Perform Sensitivity Analysis Research Articles

Study on multi-factor casing damage prediction method based on machine learning

Casing damage is one of the common problems encountered in reservoir development, which seriously affects the normal production of the oil field. In this study, through the analysis of oil field data, a casing damage model under the coupling effects of mudstone hydration-corrosion and sand production-corrosion was established. Thirty-four influencing factors of casing damage were determined in four categories: geology, engineering, development, and corrosion. Six machine learning methods were used to predict the probability of casing damage under the coupling effects of multiple factors. The generalization performance of the model was evaluated using the recall rate of casing damage wells and accuracy. The results show that the random forest and LightGBM models show the best generalization performance. The prediction accuracy rates of the two models on the test set were 84.2% and 85.9%, respectively, and the random forest model showed an excellent performance of 92.3% on the recall rate of casing damage wells. Finally, the optimized model was used to perform sensitivity analysis on each influencing factor, and the main controlling factors of casing damage were obtained. Engineering measures to prevent casing damage are proposed. This study has made outstanding contributions to improving the economic benefits of the oilfield.

The role of Neutrophil counts, infections and Smoking in mediating the Effect of Bronchiectasis on Chronic Obstructive Pulmonary Disease: a mendelian randomization study.

The causality of the relationship between bronchiectasis and chronic obstructive pulmonary disease (COPD) remains unclear. This study aims to investigate the potential causal relationship between them, with a specific focus on the role of airway inflammation, infections, smoking as the mediators in the development of COPD. We conducted a two-sample Mendelian randomization (MR) analysis to assess: (1) the causal impact of bronchiectasis on COPD, sex, smoking status, infections, eosinophil and neutrophil counts, as well as the causal impact of COPD on bronchiectasis; (2) the causal effect of smoking status, infections and neutrophil counts on COPD; and (3) the extent to which the smoking status, infections and neutrophil counts might mediate any influence of bronchiectasis on the development of COPD. COPD was associated with a higher risk of bronchiectasis (OR 1.28 [95% CI 1.05, 1.56]). Bronchiectasis was associated with a higher risk of COPD (OR 1.08 [95% CI 1.04, 1.13]), higher levels of neutrophil (OR 1.01 [95% CI 1.00, 1.01]), higher risk of respiratory infections (OR 1.04 [95% CI 1.02, 1.06]) and lower risk of smoking. The causal associations of higher neutrophil cells, respiratory infections and smoking with higher COPD risk remained after performing sensitivity analyses that considered different models of horizontal pleiotropy, with OR 1.17, 1.69 and 95.13, respectively. The bronchiectasis-COPD effect was 0.99, 0.85 and 122.79 with genetic adjustment for neutrophils, respiratory infections and smoking. COPD and bronchiectasis are mutually causal. And increased neutrophil cell count and respiratory infections appears to mediate much of the effect of bronchiectasis on COPD.

Mutations make pandemics worse or better: modeling SARS-CoV-2 variants and imperfect vaccination.

COVID-19 is a respiratory disease triggered by an RNA virus inclined to mutations. Since December 2020, variants of COVID-19 (especially Delta and Omicron) continuously appeared with different characteristics that influenced death and transmissibility emerged around the world. To address the novel dynamics of the disease, we propose and analyze a dynamical model of two strains, namely native and mutant, transmission dynamics with mutation and imperfect vaccination. It is also assumed that the recuperated individuals from the native strain can be infected with mutant strain through the direct contact with individual or contaminated surfaces or aerosols. We compute the basic reproduction number, , which is the maximum of the basic reproduction numbers of native and mutant strains. We prove the nonexistence of backward bifurcation using the center manifold theory, and global stability of disease-free equilibrium when , that is, vaccine is effective enough to eliminate the native and mutant strains even if it cannot provide full protection. Hopf bifurcation appears when the endemic equilibrium loses its stability. An intermediate mutation rate leads to oscillations. When increases over a threshold, the system regains its stability and exhibits an interesting dynamics called endemic bubble. An analytical expression for vaccine-induced herd immunity is derived. The epidemiological implication of the herd immunity threshold is that the disease may effectively be eradicated if the minimum herd immunity threshold is attained in the community. Furthermore, the model is parameterized using the Indian data of the cumulative number of confirmed cases and deaths of COVID-19 from March 1 to September 27 in 2021, using MCMC method. The cumulative cases and deaths can be reduced by increasing the vaccine efficacies to both native and mutant strains. We observe that by considering the vaccine efficacy against native strain as 90%, both cumulative cases and deaths would be reduced by 0.40%. It is concluded that increasing immunity against mutant strain is more influential than the vaccine efficacy against it in controlling the total cases. Our study demonstrates that the COVID-19 pandemic may be worse due to the occurrence of oscillations for certain mutation rates (i.e., outbreaks will occur repeatedly) but better due to stability at a lower infection level with a larger mutation rate. We perform sensitivity analysis using the Latin Hypercube Sampling methodology and partial rank correlation coefficients to illustrate the impact of parameters on the basic reproduction number, the number of cumulative cases and deaths, which ultimately sheds light on disease mitigation.

Enhanced prediction of highway flood inundation through Bayesian generalized linear geostatistical models

Transportation infrastructure facilitates the mobility of goods and humans. Following flooding, blocked road access would prevent vulnerable communities from accessing essential services and disaster relief resources. To reduce the impact of damaged transportation infrastructure on community lifelines, efficient infrastructure restoration is desired. Conventionally, damage identification is often performed via field inspections. However, due to the blocked road access and safety concerns, a limited amount of damage inspection data can be collected immediately following flooding. Aimed at providing a quick prediction of highway inundation status, this research proposes a novel approach that integrates geospatial correlation to address the data sparsity issue. At the core of this approach is a Bayesian generalized linear geostatistical model (BGLGM) that measures (1) correlations between highway inundation status and the associated geospatial variables (e.g., road elevation and flood depth), (2) spatially correlated residuals that cannot be explained by the geospatial variables, and (3) parametric uncertainties. To verify and validate the proposed approach, a case study on highway flood inundation in Harris County, Texas, following Hurricane Harvey was conducted. A sensitivity analysis of the model performance to the availability of damage inspection data was conducted. The results show that the proposed approach is capable of providing accurate highway inundation prediction using limited damage inspection data, which validates the concept of integrating geospatial correlation for more accurate highway inundation representation and prediction. In addition to supporting rapid damage inspection, the validation opens up possibilities for integrating geospatial correlation into machine learning and deep learning models to enhance model performance. The region-specific geospatial correlation also has the potential to recalibrate pre-trained models, improving their generalizability to other regions.

Causal relationship between feelings and cognitive decline: An univariable and multivariable Mendelian randomization study.

While the impact of depression on cognition is well-documented, the relationship between feelings and cognition has received limited attention. To explore the potential association between feelings and cognition with a two-sample Mendelian randomization (MR) analysis. Our analysis utilized genome-wide association data on various feelings (fed-up feelings, n = 453071; worrier/anxious feelings, n = 450765; guilty feelings, n = 450704; nervous feelings, n = 450700; sensitivity/hurt feelings, n = 449419; miserableness, n = 454982; loneliness/isolation, n = 455364; happiness, n = 152348) in the European population and their impact on cognitive functions (intelligence, n = 269867). Conducting a univariable MR (UVMR) analysis to assess the relationship between feelings and cognition. In this analysis, we applied the inverse variance weighting (IVW), weighted median, and MR Egger methods. Additionally, we performed sensitivity analysis (leave-one-out analysis), assessed heterogeneity (using MR-PRESSO and Cochran's Q test), and conducted multiple validity test (employing MR-Egger regression). Subsequently, a multivariable MR (MVMR) analysis was employed to examine the impact of feelings on cognition. IVW served as the primary method in the multivariable analysis, complemented by median-based and MR-Egger methods. In this study, UVMR indicated that sensitivity/hurt feelings may have a negative causal effect on cognition (OR = 0.63, 95%CI: 0.43-0.92, P = 0.017). After adjustment of other feelings using MVMR, a direct adverse causal effect on cognition was observed (ORMVMR = 0.39, 95%CI: 0.17-0.90, PMVMR = 0.027). While a potential increased risk of cognitive decline was observed for fed-up feelings in the UVMR analysis (ORUVMR = 0.64, 95%CI: 0.42-0.97, PUVMR = 0.037), this effect disappeared after adjusting for other feelings (ORMVMR = 1.42, 95%CI: 0.43-4.74, PMVMR = 0.569). These findings were generally consistent across MV-IVW, median-based, and MR-Egger analyses. MR-Egger regression revealed pleiotropy in the impact of worrier/anxious feelings on cognition, presenting a challenge in identifying the effect. Notably, this study did not demonstrate any significant impact of guilty feelings, nervous feelings, miserableness, or loneliness/isolation on cognition. Due to a limited number of instrumental variables for happiness, this study was unable to analyze the relationship between happiness and cognition. This MR study finds that sensitivity/hurt feelings are associated with cognitive decline, while the link between worrier/anxious feelings and cognition remains inconclusive. Insufficient evidence supports direct associations between happiness, guilty feelings, nervous feelings, miserableness, loneliness/isolation, and cognition.

Contribution of expanded marine sulfur chemistry to the seasonal variability of dimethyl sulfide oxidation products and size-resolved sulfate aerosol

Abstract. Marine emissions of dimethyl sulfide (DMS) and the subsequent formation of its oxidation products methanesulfonic acid (MSA) and sulfuric acid (H2SO4) are well-known natural precursors of atmospheric aerosols, contributing to particle mass and cloud formation over ocean and coastal regions. Despite a long-recognized and well-studied role in the marine troposphere, DMS oxidation chemistry remains a work in progress within many current air quality and climate models, with recent advances exploring heterogeneous chemistry and uncovering previously unknown intermediate species. With the identification of additional DMS oxidation pathways and intermediate species that influence the eventual fate of DMS, it is important to understand the impact of these pathways on the overall sulfate aerosol budget and aerosol size distribution. In this work, we update and evaluate the DMS oxidation mechanism of the chemical transport model GEOS-Chem by implementing expanded DMS oxidation pathways in the model. These updates include gas- and aqueous-phase reactions, the formation of the intermediates dimethyl sulfoxide (DMSO) and methanesulfinic acid (MSIA), and cloud loss and aerosol uptake of the recently quantified intermediate hydroperoxymethyl thioformate (HPMTF). We find that this updated mechanism collectively decreases the global mean surface-layer gas-phase sulfur dioxide (SO2) mixing ratio by 40 % and enhances the sulfate aerosol (SO42-) mixing ratio by 17 %. We further perform sensitivity analyses exploring the contribution of cloud loss and aerosol uptake of HPMTF to the overall sulfur budget. Comparing modeled concentrations to available observations, we find improved biases relative to previous studies. To quantify the impacts of these chemistry updates on global particle size distributions and the mass concentration, we use the TwO-Moment Aerosol Sectional (TOMAS) aerosol microphysics module coupled to GEOS-Chem and find that changes in particle formation and growth affect the size distribution of aerosol. With this new DMS-oxidation scheme, the global annual mean surface-layer number concentration of particles with diameters smaller than 80 nm decreases by 16.8 %, with cloud loss processes related to HPMTF being mostly responsible for this reduction. However, the global annual mean number of particles larger than 80 nm (corresponding to particles capable of acting as cloud condensation nuclei, CCN) increases by 3.8 %, suggesting that the new scheme promotes seasonal particle growth to these sizes.

Association between 19 medication use and risk of common cancers: A cross-sectional and Mendelian randomisation study.

Previous studies have yielded inconsistent results concerning drug use and the risk of cancers. We conducted a large-scale cross-sectional study and a two-sample Mendelian randomisation (MR) study to reveal the causal effect between the use of 19 medications and the risk of four common cancers (breast, lung, colorectal, and prostate). We obtained information on medication use and cancer diagnosis from National Health and Nutrition Examination Survey participants. After propensity score matching, we conducted survey-weighted multivariate logistic regression and restricted cubic spline analysis to assess the observed correlation between medication use and cancer while adjusting for multiple covariates. We also performed MR analysis to investigate causality based on summary data from genome-wide association studies on medication use and cancers. We performed sensitivity analyses, replication analysis, genetic correlation analysis, and reverse MR analysis to improve the reliability of MR findings. We found that the use of agents acting on the renin-angiotensin system was associated with reduced risk of prostate cancer (odds ratio (OR) = 0.42; 95% confidence interval (CI) = 0.27-0.63, P < 0.001), and there was a nonlinear association of 'decrease-to-increase-to-decrease' (P < 0.0001). The random-effects inverse variance weighted (IVW) model-based primary MR analysis (OR = 0.94, 95% CI = 0.91-0.97, P = 0.0007) and replication MR analysis (OR = 0.90, 95% CI = 0.85-0.96, P = 0.0006) both provided robust evidence of the causality of genetic liability for the use of agents acting on the renin-angiotensin system on a decreased risk of prostate cancer. Our study provides robust evidence that the use of drugs acting on the renin-angiotensin system can reduce prostate cancer risk. Given the high prevalence of prostate cancer, these findings have important implications for drug selection and prostate cancer prevention in patients with cardiovascular disease.

Design study on fuel buffer system for continuous processing of tritium processes considering burn and dwell operation scenarios

The most important purpose of the tritium processes in the fusion fuel cycle is to stably supply tritium and deuterium to reduce operational uncertainty in the Tokamak. To achieve this, it is desirable for the process operation to be designed as a continuous process with a steady flowrate, pressure and composition. In this study, we aim to propose a modified concept of storage and delivery system, the Fuel Buffer System which is the main interface system between the tritium processing systems and Tokamak for the continuous operation in tritium processes. The buffer vessel is considered as a key interface equipment in the FBS to make steady feed or product stream. We evaluate the tritium inventory and the design impact of the FBS in a tokamak burn-and-dwell operating environment. By applying known ITER operating scenarios, we perform sensitivity analysis on the operating pressure of the buffer vessel and fuel cycle operation time. Based on the results, we suggest the design strategy for fusion fuel cycle in the K-DEMO.

Impacts of solid rock components on resistivity-based reserves evaluation in organic-rich mudrocks

The effective electrical conductivity of organic-rich mudrocks can be influenced by the presence of clay, pyrite, and thermally mature organic matter. In this paper, we investigate the impacts of conductivity, volumetric concentrations, and spatial distribution/connectivity of clay, pyrite, and kerogen networks on the effective conductivity of the rock and how this affects the assessment of water/hydrocarbon saturation. This quantification enables an understanding of when such components need to be considered in interpreting resistivity measurements in organic-rich mudrocks. We perform sensitivity analysis on the impacts of (a) thermal-maturity-dependent kerogen conductivity, (b) volumetric concentration/spatial distribution of kerogen, (c) conductivity and volumetric concentration/spatial distribution of pyrite, and (d) cation exchange capacities of various clay minerals and their concentration/spatial distribution on the effective conductivity of the rock. The sensitivity analysis showed that with increasing pyrite concentration, the effective conductivity of the rock increased. This increment became significant when the concentration of pyrite was above 2%. Lastly, we observed that thermally mature kerogen increased the conductivity of the rock, with a significant increment occurring when the kerogen conductivity was above 0.01 S m −1 . Results confirmed that the incorporation of conductive rock components such as clay, pyrite, and kerogen into the electrical rock physics model improved the estimates of hydrocarbon reserves.

Exploring the link between walking and lung cancer risk: a two-stage Mendelian randomization analysis

BackgroundPrevious observational research showed a potential link between physical activities such as walking and the risk of lung cancer. However, Mendelian randomization (MR) studies suggested there was no association between moderate to vigorous physical activity and lung cancer risk. We speculated that specific physical activities may be associated with lung cancer risk. Consequently, we conducted an MR study to examine the potential relationship between walking and the risk of lung cancer.MethodsWe collected genetic summary data from UK Biobank. After excluding SNPs with F values less than 10 and those associated with confounding factors, we conducted a MR analysis to assess the causal effects between different types of walk and lung cancer. We also performed sensitivity analysis to validate the robustness of our findings. Finally, we analyzed the possible mediators.ResultsMR analysis showed number of days/week walked for 10 + minutes was associated with a reduced risk of lung cancer risk (OR = 0.993, 95% CI = 0.987–0.998, P = 0.009). Additionally, usual walking pace was identified as a potentially significant factor in lowering the risk (OR = 0.989, 95% CI = 0.980–0.998, P = 0.015). However, duration of walks alone did not show a significant association with lung cancer risk (OR = 0.991, 95%CI = 0.977–1.005, P = 0.216). The sensitivity analysis confirmed the robustness of these findings. And number of days/week walked for 10 + minutes could affect fed-up feelings and then lung cancer risk. There was a bidirectional relationship between usual walking pace and sedentary behaviors (time spent watching TV).ConclusionThe study unveiled a genetically predicted causal relationship between number of days/week walked for 10 + minutes, usual walking pace, and the risk of lung cancer. The exploration of potential mediators of walking phenotypes and their impact on lung cancer risk suggests that specific physical activities may reduce the risk of lung cancer.

Association between postpartum depression and postpartum hemorrhage: A systematic review and meta-analysis.

Postpartum depression (PPD) is a growing mental health concern worldwide and has detrimental effects on the social and cognitive health of both mothers and infants. This review was performed to assess the risk of PPD in women with postpartum hemorrhage (PPH) and to identify potential moderators. The review protocol was registered in the PROSPERO database on June 17, 2023 (registration number: CRD42023432955). Two researchers independently performed a literature search of the PubMed, Embase, and Web of Science databases for articles published before May 25, 2023, with no filters and no language or location restrictions. Study quality was evaluated using the Newcastle-Ottawa Scale. The primary outcome was the odds ratio (OR) and 95% confidence interval (CI) of PPD in women with vs. without PPH. We performed sensitivity analyses and meta-regression analyses to resolve heterogeneity. Meta-regression analyses included the effects of age, maternal smoking, marital status, preterm labor, maternal education level, preeclampsia, anemia during pregnancy, and cesarean section. In total, seven studies involving 540 558 participants met the eligibility criteria and were included in the meta-analysis. Women with PPH were at increased risk of PPD compared with women without PPH (OR 1.10; 95% CI 1.03-1.16), and heterogeneity was low (I2 = 23%; τ2 = 0.0007; p = 0.25). Moreover, the results of the sensitivity analyses showed that the I2 value decreased from 23% to 0% after excluding one particular study, which may have been a source of heterogeneity. In the meta-regression analyses, the OR of PPD was greatly affected by maternal smoking (OR -0.26; 95% CI -0.30 to -0.22; p < 0.001). However, we did not observe any effects for maternal age, marital status, preterm labor, maternal education level, preeclampsia, anemia during pregnancy, or cesarean section. Women with PPH must be closely monitored because they have a higher risk of PPD than women without PPH. Early recognition and management of these patients will improve treatment outcomes, maternal health, and newborn development.

Mortality After Alcohol Septal Ablation vs. Septal Myectomy in Patients With Obstructive Hypertrophic Cardiomyopathy.

Background: Alcohol septal ablation (ASA) and septal myectomy (SM) are 2 options for septal reduction therapy (SRT) to treat medication-resistant symptomatic obstructive hypertrophic cardiomyopathy (HCM). Because differences in mortality rates after these different SRT methods have not been extensively investigated in real-world settings, in this study compared the 1-year mortality rates after ASA and SM using population-based database. Methods and Results: Utilizing New York Statewide Planning and Research Cooperative System (SPARCS) data from 2005 to 2016, we performed a comparative effectiveness study of ASA vs. SM in patients with HCM. The outcome was all-cause death up to 360 days after SRT. We constructed a multivariable logistic regression model and performed sensitivity analysis with propensity score (PS)-matching and inverse probability of treatment weighting (IPTW) methods. We identified 755 patients with HCM who underwent SRT: 348 with ASA and 407 with SM. The multivariable analysis showed that all-cause deaths were significantly fewer in the ASA group at 360 days after SRT (adjusted odds ratio=0.34; 95% confidence interval [CI] 0.13-0.84; P=0.02). The PS-matching and IPTW methods also supported a lower mortality rate in the ASA group at 360 days post-SRT. Conclusions: In this population-based study of patients with HCM who underwent SRT in a real-world setting, the 1-year all-cause mortality rate was significantly lower in patients who underwent ASA compared with SM.

Intravenous Thrombolysis Before Thrombectomy Improves Functional Outcome After Stroke Independent of Reperfusion Grade.

We studied the association of bridging intravenous thrombolysis (IVT) before thrombectomy for anterior circulation large-vessel occlusion and functional outcome and scrutinized its dependence on grade of reperfusion and distal thrombus migration. We included consecutive patients withanterior circulation large-vessel occlusion from our prospective registry of thrombectomy-eligible patients treated from January 1, 2017 to January 1, 2023 at a tertiary stroke center in Germany in this retrospective cohort study. To evaluate the association of bridging IVT and functional outcome quantified via modified Rankin Scale score at 90 days we used multivariable logistic and lasso regression including interaction terms with grade of reperfusion quantified via modified Thrombolysis in Cerebral Infarction (mTICI) scale and distal thrombus migration adjusted for demographic and cardiovascular risk profiles, clinical and imaging stroke characteristics, onset-to-recanalization time and distal thrombus migration. We performed sensitivity analysis using propensity score matching. In our study population of 1000 thrombectomy-eligible patients (513 women; median age, 77 years [interquartile range, 67-84]), IVT emerged as a predictor of favorable functional outcome (modified Rankin Scale score, 0-2) independent of modified mTICI score(adjusted odds ratio, 0.49 [95% CI, 0.32-0.75]; P=0.001). In those who underwent thrombectomy (n=812), the association of IVT and favorable functional outcome was reproduced (adjusted odds ratio, 0.49 [95% CI, 0.31-0.74]; P=0.001) and was further confirmed on propensity score analysis, where IVT led to a 0.35-point decrease in 90-day modified Rankin Scale score (ß=-0.35 [95 CI%, -0.68 to 0.01]; P=0.04). The additive benefit of IVT remained independent of modified mTICI score (ß=-1.79 [95% CI, -3.43 to -0.15]; P=0.03) and distal thrombus migration (ß=-0.41 [95% CI, -0.69 to -0.13]; P=0.004) on interaction analysis. Consequently, IVT showed an additive association with functional outcome in the subpopulation of patients undergoing thrombectomy who achieved successful reperfusion (mTICI ≥2b; ß=-0.46 [95% CI, -0.74 to -0.17]; P=0.002) and remained beneficial in those with unsuccessful reperfusion (mTICI ≤2a; ß=-0.47 [95% CI, -0.96 to 0.01]; P=0.05). In thrombectomy-eligible patients with anterior circulation large-vessel occlusion, IVT improves functional outcome independent of grade of reperfusion and distal thrombus migration.

Causal Effects of COVID-19 on the Risk of Thrombosis: A Two-Sample Mendel Randomization Study.

Coronavirus disease 2019 (COVID-19) and thrombosis are linked, but the biomolecular mechanism is unclear. We aimed to investigate the causal relationship between COVID-19 and thrombotic biomarkers. We used two-sample Mendelian randomization (MR) to assess the effect of COVID-19 on 20 thrombotic biomarkers. We estimated causality using inverse variance weighting with multiplicative random effect, and performed sensitivity analysis using weighted median, MR-Egger regression and MR Pleiotropy Residual Sum and Outlier (MR-PRESSO) methods. All the results were examined by false discovery rate (FDR) with the Benjamin and Hochberg method for this correction to minimize false positives. We used R language for the analysis. All COVID-19 classes showed lower levels of tissue factor pathway inhibitor (TFPI) and interleukin-1 receptor type 1 (IL-1R1). COVID-19 significantly reduced TFPI (odds ratio [OR] = 0.639, 95% confidence interval [CI]: 0.435-0.938) and IL-1R1 (OR = 0.603, 95% CI = 0.417-0.872), nearly doubling the odds. We also found that COVID-19 lowered multiple coagulation factor deficiency protein 2 and increased C-C motif chemokine 3. Hospitalized COVID-19 cases had less plasminogen activator, tissue type (tPA) and P-selectin glycoprotein ligand 1 (PSGL-1), while severe cases had higher mean platelet volume (MPV) and lower platelet count. These changes in TFPI, tPA, IL-1R1, MPV, and platelet count suggested a higher risk of thrombosis. Decreased PSGL-1 indicated a lower risk of thrombosis. TFPI, IL-1R, and seven other indicators provide causal clues of the pathogenesis of COVID-19 and thrombosis. This study demonstrated that COVID-19 causally influences thrombosis at the biomolecular level.

Seasonal and decadal subsurface thaw dynamics of an Aufeis feature investigated through numerical simulations

AbstractAufeis (also known as icings) are large sheet‐like masses of layered ice that form in river channels in arctic environments in the winter as groundwater discharges to the land surface and subsequently freezes. Aufeis are important sources of water for Arctic river ecosystems, bolstering late summer river discharge and providing habitat for caribou escaping insect harassment. The aim of this research is to use numerical simulations to evaluate a conceptual model of subsurface hydrogeothermal conditions that can lead to the formation of aufeis. We used a conceptual model based on geophysical data from the Kuparuk aufeis field on the North Slope of Alaska to develop a two‐dimensional heterogeneous vertical profile model of groundwater flow, heat transport, and freeze/thaw dynamics. Modelling results showed that groundwater can flow to the land surface through subvertical high permeability pathways during winter months when the lower permeability soils near the land surface are frozen. The groundwater discharge can freeze on the surface, contributing to aufeis formation throughout the winter. We performed sensitivity analyses on subsurface properties and surface temperature and found that aufeis formation is most sensitive to the volume of unfrozen water available in the subsurface and the rate at which the subsurface water travels to the land surface. Although a trend of warming air temperatures will lead to a greater volume of unfrozen subsurface water, the aufeis volume can be reduced under warming conditions if the period of time for which air temperatures are below freezing is reduced.

High-resolution, open-source modeling of inland flooding impacts on the North Carolina bulk electric power grid

Although damages to local distribution systems from wind and fallen trees are typically responsible for the largest fraction of electricity outages during hurricanes, outages caused by flooding of electrical substations pose a unique risk. Electrical substations are a key component of electric power systems, and in some areas, the loss of a single substation can cause widespread power outages. Before repairing damaged substations, utilities must first allow floodwaters to recede, potentially leaving some customers without power for weeks following storms. As economic losses from flooding continue to increase in the U.S., there has been increasing attention paid to the potential impacts of flooding on power systems. Yet, this attention has mostly been limited to geospatial risk assessments that identify what assets are in the path of flooding. Here, we present the first major attempt to understand how flooding from hurricanes and other extreme precipitation events affects the dynamic behavior of power networks, including losses of demand and generation, and altered power flows through transmission lines. We use North Carolina, hit by major hurricanes in three of the past seven years, as a test case. Using open-source data of grid infrastructure, we develop a high-resolution direct current optimal power flow model that simulates electricity production and generators and power flows through a network consisting of 662 nodes and 790 lines. We then simulate grid operations during the historical (2018) storm Hurricane Florence. Time series of flooding depth at a discrete set of ‘high water’ mark points from the storm are used to spatially interpolate flooding depth across the footprint area of the storms on an hourly basis. Outages of substations and solar farms due to flooding are translated to location-specific losses of demand and solar power production throughout the network. We perform sensitivity analysis to explore grid impacts as a function of the height of sensitive equipment at substations. Results shed light on the potential for localized impacts from flooding to have wider impacts throughout the grid (including in areas not affected by flooding), with performance tracked in terms of transmission line flows/congestion, generation outputs, and customer outages.

Associations between obesity and hyperuricemia combing mendelian randomization with network pharmacology

ObjectiveObesity has become a global health issue and a risk factor for hyperuricemia. However, the associations between obesity and hyperuricemia are sometimes confounding. In the present study, we performed mendelian randomization (MR) analysis to study their relationship and investigate the underlying mechanism by network pharmacology. MethodBody mass index (BMI) and uric acid related to single nucleotide polymorphism were selected as instrumental variables for MR analysis. Three robust analytical methods are used for bidirectional MR analysis such as inverse-variance weighting, weighted median and MR-Egger regression. Then, we further performed sensitivity analysis to evaluate the horizontal pleiotropy, heterogeneities, and stability. The targets related to obesity and hyperuricemia were collected, screened and further conducted for Kyoto Encyclopedia of Genes and Genomes pathway enrichment to explore the mechanism of obesity and hyperuricemia using network pharmacology. ResultsThe positive causality was indicated between BMI and hyperuricemia based on inverse variance-weighted analysis [odds ratio:1.23, 95% confidence interval: 1.11 to 1.30 for each standard deviation increase in BMI (4.6 kg/m2)]. Conversely, hyperuricemia did not influence BMI. 235 intersected targets from obesity and hyperuricemia were collected. Insulin resistance were the top 1 key target. The mechanism between obesity and hyperuricemia are associated with important pathways including adipocytokine signaling pathway, insulin resistance and cholesterol metabolism et al. ConclusionsOur MR analysis supported the causal association between obesity and hyperuricemia based on availablegenome-wide association analysis summary statistics. Obesity leads to hyperuricemia via insulin resistance, which is a key link in the huge network pathways using network pharmacology.

Sensitivity of long-term productivity estimations in mixed forests to uncertain parameters related to fine roots

Forest growth models are increasingly being used in forestry and ecology research as predictive tools to help developing practical guidelines and to improve understanding of the drivers of forest ecosystem functioning. Models are usually calibrated using parameters directly obtained or estimated from empirical field observation, and hence are subject to uncertainty. Thus, output accuracy depends on input parameters precision and on how influential is each parameter on model behaviour. Hence, it is important to analyse parameter-related uncertainty and its effects on model outputs. This can be done by performing sensitivity analyses, which allow to explore the influence of one or several calibration parameters on model outputs. As studies on tree root parameters are particularly scarce, the aim of the present work was to evaluate the influence of parameters related to fine roots on estimations of long-term forest growth patterns in pure and mixed forests, using FORECAST (a hybrid forest growth model) as a virtual lab. The fine root parameters assessed were biomass, turnover rate, and nitrogen content. The analysis was performed by simulating monospecific stands of two contrasting species (Pinus sylvestris L. and Fagus sylvatica L.), and mixed stands formed by both species. In all cases, FORECAST showed good capability to contain uncertainty propagation during the first and middle stages of stand development (<40 years). After that moment, model output uncertainty steadily increased, but it reached different maximum uncertainty levels depending on stand type. Simulations of the less nutrient demanding P. sylvestris manifested very little sensitivity when growing in monospecific stands. However, F. sylvatica monospecific stands showed intermediate sensitivity, but significant species interactions occurred in mixed stands that determined the biggest impact detected of uncertainty related to fine root parameters over model outputs. All things considered, FORECAST displayed an interesting capability to capture some of the interspecific interactions that are key in mixed forests functioning. Our results suggest an acceptable model performance under uncertain parameterization but also caution against expecting accurate quantitative estimations of forest growth, especially when considering long-term scenarios in complex mixed stands.

Fires as a source of annual ambient PM2.5 exposure and chronic health impacts in Europe

Chronic exposure to ambient PM2.5 is the largest environmental health risk in Europe. We used a chemical transport model and recent exposure response functions to simulate ambient PM2.5, contribution from fires and related health impacts over Europe from 1990 to 2019. Our estimation indicates that the excess death burden from exposure to ambient PM2.5 declined across Europe at a rate of 10,000 deaths per year, from 0.57 million (95 % confidence intervals: 0.44–0.75 million) in 1990 to 0.28 million (0.19–0.42 million) in the specified period. Among these excess deaths, approximately 99 % were among adults, while only around 1 % occurred among children. Our findings reveal a steady increase in fire mortality fractions (excess deaths from fires per 1000 deaths from ambient PM2.5) from 2 in 1990 to 13 in 2019. Notably, countries in Eastern Europe exhibited significantly higher fire mortality fractions and experienced more pronounced increases compared to those in Western and Central Europe. We performed sensitivity analyses by considering fire PM2.5 to be more toxic as compared to other sources, as indicated by recent studies. By considering fire PM2.5 to be more toxic than other PM2.5 sources results in an increased relative contribution of fires to excess deaths, reaching 2.5–13 % in 2019. Our results indicate the requirement of larger mitigation and adaptation efforts and more sustainable forest management policies to avert the rising health burden from fires.

Sensitivity analysis and thermodynamic evaluation of a combined cooling, heating and power system utilizing exhaust gases of smelting furnace

Exhaust gases from the smelting furnace have high temperature and mass flow rate, and there is huge potential to use them for energy-related purposes such as electricity generation, cooling and heating. Utilization of the gases for energy-related purposes would lead to fuel savings and emissions reduction. To use this potential, it is necessary to design proper systems and cycles and apply a heat recovery unit. Several technologies are useable for heat recovery depending on the characteristics of exhaust gases, such as their mass flow rate, temperature and compositions. Due to the higher potential of combined heating, cooling and power (CCHP) generation systems compared with the systems with a single output, a CCHP is designed and investigated in the present study by consideration of the specifications of the exhaust gases. The applied system in this study comprises a Supercritical CO2 (SCO2) cycle, heat exchanger and single-stage absorption chiller for simultaneous heating, cooling and power production. Engineering Equation Solver (EES) is employed to model the proposed system by considering the properties of the flows and characteristics of the components. To get deep insight into the effective parameters on the outputs of the designed system, the impact of three factors, namely the mass flow rate of the gases, the effectiveness of heat exchanger and temperature of exhaust gases, are analyzed and investigated by the implementation of sensitivity analysis. As one of the main conclusions, it is found that an increment in the mass flow rate of exhaust gases from 30 kg/s to 70 kg/s causes augmentation in the power generation from 2037 kW to 4754 kW. Furthermore, exergy analysis is carried out, and it is found that an increase in the temperature or mass flow rate of exhaust gases or a decrease in the effectiveness of heat exchangers would lead to decrement in the exergy efficiency of the system. According to the performed sensitivity analysis, the mass flow rate of exhaust gases has the most remarkable influence on the heating and cycle-generated power among the considered factors.