrected by four methods: Proportion method, power-corrected proportion method, covariate regression method, and residual method. STATISTICAL TESTS: Cohen's d for examining group distribution disparities, t-tests for probing mean differences, correlation coefficients to assess the relationships between intracranial substructure measurements and TIV. Multiple comparison corrections were applied to the results. RESULTS: The correlation coefficients between TIV and the volumes of intracranial structures ranged from 0.033 to 0.883, with an average of 0.467. Thirty significant volume differences were found among 36 structures in the original sample, while no differences were observed in the TIV-matched sample. Among the four correction methods, the residual method had highest consistency (similarity 94.4%) with the TIV-matched group. DATA CONCLUSION: The variation in intracranial structure sizes between genders was largely attributable to TIV. The residual method offers a more accurate and effective approach for correcting the effects of TIV on intracranial structures. EVIDENCE LEVEL: 2 TECHNICAL EFFICACY: Stage 1.

Effects of diets containing proteins from fish muscles or fish by-products on the circulating cholesterol concentration in rodents: a systematic review and meta-analysis.

A high circulating cholesterol concentration is considered an important risk factor for the development of CVD. Since lean fish intake and fish protein supplementation have been associated with lower cholesterol concentration in some but not all clinical studies, the main aim of this study was to investigate the effect of diets containing proteins from fish muscles and fish by-products on the serum/plasma total cholesterol (TC) concentration in rodents. A systematic literature search was performed using the databases PubMed, Web of Science and Embase, structured around the population (rodents), intervention (type of fish and fraction, protein dose and duration), comparator (casein) and the primary outcome (circulating TC). Articles were assessed for risk of bias using the SYRCLE's tool. A meta-analysis was conducted in Review Manager v. 5·4·1 (the Cochrane Collaboration) to determine the effectiveness of proteins from fish on the circulating TC concentration. Thirty-nine articles were included in the systematic review and meta-analysis, with data from 935 rodents. The risk of bias is unclear since few of the entries in the SYRCLE's tool were addressed. Consumption of proteins from fish resulted in a significantly lower circulating TC concentration when compared with control groups (mean difference -0·24 mmol/l, 95 % CI - 0·34, -0·15, P < 0·00001), with high statistical heterogeneity (I2 = 71 %). To conclude, proteins from fish muscles and by-products show promise as a functional dietary ingredient or supplement by preventing high cholesterol concentration in rodents, thus reducing one of the most important risk factors for developing CVD.

Efficient surface passivation of germanium nanostructures with 1% reflectance.

Germanium (Ge) is a vital element for applications that operate in near-infrared wavelengths. Recent progress in developing nanostructured Ge surfaces has resulted in >99% absorption in a wide wavelength range (300-1700 nm), promising unprecedented performance for optoelectronic devices. However, excellent optics alone is not enough for most of the devices (e.g. PIN photodiodes and solar cells) but efficient surface passivation is also essential. In this work, we tackle this challenge by applying extensive surface and interface characterization including transmission electron microscopy and x-ray photoelectron spectroscopy, which reveals the limiting factors for surface recombination velocity (SRV) of the nanostructures. With the help of the obtained results, we develop a surface passivation scheme consisting of atomic-layer-deposited aluminum oxide and sequential chemical treatment. We achieve SRV as low as 30 cm s-1combined with ∼1% reflectance all the way from ultraviolet to NIR. Finally, we discuss the impact of the achieved results on the performance of Ge-based optoelectronic applications, such as photodetectors and thermophotovoltaic cells.

The reciprocal relations between well-being and maternal and peer warmth in adolescents involved in the juvenile justice system.

INTRODUCTION: Although justice system involvement increases the risk of negative outcomes for adolescents, many justice-involved youth desist from crime as adults (Sampson & Laub, 2005). There are few studies examining predictors of positive development in justice-involved adolescents. In the current study, we assess the influence of maternal and peer warmth on the development of well-being in adolescents involved in the US justice system over the course of 5 years. METHODS: Participants included 1216 adolescent males who experienced their first arrest. Interviews were given every year for 5 years. Well-being was measured using the EPOCH questionnaire (Kern et al., 2016) and relationship warmth was measured using a scale adapted from Conger et al. (1994). Hypotheses were tested using latent curve models with structured residuals. RESULTS: Baseline levels of well-being were associated with maternal (ß = 0.49, p < .001) and peer warmth, ß = 0.52, p < .001. When an individual's maternal warmth was higher than predicted given their maternal warmth trajectory, their subsequent well-being was higher than expected given their well-being trajectory, b = 0.07, p < .001. When an individual's peer warmth was higher than predicted, their subsequent well-being was higher than expected, b = 0.06, p < .001. These relations were reciprocal, such that well-being also predicted increased maternal and peer warmth. CONCLUSIONS: These findings suggest that increasing maternal or peer warmth may have cascading effects on the well-being of justice-involved adolescents. Interventions for justice-involved youth may benefit from targeting factors that increase positive development for these youth.

Unique Variable Analysis: A Network Psychometrics Method to Detect Local Dependence.

The local independence assumption states that variables are unrelated after conditioning on a latent variable. Common problems that arise from violations of this assumption include model misspecification, biased model parameters, and inaccurate estimates of internal structure. These problems are not limited to latent variable models but also apply to network psychometrics. This paper proposes a novel network psychometric approach to detect locally dependent pairs of variables using network modeling and a graph theory measure called weighted topological overlap (wTO). Using simulation, this approach is compared to contemporary local dependence detection methods such as exploratory structural equation modeling with standardized expected parameter change and a recently developed approach using partial correlations and a resampling procedure. Different approaches to determine local dependence using statistical significance and cutoff values are also compared. Continuous, polytomous (5-point Likert scale), and dichotomous (binary) data were generated with skew across a variety of conditions. Our results indicate that cutoff values work better than significance approaches. Overall, the network psychometrics approaches using wTO with graphical least absolute shrinkage and selector operator with extended Bayesian information criterion and wTO with Bayesian Gaussian graphical model were the best performing local dependence detection methods overall.

BDS Dual-Frequency Carrier Phase Multipath Hemispherical Map Model and Its Application in Real-Time Deformation Monitoring.

The BDS multipath delay error is highly related to the surrounding monitoring environment, which cannot be eliminated or mitigated by applying the double difference observation model. In the actual monitoring environment, due to the complexity of the BDS constellation, it is difficult for existing algorithms to consider GEO, IGSO, MEO and other different orbital types of satellites for real-time and efficient multipath error reduction. Therefore, we propose a novel BDS dual-frequency multipath error reduction method for real deformation monitoring for BDS considering various satellite orbit types. This method extracts the single error residual of each satellite based on the assumption of "zero mean" and divides the appropriate grid density of GEO and IGSO/MEO, respectively, to construct a dual-frequency multipath hemispherical map model suitable for BDS satellites with different orbital types. This method can realize the multipath error elimination of the observed values of different orbits and different frequencies. The results of simulation experiments and real deformation monitoring data demonstrate that this method can effectively eliminate low-frequency multipath delay errors in the observation domain and coordinate domain. After multipath correction, the precision of the horizontal coordinates and height coordinates are 1.7 mm and 4.6 mm. The precision of the horizontal coordinate and height coordinate is increased by 50% and 60%, respectively. The fixed rate of ambiguity increased by 5-7%.

DTFusion: Infrared and Visible Image Fusion Based on Dense Residual PConv-ConvNeXt and Texture-Contrast Compensation.

Infrared and visible image fusion aims to produce an informative fused image for the same scene by integrating the complementary information from two source images. Most deep-learning-based fusion networks utilize small kernel-size convolution to extract features from a local receptive field or design unlearnable fusion strategies to fuse features, which limits the feature representation capabilities and fusion performance of the network. Therefore, a novel end-to-end infrared and visible image fusion framework called DTFusion is proposed to address these problems. A residual PConv-ConvNeXt module (RPCM) and dense connections are introduced into the encoder network to efficiently extract features with larger receptive fields. In addition, a texture-contrast compensation module (TCCM) with gradient residuals and an attention mechanism is designed to compensate for the texture details and contrast of features. The fused features are reconstructed through four convolutional layers to generate a fused image with rich scene information. Experiments on public datasets show that DTFusion outperforms other state-of-the-art fusion methods in both subjective vision and objective metrics.

Spearman-like correlation measure adjusting for covariates in bivariate survival data.

We propose an extension of Spearman's correlation for censored continuous and discrete data that permits covariate adjustment. Previously proposed nonparametric and semiparametric Spearman's correlation estimators require either nonparametric estimation of the bivariate survival surface or parametric assumptions about the dependence structure. In practice, nonparametric estimation of the bivariate survival surface is difficult, and parametric assumptions about the correlation structure may not be satisfied. Therefore, we propose a method that requires neither and uses only the marginal survival distributions. Our method estimates the correlation of probability-scale residuals, which has been shown to equal Spearman's correlation when there is no censoring. Because this method relies only on marginal distributions, it tends to be less variable than the previously suggested nonparametric estimators, and the confidence intervals are easily constructed. Although under censoring, it is biased for Spearman's correlation as our simulations show, it performs well under moderate censoring, with a smaller mean squared error than nonparametric approaches. We also extend it to partial (adjusted), conditional, and partial-conditional correlation, which makes it particularly relevant for practical applications. We apply our method to estimate the correlation between time to viral failure and time to regimen change in a multisite cohort of persons living with HIV in Latin America.

Assessment of healthcare waste treatment methods using an interval-valued intuitionistic fuzzy double normalization-based multiple aggregation approach.

Healthcare waste management has been an extensively attractive topic recently since it is one of the key concerns regarding both environment and public health, predominantly in developing nations. The optimization of the treatment procedure for healthcare waste is indeed a complex "multi-criteria decision-making (MCDM)" problem that involves contradictory and interweaved critical criteria. To successfully handle this issue, this study extends the original method, named the "double normalization-based multi-aggregation (DNMA)" approach, with "interval-valued intuitionistic fuzzy sets (IVIFSs)" for decision-making problems taking criteria in terms of benefit or cost types. This method involves two target-based normalizations and three subordinate utility models. To estimate the criteria weights, we propose a new parametric divergence measure and discuss the feasibility of the developed divergence measure based on existing divergence measures for IVIFSs. Further, the developed framework is implemented to elucidate the "healthcare waste treatment (HCWT)" problem. The comparative and sensitivity analyses of the outcomes indicate that the proposed approach efficiently tackles the problem of HCWT selection. The outcomes show that steam sterilization (0.462) is the optimal one for HCWT. The prioritization options, obtained by presented approach, are dependable and suitable, which are steam sterilization ≻ microwave ≻ incineration ≻ landfilling.