Perform Sensitivity Analysis Research Articles

Sensitivity analysis of asphalt pavement performance under freeze-thaw cycles by applying reliability method

Asphalt pavement performance remains pivotal in ensuring the longevity and safety of our transport networks, especially in regions that face unique environmental challenges such as the Qinghai-Tibet plateau. This study presents a groundbreaking reliability model for the evaluation of asphalt pavement performance, based on the esteemed AASHTO design equation. Four cardinal uncertainty factors, encompassing the environment, traffic loads, pavement structures and materials, and anthropogenic activities, were meticulously integrated into the performance analysis. Delving deep into the intricate dynamics of the pavement structure strength, five random variables - SN, ΔPSI, Mr, ESAL0and r - were identified and analyzed, reflecting the inherent uncertainties during its lifecycle. A notable observation was the linear decline of SNF-T with increasing freeze-thaw actions. Additionally, a comprehensive sensitivity analysis highlighted the profound influence of ESAL0 on the sensitivity of the load equation and emphasized the dominant role of SNF-T in pavement structural performance evaluation. This investigation not only unravels the complex interactions governing asphalt pavement behavior but also provides an invaluable framework for future research, design optimization, and infrastructure maintenance strategies in challenging terrains.

Use of Perioperative Advanced Practice Providers to Reduce Cost and Readmission in the Postoperative Hepatopancreatobiliary Population: Results of a Simulation Study.

Postoperative healthcare use and readmissions are common among the hepatopancreatobiliary (HPB) population. We evaluated the surgical volume required to sustain advanced practice providers (APPs) in the perioperative setting for cost reduction. Using decision analysis modeling, we evaluated costs of employing dedicated perioperative APP navigators compared with no APPs navigators. Simulated subjects could: (1) present to an emergency department, with or without readmission, (2) present for direct readmission, (3) require additional office visits, or (4) require no additional care. We informed our model using the most current available published data and performed sensitivity analyses to evaluate thresholds under which dedicated perioperative APP navigators are beneficial. Subjects within the APP navigator cohort accumulated $1,270 and a readmission rate of 6.9%, compared with $2,170 and 13.5% with no APP navigators, yielding a cost savings of $905 and 48% relative reduction in readmission. Based on these estimated cost savings and national salary ranges, a perioperative APPs become financially self-sustaining with 113 to 139 annual HPB cases, equating to 2 to 3 HPB cases weekly. Sensitivity analyses revealed that perioperative APP navigators were no longer cost saving when direct readmission rates exceeded 8.9% (base case 3.7%). We show that readmissions are reduced by nearly 50% with an associated cost savings of $900 when employing dedicated perioperative APPs. This position becomes financially self-sufficient with an annual HPB case load of 113 to 139 cases. High-volume HPB centers could benefit from postdischarge APP navigators to optimize outcomes, minimize high-value resource use, and ultimately save costs.

Life cycle assessment of high nickel ternary lithium (NCM811) power batteries

ABSTRACT The rapid growth in the ownership of electric vehicles has led to a rapid expansion in the installed capacity of power batteries. While electric vehicles reduce emissions during use, they also bring a huge environmental burden. This study focuses on the NCM811 battery, a major component of electric vehicles. By analyzing the three stages of production, use and recycling of power batteries, the energy consumption and emissions of each stage are determined. The results find that the production stage of the battery is the stage with the highest environmental impact throughout its life cycle, while the recycling stage effectively offset some of the environmental impact, as well as identifying the battery materials that has the highest environmental impact during the production process. In addition, different power generation mix and different cathode chemistry ratios are varied to perform sensitivity analyses. Based on these findings, recommendations for the development of lithium-ion batteries are made.

Fast uncertainty quantification of spent nuclear fuel with neural networks

The accurate calculation and uncertainty quantification of the characteristics of spent nuclear fuel (SNF) play a crucial role in ensuring the safety, efficiency, and sustainability of nuclear energy production, waste management, and nuclear safeguards. State of the art physics-based models, while reliable, are computationally intensive and time-consuming. This paper presents a surrogate modeling approach using neural networks (NN) to predict a number of SNF characteristics with reduced computational costs compared to physics-based models. An NN is trained using data generated from CASMO5 lattice calculations. The trained NN accurately predicts decay heat and nuclide concentrations of SNF, as a function of key input parameters, such as enrichment, burnup, cooling time between cycles, mean boron concentration and fuel temperature. The model is validated against physics-based decay heat simulations and measurements of different uranium oxide fuel assemblies from two different pressurized water reactors. In addition, the NN is used to perform sensitivity analysis and uncertainty quantification. The results are in very good alignment to CASMO5, while the computational costs (taking into account the costs of generating training samples) are reduced by a factor of 10 or more. Our findings demonstrate the feasibility of using NNs as surrogate models for fast characterization of SNF, providing a promising avenue for improving computational efficiency in assessing nuclear fuel behavior and associated risks.

Validation of a Multiscale Computational Model Using a Mock Circulatory Loop to Simulate Cardiogenic Shock.

The objectives of this study are to characterize the hemodynamics of cardiogenic shock (CS) through a computational model validated using a mock circulatory loop (MCL) and to perform sensitivity analysis and uncertainty propagation studies after the American Society of Mechanical Engineers (ASME) Validation and Verification (V&V) guidelines. The uncertainties in cardiac cycle time ( ), total resistance ( ), and total volume ( ) were quantified in the MCL and propagated in the computational model. Both models were used to quantify the pressure in the left atrium, aorta (Ao), and left ventricle (LV), along with the flow through the aortic valve, reaching a good agreement. The results suggest that 1) is the main source of uncertainty in the variables under study, 2) showed its greatest impact on the uncertainty of Ao hemodynamics, and 3) mostly affected the uncertainty of LV pressure and Ao flow at the late-systolic phase. Comparison of uncertainty levels in the computational and experimental results was used to infer the presence of additional contributing factors that were not captured and propagated during a first analysis. Future work will expand upon this study to analyze the impact of mechanical circulatory support devices, such as ventricular assist devices, under CS conditions.

Iron, copper, zinc and magnesium on rheumatoid arthritis: a two-sample Mendelian randomization study

ABSTRACT This study aimed to elucidate the causal genetic relationships between iron, copper, zinc, magnesium, and rheumatoid arthritis (RA). A two-sample Mendelian randomization (MR) analysis was conducted using the “TwoSampleMR” and “MendelianRandomization” packages in R. The random-effects inverse variance-weighted (IVW) method was used as the primary approach. We performed sensitivity analyses to test the reliability of the results. The random-effects IVW analysis revealed that there was no genetic causal relationship between iron (P = 0.429, odds ratio [OR] 95% confidence interval [CI] = 0.919 [0.746–1.133]), copper (P = 0.313, OR 95% CI = 0.973 [0.921–1.027]), zinc (P = 0.633, OR 95% CI = 0.978 [0.891–1.073]), or magnesium (P = 0.218, OR 95% CI = 0.792 [0.546–1.148]) and RA. Sensitivity analysis verified the reliability of the results. Therefore, there is no evidence to support a causal relationship between iron, copper, zinc, and magnesium intake at the genetic level and the development of RA.

Building Resilient communities: Techno-economic assessment of standalone off-grid PV powered net zero energy (NZE) villages

The use of renewable energy resources for off-grid electricity production has gained more importance in recent decades for meeting the energy needs of remote areas, even with limited resources. This research aims to provide an optimized and cost-effective approach for generating electricity in rural areas. By using current methodology, a stand alone energy source of PV is designed for development of NZE village. Solar irradiance of the selected location is 6.16 kWh/m2/day while the estimated electric load data for whole village is 64.259 kWh. Electric load and solar irradiance of the loaction is used in the Hybrid Optimization Model for Electric Renewable (HOMER) to design and analyze the techno-economic feasibility of the stand alone PV system to meet the load requirements. The study obtained the total Net Present Cost (NPC) of $0.511 M and the Cost Of Electricity (COE) is 2.26$/unit through the HOMER analysis, which is further refined by performing sensitivity analysis using parameters such as PV panel price, battery price, solar irradiance, variations in electric load and discount rates. According to the results, system is feasibile by annual electricity production of 30,078 kWh with initial capital investment of $0.434 M. This analysis compared the system performance and showed that it is economically and technically viable to meet the complete electricity needs of the village with a payback period of 7.2 years. Research can be utilized for policy making and implementation of NZE approach in remote areas by the government.

Monitoring the reproductive number of COVID-19 in France: Comparative estimates from three datasets.

The effective reproduction number (Rt) quantifies the average number of secondary cases caused by one person with an infectious disease. Near-real-time monitoring of Rt during an outbreak is a major indicator used to monitor changes in disease transmission and assess the effectiveness of interventions. The estimation of Rt usually requires the identification of infected cases in the population, which can prove challenging with the available data, especially when asymptomatic people or with mild symptoms are not usually screened. The purpose of this study was to perform sensitivity analysis of Rt estimates for COVID-19 surveillance in France based on three data sources with different sensitivities and specificities for identifying infected cases. We applied a statistical method developed by Cori et al. to estimate Rt using (1) confirmed cases identified from positive virological tests in the population, (2) suspected cases recorded by a national network of emergency departments, and (3) COVID-19 hospital admissions recorded by a national administrative system to manage hospital organization. Rt estimates in France from May 27, 2020, to August 12, 2022, showed similar temporal trends regardless of the dataset. Estimates based on the daily number of confirmed cases provided an earlier signal than the two other sources, with an average lag of 3 and 6 days for estimates based on emergency department visits and hospital admissions, respectively. The COVID-19 experience confirmed that monitoring temporal changes in Rt was a key indicator to help the public health authorities control the outbreak in real time. However, gaining access to data on all infected people in the population in order to estimate Rt is not straightforward in practice. As this analysis has shown, the opportunity to use more readily available data to estimate Rt trends, provided that it is highly correlated with the spread of infection, provides a practical solution for monitoring the COVID-19 pandemic and indeed any other epidemic.

Modeling and assessing seismic resilience leveraging systems dynamic approach: A case study of society 5.0

Society 5.0 is a transformation strategy centered on integrating digital technologies unveiled by the Japanese government to create a human-centric society for economic development and mitigate sustainability issues. Since societies are constantly exposed to various natural disasters like earthquakes, many precautions must be taken both before and after a disaster to minimize the damage. Seismic resilience is one of the practical assessments that may be taken in this regard. Quantifying the functionality of seismic resilience requires a systematic examination of the relevant components and their functional impact. We present a framework based on fragility, consequence and recoverability stages for evaluating the impact of a component on its functionality for earthquake events. Within this study, we introduce a novel set of indicators, which are derived from the key variables impacted by earthquakes, including hospitals, grids, and infrastructures. To that end, we have developed a system dynamics (SD) model to assess earthquake resilience in the context of Society 5.0, considering three earthquake magnitudes (7, 8, and 9 Mw) to simulate societal seismic resilience. We also perform sensitivity analysis to validate the outcomes of the policy simulations. Our findings affirm that by scrutinizing the seismic resilience of critical infrastructure and proposing relevant policies, it is possible to minimize disaster-related damage. This represents a pragmatic step forward in the field of disaster risk management.

Outcomes of electroconvulsive therapy in patients with depressive symptoms with versus without comorbid personality disorders/traits: A systematic review and meta-analysis.

To assess electroconvulsive therapy (ECT) outcomes in patients affected by depressive symptoms with versus without additional comorbid personality disorders/traits. We identified observational studies investigating ECT clinical outcomes in patients affected by depressive symptoms with versus without comorbid personality disorders/traits in Embase/Medline in 11/2022. Our protocol was registered with PROSPERO (CRD42023390833). Study quality was evaluated using the Newcastle-Ottawa-Scale. Our primary outcomes were ECT response and remission rates. Meta-regression analyses included effects of in/outpatient percentages, age, number of ECT sessions, and electrode placement; subgroup analyses included the assessment methods for personality disorders/traits. We performed sensitivity analyses after excluding poor-quality studies. A total of 20 studies (n = 11,390) were included in our analysis. Patients with comorbid personality disorders/traits had lower remission rates (OR = 0.42, 95% CI = 0.31, 0.58, p < 0.001) with substantial heterogeneity (I2 = 93.0%) as well as lower response rates (OR = 0.35, 95% CI = 0.24, 0.51, n = 5129, p < 0.001) with substantial heterogeneity (I2 = 93.0%) compared with patients without comorbid personality disorders/traits. Relapse rates were higher in patients with versus without comorbid personality disorders/traits (OR = 3.23, 95% CI = 1.40, 7.45, k = 4, n = 239, p = 0.006) with moderate heterogeneity (I2 = 75.0%) and post-ECT memory impairment was more frequent in patients with versus without comorbid personality disorders/traits (OR = 1.41, 95% CI = 1.36, 1.46, k = 4, n = 471, p < 0.001) with minimal heterogeneity (I2 = 0.0%). Dropout rates were higher in patients with versus without comorbid personality disorders/traits (OR = 1.58, 95% CI = 1.13, 2.21, k = 3, n = 6145, p = 0.008). Patients with comorbid personality disorders/traits treated with ECT are reported to have lower response and remission rates and higher rates of side effects and relapse rates compared with patients without personality disorders/traits.

Investigations on the Effects of Rail Longitudinal Forces in Train-Track Dynamic Interaction

Nowadays, attention to railways has been increased as one of the important transportation methods. Various factors can cause longitudinal forces on the railway tracks, so the most important factors are the temperature changes due to the rail contraction and expansion (thermal forces), train braking system forces and acceleration, etc. Increasing longitudinal force can lead to buckling phenomena in railway tracks. In this paper, the effects of tensile and compressive longitudinal forces on the parameters of rail, sleeper, and ballast layers under vertical moving loads are investigated by using the finite element method. In this regard, by performing sensitivity analyses for different values of longitudinal forces and train speeds dynamic responses (displacement, velocity, and acceleration) of railway track components like rail, sleeper, and ballast have been studied. The results show that increasing the values of longitudinal axial force from –2000 [kN] up to 2000 [kN] as well as increasing the train speed from 10 [m/s] (36 [km/h]) up to 100 [m/s] (360 [km/h]) increases the rail displacement and velocity in the range of 25% up to 37% also the rail acceleration in the range of 9% up to 14%. The velocity and acceleration of the sleeper also the ballast velocity increase in the range of 24% up to 30% and 7% up to 9%, respectively, by increasing the speed of the train from 10 [m/s] (36 [km/h]) to 100 [m/s] (360 [km/h]) in all three modes without applying axial force, considering compressive and tensile one in the train-track interaction system.

Methodology of load effect analysis and ultimate limit state design of semi-submersible hulls of floating wind turbines: With a focus on floater column design

A main challenge in the design of hull structures of floating wind turbines (FWTs) is to properly estimate the load effects and resistance to perform the safety design check. While great efforts have been devoted to the load effects in terms of global motions, there is a lack of effective methods to calculate internal loads and lateral pressures of hulls in a fully coupled aero-hydro-servo-elastic environment, which should serve as the basis for the assessment of load effects in hulls. Moreover, very limited knowledge and experience exist in the current guidelines and research publications to deal with the detailed design of hull structures of FWTs. This study outlines an effective methodology and procedure to determine load effects for structural design of semi-submersible hulls, and demonstrates their application in a case study of column design based on the ultimate limit state (ULS) criteria. First, a novel multi-body floater model is established in a fully coupled FWT numerical system, which is capable of calculating floater internal loads. Second, the environmental contour approach is employed to initially choose load cases for ULS design, based on important wind speed conditions and criterial wave periods. Next, a screening of the critical load cases is made based on Von Mises stress analysis of columns considering global loads. Then, lateral pressure on columns is calculated, and a detailed design check is made based on combined global loads and lateral pressure as well as relevant safety factors for loads and resistance. Furthermore, the relative influence of global loads and lateral pressure on ULS performance of columns is investigated, which serves as the basis to reset the critical load cases for ULS design check. Subsequently, sensitivity analysis of ULS performance with respect to column design parameters, extreme value estimation, and shell buckling design checks, are performed to determine the plate thickness and ring stiffener distances of central and outer columns. Finally, optimized ring stiffener dimensions are estimated for different design cases based on panel buckling design criteria, and detailed design parameters for the central and outer columns are obtained. The methodology and procedure established and applied in a case study in this work, contribute to improving engineering practice and establishing improved design standards.

Extracorporeal life support in cardiac arrest: a post hoc Bayesian re-analysis of the INCEPTION trial.

Previously, we performed the multicentre INCEPTION trial, randomizing patients with refractory out-of-hospital cardiac arrest (OHCA) to extracorporeal cardiopulmonary resuscitation (ECPR) or conventional cardiopulmonary resuscitation (CCPR). Frequentist analysis showed no statistically significant treatment effect for the primary outcome; 30-day survival with a favourable neurologic outcome (cerebral performance category score of 1-2). To facilitate a probabilistic interpretation of the results, we present a Bayesian re-analysis of the INCEPTION trial. We analysed survival with a favourable neurologic outcome at 30 days and 6 months under a minimally informative prior in the intention-to-treat population. Effect sizes are presented as absolute risk differences (ARDs) and relative risks (RRs), with 95% credible intervals (CrIs). We estimated posterior probabilities at various thresholds, including the minimal clinically important difference (MCID) (5% ARD), based on expert consensus, and performed sensitivity analyses under sceptical and literature-based priors. The mean ARD for 30-day survival with a favourable neurologic outcome was 3.6% (95% CrI -9.5-16.7%), favouring ECPR, with a median RR of 1.22 (95% CrI 0.59-2.51). The posterior probability of an MCID was 42% at 30 days and 42% at 6 months, in favour of ECPR. Bayesian re-analysis of the INCEPTION trial estimated a 42% probability of an MCID between ECPR and CCPR in refractory OHCA in terms of 30-day survival with a favourable neurologic outcome. Clinicaltrials.gov (NCT03101787, registered 5 April 2017).

Exploring the Causality Between Plasma Brain-Derived Neurotrophic Factor and Neurological Diseases: A Mendelian Randomization Study.

Brain-derived neurotrophic factor (BDNF) is a protein synthesized in the brain and widely expressed in the nervous system. Previous studies have demonstrated a controversial role of BDNF in neurological diseases. In this study, we aimed to assess the association between BDNF levels and the risk of neurological diseases by Mendelian randomization analysis. From a genome-wide association analysis of plasma proteins comprising 3,301 European participants, we isolated 25 genetic variations as instrumental variables for BDNF levels. Summary statistics data on six common neurological diseases as outcome variables. Two-sample Mendelian randomization (MR) analysis was used to assess whether plasma BDNF is causally related to neurological diseases. We also performed sensitivity analysis to ensure the robustness of the results and reverse MR to exclude potential reverse causality. We confirmed the significant causal relationship between BDNF levels and the risk of Alzheimer's disease (AD) (OR, 0.91; 95% CI, 0.83, 0.99; p = 0.033). Other methods have also shown similar results. We infer that BDNF also reduces the risk of epilepsy (OR, 0.94; 95% CI, 0.90, 0.99; p = 0.0103). In reverse MR analysis, we also found that AD can affect the level of BDNF. Our study suggests higher plasma BDNF was associated with the reduced risk of AD. Moreover, higher plasma BDNF is a protective factor on AD and focal epilepsy. The results provide credence to the idea that BDNF may play a significant role in the development of focal epilepsy and AD.

Comprehensive performance evaluation and sensitivity analysis method of a cutter-changing robot for a large-diameter shield machine

Cutter-changing robot is a new product to automatically replace the cutter in shield machine, whose performance is affected by multiple criteria with diversity, multiplicity and correlation, which is a complex multiple criteria decision-making problem with limited and fuzzy available information. This paper proposes a comprehensive performance evaluation model including the performance indicator hierarchy, weight determination, measurement and assessment methods of indicators and sensitivity analysis. Fuzzy Analytical Hierarchy Process (FAHP) is applied to identify the construction requirements from external shield machine and environment and design characteristics in the whole cutter-changing process, weight for 3 first-level indicators and 14 s-level indicators are determined. The objective evaluation indexes measured by instrumentation and subjective evaluation indexes quantified by fuzzy logic are normalized within the range of 0–1. The scores and proportion of assessment grade for each indicator at each level are obtained through Fuzzy Comprehensive Evaluation (FCE). Furthermore, the sensitivity of weight and evaluation data on performance evaluation result are deeply analyzed. The applicability and validity of the proposed approach were demonstrated by a case study of a cutter-changing robot made by China Railway Construction Heavy Industry Co., Ltd. According to the result, operational capacity is the most important criteria in the first level, and cutter grasping ability is the most important indicator in the second level. Path planning ability is the weakest indicators. Cutter grasping ability and time of replacement are the most critical affecting factors. The proposed method not only helps the designers capture the weak indicators accurately and improve the design quality, but also provides engineers with a holistic perspective to the cutter-changing robot before actual application.

Power distribution in developing countries — Planning for effectiveness and equity

Power grid expansion planning is a challenging problem that typically considers the facility location, sizing, and transmission line upgrade aspects, with the objective of minimizing the upgrade and operational costs. The consideration of fairness and equity between the populations being served by the power grid has not been addressed previously in the literature. These issues are of special interest regarding the power grid infrastructure in developing countries, where certain populations might be “last in line” to be connected to the grid. In this paper, we develop a power grid expansion optimization model that considers both effectiveness and equity, given a budget constraint on upgrade expenditure. Effectiveness is measured by the deprivation costs of all populations served by the power grid, while equity is measured by their Gini mean absolute difference. Node upgrade rules are applied, and the upgrade plan is provided over a given planning horizon.We optimally solved our model for small instances and performed sensitivity analysis on its parameters. We then developed an LNS (Large Neighborhood Search) heuristic for solving large instances and using publicly available data. Additional instances are generated based on the Myanmar power grid. Our analysis shows that the LNS can provide good solutions relative to a greedy approach.The approach taken in this paper can be applied to a wide range of infrastructure planning problems in which both effectiveness and equity should be considered.

Association between gut microbiota and sensorineural hearing loss: a Mendelian randomization study.

Several recent studies speculated that the gut microbiota is associated with sensorineural hearing loss (SNHL) and proposed the concept of the gut-inner ear axis. However, the causal effect of gut microbiota on SNHL is still unknown. In this study, we performed a two-sample Mendelian randomization (MR) analysis to estimate the causal effect of gut microbiota on SNHL. Gut microbiota data were obtained from the largest available genome-wide association study (n = 18,340) conducted by the MiBioGen consortium. The summary statistics of SNHL were obtained from the FinnGen consortium R8 release data (28,310 cases and 302,750 controls). The causal effects were estimated with inverse-variance weighted, MR-Egger, and weighted median. Reverse Mendelian randomization analysis was performed on the bacteria that were found to be associated with SNHL in forward Mendelian randomization analysis. We then performed sensitivity analyses, including Cochran's Q-test, MR-Egger intercept test, MR-PRESSO, cML-MA-BIC, and leave-one-out analysis, to detect heterogeneity and pleiotropy. The inverse-variance weighted results suggested that Lachnospiraceae (UCG001) had a significant protective effect against SNHL (odds ratio = 0.85, 95% confidence interval: 0.78-0.93, P = 6.99 × 10-4). In addition, Intestinimonas (odds ratio = 0.89, 95% confidence interval: 0.82-0.97, P = 8.53 × 10-3) presented a suggestively protective effect on SNHL. Rikenellaceae (RC9gutgroup) (odds ratio = 1.08, 95% confidence interval: 1.02-1.15, P = 0.01) and Eubacterium (hallii group) (odds ratio = 1.12, 95% confidence interval: 1.00-1.24, P = 0.048) suggestively increase the risk of SNHL. The results of the reverse MR analysis showed that there is no significant causal effect of SNHL on the gut microbiota. No significant heterogeneity of instrumental variables or pleiotropy was detected. The evidence that the four genera mentioned above are associated with SNHL supports the hypothesis of a gut-inner ear axis. Our study provides microbial markers for the prevention and treatment of SNHL, and further studies are needed to explore the mechanisms of the gut microbiome-inner ear axis in health and diseases.

Causal relationship between asthma and inflammatory bowel disease : A two-sample bidirectional mendelian randomization analysis

Background: Based on the findings of current observational studies, asthma and inflammatory bowel disease (including Crohn's disease and ulcerative colitis) are associated; however, their causal association cannot be established due to methodological limitations. Objectives: we use two-sample bidirectional mendelian randomization (MR) to overcome the confounding factors and explore the causal link between asthma and inflammatory bowel disease. Methods: After selecting asthma and IBD-related genome-wide association studies (GWAS) data and screening single nucleotide polymorphisms (SNPs), MR analysis was performed by four methods: inverse variance weighted (IVW), MR-Egger, maximum likelihood, and weighted median (WM), while Cochran's Q test was used to detect heterogeneity and MR-Egger intercept to detect horizontal pleiotropy. Finally, we used the leave-one-out method and funnel plot to perform sensitivity analysis. Results: We screened 57, 59, and 60 SNPs in the association analysis of asthma and IBD, CD, and UC, respectively. The results of MR analysis showed that asthma only increased the risk of CD (IVW: OR = 1.1712, 95% CI = 1.0418-1.3167, P value = 0.0082; maximum likelihood: OR = 1.1739, 95% CI = 1.0428-1.3215, P value = 0.0080). Neither forward nor reverse MR analysis revealed heterogeneity or horizontal pleiotropy. Similarly, we did not find potential directional pleiotropy by funnel plot, and the leave-one-out method did not suggest a significant effect of a single SNP on the overall results. Conclusions: we found a negative correlation between asthma and Crohn's disease, but more research is needed to confirm this.

Relationship between preoperative neutrophil-to-lymphocyte ratio and postoperative delirium: The PNDABLE and the PNDRFAP cohort studies.

In this study, the relationship between preoperative neutrophil-to-lymphocyte ratio (NLR) and Alzheimer-related biomarkers in cerebrospinal fluid (CSF) was investigated to determine whether high NLR is a potential risk factor for postoperative delirium (POD) and to evaluate its predictive efficacy. We selected 1000 patients from the perioperative neurocognitive disorder risk factor and prognosis (PNDRFAP) database and 999 patients from the perioperative neurocognitive disorder and biomarker lifestyle (PNDABLE) database. Patients in the PNDABLE database have been measured for Alzheimer-related biomarkers in CSF (Aβ40 , Aβ42 , P-tau, and tau protein). Mini-mental state examination was used to assess the preoperative mental status of patients. POD was diagnosed using the confusion assessment method and assessed for severity using the memorial delirium assessment scale. Logistic regression analysis was utilized to explore the association of preoperative NLR with POD. What's more, we also performed sensitivity analysis by adding corrected confounders, and the results were almost unchanged. Spearman's rank correlation was used to determine the associations between NLR and Alzheimer-related biomarkers. Mediation analyses with 10,000 bootstrapped iterations were used to explore the mediation effects. Finally, we use decision curves and the nomogram model to evaluate the efficacy of preoperative NLR in predicting POD; we also performed external validation using data from Qilu Hospital. Logistic regression results showed that an elevated preoperative NLR was a risk factor for the development of POD in patients (PNDRFAP: OR=1.067, 95% CI 1.020-1.116; PNDABLE: OR=1.182, 95% CI 1.048-1.335, p<.05). Spearman's rank correlation analysis showed a positive but weak correlation between NLR and P-tau/T-tau (R=.065). The mediating effect results indicate that NLR likely mediates the occurrence of POD through elevated tau protein levels (proportion: 47.47%). The results of the box plots showed statistically significant NLR and CSF biomarkers between the POD and non-POD (NPOD) groups (p<.05), with higher NLR, P-tau, and T-tau in the POD group than in the NPOD group. In contrast, the NPOD group had higher Aβ42 levels compared to the POD group. In addition, we used R package to plot the decision curve and nomogram both suggesting a good predictive effect of preoperative NLR on the occurrence of POD. Elevated preoperative NLR levels may be a risk factor for POD and likely mediate the development of POD through elevated P-tau/T-tau levels.

Evaluating sustainability of urban mobility of Asian cities: An integrated approach of interval type-2 fuzzy best-worst method and MULTIMOORA

The motivation for sustainable urban mobility is driven by the need to create environmentally friendly, socially inclusive, economically viable, and healthier cities for current and future generations. It requires a comprehensive approach that combines infrastructure development, policy changes, behavioral shifts, and technological advancements to transform urban transportation systems. The importance of creating increasingly sustainable urban transportation is now greater than it has ever been. One of the attempts in this field is the sustainable urban transport index (SUTI) developed by the United Nations (UN). Although the SUTI has a comprehensive pool of criteria for the evaluation of mobility sustainability of cities, considering the criteria weights as equal and disregarding the uncertainties present in expert opinions is viewed as a significant drawback. To satisfy the aforementioned deficiency, a novel integrated approach including the best-worst method (BWM), multi-objective optimization by ratio analysis (MOORA) and MOORA plus full multiplicative form (MULTIMOORA) method is develloped and applied for the SUTI evaluation. In order to manage ambiguous linguistic approximations, the utilization of BWM is applied within the framework of interval type-2 fuzzy (IT2F) sets. While IT2F-BWM is used to prioritize the main and sub-indicators of SUTI, MULTIMOORA approach is applied to rank the Asian cities. The findings imply that the proposed approach not only provides a more scientific perspective but also provides the opportunity to perform sensitivity analysis on criteria weights. Therefore, the results obtained in this study are expected to help cities to create a road map for planning sustainable urban mobility (e.g. Sustainable Urban Mobility Plan).