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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]

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]

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 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]

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]

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]

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]

The model divides the population into five sub-populations of humans and three vectors. Boundedness, non-negativity, and continuous dependence of solutions on initial data prove their well-posedness. An explicit expression of the basic reproduction number and stability analysis of equilibria offer insights into disease mitigation and persistence. The Disease-Free Equilibrium is globally stable when the basic reproduction number is below 1, while the Endemic Equilibrium is globally stable when it is greater than 1. Sensitivity analysis identifies key mitigation parameters, and bifurcation analysis reveals the bifurcation parameter. Optimal control strategies are identified through the maximum principle, and numerical simulations exemplify the analytical results. [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]

Introduction: Observational studies suggest associations between gut microbiota and polymyositis (PM) and dermatomyositis (DM), but causal relationships are unclear. We investigate the causal effects of gut microbiota on PM and DM, providing insights hoping to provide insights for future treatment and prevention. Methods: Summary statistics of gut microbiota were obtained from a multi-ethnic Genome Wide Association Studies (GWAS) meta-analysis, including 119 taxa from 18,340 Europeans. PM/DM statistics were extracted from GWAS analyses. Mendelian randomization (MR) with IVW, MR-Egger, and weighted median methods was performed. Sensitivity analyses addressed heterogeneity and pleiotropy. Of the 119 bacterial genera studied, six showed causal links. Results: Alloprevotella (OR: 3.075, 95% CI: 1.127-8.386, p = 0.028), Ruminococcaceae UCG003 (OR: 4.219, 95% CI: 1.227-14.511, p = 0.022), Dialister (OR: 0.273, 95% CI: 0.077-0.974, p = 0.045) were associated with PM. Anaerotruncus (OR: 0.314, 95% CI: 0.112-0.882, p = 0.028), Ruminococcaceae UCG002 (OR: 2.439, 95% CI: 1.173-5.071, p = 0.017), Sutterella (OR: 3.392, 95% CI: 1.302-8.839, p = 0.012) were related to DM. Sensitivity analyses validated these associations. Discussion: We establish causal relationships between Ruminococcaceae, Sutterella, Anaerotruncus with DM, Alloprevotella, Ruminococcaceae UCG003, and Dialister with PM. Common microbiota, like Ruminococcaceae, have significant clinical implications. These findings open up greater possibilities for the gut microbiota to contribute to the development of PM/DM and for future monitoring of the gut microbiota in patients with PM/DM. [ABSTRACT FROM AUTHOR]

Natural gas hydrates (NGH) are considered a very promising source of clean energy due to their widespread distribution, high energy density, and pure combustion products. Currently, there are few studies on NGH reservoir well testing, and the models are often idealistic, lacking practical guidance for field application. In this paper, a well-testing model for partially perforated wells in the NGH reservoir is proposed, which takes into account the dynamic decomposition of hydrates. This model can simulate the performance of the perforated NGH well with a dynamic dissociation interface, which divides the reservoir into decomposed and undecomposed regions. Governing equations in cylindrical coordinates are formulated to depict fluid flow. Moving boundaries and dissociation coefficients are incorporated to describe the solid-to-gas transition within hydrates. Analytical solutions including the pressure transient behaviors of the NGH reservoir and the bottomhole pressure (BHP) of partially perforated wells are derived by utilizing the Laplace transform method of the separation of variables and the Stehfest numerical inversion algorithm. Sensitivity analysis is conducted using the parameters from partially perforated wells and NGH formation properties. We plot the pressure and pressure derivative curves in double logarithmic coordinates to study the pressure transient behaviors. There are seven flow regimes that are typical for partially perforated wells in the NGH reservoir, namely, pure wellbore storage, skin effect, spherical flow, pseudoradial flow, composite effect, improvement, and radial flow regimes. [ABSTRACT FROM AUTHOR]

Background: Observational studies have suggested an association between gut microbiota and Alzheimer's disease (AD); however, the causal relationship remains unclear, and the role of blood metabolites in this association remains elusive. Purpose: To elucidate the causal relationship between gut microbiota and AD and to investigate whether blood metabolites serve as potential mediators. Materials and methods: Univariable Mendelian randomization (UVMR) analysis was employed to assess the causal relationship between gut microbiota and AD, while multivariable MR (MVMR) was utilized to mitigate confounding factors. Subsequently, a two-step mediation MR approach was employed to explore the role of blood metabolites as potential mediators. We primarily utilized the inverse variance-weighted method to evaluate the causal relationship between exposure and outcome, and sensitivity analyses including Contamination mixture, Maximum-likelihood, Debiased inverse-variance weighted, MR-Egger, Bayesian Weighted Mendelian randomization, and MR pleiotropy residual sum and outlier were conducted to address pleiotropy. Results: After adjustment for reverse causality and MVMR correction, class Actinobacteria (OR: 1.03, 95% CI: 1.01-1.06, p = 0.006), family Lactobacillaceae (OR: 1.03, 95% CI: 1.00-1.05, p = 0.017), genus Lachnoclostridium (OR: 1.03, 95% CI: 1.00-1.06, p = 0.019), genus Ruminiclostridium9 (OR: 0.97, 95% CI: 0.94-1.00, p = 0.027) and genus Ruminiclostridium6 (OR: 1.03, 95% CI: 1.01-1.05, p = 0.009) exhibited causal effects on AD. Moreover, 1-ribosyl-imidazoleacetate levels (-6.62%), Metabolonic lactone sulfate levels (2.90%), and Nonadecanoate (19:0) levels (-12.17%) mediated the total genetic predictive effects of class Actinobacteria on AD risk. Similarly, 2-stearoyl-GPE (18:0) levels (-9.87%), Octadecanedioylcarnitine (C18-DC) levels (4.44%), 1-(1-enyl-stearoyl)-2-oleoyl-GPE (p-18:0/18:1) levels (38.66%), and X-23639 levels (13.28%) respectively mediated the total genetic predictive effects of family Lactobacillaceae on AD risk. Furthermore, Hexadecanedioate (C16-DC) levels (5.45%) mediated the total genetic predictive effects of genus Ruminiclostridium 6 on AD risk; Indole-3-carboxylate levels (13.91%), X-13431 levels (7.08%), Alpha-ketoglutarate to succinate ratio (-13.91%), 3-phosphoglycerate to glycerate ratio (15.27%), and Succinate to proline ratio (-14.64%) respectively mediated the total genetic predictive effects of genus Ruminiclostridium 9 on AD risk. Conclusion: Our mediation MR analysis provides genetic evidence suggesting the potential mediating role of blood metabolites in the causal relationship between gut microbiota and AD. Further large-scale randomized controlled trials are warranted to validate the role of blood metabolites in the specific mechanisms by which gut microbiota influence AD. [ABSTRACT FROM AUTHOR]

Background: Observational researches have suggested a connection between iron deficiency anemia (IDA) and an increased likelihood of ischemic stroke (IS), yet establishing causality is challenging owing to the inherent limitations of such studies, including their vulnerability to confounding factors and the potential for reverse causation. This study employs a bidirectional two-sample Mendelian randomization (MR) approach to assess the causal linkage between IDA and IS and its subtypes. Methods: Identifiable single nucleotide polymorphisms (SNPs) with significant links to either IDA or IS and its subtypes were employed as instrumental variables (IVs). The relationship between IDA and any IS, small vessel stroke (SVS), cardioembolic stroke (CES), and large artery stroke (LAS), was quantified using the inverse variance weighted (IVW) method. Complementary analyses utilizing MR-Egger and weighted median methods further supplemented the IVW findings. Moreover, the leave-one-out analysis, MR-Egger intercept test, MR-PRESSO global test, and Cochrane's Q test were conducted for sensitivity analyses. Results: This study revealed no correlation between IDA and any IS (IVW method: OR [95% CI] = 0.977 [0.863-1.106]; p = 0.716), LAS (OR [95% CI] = 1.158 [0.771-1.740]; p = 0.479), CES (OR [95% CI] = 1.065 [0.882-1.285]; p = 0.512), or SVS (OR [95% CI] = 1.138 [0.865-1.498]; p = 0.357). Conducting a reverse MR analysis, it was determined that there is no causal connection between any IS, LAS, CES, SVS, and IDA (all p > 0.05). Sensitivity analysis indicated that heterogeneity was not significant and no evidence of horizontal pleiotropy was detected. Conclusion: This MR study suggested no causal effect of IDA on IS, LAS, CES, and SVS. Through reverse MR analyses, it was determined that IS and its subtypes did not exert a causal impact on IDA. [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]