Search

The robotic live-cube compact storage system can provide a higher storage capacity and more flexible throughput than other automated warehousing systems. The extant studies usually fix the input/output point at the bottom-left of such storage systems and have not investigated other configurations of the input/output point. In this study, we propose six policies for the configuration of the input/output point and evaluate their performance by using an analytical model and simulations. A sensitivity analysis is carried out by considering different shape-related factors, system heights, and energy consumption to explore the optimal configuration of the input/output point. For most input/output point policies, a shape factor (length divided by depth) of 0.5 results in good system performance in terms of retrieval time. When using optimal system dimension, the input/output point located in the upper-middle part along the length and depth performs best compared with other policies. Specifically, this configuration reduces the retrieval time by 40.23% and energy consumption by 42.31% compared with the bottom-left corner input/output point. We find that there exists a trade-off among the cost of setup, number of empty grids, and retrieval time when varying the number of input/output points. [ABSTRACT FROM AUTHOR]

The Fayans energy density functional (EDF) has been very successful in describing global nuclear properties (binding energies, charge radii, and especially differences of radii) within nuclear density functional theory. In a recent study, supervised machine learning methods were used to calibrate the Fayans EDF. Building on this experience, in this work we explore the effect of adding isovector pairing terms, which are responsible for different proton and neutron pairing fields, by comparing a 13D model without the isovector pairing term against the extended 14D model. At the heart of the calibration is a carefully selected heterogeneous dataset of experimental observables representing ground-state properties of spherical even-even nuclei. To quantify the impact of the calibration dataset on model parameters and the importance of the new terms, we carry out advanced sensitivity and correlation analysis on both models. The extension to 14D improves the overall quality of the model by about 30%. The enhanced degrees of freedom of the 14D model reduce correlations between model parameters and enhance sensitivity.

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]

Lot splitting is one of the effective technologies of time-based strategy and has been widely studied in a variety of production environments. Nevertheless, the literature on its application in seru production has been highly scarce until now. Seru system is composed of simple equipment and multi-skilled workers, which can be quickly converted from the traditional assembly line to seru units. As an innovative production mode, seru production inevitably allows applying lot splitting in the real world. Therefore, a multi-objective model is studied for line–cell conversion with lot splitting, aiming at determining the trade-off among makespan, inter-seru system balancing, and intra-seru system balancing. Due to the proposed model's NP-hard nature, an improved NSGA-II was developed to solve it. Finally, extensive numerical simulations are conducted. Compared to no lot splitting, lot splitting is improved by 4.2% and 3.7% in terms of inter-seru system balancing and makespan respectively. The better efficiency and effectiveness of the improved NSGA-II are proved by comparisons with other state-of-the-art algorithms. Additionally, a sensitivity analysis is conducted to ascertain the degree of contribution of the model parameters towards the value of objectives, which provides management implications to support the decision-making of seru production for the enterprise. [ABSTRACT FROM AUTHOR]

We considered two periodic review perishable inventory systems. The first system is controlled by a periodic review (T , S) policy, where orders are triggered at each review period T and when the stock is less than S. The second one is controlled by a periodic review (s , S) policy, where orders are triggered only if the stock level is below s. For both models, the lifetime of each product was assumed to follow a general distribution, whereas the lead time was constant. The demands followed a Poisson process, and all unsatisfied demand was lost. Using an approximate solution of the steady-state probabilities of the inventory level, we obtained a closed form of cost expression. Next, we conducted a simulation study to identify our solution accuracy. Finally, we performed a sensitivity analysis to evaluate the effect of the lifetime variation and cost parameters on the best policy and its cost. The obtained results were close to those of the optimal policy with an average accuracy of 3.7% for the periodic review (T , S) policy and 3.8% for the periodic review (s , S) policy. For the worst cases, where the accuracy is higher than 7%, we provided an analytical approach to improve the system accuracy. [ABSTRACT FROM AUTHOR]

Pulmonary hypertension is a cardiovascular disorder manifested by elevated mean arterial blood pressure (>20 mmHg) together with vessel wall stiffening and thickening due to alterations in collagen, elastin, and smooth muscle cells. Hypoxia-induced (type 3) pulmonary hypertension can be studied in animals exposed to a low oxygen environment for prolonged time periods leading to biomechanical alterations in vessel wall structure. This study introduces a novel approach to formulating a reduced order nonlinear elastic structural wall model for a large pulmonary artery. The model relating blood pressure and area is calibrated using ex vivo measurements of vessel diameter and wall thickness changes, under controlled pressure conditions, in left pulmonary arteries isolated from control and hypertensive mice. A two-layer, hyperelastic, and anisotropic model incorporating residual stresses is formulated using the Holzapfel-Gasser-Ogden model. Complex relations predicting vessel area and wall thickness with increasing blood pressure are derived and calibrated using the data. Sensitivity analysis, parameter estimation, subset selection, and physical plausibility arguments are used to systematically reduce the 16-parameter model to one in which a much smaller subset of identifiable parameters is estimated via solution of an inverse problem. Our final reduced one layer model includes a single set of three elastic moduli. Estimated ranges of these parameters demonstrate that nonlinear stiffening is dominated by elastin in the control animals and by collagen in the hypertensive animals. The pressure-area relation developed in this novel manner has potential impact on one-dimensional fluids network models of vessel wall remodeling in the presence of cardiovascular disease.

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]

With the development of information technology, big data analysis has been highlighted in operations and management. From this viewpoint, this paper studies a buyer's optimal purchasing decisions in combined procurement. For combined procurement, a buyer first signs a long-term contract with a supplier to guarantee a certain level of commodity supply, and can then replenish the commodities from the spot market if necessary. The optimal purchasing quantity in the long-term contract is examined to maximise the buyer's expected profit from combined procurement. In view of the imperfectness in the spot market, the spot trading liquidity is considered in the buyer's optimal purchasing decision. The properties of the two optimal purchasing quantities are examined and several interesting results are obtained. For example, it is illustrated that a buyer's expected profit may decrease in the spot capacity, a result that has never appeared in the existing literature, which reveals the importance of a buyer's optimal order decision in the presence of spot replenishment. Numerical results and sensitivity analysis are performed to verify the results. Management insights are suggested for a buyer's optimal purchasing decisions in combined procurement with a long-term contract and spot replenishment. [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]

Objective: Whether the role of post-traumatic stress disorder (PTSD) on type 2 diabetes (T2D) is mediated by obesity or other mediating factors is controversial. This study was designed to assess the impact of PTSD on genetic susceptibility to T2D and mediating factors. Methods: The datasets for PTSD, T2D, obesity, hypertension, hyperlipidemia, smoking status, and alcohol consumption were obtained from genome-wide association studies. Mendelian randomization (MR) was used to assess exposureoutcome causality, and inverse variance weighted was used as the primary tool for MR analysis. MR-Egger intercept, Cochran's Q, and leave-one-out sensitivity analysis were employed to assess horizontal pleiotropy, heterogeneity, and robustness, respectively. Results: The MR analysis showed that PTSD was associated with increased genetic susceptibility to T2D (OR, 1.036; 95% CI, 1.008-1.064; p = 0.011), obesity (OR, 1.033; 95% CI, 1.016-1.050; p < 0.001), and hypertension (OR, 1.002; 95% CI, 1.000-1.003; p = 0.015), but not not with genetic susceptibility to hyperlipidemia, alcohol consumption, and smoking status (p = 0.05). Mediated effect analysis showed that PTSD increased genetic susceptibility to T2D by increasing genetic susceptibility to obesity and hypertension, with obesity accounting for 9.51% and hypertension accounting for 2.09%. MR-Egger intercept showed no horizontal pleiotropy (p = 0.05). Cochran's Q showed no heterogeneity (p = 0.05). Leave-one-out sensitivity analysis showed that the results were robust. Conclusion: This MR analysis suggests that PTSD increases the risk of T2D and that this effect is partially mediated by obesity and hypertension. Active prevention and treatment of PTSD can help reduce the risk of T2D. [ABSTRACT FROM AUTHOR]

Background: While the hepatic enzymes Aspartate Aminotransferase (AST) and Alanine Aminotransferase (ALT) are crucial for liver function, their role in Spontaneous Abortion (SA) has not been thoroughly explored. Utilizing Mendelian Randomization (MR), this study aims to clarify the putative causal relationship between AST/ALT levels and SA. Methods: Genome-wide association study (GWAS) summary data for SA (finnb-O15_ABORT_SPONTAN), AST (ukb-d-30650_raw), and ALT (ukb-d-30620_raw) were acquired from the Integrative Epidemiology Unit OpenGWAS database. Bidirectional MR analysis was conducted using MR-Egger, Weighted Median, Simple Mode, Weighted Mode, and Inverse Variance Weighted (IVW) algorithms, and the robustness of MR results was assessed through sensitivity analyses including Heterogeneity, Horizontal Pleiotropy, and Leave-One-Out (LOO) tests. The causal role of AST and ALT's coaction in SA was explored via multivariable MR (MVMR) analysis. Results: The MR results via the IVW algorithm revealed a causal relation between both AST and ALT and SA (AST: P = 0.013; ALT: P = 0.017), identifying them as risk factors for SA (AST: odd ratio (OR) = 1.019; ALT: OR = 1.012). Sensitivity analysis substantiated the reliability of these results. Moreover, not notably causality was found between SA and AST/ALT (P > 0.05). Through MVMR analysis, AST and ALT demonstrated functional complementarity in the occurrence of SA, attributable to counterbalanced causalities (AST: P = 0.128; ALT: P = 0.899). Conclusion: The study substantiates a causal linkage between transaminase levels and SA, enhancing our understanding of their biological interaction and the regulatory mechanisms at play. These insights could have implications for identifying novel biomarkers and therapeutic targets for SA. [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]

In recent times, various real word problems including infectious diseases, engineering problems, and chemical processes, etc are modelled by using differential equations with fractals and fractional orders. The mentioned area provides a powerful tool to investigate the aforesaid problems from different perspectives like analysis, numerical investigation, and qualitative theory. Similarly, one of the global issue is devoted to re-infection of viral diseases like COVID-19. The said behaviour has produced very saviour impact on human life worldwide. Also, this is very critical for health system of a country as well as for its economics situation. To study the aforesaid phenomenon of re-infection, a hybrid type fractal–fractional three compartments model is formulated. The basic results related to boundedness, positivity, equilibrium points, basic reproductive number, and sensitivity analysis of the basic threshold are included in the theoretical aspect. Additionally, the Volterra–Lyapunov approach is used to establish both local and global stability analysis under fractals and fractional derivative. Furthermore, utilizing techniques from numerical functional analysis and fixed point theory, the existence and Hyers–Ulam stability of the solutions are also examined. A numerical algorithm based on Lagrange's interpolation polynomials is extended to simulate the results of various compartments. Graphical interpretations corresponding to different fractals–fractional order values are presented. For verification of our numerical results, a comparison is provided between the real and simulated data. Here it should be kept in mind that the differential operator of fractals–fractional order is considered with Mittag-Leffler kernel. The mentioned operator is more general among the available fractals fractional differential operators in literature. [ABSTRACT FROM AUTHOR]

Objective: The causal relationship and mechanisms between lipids and glioblastoma (GBM) remain unclear. This study aims to investigate the independent causal relationship between liposomal phosphatidylcholine 16:0_22:6 (PC16) and GBM, and to identify the potential mediating role of the inflammatory factor-fibroblast growth factor 21(FGF21). Methods: Utilizing summary statistics from genome-wide association studies (GWAS) of lipids (179 types in 7174 Finnish individuals), GBM (243 cases and 287137 controls), and inflammatory factors (91 types in 14824 European individuals), a two-sample Mendelian Randomization (MR) approach was employed to establish the causal link between liposomal PC16 and GBM. Additionally, a two-step MR method was used to quantify the proportion of the causal effect of PC16 on GBM that is mediated by the inflammatory factor FGF21. Results: MR analyses revealed a strong causal relationship between PC16 and GBM (OR=1.72, 95% CI: 1.11-2.68, P=0.016), but no reverse causality was observed from GBM to PC16 (OR=1.01, 95% CI: 0.99-1.02, P=0.38). Mediation analysis showed a strong causal relationship between PC16 and the FGF21 (OR = 0.94, 95% CI: 0.89-0.99, P=0.018) as well as between FGF21 and GBM (OR = 0.42, 95% CI: 0.25-0.71, P=0.001), with the mediation effect accounting for 9.78% of the total effect. This suggests that the causal relationship between PC16 and GBM is likely mediated by the intermediary factor FGF21. No evidence of pleiotropy was found in the sensitivity analysis of these positive results. Conclusion: In summary, the findings of this study suggest that liposomal PC16 may increase the risk of GBM occurrence, and FGF21 may play a significant mediating role in this causal relationship. [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]

Developing efficient and secure image encryption techniques for transmitting multiple images has become crucial due to the inadequacy of single-image encryption techniques in handling the increasing volume of big data over unprotected networks. This paper introduces a novel multiple-image encryption (MIE) technique that utilizes mixed image elements in conjunction with the RSA cryptosystem, fractional discrete cosine transform (FrDCT), and Henon chaotic map (HCM). To encrypt k images together, the first step involves making three big images B 1 , B 2 and B 3 from these k images using matrix theory. The three images B 1 , B 2 and B 3 are converted into three indexed images I 1 , I 2 , and I 3 by extracting their color maps. Indexed images I 1 , I 2 and I 3 are then treated as a single RGB image by taking I 1 as a red (R) component, I 2 as a green (G) component and I 3 as a blue (B) component. The RSA cryptosystem is then applied on each component individually, followed by FrDCT and HCM to enhance security and key space. The resulting encrypted image is a single-channel real-valued image that is easy to display, store, and transmit over an unsecured network. The suggested technique offers multi-layer security in frequency, time and coordinate domains. Private keys, their arrangements and parameter positions are critical for decryption. Simulation analysis supports the robustness and effectiveness of the proposed technique. The sensitivity analysis demonstrates its extreme sensitivity towards private keys and their arrangement. Statistical analysis, including measures such as MSE, SSIM, PSNR, NPCR, UACI, entropy analysis, correlation coefficient, histogram analysis, computational complexity and comparison analysis confirm the effectiveness and viability of the introduced algorithm. [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]