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Spectroscopic ellipsometry and some other optical metrology techniques, such as reflectometry and scatterometry, are model-based optical measurements and, therefore, require appropriate modeling to determine the geometric and material properties of substrates, thin films, and multilayer structures. Parametric sensitivity analysis (SA) provides essential assistance in the model-building process to quantify the relative importance of model parameters for model output and to identify those with high/little influence. SA can be performed in a variety of ways, and this article discusses an application of the Morris or elementary effect (EE) method, a screening type SA procedure, to spectroscopic ellipsometry modeling. The method is a global SA technique and uses a stepping of m parameters along certain so-called "trajectories" or sequences of points in parameter space, randomly constructed in order to maximally fill the volume of the m -dimensional parameter space. However, it was thought that the EE method relies greatly on a sampling strategy or a way of selecting "optimized trajectories" in the parameter space, i.e., a necessary number of trajectories chosen to be well spread over the space to properly cover the entire realistic ranges of all input factors. Here, we use two sampling methods for selecting trajectories with possibly different distributions and investigate their effects on the estimation of various sensitivity measures in spectroscopic ellipsometry data modeling. The SA results indicate that the performance of the sampling strategy should not be judged only by maximizing the trajectory spread but also include some additional convergence criteria for the sensitivity measures. [ABSTRACT FROM AUTHOR]

Typically, the parameters entering a physical simulation model carry some kind of uncertainty, e.g., due to the intrinsic approximations in a higher fidelity theory from which they have been obtained. Global sensitivity analysis (GSA) targets quantifying which parameter uncertainties impact the accuracy of the simulation results, e.g., to identify which parameters need to be determined more accurately. We present a GSA approach based on the Cramérs–von Mises distance. Unlike prevalent approaches, it combines the following properties: (i) it is equally suited for deterministic as well as stochastic model outputs, (ii) it does not require gradients, and (iii) it can be estimated from numerical quadrature without further numerical approximations. Using quasi-Monte Carlo for numerical integration and a first-principles kinetic Monte Carlo model for the CO oxidation on RuO2(110), we examine the performance of the approach. We find that the results agree very well with what is known in the literature about the sensitivity of this model and that the approach converges in a modest number of quadrature points. Furthermore, it appears to be robust against even extreme relative noise. All these properties make the method particularly suited for expensive (kinetic) Monte Carlo models because we can reduce the number of simulations as well as the target variance of each of these. [ABSTRACT FROM AUTHOR]

Computational and machine learning frameworks have greatly contributed to early diagnosis and rapid therapeutic interventions due to breakthroughs in science and technology. This study aims to develop a mathematical model that utilizes machine learning and integrates both innate and adaptive immunological components to examine the progression of breast cancer. The proposed framework utilizes a set of ordinary differential equations to represent the interactions between breast cancer cells, cytotoxic T lymphocytes, T-helper cells, and macrophages. We used machine learning optimization techniques to enhance the computational framework, enabling accurate modeling of the dynamic alterations occurring in the tumor microenvironment. This approach also considers the different properties and responses of immune cells. Our validated results, obtained using metaheuristic algorithms and sensitivity analysis, provide significant insights into the correlation between the advancement of breast cancer and the innate and adaptive immune systems. This study supports the theory that advanced programming tools may enhance healthcare systems by offering reliable techniques for understanding and possibly controlling the progression of cancer via the manipulation of the immune system. [ABSTRACT FROM AUTHOR]

BACKGROUND: Osteonecrosis due to drugs is a serious adverse reaction occurring after the application of such drugs. Increasing evidence suggests that the gut microbiota composition is associated with osteonecrosis due to drugs. However, the causal relationship of the gut microbiota to osteonecrosis due to drugs is still unclear. OBJECTIVE: To evaluate the potential causal relationship between the gut microbiota and the risk of osteonecrosis due to drugs using the Mendelian randomization method. METHODS: A two-sample Mendelian randomization study was performed using the summary statistics of gut microbiota from the largest available genomewide association study meta-analysis (n=13 266) conducted by the MiBioGen consortium as well as the summary statistics of osteonecrosis due to drugs obtained from the FinnGen consortium R9 release data (264 cases and 377 013 controls). Inverse variance weighted, MR-Egger, weighted median, weighted model and simple model were used to examine the causal association between gut microbiota and osteonecrosis due to drugs. Sensitivity analysis was used to test whether the results of the Mendelian randomization analysis were reliable. Reverse Mendelian randomization analysis was performed on all the bacteria as an outcome for effect analysis and sensitivity analysis. RESULTS AND CONCLUSION: Inverse variance weighted estimates suggested that Lentisphaerae (phylum), Lentisphaeria (class), Melainabacteria (class), Gastranaerophilales (order), Rhodospirillales (order), Victivallales (order) and Bifidobacterium (genus) had protective causal effects on osteonecrosis due to drugs. Methanobacteria (class), Bacillales (order), Methanobacteriaceae (family), Lachnospiraceae (family), Methanobacteriales (order), Holdemania (genus), Holdemania (UCG010 group) (genus), Odoribacter (genus) and Tyzzerella3 (genus) had negative causal effects on osteonecrosis due to drugs. According to the results of reverse Mendelian randomization analysis, Clostridiaceae1 (family), Peptostreptococcaceae (family), Streptococcaceae (family), Clostridiumsensustricto1 (genus) and Streptococcus (genus) showed negative causal effects on osteonecrosis due to drugs. However, Eisenbergiella (genus) showed protective causal effects on osteonecrosis due to drugs. None of the bidirectional sensitivity analysis revealed heterogeneity or horizontal pleiotropy. When gut microbiota were used as exposure and osteonecrosis due to drugs as the outcome, Mendelian randomization analysis found that seven bacterial traits were positively correlated to osteonecrosis due to drugs, nine bacterial traits were negatively related to osteonecrosis due to drugs. When osteonecrosis due to drugs were used as exposure and gut microbiota as the outcome, reverse Mendelian randomization analysis found a negative correlated relationship with five bacterial traits and a positive causal relationship with one bacterial trait. By changing the diversity and composition of gut microbiota, it is expected to improve the incidence and prognosis of osteonecrosis due to drugs, providing new ideas for the study of orthopedic diseases. [ABSTRACT FROM AUTHOR]

In this paper, the effect of Solar Photovoltaic (SPV) penetration on the damping of critical and inter area modes arising from different penetration level of SPV into the Nigeria national grid has been studied. Also, the possible effects of SPV penetration on Critical Clearing Time (CCT) when a three-phase fault occurs on the grid are studied. The Nigeria 56-bus system was used for the investigation. Sensitivity analyses are carried out to obtain insight into how certain change in parameters affects both the transient and small signal stabilities of the Nigerian power systems. DigSILENT Power Factory was used for the simulation while the analysis was performed in MATLAB. Some of the important findings show that the grid developed increasing amplitude of oscillation from 20 % SPV penetration. There is no steady increase or decrease of CCT with penetration levels. The effect of changes in load on small signal conducted shows that both the real and imaginary load at Kano and Birnin kebbi bus should not exceed 257 MW, 176 MVAR and 171 MW, 134 MVAR, respectively so as to avoid system collapse. [ABSTRACT FROM AUTHOR]

Highlights Honeycomb‐reinforced ablation thermal protection (HRATP) composites are widely used in aircraft thermal protection systems as one of the most effective thermal protection methods. The conventional milling (CM) method renders it susceptible to various processing damages, which directly affects the bonding strength of the whole component. Aiming at the poor surface quality caused by the large cutting force in the cutting process, a longitudinal ultrasonic‐assisted milling (LUAM) method was proposed, which realized the periodic separation of multi‐edge micro‐tooth milling cutter and workpiece, effectively reduced the cutting force and improved the surface quality. A motion trajectory model for a multi‐edge micro‐tooth milling cutter was developed, and an experimental study of LUAM was conducted. The effects of different tools and ultrasonic characterization parameters on cutting force, cutting temperature, and surface quality were compared and analyzed. The results of the comparison between the four different tools indicate that the multi‐edge micro‐tooth milling cutter exhibits the highest machining quality. Compared with CM, applying longitudinal ultrasonic vibration to the multi‐edge micro‐tooth milling cutter reduces the inlet burr length by 33.3%, and the chip was mainly removed in a small particle manner. A multi‐edge micro‐tooth LUAM model was established of HRATP composites. Sensitivity analysis of milling and vibration parameters on temperature. LUAM reduces the cutting temperature by 12.4% and the burr by 33.3%. LUAM significantly enhanced the surface quality compared with CM. [ABSTRACT FROM AUTHOR]

In modern times, antenna design has become more demanding than ever. The escalating requirements for performance and functionality drive the development of intricately structured antennas, where parameters must be meticulously adjusted to achieve peak performance. Often, global adjustments to geometry are necessary for optimal results. However, direct manipulation of antenna responses evaluated with full-wave electromagnetic (EM) simulation models using conventional nature-inspired methods entails significant computational costs. Alternatively, surrogate-based techniques show promise but are impeded by dimensionality-related challenges and nonlinearity of antenna outputs. This study introduces an innovative technique for swiftly optimizing antennas. It leverages a machine learning framework with an infill criterion employing predicted enhancement of the merit function, utilizing a particle swarm optimizer as the primary search engine, and employs kriging for constructing the underlying surrogate model. The surrogate model operates within a reduced-dimensionality domain, guided by directions corresponding to maximum antenna response variability identified through fast global sensitivity analysis, tailored explicitly for domain determination. Operating within this reduced domain enables building dependable metamodels at a significantly lower computational cost. To address accuracy loss resulting from dimensionality reduction, the global optimization phase is supplemented by local sensitivity-based parameter adjustment. Extensive comparative experiments involving various planar antennas demonstrate the competitive operation of the presented technique over machine learning algorithms operating in full-dimensionality space and direct EM-driven bio-inspired optimization techniques. [ABSTRACT FROM AUTHOR]

Background: Epigenetic age accelerations (EAAs) are a promising new avenue of research, yet their investigation in subacute thyroiditis (SAT) remains scarce. Our study endeavors to fill this void by exploring the potential causal association between EAAs and SAT. Methods: Our study utilized publicly available genome-wide association study (GWAS) data of European ancestry to conduct a bidirectional Mendelian randomization (MR) study. Five MR methods were employed to measure causal association between EAAs and SAT multiple analyses were utilized to perform quality control. Results: Our study evaluated causal association between SAT and four EAAs, included GrimAge acceleration (GrimAA), Hannum age acceleration (HannumAA), PhenoAge acceleration (PhenoAA), intrinsic epigenetic age acceleration (IEAA). Results showed that there is a significant causal association between PhenoAA and SAT (OR 1.109, 95% CI 1.000–1.228, p = 0.049, by IVW method). On the contrary, SAT was associated with IEAA (OR 0.933, 95% CI 0.884–0.984, p = 0.011, by IVW method; OR 0.938, 95% CI 0.881–0.998, p = 0.043, by weighted median method). Leave-one-out sensitivity analysis, heterogeneity test, pleiotropy test, and MR-PRESSO analysis provide good quality control. Conclusion: The bidirectional MR analysis concluded that an increase in PhenoAA was correlated with a higher risk of SAT, indicating a potential causal relationship between PhenoAA and risk of SAT. Conversely, SAT was found to be closely associated with IEAA, suggesting that SAT may accelerate the aging process. Slowing down biological aging has emerged as a new research direction in curbing SAT. [ABSTRACT FROM AUTHOR]

Nearly 80% of women are estimated to have at least one HPV infection from high-risk types. Although the majority can clear the infection through their immune system, some are at risk for cervical cancer. Persistent high-risk HPV infections are the leading cause of cervical cancer worldwide. This paper proposes a mathematical model that examines the effects of HPV transmission on cervical cancer patients under vaccine intervention. The model's fundamental properties are investigated, including the stability of equilibrium points and the existence of forward bifurcations. Subsequently, based on cervical cancer patient data collected from Hungary between 2000 and 2020, the model's optimal parameter values are identified using a nonlinear least squares method. Further, we perform a sensitivity analysis of the key cervical cancer progression parameters. Our results indicate that both direct HPV vaccination in susceptible populations and additional vaccination in individuals who have recovered can improve immune responses and reduce the risk of cervical cancer. In addition, the study of the effects of intervention measures on cervical cancer patients in Hungary from 2000 to 2030 reveals that reducing the contact rate is conducive in the short term to curbing the development of cervical cancer; however, in the long term, relying solely on this measure is not sufficient to lead to a significant decrease in the number of cervical cancer cases. [ABSTRACT FROM AUTHOR]

The stochastic dynamical ρ4 equation is utilized as a robust framework for modeling the behavior of complex systems characterized by randomness and nonlinearity, with applications spanning various scientific fields. The aim of this paper is to employ an analytical method to identify stochastic traveling wave solutions of the dynamical ρ4 equation. Novel hyperbolic and rational functions are investigated through this method. A Galilean transformation is applied to reformulate the model into a planar dynamical system, which enables a comprehensive qualitative analysis. Additionally, the emergence of chaotic and quasi-periodic patterns following the introduction of a perturbation term is addressed. Simulation results indicate that significant changes in the systems’ dynamic behavior are caused by adjusting the amplitude and frequency parameters. Our findings indicate the impact of the method on system dynamics and its efficacy in analyzing solitons and phase behavior in nonlinear models. These discoveries provide fresh perspectives on how the suggested method can lead to notable shifts in the systems’ dynamic behavior. The effectiveness and practicality of the proposed methodology in scrutinizing soliton solutions and phase visualizations across diverse nonlinear models are underscored by these revelations. [ABSTRACT FROM AUTHOR]

This study explores an epidemic model elucidating the dynamics of COVID-19 transmission amidst vaccination and saturated treatment interventions. The investigation encompasses both deterministic and stochastic frameworks, considering constant and fluctuating environments, utilizing COVID-19 data from India for empirical validation. Through rigorous mathematical and numerical analyses, we ascertain pivotal insights. Our deterministic model unveils a critical phenomenon: the occurrence of backward bifurcation at ℛ0 = 1, underscoring that merely reducing the basic reproduction number below unity does not ensure disease eradication. Sensitivity analyses underscore the acceleration of epidemic spread with higher transmission rates, yet mitigation measures such as vaccination and comprehensive treatment can effectively reduce the basic reproduction number below unity. Within the stochastic framework, we establish the existence of a unique global positive solution. We delineate conditions for disease extinction or persistence and identify criteria for the emergence of stationary distribution, reflecting the sustained presence of infection within the community. Our findings elucidate that while smaller noise intensities sustain disease prevalence, heightened noise levels lead to complete eradication of the infection. [ABSTRACT FROM AUTHOR]

AbstractThe global population has encountered significant challenges throughout history due to infectious diseases. To comprehensively study these dynamics, a novel deterministic mathematical model, TCD IL2 Z, is developed for the early detection and treatment of lung cancer. This model incorporates IL2 cytokine and anti-PD-L1 inhibitors, enhancing the immune system’s anticancer response within five epidemiological compartments. The TCD IL2Z model is analyzed qualitatively and quantitatively, emphasizing local stability given the limited data-a critical component of epidemic modeling. The model is systematically validated by examining essential elements such as equilibrium points, the reproduction number (R0), stability, and sensitivity analysis. Next-generation techniques based on R0 that track disease transmission rates across the sub-compartments are fed into the system. At the same time, sensitivity analysis helps model how a particular parameter affects the dynamics of the system. The stability on the global level of such therapy agents retrogrades individuals with immunosuppression or treated with IL2 and anti-PD-L1 inhibitors admiring the Lyapunov functions’ applications. NSFD scheme based on the implicit method is used to find the exact value and is compared with Euler’s method and RK4, which guarantees accuracy. Thus, the simulations were conducted in the MATLAB environment. These simulations present the general symptomatic and asymptomatic consequences of lung cancer globally when detected in the middle and early stages, and measures of anticancer cells are implemented including boosting the immune system for low immune individuals. In addition, such a result provides knowledge about real-world control dynamics with IL2 and anti-PD-L1 inhibitors. The studies will contribute to the understanding of disease spread patterns and will provide the basis for evidence-based intervention development that will be geared toward actual outcomes. [ABSTRACT FROM AUTHOR]

Ancient divergences within Opisthokonta—a major lineage that includes organisms in the kingdoms Animalia, Fungi, and their unicellular relatives—remain contentious. To assess progress toward a genome-scale Opisthokonta phylogeny, we conducted the most taxon rich phylogenomic analysis using sets of genes inferred with different orthology inference methods and established the geological timeline of Opisthokonta diversification. We also conducted sensitivity analysis by subsampling genes or taxa from the full data matrix based on filtering criteria previously shown to improve phylogenomic inference. We found that approximately 85% of internal branches were congruent across data matrices and the approaches used. Notably, the use of different orthology inference methods was a substantial contributor to the observed incongruence: analyses using the same set of orthologs showed high congruence of 97% to 98%, whereas different sets of orthologs resulted in somewhat lower congruence (87% to 91%). Examination of unicellular Holozoa relationships suggests that the instability observed across varying gene sets may stem from weak phylogenetic signals. Our results provide a comprehensive Opisthokonta phylogenomic framework that will be useful for illuminating ancient evolutionary episodes concerning the origin and diversification of the 2 major eukaryotic kingdoms and emphasize the importance of investigating effects of orthology inference on phylogenetic analyses to resolve ancient divergences. Opisthokonta is the major lineage that includes animals, fungi, and their unicellular relatives, but some ancient divergences remain contentious. This study presents a genome-scale phylogeny and establishes the geological timeline of opisthokont diversification. [ABSTRACT FROM AUTHOR]

This paper incorporates generalized nonlinear vaccination rate and temporary immunity into the infectious disease models and presents the corresponding system. The qualitative results show that the disease-free equilibrium point and the endemic equilibrium point are globally asymptotically stable under some certain conditions. The local sensitivity analysis reveals that the importance of the critical parameters to the effective reproduction number Rv in the order from high to low is μ, Λ, 휃, γ and ξ, respectively. The global sensitivity analysis shows that the parameters which impact on the effective reproduction number Rv are α, 휃, μ, β, Λ, p, α, ξ and γ in the descending order. Based on the dynamical behavior and the sensitivity analysis of the considered system, the controlling measures are presented to prevent and control the infectious diseases: (1) increasing the nonlinear vaccination rate of the susceptible subpopulation, (2) controlling the number of immigrants to infectious areas, (3) increasing vaccination coverage of immigrants and newborns and (4) improving the effectiveness of vaccines and reducing the rate of immune loss. [ABSTRACT FROM AUTHOR]

In structural damage detection (SDD) studies, regularization techniques have shown potential for improving ill-posedness. However, existing methods based on regularization techniques cannot yield satisfactory results for the SDD problem involving regional damages. Based on the sparse distribution characteristics of regional damages in the finite element (FE) model, this study proposes a novel SDD method that integrates sensitivity analysis of modal parameters with overlapping group sparse regularization. First, the relationship between modal parameters and structural damages is established based on sensitivity analysis. Then, the overlapping group sparsity of regional damages is analyzed and the objective function for SDD can be defined. Finally, a grouping matrix is introduced to transform the objective function from overlapping to non-overlapping group sparse regularization. To evaluate the effectiveness of the proposed method, both numerical simulations and experimental studies are employed, and comparative studies are conducted with the SDD method based on the l1-norm regularization. The identification results indicate that the proposed method can handle regional damages in the FE model and identify both single and multiple damages well. [ABSTRACT FROM AUTHOR]

This study proposed a framework using artificial neural networks (ANNs), the designed residual regression method, and probabilistic indexing, for drought sensitivity shift analysis, associated with future yield loss risk in Anthropocene. The temporal assessment of the drought index (standardized precipitation evapotranspiration index, SPEI) illustrated prevalent occurrence of mild to moderate drought with a fluctuating trend. Furthermore, the optimal ANN model, with R2 = 0.86 for training and 0.7 for testing, was used to project yield for future analysis. The projected spatial changes in drought sensitivity patterns suggest most of the area will experience an increased sensitivity to drought in the far future (2036–2050). The estimated probability loss under the different risk levels defined by the yield reduction rate was 6.2–55.5%. Furthermore, the spatial distribution of the designed risk index (IR) revealed the districts with the highest probability of vulnerability and showed a probable increase in future vulnerability. [ABSTRACT FROM AUTHOR]

This study employs Cantera code to investigate the laminar burning velocity of different ammonia–hydrogen mixtures. Suitable models were selected from recent literature, and the one with the lowest root mean square error (RMSE) against experimental data was identified through the error function method. Bao mechanism shows an RMSE value of 4.71 at atmospheric pressure for ammonia–hydrogen mixtures, while the Otomo mechanism exhibits an RMSE of 2.11 under high‐pressure conditions. Additionally, sensitivity analysis was conducted to highlight critical reactions within each mechanism, emphasizing distinctions between different pressures. This approach aims to choose the proper mechanism to reduce computational and experimental costs in the early stages of ammonia–hydrogen research. [ABSTRACT FROM AUTHOR]

This study evaluates a capacitated facility location model enhanced with distance constraints for an emergency response problem, ensuring certain neighborhoods remain within an accessible range from facilities following a hurricane. The proposed model takes into account the capacity constraints for drones and vehicles. The model determines optimal locations for facilities and the distribution of supplies across the city. It also specifies which facilities should support the needs of each neighborhood and decides on the appropriate mode of transportation—ground vehicles if possible, or drones if roadways are obstructed. To solve the problem, a Lagrangian relaxation technique is employed, relaxing the constraints related to facility capacity and distance. The numerical results confirm the quality and efficiency of the solutions. The findings indicate that ground transportation is more frequently utilized than drones at each operational facility. A comprehensive set of sensitivity analyses is conducted to examine the impact of various variables and parameters on the solution. [ABSTRACT FROM AUTHOR]

Background: Turning and milling machines (TMMs) have diverse and complex heat sources, which pose challenges to understanding their thermal behavior and thermal critical regions. Methods: A comprehensive procedure was proposed to explore the thermal characteristics of TMMs and identify thermally critical components. This study employed a Taguchi-FEM approach along with sensitivity analysis to evaluate the significant contributing boundary conditions of TMMs to tool center point (TCP) deformation in the X-, Y-, and Z-directions. An ANOVA-driven search was further proposed to identify thermal key points significantly impacting TCP deformation across directions and time intervals. Integrating both methods facilitated the identification of the TMM thermal critical regions. Results: The spindle TCP's thermal critical regions were most significantly influenced by the spindle component, followed by the milling cutter shaft component and then the right side of the base bed. Similarly, for the sub-spindle TCP, the highest impact came from the sub-spindle components, followed by the right side of the base bed and the milling cutter shaft components. Conclusions: The proposed procedure identified the significant regions for assisting evaluation in TMMs and reduced the number of required temperature monitoring points while optimizing their placements to minimize measurement costs. [ABSTRACT FROM AUTHOR]

We determine the sensitivity of a current optimal solution to a combinatorial optimization problem to cost changes in a set of elements. In a recent study, the concept of regular set tolerances has been introduced for a combinatorial optimization problem and for three types of cost functions, namely sum, product, and bottleneck. A regular set tolerance is the supremum amount of cost changes that can be distributed in the most favorable way to multiple elements such as not to change the current optimal solution. In this paper, we introduce an alternative concept, namely the reverse set tolerance , which is a measure of the infimum amount of cost changes to multiple elements such that the current optimal solution becomes non-optimal. We characterize the specific cases in which reverse set upper and lower tolerances have positive values and in which they are infinite. We also show a criterion for the uniqueness of an optimal solution. Furthermore, we present bounds and exact formulas for reverse set upper and lower tolerances using the relation to their corresponding single tolerance counterparts. We discuss the similarities and differences in the results between regular and reverse set tolerances. Finally, we motivate this new concept by analyzing them for special combinatorial optimization problems with important practical applications. [ABSTRACT FROM AUTHOR]

Chimeric antigen receptor T (CAR T) cell therapy has shown remarkable success in treating various leukemias and lymphomas. Cellular kinetic (CK) and pharmacodynamic (PD) behavior of CAR T cell therapy is distinct from other therapies due to its living nature. CAR T CK is typically characterized by an exponential expansion driven by target binding, fast initial decline (contraction), and slow long-term decline (persistence). Due to the dependence of CK on target binding, CK and PD of CAR T therapies are inherently and bidirectionally linked. In this work, we develop a semi-mechanistic model of CAR T CK/PD, incorporating molecular-scale binding, T cell dynamics with multiple phenotypes, and tumor growth and killing. We calibrate this model to published CK and PD data for a CD19-targeting CAR T cell therapy. Using sensitivity analysis, we explore variability in response due to patient- and drug-specific properties. We further explore the impact of tumor characteristics on CAR T-cell expansion and efficacy through individual- and population-level parameter scans. [ABSTRACT FROM AUTHOR]

Groundwater is one of the most important sources of freshwater, contributing significantly for drinking and other domestic activities around the world. However, its quality is deteriorating over time due to overexploitation and anthropogenic activities. Rib watershed is located in the Tana basin (Ethiopia), is intensively cultivated and urbanizing area. Therefore, this study attempted to assess groundwater vulnerability to pollution using the GIS-based Modified DRASTIC Model. The modified DRASTIC model was selected due to it incorporates both hydrogeological parameters and anthropogenic factors for assessment. The necessary data were collected from the field, and downloaded from websites and laboratory experiments (No3-). The results showed that more than 73.24% of the watershed is under medium to high vulnerability. Highly vulnerable areas of the watershed (22.48%) was confined to the Southern parts of the watershed (under built-up and cultivated areas). Based on single parameter sensitivity analysis, its vulnerability was highly influenced by aquifer media (24.1%), net recharge (21.75%), land use/land cover (15.1%), and depth of groundwater table (13.6%). The Modified drastic model was validated based on the observed data of nitrate concentration in groundwater. Based on the observed data of nitrate concentration, high-vulnerable areas were more contaminated than medium and low-vulnerable areas. The modified drastic index has a strong correlation with nitrate concentration with (r = 0.76) and (P < 0.043). Therefore, the result indicated that the area is vulnerable to contamination calling for appropriate groundwater management. Hence, this finding helps to plan and minimize future contamination of groundwater by considering its vulnerability before high-risk activities are allowed. [ABSTRACT FROM AUTHOR]

The Visceral Adiposity Index (VAI) assesses visceral fat and related metabolic risks. However, its precise correlation with sarcopenia is unclear. This study aimed to examine this correlation. A cross-sectional analysis was conducted using NHANES data from 2011 to 2018. To correct VAI skewness, a logarithmic transformation was applied. Multiple covariates were included, and logistic regression was employed to explore the relationship between VAI and sarcopenia. Restricted cubic spline (RCS) and threshold saturation analyses were used to investigate the nonlinear relationship. Subgroup analyses evaluated the effects of various stratification factors. Sensitivity and additive analyses tested the robustness of the findings. The study included 4688 individuals. Participants with sarcopenia had significantly higher VAI values. Logistic regression revealed a significant positive connection between Log VAI and sarcopenia (OR 2.09, 95% CI 1.80–2.43) after adjusting for variables. RCS analysis showed a nonlinear correlation, identifying a breakpoint at VAI = 1.51. To the left of this breakpoint, each unit increase in VAI significantly correlated with a higher likelihood of sarcopenia (OR 2.54, 95% CI 1.74–3.79); to the right, increases in VAI did not significantly affect prevalence. Subgroup analyses suggested VAI as an independent risk factor. Sensitivity and additive analyses confirmed the main findings' robustness. Among American adults, the VAI is significantly associated with sarcopenia, with higher VAI values potentially increasing the prevalence of sarcopenia. Monitoring VAI is critical for early identification of high-risk individuals and interventions to delay or minimize the onset and progression of sarcopenia. [ABSTRACT FROM AUTHOR]

Failure Mode and Effect Analysis (FMEA) is acknowledged as a beneficial instrument for identifying and mitigating system failures. However, the traditional FMEA method has its limitations. For instance, crisp numbers fail to adequately represent the intricate information and cognitive nuances of experts. Additionally, the conventional approach overlooks the significance of weights assigned to FMEA experts and risk factors (RFs). Furthermore, the simplistic ranking of failure modes in traditional FMEA does not accurately reflect priorities. In light of these drawbacks, this paper introduces an innovative, fully data‐driven FMEA method, leveraging a probabilistic uncertain linguistic term sets (PULTSs) environment and the Weighted Aggregates Sum Product Assessment (WASPAS) method. In the assessment process, PULTSs serve as linguistic tools that express probability distribution, allowing for a more reasonable and precise description of information. To address the issue of weights for RFs, the regret theory and Modified CRITIC method are employed. Subsequently, the WASPAS method is applied to determine the risk rankings of failure modes. To illustrate the feasibility and rationality of this novel FMEA model, the paper includes an example involving the production of Lithium‐ion batteries. To emphasize the excellence of the proposed FMEA model, sensitivity and comparative analyses are carried out. [ABSTRACT FROM AUTHOR]

Meta‐analysis is an essential tool to comprehensively synthesize and quantitatively evaluate results of multiple clinical studies in evidence‐based medicine. In many meta‐analyses, the characteristics of some studies might markedly differ from those of the others, and these outlying studies can generate biases and potentially yield misleading results. In this article, we provide effective robust statistical inference methods using generalized likelihoods based on the density power divergence. The robust inference methods are designed to adjust the influences of outliers through the use of modified estimating equations based on a robust criterion, even when multiple and serious influential outliers are present. We provide the robust estimators, statistical tests, and confidence intervals via the generalized likelihoods for the fixed‐effect and random‐effects models of meta‐analysis. We also assess the contribution rates of individual studies to the robust overall estimators that indicate how the influences of outlying studies are adjusted. Through simulations and applications to two recently published systematic reviews, we demonstrate that the overall conclusions and interpretations of meta‐analyses can be markedly changed if the robust inference methods are applied and that only the conventional inference methods might produce misleading evidence. These methods would be recommended to be used at least as a sensitivity analysis method in the practice of meta‐analysis. We have also developed an R package, robustmeta, that implements the robust inference methods. [ABSTRACT FROM AUTHOR]

During patient follow-up in a randomized trial, some deaths may occur. Where death (or noncardiovascular death) is not part of an outcome of interest it is termed a competing risk. Conventional analyses (eg, Cox proportional hazards model) handle death similarly to other censored follow-up. Patients still alive are unrealistically assumed to be representative of those who died. The Fine and Gray model has been used to handle competing risks, but is often used inappropriately and can be misleading. We propose an alternative multiple imputation approach that plausibly accounts for the fact that patients who die tend also to be at high risk for the (unobserved) outcome of interest. This provides a logical framework for exploring the impact of a competing risk, recognizing that there is no unique solution. We illustrate these issues in 3 cardiovascular trials and in simulation studies. We conclude with practical recommendations for handling competing risks in future trials. • In CV trials where competing events are rare or well balanced between groups, they are unlikely to cause major problems. • The Fine and Gray model (although useful in certain specific circumstances) is usually best avoided because it is often used inappropriately and can be misleading. • Where sensitivity analyses for competing risks are warranted, a multiple imputation approach is recommended. • There is no single right answer to the competing risk problem; one needs to seek a consistency of conclusions across a range of assumptions. [ABSTRACT FROM AUTHOR]

The inability to correctly account for unmeasured confounding can lead to bias in parameter estimates, invalid uncertainty assessments, and erroneous conclusions. Sensitivity analysis is an approach to investigate the impact of unmeasured confounding in observational studies. However, the adoption of this approach has been slow given the lack of accessible software. An extensive review of available R packages to account for unmeasured confounding list deterministic sensitivity analysis methods, but no R packages were listed for probabilistic sensitivity analysis. The R package unmconf implements the first available package for probabilistic sensitivity analysis through a Bayesian unmeasured confounding model. The package allows for normal, binary, Poisson, or gamma responses, accounting for one or two unmeasured confounders from the normal or binomial distribution. The goal of unmconf is to implement a user friendly package that performs Bayesian modeling in the presence of unmeasured confounders, with simple commands on the front end while performing more intensive computation on the back end. We investigate the applicability of this package through novel simulation studies. The results indicate that credible intervals will have near nominal coverage probability and smaller bias when modeling the unmeasured confounder(s) for varying levels of internal/external validation data across various combinations of response-unmeasured confounder distributional families. [ABSTRACT FROM AUTHOR]

Introduction: To investigate whether initial tumor burden at biopsy could predict adverse features after radical prostatectomy (RP) in International Society of Urological Pathology (ISUP) 1 prostate cancer (PCa) patients. Methods: This retrospective study was conducted in six referral centers. The cohort included patients with ISUP 1 PCa at systematic and MRI-targeted biopsy. We defined a high tumor burden at biopsy if ≥ 20% of cores were positive. The endpoint of the study was adverse features at RP, defined as ≥ pT3a stage and/or N1 and/or ISUP ≥ 3. Sensitivity analyses were performed to assess associations between different thresholds on biopsy (percentage of positive cores [PPC] ≥ 25%, ≥ 33%, ≥ 50%, bilateral positivity and positive cores > 3) and adverse features. As the number of targeted biopsies sampled may influence the number of positive cores, we used a virtual biopsy model in which all targeted biopsy results were interpreted as a single targeted biopsy. Results: A total of 312 contemporary patients were included. At final pathology, 99 patients (32%) had adverse features. In multivariate logistic regression analysis, there was no statistical association between PPC > 20% and adverse features (OR = 1.22; 95%CI:0.69–2.22, p = 0.5). In sensitivity analysis, tumor burden at biopsy was not associated with the risk of adverse features, regardless of the definition used (all p > 0.05). When we considered a unique virtual targeted biopsy, tumor burden remained not associated with adverse features (all p > 0.05). Conclusions: ISUP 1 PCa tumor burden at biopsy did not predict adverse features in this study, suggesting that it should not be used alone as an exclusion criterion when assessing eligibility for active surveillance. [ABSTRACT FROM AUTHOR]

Heat transfer in the human eyeball, a complex organ, is significantly influenced by various pathophysiological and external parameters. Particularly, heat transfer critically affects fluid behavior within the eye and ocular drug delivery processes. Overcoming the challenges of experimental analysis, this study introduces a comprehensive three‐dimensional mathematical and computational model to simulate the heat transfer in a realistic geometry. Our work includes an extensive sensitivity analysis to address uncertainties and delineate the impact of different variables on heat distribution in ocular tissues. To manage the model's complexity, we employed a very fast model reduction technique with certified sharp error bounds, ensuring computational efficiency without compromising accuracy. Our results demonstrate remarkable consistency with experimental observations and align closely with existing numerical findings in the literature. Crucially, our findings underscore the significant role of blood flow and environmental conditions, particularly in the eye's internal tissues. Clinically, this model offers a promising tool for examining the temperature‐related effects of various therapeutic interventions on the eye. Such insights are invaluable for optimizing treatment strategies in ophthalmology. [ABSTRACT FROM AUTHOR]

Adversarial Risk Analysis (ARA) can be a more realistic and practical alternative to traditional game theoretic or decision theoretic approaches for modeling strategic decision‐making in the presence of an adversary. ARA relies on quantifying the decision‐maker's (DM's) uncertainties about the adversary's strategic thinking, choices and utilities via probability distributions to identify the optimal solution for the DM. ARA solution will be sensitive to the choices of prior distributions used for modelling the expert beliefs. Yet, to date, no mathematical results to characterize the robustness of the ARA solution to the misspecification of one or more prior distributions exist. Prior elicitation is known to be challenging. We present the very first mathematical results on the global robustness of the ARA solution. We use the distorted band class of priors and establish the conditions under which an ordering on the ARA solution can be established when modelling the first‐price sealed‐bid auctions using the nonstrategic play and level‐k$$ k $$ thinking solution concepts. We illustrate these results using numerical examples and discuss further areas of research. [ABSTRACT FROM AUTHOR]

The Windkessel (WK) model is a simplified mathematical model used to represent the systemic arterial circulation. While the WK model is useful for studying blood flow dynamics, it suffers from inaccuracies or uncertainties that should be considered when using it to make physiological predictions. This paper aims to develop an efficient and easy‐to‐implement uncertainty quantification method based on a local gradient‐based formulation to quantify the uncertainty of the pressure waveform resulting from aleatory uncertainties of the WK parameters and flow waveform. The proposed methodology, tested against Monte Carlo simulations, demonstrates good agreement in estimating blood pressure uncertainties due to uncertain Windkessel parameters, but less agreement considering uncertain blood‐flow waveforms. To illustrate our methodology's applicability, we assessed the aortic pressure uncertainty generated by Windkessel parameters‐sets from an available in silico database representing healthy adults. The results from the proposed formulation align qualitatively with those in the database and in vivo data. Furthermore, we investigated how changes in the uncertainty of the Windkessel parameters affect the uncertainty of systolic, diastolic, and pulse pressures. We found that peripheral resistance uncertainty produces the most significant change in the systolic and diastolic blood pressure uncertainties. On the other hand, compliance uncertainty considerably modifies the pulse pressure standard deviation. The presented expansion‐based method is a tool for efficiently propagating the Windkessel parameters' uncertainty to the pressure waveform. The Windkessel model's clinical use depends on the reliability of the pressure in the presence of input uncertainties, which can be efficiently investigated with the proposed methodology. For instance, in wearable technology that uses sensor data and the Windkessel model to estimate systolic and diastolic blood pressures, it is important to check the confidence level in these calculations to ensure that the pressures accurately reflect the patient's cardiovascular condition. [ABSTRACT FROM AUTHOR]

AbstractNuclear energy and renewables, both being low-carbon energy sources that are likely to play an increasingly larger role in energy systems of the future, are increasingly being considered from an integrated standpoint. However, the deployment of baseload nuclear reactors as part of such integrated systems may present some challenges in long-term planning, such as surplus energy generation, inflexibility, and increased energy storage requirements. On the other hand, flexible advanced nuclear reactors can be utilized to tackle the limitations of baseload reactors, such as inflexibility of load-following and high initial capital cost.This paper aims to investigate from techno-economic aspects whether energy modelers should use a flexible nuclear reactor model, specifically a small modular pressurized water reactor technology, or a baseload reactor model of comparable size in long-term integrated nuclear renewable (NR) integrated energy system planning simulations. We mathematically develop an off-grid NR integrated system in a MATLAB environment. An advanced small modular reactor is incorporated in this study and is operated in a baseload and flexible mode of operation for comparative analysis. Two metaheuristic optimization algorithms, pelican optimization algorithm and particle swarm optimization, are employed to obtain and validate the optimal configurations of two different NR systems (e.g. baseload reactor system and flexible reactor system). A sensitivity analysis is conducted to reinforce the key research findings.The results indicate that a flexible nuclear reactor reduces the total annualized cost by a very small amount (roughly 2%) and the energy storage sizing by around 3% for NR integrated system planning, compared to a baseload reactor. This study provides insights into the operational assumptions (e.g. baseload or flexible operation) to consider during the modeling of a long-term planning problem of for NR integrated systems. [ABSTRACT FROM AUTHOR]

Background: Many studies have investigated the association between hypothyroidism and frozen shoulder, but their findings have been inconsistent. Furthermore, earlier research has been primarily observational, which may introduce bias and does not establish a cause-and-effect relationship. To ascertain the causal association, we performed a two-sample bidirectional Mendelian randomization (MR) analysis. Methods: We obtained data on "Hypothyroidism" and "Frozen Shoulder" from Summary-level Genome-Wide Association Studies (GWAS) datasets that have been published. The information came from European population samples. The primary analysis utilized the inverse-variance weighted (IVW) method. Additionally, a sensitivity analysis was conducted to assess the robustness of the results. Results: We ultimately chose 39 SNPs as IVs for the final analysis. The results of the two MR methods we utilized in the investigation indicated that a possible causal relationship between hypothyroidism and frozen shoulder. The most significant analytical outcome demonstrated an odds ratio (OR) of 1.0577 (95% Confidence Interval (CI):1.0057–1.1123), P = 0.029, using the IVW approach. Furthermore, using the MR Egger method as a supplementary analytical outcome showed an OR of 1.1608 (95% CI:1.0318–1.3060), P = 0.017. Furthermore, the results of our sensitivity analysis indicate that there is no heterogeneity or pleiotropy in our MR analysis. In the reverse Mendelian analysis, no causal relationship was found between frozen shoulders and hypothyroidism. Conclusion: Our MR analysis suggests that there may be a causal relationship between hypothyroidism and frozen shoulder. [ABSTRACT FROM AUTHOR]

Background: The health effects of Life's Essential 8 (LE8) on chronic diseases have been disclosed, but its association with hypertension remains unknown. The current study aimed to explore the potential link between 10-year LE8 trajectory and the incidence of hypertension. Methods: LE8 was constructed from four behaviors and four metabolic factors, ranging from 0 to 100. Latent mixture models were used to identify trajectories of LE8 scores during 2006 to 2016. Incident hypertension was diagnosed based on self-reported clinical diagnoses and physical examinations from 2016 to 2020. Cox models were employed to assess the association of LE8 trajectories with hypertension. In addition to incorporating the mean hs-CRP levels from 2006 to 2016, age, sex, monthly income, educational level, and occupation at recruitment were adjusted for as confounding factors. Results: 7500 participants aged 40.28 ± 10.35 years were included in the study, of whom 2907 (38.76%) were women. Five LE8 trajectory patterns were identified. After around four-year follow-up, 667 hypertension events were observed. Compared to the Low-Stable trajectory, the hazard ratios and 95% confidence intervals for the Moderate-Increasing, Moderate-Decreasing, Moderate-Stable, and High-Stable trajectories were 0.51 (0.40, 0.65), 0.81 (0.64, 1.02), 0.45 (0.36, 0.58), 0.23 (0.16, 0.33), respectively. The risk of incident hypertension decreased as participants improved their LE8 status. The robustness of the primary results was confirmed through several sensitivity analyses. Conclusions: LE8 trajectories were associated with the incident hypertension. People who improved their LE8 scores over time experienced a decreased risk of hypertension, even if they started with lower LE8 scores initially. [ABSTRACT FROM AUTHOR]

In physical domains, Beta derivatives are necessary to comprehend wave propagation across various nonlinear models. In this research work, the modified Sardar sub-equation approach is employed to find the soliton solutions of (1+1)-dimensional time-fractional coupled nonlinear Schrödinger model with Beta fractional derivative. These models are fundamental in real-world applications such as control systems, processing of signals, and fiber optic networks. By using this strategy, we are able to obtain various unique optical solutions, including combo, dark, bright, periodic, singular, and rational wave solutions. In addition, We address the sensitivity analysis of the proposed model to investigate the truth that it is extremely sensitive. These studies are novel and have not been performed before in relation to the nonlinear dynamic features of these solutions. We show these behaviors in 2-D, contour 3-D structures across the associated physical characteristics. Our results demonstrate that the proposed approach offers useful results for producing solutions of nonlinear fractional models in application of mathematics and wave propagation in fiber optics. [ABSTRACT FROM AUTHOR]

Background: Accumulating evidence has highlighted that lncRNA ABHD11-AS1 plays an essential role in tumorigenesis and is expected to become a new predictive biomarker and ideal target for cancer therapy, whereas some of their findings are conflicting due to the relatively small sample size of individual studies. Thus, this meta-analysis aimed to quantitatively ascertain the association of ABHD11-AS1 with diverse human malignancies. Methods: Eight databases were comprehensively screened for relevant articles on January 1, 2024. The significance of ABHD11-AS1 in malignancies was determined by odds ratios (ORs) or hazard ratios (HRs) with corresponding 95% confidence interval (CI). Subgroup analyses and sensitivity analyses were applied to verify the reliability and robustness of the pooled results. Simultaneously, the GEPIA2021 and UCSC Xena databases were applied to further strengthen the results. Results: Fourteen clinical studies comprising eight kinds of malignancies and 1215 malignancy cases were enrolled into this meta-analysis. The pooled results showed that increased ABHD11-AS1 expression was remarkably associated with lymph node metastasis (OR = 2.73, 95%CI [1.97, 3.77], I2 = 0%, p < 0.00001), advanced tumor stage (OR = 3.14, 95%CI [2.34, 4.21], I2 = 39%, p < 0.00001), and unfavorable overall survival (OS) (HR = 1.81, 95%CI [1.58, 2.06], I2 = 0%, p < 0.00001). Subgroup analyses and sensitivity analyses indicated that the pooled results were reliable and robust. Additionally, ABHD11-AS1 was significantly increased in eight kinds of malignancies according to the validation of the GEPIA2021 database. Meanwhile, the UCSC Xena databases further revealed that elevated ABHD11-AS1 expression was significantly associated with poor prognosis as assessed by progression free interval (PFI), disease free interval (DFI), disease specific survival (DSS), and OS. Conclusions: Current evidence supports the association of elevated ABHD11-AS1 expression with poor prognosis. Thereby, ABHD11-AS1 may be considered as a promising biomarker to screen cancer and predict malignancy prognosis. Also, there is a necessity for larger-scale multicenter studies with uniform study protocols from different countries to further validate the conclusions. [ABSTRACT FROM AUTHOR]

Introduction Computational head injury models are promising tools for understanding and predicting traumatic brain injuries. However, most available head injury models are "average" models that employ a single set of head geometry (e.g. 50th-percentile U.S. male) without considering variability in these parameters across the human population. A significant variability of head shapes exists in U.S. Army soldiers, evident from the Anthropometric Survey of U.S. Army Personnel (ANSUR II). The objective of this study is to elucidate the effects of head shape on the predicted risk of traumatic brain injury from computational head injury models. Materials and Methods Magnetic resonance imaging scans of 25 human subjects are collected. These images are registered to the standard MNI152 brain atlas, and the resulting transformation matrix components (called head shape parameters) are used to quantify head shapes of the subjects. A generative machine learning model is used to generate 25 additional head shape parameter datasets to augment our database. Head injury models are developed for these head shapes, and a rapid injurious head rotation event is simulated to obtain several brain injury predictor variables (BIPVs): Peak cumulative maximum principal strain (CMPS), average CMPS, and the volume fraction of brain exceeding an injurious CMPS threshold. A Gaussian process regression model is trained between head shape parameters and BIPVs, which is then used to study the relative sensitivity of the various BIPVs on individual head shape parameters. We distinguish head shape parameters into 2 types: Scaling components |${T_{xx}}$|⁠ , |${T_{yy}}$|⁠ , and |${T_{zz}}$| that capture the breadth, length, and height of the head, respectively, and shearing components (⁠|${T_{xy}},{T_{xz}},{T_{yx}},{T_{yz}},{T_{zx}}$|⁠ , and |${T_{zy}}$|⁠) that capture the relative skewness of the head shape. Results An overall positive correlation is evident between scaling components and BIPVs. Notably, a very high, positive correlation is seen between the BIPVs and the head volume. As an example, a 57% increase in peak CMPS was noted between the smallest and the largest investigated head volume parameters. The variation in shearing components |${T_{xy}},{T_{xz}},{T_{yx}},{T_{yz}},{T_{zx}}$|⁠ , and |${T_{zy}}$| on average does not cause notable changes in the BIPVs. From the Gaussian process regression model, all 3 BIPVs showed an increasing trend with each of the 3 scaling components, but the BIPVs are found to be most sensitive to the height dimension of the head. From the Sobol sensitivity analysis, the |${T_{zz}}$| scaling parameter contributes nearly 60% to the total variance in peak and average CMPS; |${T_{yy}}$| contributes approximately 20%, whereas |${T_{xx}}$| contributes less than 5%. The remaining contribution is from the 6 shearing components. Unlike peak and average CMPS, the VF-CMPS BIPV is associated with relatively evenly distributed Sobol indices across the 3 scaling parameters. Furthermore, the contribution of shearing components on the total variance in this case is negligible. Conclusions Head shape has a considerable influence on the injury predictions of computational head injury models. Available "average" head injury models based on a 50th-percentile U.S. male are likely associated with considerable uncertainty. In general, larger head sizes correspond to greater BIPV magnitudes, which point to potentially a greater injury risk under rapid neck rotation for people with larger heads. [ABSTRACT FROM AUTHOR]

Introduction Military personnel frequently experience stressful, morally challenging situations that can lead to posttraumatic stress disorder (PTSD). The relationships between moral identity, transgressive acts, and symptoms related to posttraumatic stress disorder (i.e. posttraumatic stress symptoms; PTSS) among U.S. Army Soldiers were assessed. Materials and Methods A convenience cohort of 1,547 soldiers completed a survey assessing moral identity and PTSS before deployment. A subset of 505 soldiers completed another survey assessing transgressive acts and PTSS during deployment. Exploratory and confirmatory factor analyses were conducted to determine the underlying factor structure of the measures of moral identity and transgressive acts. Generalized linear modeling, robustness checks, and sensitivity analysis were used to evaluate the predictive relationships. Results Moral identity did not directly predict PTSS during deployment. However, self-attributed and betrayal-based transgressive acts were significant predictors. Specifically, self-attributed transgressive acts and PTSS were moderated by internalized moral identity; individuals with a higher internalized moral identity experienced more severe PTSS following self-attributed transgressive acts. These findings were consistent across various model checks, including covariate adjustments, data imputation, and the application of a data quality filter. Conclusions The study highlights the significant role of self-attributed moral transgressions during deployment in the development of PTSS among military service members—especially in those with a strong internalized moral identity. This finding suggests a "target of opportunity" for the development of intervention strategies that mitigate PTSS by addressing the moral dimensions of military service. [ABSTRACT FROM AUTHOR]

Background: Rectal indomethacin reduces pancreatitis following endoscopic retrograde cholangiopancreatography (ERCP). However, there is insufficient evidence regarding its added benefits in patients already receiving prophylactic pancreatic stenting. Our goal was to evaluate the impact of indomethacin in high-risk patients undergoing pancreatic stenting. Methods: A cohort study was conducted on all patients who underwent the rescue cannulation technique for challenging bile duct cannulation (selected high-risk patients). Patients were split into two groups based on the prophylaxis method for post-ERCP pancreatitis (PEP): one receiving a combination of indomethacin and pancreatic stenting, while the other received pancreatic stenting alone. Comparative analyses were carried out on PEP, hyperamylasemia, gastrointestinal bleeding, and postoperative hospital stay among post-ERCP pancreatitis patients. Results: Between November 2017 and May 2023, a total of 607 patients with native papillae were enrolled, with 140 grouped into the indomethacin plus stent group and 467 into the stent alone group. The overall PEP rate was 4.4% in the entire cohort, with no statistical differences observed between the groups in terms of PEP rates (P = 0.407), mild PEP (P = 0.340), moderate to severe PEP (P = 1.000), hyperamylasemia (P = 0.543), gastrointestinal bleeding (P = 0.392), and postoperative hospital stay (P = 0.521). Furthermore, sensitivity analysis using multivariable analysis also validated these findings. Conclusions: Indomethacin did not reduce the incidence or severity of PEP in high-risk patients who routinely received prophylactic pancreatic stent placement. Therefore, the additional administration of rectal indomethacin to further mitigate PEP appears to be not necessary. [ABSTRACT FROM AUTHOR]

The efficiency of mixed convection peristaltic flow can be investigated through pressure rise per wavelength (Δ P λ) and frictional forces ( F λ ). The main aim of the present study is to discover the sensitivity analysis of non-Newtonian fluids using the Bingham plastic fluid model. In order to achieve this objective, we have empirically modeled the pressure rise per wavelength (Δ P λ) and frictional forces ( F λ ) as a function varying with leading parameters of problem. The flow problem is governed by three coupled nonlinear partial differential equations. They are reduced to nonlinear coupled ordinary differential equations by using the long wavelength and low Reynolds number approximations. They are solved numerically using MATLAB built-in routine bvp4c to analyze the sensitivity of pressure rise per wavelength ( Δ P λ ) and frictional forces ( F λ ). We first derive the empirical model among each of responses Δ P λ and F λ and physical parameters which govern the flow using response surface methodology. The goodness of fit of empirical model is decided on the basis of coefficient of determination ( R 2 ) obtained from the analysis of variance (ANOVA). The coefficients of determination ( R 2 ) are 99.78% both for Δ P λ and F λ . The higher values of R 2 determine the goodness of fit of empirical model. No correlation has been developed to optimize Δ P λ and F λ in peristaltic flow for Bingham plastic fluids using RSM. The results of sensitivity analysis revealed that Δ P λ and F λ are most sensitive to flow rate (q) at all levels such as low (− 1), medium (0) and high (+ 1). The sensitivity of Δ P λ to Bingham number (Bn) shows a distinct behavior with varying levels of flow rate (q). At low level (− 1) of flow rate (q), the sensitivity is positive, and at high level (+ 1) of flow rate (q), the sensitivity becomes negative. Conversely, the sensitivity of F λ to Bingham number (Bn) at low to high level of flow rate (q). [ABSTRACT FROM AUTHOR]

Background: We assessed the prevalence of complications from percutaneous ethanol injection (PEI) for benign and cystic thyroid nodules (CTNs) and their management. Methods: We conducted a systematic review with meta-analysis of data from published observational studies on PEI of CTNs. We also included unpublished retrospectively collected data on complications after PEI from all consecutive patients with cytologically benign CTNs who underwent PEI at the Unit of Endocrinology and Metabolic Diseases, AOU University of Campania Luigi Vanvitelli (Naples, Italy) between June 1, 2021, and March 31, 2024. A random effects meta-analysis was performed on the prevalence rate data. Pooled prevalence data were presented with confidence intervals (CIs). The I2 statistic index was used to quantify the heterogeneity. The details of the complications and the management were qualitatively described. Results: The literature search yielded 1189 studies, of which 48 studies were included in the systematic review and meta-analysis, in addition to our institutional experience (3670 CTNs in total). The overall quality of each included study was judged as fair. The prevalence of "Overall" complications of PEI was 32% ([CI 25–40%], I2 92.7%, 967 of 3195 thyroid nodules [TNs]). The prevalence of "Minor" complications of PEI was 32% ([CI 25–40%], I2 92.7%, 952 of 3195 TNs). The prevalence of "Major" complications of PEI was 2% ([CI 1–2%], I2 0%, 22 of 3670 TNs). Sensitivity analyses did not modify the results. The pooled prevalence rate of local pain was 21% (CI [16–27] I2 90.3). Local pain was typically transient and mild, sometimes moderate, and requiring analgesics for few days. The pooled prevalence rate of dysphonia was 1% (CI [1–2], I2 0). Dysphonia was transient and could last from several hours to 12 months after PEI. Conclusions: Complications of PEI for benign and CTNs are relatively common, but most are minor and usually transient, not requiring treatment. Dysphonia was a major complication, but it was uncommon and transient. PEI for CTNs could be considered a generally safe technique. [ABSTRACT FROM AUTHOR]

Shapley value is originally a concept in econometrics to fairly distribute both gains and costs to players in a coalition game. In the recent decades, its application has been extended to other areas such as marketing, engineering and machine learning. For example, it produces reasonable solutions for problems in sensitivity analysis, local model explanation toward the interpretable machine learning, node importance in social network, attribution models, etc. However, it could be very expensive to compute the Shapley value. Specifically, in a d-player coalition game, calculating a Shapley value requires the evaluation of d ! or 2 d marginal contribution values, depending on whether we are taking the permutation or combination formulation of the Shapley value. Hence, it becomes infeasible to calculate the Shapley value when d is reasonably large. A common remedy is to take a random sample of the permutations to surrogate for the complete list of permutations. We find an advanced sampling scheme can be designed to yield much more accurate estimation of the Shapley value than the simple random sampling (SRS). Our sampling scheme is based on combinatorial structures in the field of design of experiments (DOE), particularly the order-of-addition experimental designs for the study of how the orderings of components would affect the output. We show that the obtained estimates are unbiased, and can sometimes deterministically recover the original Shapley value. Both theoretical and simulations results show that our DOE-based sampling scheme outperforms SRS in terms of estimation accuracy. Surprisingly, it is also slightly faster than SRS. Lastly, real data analysis is conducted for the C. elegans nervous system and the 9/11 terrorist network. for this article are available online. [ABSTRACT FROM AUTHOR]

The method of difference-in-differences (DID) is widely used to study the causal effect of policy interventions in observational studies. DID employs a before and after comparison of the treated and control units to remove bias due to time-invariant unmeasured confounders under the parallel trends assumption. Estimates from DID, however, will be biased if the outcomes for the treated and control units evolve differently in the absence of treatment, namely if the parallel trends assumption is violated. We propose a general identification strategy that leverages two groups of control units whose outcomes relative to the treated units exhibit a negative correlation, and achieves partial identification of the average treatment effect for the treated. The identified set is of a union bounds form that involves the minimum and maximum operators, which makes the canonical bootstrap generally inconsistent and naive methods overly conservative. By using the directional inconsistency of the bootstrap distribution, we develop a novel bootstrap method to construct confidence intervals for the identified set and parameter of interest when the identified set is of a union bounds form, and we theoretically establish the asymptotic validity of the proposed method. We develop a simple falsification test and sensitivity analysis. We apply the proposed strategy for bracketing to study whether minimum wage laws affect employment levels. for this article are available online. [ABSTRACT FROM AUTHOR]

In this work, we propose an approach for assessing sensitivity to unobserved confounding in studies with multiple outcomes. We demonstrate how prior knowledge unique to the multi-outcome setting can be leveraged to strengthen causal conclusions beyond what can be achieved from analyzing individual outcomes in isolation. We argue that it is often reasonable to make a shared confounding assumption, under which residual dependence amongst outcomes can be used to simplify and sharpen sensitivity analyses. We focus on a class of factor models for which we can bound the causal effects for all outcomes conditional on a single sensitivity parameter that represents the fraction of treatment variance explained by unobserved confounders. We characterize how causal ignorance regions shrink under additional prior assumptions about the presence of null control outcomes, and provide new approaches for quantifying the robustness of causal effect estimates. Finally, we illustrate our sensitivity analysis workflow in practice, in an analysis of both simulated data and a case study with data from the National Health and Nutrition Examination Survey (NHANES). for this article are available online. [ABSTRACT FROM AUTHOR]

To be convincing, an observational or nonrandomized study of causal effects must demonstrate that its conclusions cannot be readily explained by a small unmeasured bias in the way individuals were assigned to treatment or control. The Bahadur relative efficiency of a sensitivity analysis compares the performance of different test statistics or different research designs when sensitivity to unmeasured bias is appraised: better statistics and better designs exhibit insensitivity to larger biases. Bahadur efficiency is relevant here because, unlike Pitman efficiency, it can depict efficiency with an effect that is not trivially small: every trivially small treatment effect is sensitive to trivially small biases. The Bahadur efficiency of a sensitivity analysis has been used by various authors in the simple case of matched pairs, but the technical issues are more complex in the case of blocks larger than pairs, and they are developed here for the first time. Choosing a better test statistic for a block design, or choosing a better block size—larger than pairs—can result in enormous increases in the efficiency of a sensitivity analysis. An adaptive choice of test statistic can ensure the better Bahadur efficiency of two competing statistics. An R package weightedRank implements the methods, contains the example and reproduces its analysis. for this article are available online. [ABSTRACT FROM AUTHOR]

This paper proposes a Topology Optimization (TO) method for the design of microstructures within All-Solid-State Batteries (ASSBs), using the homogenization method. ASSBs have attracted significant attention because of their possibilities to surpass the problems of conventional liquid lithium-ion batteries regarding safety, energy density, and longevity. To improve the performance of ASSBs, many experimental research results based on costly trial-and-error approaches have previously been presented. In order to mitigate the cost and time involved in trial-and-error, several numerical approaches have also been proposed to deal with the multiple materials in various batteries. TO is one of the most flexible structural optimization methods that can allow topological changes as well as boundary changes, and has the potential to provide high-performance structures. TO methods have also been successfully applied to the design of several kinds of batteries, such as Solid Oxide Fuel Cells (SOFC) and Redox flow batteries. However, to the best of our knowledge, research concerning the structural optimization of ASSBs has not been found. In this paper, we propose the TO method targeting microstructure designs of ASSBs using the homogenization method to associate microstructural configurations and macroscopic properties. First, we define a new objective function that can appropriately evaluate the performance of ASSBs and can reduce computational costs in the optimization process. Next, the design sensitivities addressing the time-dependent and nonlinear aspects of the problem are derived using the Lagrange and continuous adjoint methods. Finally, we provide several numerical results under various conditions, including different C-rates, to confirm the validity and efficiency of our proposed method. [ABSTRACT FROM AUTHOR]

Non-toroidal-shaped primary pass-through protection current transformers (CTs) are used to measure high currents. Their design provides them with a big airgap that allow the passing of several cables per phase though them, which is the main advantage versus toroidal types, as the number of CTs required to measure the whole phase current is drastically reduced. The cables passed through the transformer window can be in several positions. As the isolines of the magnetic field generated by the primary currents are centered in the cables, if these cables are not centered in the transformer window, then the magnetic field will be non-uniform along the transformer core. Consequently, local saturations can appear if the cables are not properly disposed, causing the malfunction of the CT. In this paper, the performance of a non-toroidal-shaped protection CT is studied. This research is focused on the influence of the cable position on possible partial saturations of the CT when it is operating near to its accuracy limit. Depending on the cable position, the ratio of the primary and secondary currents can depart from the assigned ratio. The validation of this phenomenon was carried out via finite element analysis (FEA), showing that partial transformer core saturations appear in areas of the magnetic core close to the cable. By applying FEA, the admissible accuracy region for cable positioning inside the CT is also delimited. Finally, the simulation results are ratified with experimental tests performed in non-toroidal protection CTs, varying the primary cables' positions, which are subjected to currents up to 5 kA, achieving satisfactory results. From this analysis, installation recommendations are given. [ABSTRACT FROM AUTHOR]