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The aim of this research is to simulate and analyze a combined power cycle (Steam turbine and gas turbine cycles) by studying the effect of changing the natural gas flow rate on the developed power. Therefore, reducing the amount of used natural gas in the combustion chamber of the gas turbine cycle from 9.2 to 4 kg/s showed a significant drop in the power produced by the gas turbine, i.e., from 123.7 to 57.7 MW. Additionally, this change in the combusted natural gas amount affected the heat recovered in both heat recovery steam generators (HRSGs), i.e., from 219.79 to 100.35 MW, respectively. Consequently, the amount of generated steam in the high pressure HRSGs and the power developed in the steam turbine changed from 60.88 to 27.79 kg/s and from 56.39 to 27.13 MW, respectively. A heat exchanger (HFHX) utilizing a heating fluid was used as an external source of energy to compensate the reduction in the generated heat and to increase the amount of generated steam up to 157.32 kg/s, which keeps the power plant capacity at 180 MW. Existing combined local plant data were used in this study and were simulated in Aspen Plus software V11. A sensitivity analysis was made to optimize the cycle operating conditions that use less natural gas and produce the same amount of power. [ABSTRACT FROM AUTHOR]

U-tube steam generators (UTSGs) are crucial in nuclear power plants, serving as the interface between the primary and secondary coolant loops. UTSGs ensure efficient heat exchange, operational stability, and safety, directly impacting the plant's efficiency and reliability. Existing UTSG models have fixed structures, which can only be used when certain parameters are given as model input. Such constraints hinder their ability to accommodate the diverse operating conditions, where input and output parameters can vary significantly. To address this challenge, we propose a machine learning-based method for developing a high-degree-of-freedom UTSG thermal model. The most notable feature of this approach is its capacity to flexibly interchange input and output parameters. By adopting comprehensive parameter sensitivity analysis, the most efficient method for training dataset generation is determined. Leveraging a lightweight machine learning method, the prediction accuracy for all UTSG parameters is improved to within 2.1%. The flexibility of the proposed machine learning approach ensures that the UTSG model can accommodate any type of parameter input without extensive reconfiguration of the model structure, thereby enhancing its applicability and robustness in real-world applications. [ABSTRACT FROM AUTHOR]

This work continues the review and illustrative application to energy systems of the "Fourth-Order Best-Estimate Results with Reduced Uncertainties Predictive Modeling" (4th-BERRU-PM) methodology. The 4th-BERRU-PM methodology uses the Maximum Entropy (MaxEnt) principle to incorporate fourth-order experimental and computational information, including fourth (and higher) order sensitivities of computed model responses with respect to model parameters. The 4th-BERRU-PM methodology yields the fourth-order MaxEnt posterior distribution of experimentally measured and computed model responses and parameters in the combined phase space of model responses and parameters. The 4th-BERRU-PM methodology encompasses fourth-order sensitivity analysis (SA) and uncertainty quantification (UQ), which were reviewed in the accompanying work (Part 1), as well as fourth-order data assimilation (DA) and model calibration (MC) capabilities, which will be reviewed and illustrated in this work (Part 2). The applicability of the 4th-BERRU-PM methodology to energy systems is illustrated by using the Polyethylene-Reflected Plutonium (acronym: PERP) OECD/NEA reactor physics benchmark, which is modeled using the linear neutron transport Boltzmann equation, involving 21,976 imprecisely known parameters. This benchmark is representative of "large-scale computations" such as those involved in the modeling of energy systems. The result ("response") of interest for the PERP benchmark is the leakage of neutrons through the outer surface of this spherical benchmark, which can be computed numerically and measured experimentally. The impact of the high-order sensitivities of the response with respect to the PERP model parameters is quantified for "high-precision" parameters (2% standard deviations) and "typical-precision" parameters (5% standard deviations). Analyzing the best-estimate results with reduced uncertainties for the 1st—through 4th-order moments (mean values, covariance, skewness, and kurtosis) produced by the 4th-BERRU-PM methodology for the PERP benchmark indicates that, even for systems modeled by linear equations (e.g., the PERP benchmark), retaining only first-order sensitivities is insufficient for reliable predictive modeling (including SA, UQ, DA, and MC). At least second-order sensitivities should be retained in order to obtain reliable predictions. [ABSTRACT FROM AUTHOR]

This work (in two parts) will review the recently developed predictive modeling methodology called "4th-BERRU-PM" and its applicability to nuclear energy systems as exemplified by an illustrative application to the Polyethylene-Reflected Plutonium (acronym: PERP) OECD/NEA reactor physics benchmark. The acronym 4th-BERRU-PM designates the "Fourth-Order Best-Estimate Results with Reduced Uncertainties Predictive Modeling" methodology, which uses the Maximum Entropy (MaxEnt) principle to incorporate fourth-order experimental and computational information, including fourth (and higher) order sensitivities of computed model responses to model parameters, while yielding best-estimate results with reduced uncertainties for the first fourth-order moments (mean values, covariance, skewness, and kurtosis) of the optimally predicted posterior distribution of model results and calibrated model parameters. The 4th-BERRU-PM methodology encompasses the scopes of high-order sensitivity analysis (SA), uncertainty quantification (UQ), data assimilation (DA) and model calibration (MC), as will be illustrated in this work by means of the above-mentioned OECD/NEA reactor physics benchmark. This benchmark is modeled using the neutron transport Boltzmann equation involving 21,976 imprecisely known parameters, the solution of which is representative of "large-scale computations". The model result ("response") of interest is the leakage of neutrons through the outer surface of this spherical benchmark, which can be computed numerically and measured experimentally. Part 1 of this work illustrates the impact of high-order sensitivities, in conjunction with parameter standard deviations of various magnitudes, on the determination of the expected value and variance of the computed response in terms of the first four moments of the distribution of the uncertain model parameters. Part 2 of this work will illustrate the capabilities of the 4th-BERRU-PM methodology for combining computational and experimental information, up to and including forth-order sensitivities and distributional moments, for producing best-estimate values for the predicted responses and model parameters while reducing their accompanying uncertainties. [ABSTRACT FROM AUTHOR]

Aerospace-grade SRAM-based field-programmable gate arrays (FPGAs) used in space applications are highly susceptible to single event effects, leading to soft errors in FPGAs. Additionally, as FPGAs scale up, the difficulty of correcting soft errors also increases. This paper proposes that performing soft error sensitivity analysis on FPGAs can help target the more sensitive areas for detection and correction, thereby improving the efficiency of soft error repair. Firstly, in accordance with the dual-layer architecture of SRAM-based FPGAs, methods for the soft error sensitivity analysis of FPGA application layer resources and configuration bitstreams are reviewed. Subsequently, based on the analysis results, it also covers corresponding application layer memory scrubbing and configuration scrubbing techniques. A prospective look at emerging soft error mitigation technologies is discussed at the end of this review, supporting the development of highly reliable aerospace-grade SRAM-based FPGAs. [ABSTRACT FROM AUTHOR]

In China, several expressways have been designed as prefabricated box culverts with hinge connections, which have different structural features from the prefabricated culverts in other countries. The difference would contribute to the culvert–soil interaction of prefabricated box culverts, which could affect the earth pressure on the culvert. Based on the field test and numerical simulation method, a hinged prefabricated box culvert (HPBC) with a span of 4 m and a rise of 4 m was investigated, which was applied to the Xi-Yu expressway in China. The objective of this research was to investigate the vertical earth pressure on the top slab of the HPBC culvert at different backfill heights through the field tests. The FLAC3D software was employed to conduct further analysis of the effects of backfill height, backfill modulus, and foundation modulus on the vertical earth pressure on the top slab of HPBC. The differences between the HPBC and monolithic box culvert (MBC) were also examined. Furthermore, a revised method for calculating the vertical earth pressure on the top slab was put proposed and compared with the AASHTO method for calculating earth pressure on the top of culverts and the values taken from the Chinese culvert design code. The proposed method is capable of improving the accuracy of the earth pressure approach, making it more representative of actual conditions. Subsequently, the sensitivity analysis of backfill height, backfill modulus and foundation modulus to the vertical earth pressure concentration coefficient of the top slab was carried out by using the principle of orthogonal array analysis and the modified calculation method proposed in this paper. The findings of this study offer valuable insights into the determination of culvert top earth pressure of HPBC. [ABSTRACT FROM AUTHOR]

This paper presents a novel approach to studying divorce dynamics and elimination strategies using nonlinear differential equations. A mathematical model is formulated to capture the key factors influencing divorce rates. The model undergoes a rigorous theoretical analysis, including parameter estimation, solution existence/uniqueness, positivity, boundedness, and invariant regions. A qualitative analysis explores equilibria, stability conditions, and a sensitivity analysis. Numerical simulations and discussions are presented to validate the model and shed light on divorce dynamics. Finally, conclusions and future research directions are outlined. This work offers valuable insights for understanding and potentially mitigating divorce rates through targeted interventions. [ABSTRACT FROM AUTHOR]

Aiming at the problem of excessive torque fluctuation of double-layer permanent magnet synchronous motor (PMSM) for vehicle, based on the evolutionary algorithm (EA), the parameters of rotor skew angle, stator slot height, stator slot width and width and thickness of double-layer magnet steel for a double-layerpermanent magnet synchronous motor(PMSM )applied to dedicated hybrid transmission (DHT) were optimized. The sensitivity of each parameter was analyzed by design of experiment(DOE) method to derive the influence weights and positive and negative correlations of the parameters on the optimization target, and then the optimal parameter combination was derived by combining with the multi-round EA, and the results showed that the torque fluctuation of the optimized PMSM decreased by 3. 5% . In addition, the maximum torque per ampere (MTPA) control of the optimized PMSM was simulated by joint simulation, and the results showed that the torque fluctuation of the optimized motor is obviously reduced in the high torque speedup stage, which verified the effectiveness of the optimization process. The simulation process improves the efficiency of the optimization process, reduces the number of physical tests, and lowers the development cost and development cycle for DHT. [ABSTRACT FROM AUTHOR]

The Arnold Cat Map (ACM) is a popular chaotic map used in image encryption. Chaotic maps are known for their sensitivity to initial conditions and their ability to permute, or rearrange, pixels. However, ACM is periodic, and its period is relatively short. This periodicity decreases the effective key-space and security of a cryptosystem using ACM. Further, ACM is typically only able to be performed on square images. To solve the low periodicity and typical limitation to square images, this paper proposes performing ACM on overlapping square partitions which cover the entirety of an image. The presence of overlap results in a greatly increased image period. The resulting system will be referred to as overlapping ACM or OACM. Several papers have already discussed systems involving overlapping ACM. However, they did not discuss the implementation or periodicity of such a system in detail. This paper does cover the implementation and periodicity analysis of OACM and proposes a simple symmetric encryption system which uses OACM. The proposed encryption system is not as sophisticated or secure as other modern encryption schemes, since it is mainly intended as an initial test of OACM's utility. Histogram and sensitivity analyses did however indicate a level of security against various cryptographic attacks, and OACM performed reasonably in both the permutation and diffusion stages of the cryptosystem. [ABSTRACT FROM AUTHOR]

Translation abstract Safety assessment of buildings constructed in large, slow moving landslidesLarge slow moving landslides (SMLS) with typical deformation rates in a range of some mm to several dm per year and volumes of several 100,000 m3 to more than 100 mio. of m3 are common phenomenon in Alpin regions. Settlement areas, infrastructural buildings, bridges, tunnels, water reservoirs as well as protective structures are influenced in many cases by such SMLS. Whereas small landslides can be stabilized by support measures in a way that factors of safety acc. to standards can be proofed, this is often not possible for large landslides due to technical and economical reasons. In this paper, alternative verification procedures and assessment methods for building constructions in such SMLS and possible discrepancies to requirements of actual standards are discussed. Based on previous experience with practical examples, specific assessment criteria for an assessment of the use of settlement areas and structural facilities in SMLS are given depending on the change in the safety factor ΔFOS, the changes in movement rates, sensitivity analyses and the observation method. [ABSTRACT FROM AUTHOR]

In sparse data meta‐analyses (with few trials or zero events), conventional methods may distort results. Although better‐performing one‐stage methods have become available in recent years, their implementation remains limited in practice. This study examines the impact of using conventional methods compared to one‐stage models by re‐analysing meta‐analyses from the Cochrane Database of Systematic Reviews in scenarios with zero event trials and few trials. For each scenario, we computed one‐stage methods (Generalised linear mixed model [GLMM], Beta‐binomial model [BBM], Bayesian binomial‐normal hierarchical model using a weakly informative prior [BNHM‐WIP]) and compared them with conventional methods (Peto‐Odds‐ratio [PETO], DerSimonian‐Laird method [DL] for zero event trials; DL, Paule‐Mandel [PM], Restricted maximum likelihood [REML] method for few trials). While all methods showed similar treatment effect estimates, substantial variability in statistical precision emerged. Conventional methods generally resulted in smaller confidence intervals (CIs) compared to one‐stage models in the zero event situation. In the few trials scenario, the CI lengths were widest for the BBM on average and significance often changed compared to the PM and REML, despite the relatively wide CIs of the latter. In agreement with simulations and guidelines for meta‐analyses with zero event trials, our results suggest that one‐stage models are preferable. The best model can be either selected based on the data situation or, using a method that can be used in various situations. In the few trial situation, using BBM and additionally PM or REML for sensitivity analyses appears reasonable when conservative results are desired. Overall, our results encourage careful method selection. [ABSTRACT FROM AUTHOR]

Non-equilibrium plasma is applied for the first time to ignite the ammonium-hydrogen mixture and reduce the NO x /N 2 O emission with effect in this work. Gas chromatography (GC) experiments as well as a detailed kinetic model, including neutral molecules, free radicals, charged particles, vibratory excited states, and electron excited states, are integrated to investigate the kinetic process of ammonia-hydrogen doped ignition and NO x /N 2 O emission facilitated by nanosecond pulsed discharge. The numerical model closely matches the GC-measured values of NH 3 consumption, H 2 increase, and N 2 O production. Pathway fluxes of important species and sensitivity analysis of ignition delay time reveal that the addition of non-equilibrium plasma has an obvious promotion effect on ammonia and hydrogen-doped oxidation and ignition. The initial ignition temperature significantly impacts the ignition delay time, with a more pronounced plasma-promotion effect observed at lower temperatures. Conversely, as the hydrogen mixing ratio increases, the ignition delay time decreases, yet the generation of NO and N 2 O increases. This is attributed to the heightened production of H and NH through elementary reactions such as NH 3 +H NH 2 + H 2 , H + O 2 O + OH, and OH + H 2 H + H 2 O. Nevertheless, NO and N 2 O emissions are reduced by 1-2 orders of magnitude with plasma assistance compared to auto-ignition. The reduction in NO emissions can be attributed to increased consumption in the pathway of NO + NH 2 N 2 + H 2 O and decreased formation in the pathways of HNO + O 2 HO 2 + NO and NH + NO N 2 O + H. Additionally, it was discovered that the electron attachment dissociation reaction e + N 2 O → O− + N 2 predominantly contributes to the reduction of N 2 O. These research findings significantly contribute to advancing our understanding of the kinetics involved in ammonia-hydrogen doped ignition and emissions, particularly with plasma assistance, for potential engine applications. • Kinetic models for plasma-assisted NH 3 /H 2 /air ignition are established. • Measurements of products in plasma-activated NH 3 /H 2 /air pyrolysis are provided. • NO and N 2 O emissions are reduced with plasma assistance compared to auto-ignition. • Pathways for NO and N 2 O generation during NH 3 /H 2 /air ignition are elucidated. [ABSTRACT FROM AUTHOR]

A major global health problem continues to be malaria, a disease that can be fatal brought on by Plasmodium parasites and spread by the bite of infected Anopheles mosquitoes. We provide a deterministic mathematical model in this study to simulate the dynamics of malaria transmission between humans and mosquitoes. We present a new compartment for hospitalized patients as well as fractional calculus. The next-generation matrix technique is used to obtain the fundamental reproduction number, R 0 , and stability conditions for the model's equilibrium points are derived. Both locally and globally, the sensitivity analysis for the fundamental reproduction number R 0 is satisfied. In MAPLE, the Runge–Kutta fourth-order approach is used to simulate the model. [ABSTRACT FROM AUTHOR]

Accurately modeling gross primary productivity (GPP) is of great importance for diagnosing terrestrial carbon–climate feedbacks. Process-based terrestrial ecosystem models are often subject to substantial uncertainties, primarily attributed to inadequately calibrated parameters. Recent research has identified carbonyl sulfide (COS) as a promising proxy of GPP due to the close linkage between leaf exchange of COS and carbon dioxide (CO2) through their shared pathway of stomatal diffusion. However, most of the current modeling approaches for COS and CO2 do not explicitly consider the vegetation structural impacts, i.e., the differences between the sunlit and shaded leaves in COS uptake. This study used ecosystem COS fluxes from seven sites to optimize GPP estimation across various ecosystems with the Biosphere-atmosphere Exchange Process Simulator (BEPS), which was further developed to simulate the canopy COS uptake under its state-of-the-art two-leaf framework. Our results demonstrated substantial improvement in GPP simulation across various ecosystems through the data assimilation of COS flux into the two-leaf model, with the ensemble mean of the root mean square error (RMSE) for simulated GPP reduced by 20.16 % to 64.12 %. Notably, we also shed light on the remarkable identifiability of key parameters within the BEPS model, including the maximum carboxylation rate of RuBisCO at 25 °C (Vcmax25), minimum stomatal conductance (bH2O), and leaf nitrogen content (Nleaf), despite intricate interactions among COS-related parameters. Furthermore, our global sensitivity analysis delineated both shared and disparate sensitivities of COS and GPP to model parameters and suggested the unique treatment of parameters for each site in COS and GPP modeling. In summary, our study deepened insights into the sensitivity, identifiability, and interactions of parameters related to COS and showcased the efficacy of COS in reducing uncertainty in GPP simulations. [ABSTRACT FROM AUTHOR]

To clarify the influence range and saturation distribution after the biogas desaturation method is applied, a three-dimensional model is established with TOUGH2 software to analyze the effect of construction parameters such as grouting volume, grouting rate, grouting depth, nitrogen source concentration, and soil porosity. After that, the sensitivity of the parameters on the influence range is determined. The grouting volume and soil porosity are the most sensitive to the lateral and vertical influence range, respectively. This study provides a basis for the engineering practice of treating liquefiable subgrade by the biogas desaturation method. [ABSTRACT FROM AUTHOR]

The most widely used equation to calculate water saturation or suitable shaly water saturation in clean or shaly formation, respectively, is the modified Archie formula. The quality of Archie parameters including saturation exponent affects the preciseness of water saturation, and thus estimated oil and gas in place. Therefore, estimating the saturation exponent by the soft computation methods deems to be necessary. In this study, intelligent models such as multilayer perceptron neural network, least squares support vector machine, radial basis function neural network, and adaptive neuro-fuzzy inference system are developed to predict saturation exponent in terms of petrophysical data including porosity, absolute permeability, water saturation, true resistivity, and resistivity index by utilizing a databank for middle east oil and gas reservoirs. The introduced models are optimized using particle swarm optimization, genetic algorithm, and levenberg marquardt techniques. Graphical and statistical methods are used to demonstrate the capability of the constructed models. Based on the statistical indexes obtained for each model, it is found that radial basis function neural network, multilayer perceptron neural network, and least squares support vector machine are the most robust models as they possess the smallest mean squared error, root mean squared error and average absolute relative error as well as highest coefficient of determination. Moreover, the sensitivity analysis indicates that water saturation has the most effect and porosity has the least effect on the saturation exponent. The developed models are simple-to-use and time-consuming tools to predict saturation exponent without needing laboratory methods which are tedious and arduous. [ABSTRACT FROM AUTHOR]

This study revisits the freely vibrating problem of asymmetric L-shaped membranes using a three-segmented domain decomposition (3-SDD) strategy. Motivated by the need for more accurate and flexible methods, the 3-SDD strategy is compared with the previously proposed two-segmented domain decomposition (2-SDD) strategy. The region-matching technique is used to derive an alternative series solution, and the eigenvalues obtained are compared with those in existing research. The convergence behavior and sensitivity analyses reveal that the 3-SDD strategy offers improved accuracy and stability, particularly for higher truncation terms. Detailed comparisons of the first four eigenvalue squares show strong agreement between the 3-SDD and 2-SDD strategies, confirming the reliability of both methods. This research establishes a foundation for the vibration analysis of complex membrane structures, emphasizing the benefits of the 3-SDD approach for upcoming engineering applications and showcasing its potential for broader applicability in practical scenarios. The findings underscore the importance of utilizing multi-segmented decomposition strategies to enhance the accuracy and flexibility of free vibration analysis. [ABSTRACT FROM AUTHOR]

With rapid urbanization growth, considerable amounts of construction waste are generated on an annual basis, posing significant economic and environmental challenges worldwide. Re-cycling construction waste is a sustainable way for waste disposal, leading to the necessity of meticulous planning of recycling centers. A well-designed plan for constructing recycling centers can effectively improve the recycling rate of construction waste while minimizing investment. This paper formulates a two-stage stochastic model for planning recycling centers with the objective of maximizing the recycling rate under different scenarios. This study comprehensively considers various uncertain factors, such as the amount of construction waste generated and the demand for recycled materials. A case study of Guangzhou is used for validation, which demonstrates the effectiveness of the developed model in planning recycling center construction. The comparison between the proposed model and a conventional mean value model shows the importance of accounting for uncertainties. Specifically, the derived results indicate that 7% more construction waste is recycled with the same investment in constructing recycling centers. Additionally, via a sensitivity analysis, valuable managerial insights on investing resources in recycling center construction are provided to decision makers. Ultimately, the research findings are expected to enhance the recycling rate of construction waste, thereby contributing to sustainable industry development. [ABSTRACT FROM AUTHOR]

Dynamic models generating time‐dependent model predictions are typically associated with high‐dimensional input spaces and high‐dimensional output spaces, in particular if time is discretized. It is computationally prohibitive to apply traditional global sensitivity analysis (SA) separately on each time output, as is common in the literature on multivariate SA. As an alternative, we propose a novel method for efficient global SA of dynamic models with high‐dimensional inputs by combining a new polynomial chaos expansion (PCE)‐driven partial least squares (PLS) algorithm with the analysis of variance. PLS is used to simultaneously reduce the dimensionality of the input and output variables spaces, by identifying the input and output latent variables that account for most of their joint variability. PCE is incorporated into the PLS algorithm to capture the non‐linear behavior of the physical system. We derive the sensitivity indices associated with each output latent variable, based on which we propose generalized sensitivity indices that synthesize the influence of each input on the variance of entire output time series. All sensitivities can be computed analytically by post‐processing the coefficients of the PLS‐PCE representation. Hence, the computational cost of global SA for dynamic models essentially reduces to the cost for estimating these coefficients. We numerically compare the proposed method with existing methods by several dynamic models with high‐dimensional inputs. The results show that the PLS‐PCE method can obtain accurate sensitivity indices at low computational cost, even for models with strong interaction among the inputs. [ABSTRACT FROM AUTHOR]

Introduction: In virtual bioequivalence (VBE) assessments, pharmacokinetic models informed with in vitro data and verified with small clinical trials' data are used to simulate otherwise unfeasibly large trials. Simulated VBE trials are assessed in a frequentist framework as if they were real despite the unlimited number of virtual subjects they can use. This may adequately control consumer risk but imposes unnecessary risks on producers. We propose a fully Bayesian model-integrated VBE assessment framework that circumvents these limitations. Methods: We illustrate our approach with a case study on a hypothetical paliperidone palmitate (PP) generic long-acting injectable suspension formulation using a validated population pharmacokinetic model published for the reference formulation. BE testing, study power, type I and type II error analyses or their Bayesian equivalents, and safe-space analyses are demonstrated. Results: The fully Bayesian workflow is more precise than the frequentist workflow. Decisions about bioequivalence and safe space analyses in the two workflows can differ markedly because the Bayesian analyses are more accurate. Discussion: A Bayesian framework can adequately control consumer risk and minimize producer risk. It rewards data gathering and model integration to make the best use of prior information. The frequentist approach is less precise but faster to compute, and it can still be used as a first step to narrow down the parameter space to explore in safe-space analyses. [ABSTRACT FROM AUTHOR]

The critical role of groundwater in meeting diverse needs, including drinking, industrial, and agricultural, highlights the urgency of effective resource management. Excessive groundwater extraction, especially in coastal regions including Urmia Plain in NW Iran, disrupts the equilibrium between freshwater and saline boundaries within aquifers. Influential parameters governing seawater intrusion—groundwater occurrence (G), aquifer hydraulic conductivity (A), the height of groundwater level above the mean sea level (L), distance from the shore (D), impact of the existing status of seawater intrusion (I), and thickness of the saturated aquifer (T)—merge to shape the GALDIT vulnerability index for coastal aquifers. This study enriches the GALDIT framework by incorporating two additional hydrogeological variables: hydraulic gradient (i) and pumping rate (P). This expansion produces seven distinct vulnerability maps (GALDIT, GAiDIT, GAiDIT-P, GALDIT-i, GALDIT-iP, GALDIT-P, GAPDIT). In the Urmia Plain, the traditional GALDIT index reveals vulnerability values ranging from 2 to 8.1, categorized into six classes from negligible to very high vulnerability. However, the modified indices, GAiDIT and GAiDIT-P, yield a three-class categorization, ranging from low to high vulnerability. The introduction of the "i" and "P" parameters in GALDIT-i and GALDIT-iP enhances the precision of vulnerability mapping, altering class distribution and intensifying vulnerability ratings. The eastern, central, and coastal areas of the Urmia Plain demonstrate high to very high vulnerability levels, in contrast to the lower vulnerability observed in the western regions. Both the GALDIT-P (r = 0.82) and GALDIT-iP (r = 0.81) indices show strong correlations with Cl concentration, thereby improving mapping accuracy over the traditional GALDIT index (r = 0.72). A sensitivity analysis highlights the critical influence of the "i" parameter, suggesting its weighting should be revised. Parameter recalibration serves to amplify the significance of "G," "L," "D," and "i" parameters, while diminishing others. The integration of multiple hydrogeological variables considerably enhances the precision of groundwater vulnerability assessments. [ABSTRACT FROM AUTHOR]

This study aims to estimate the familial risks of pterygium and assess its relative contributions to environmental and genetic factors using the 2000–2017 Taiwan National Health Insurance Research Database. The marginal Cox's model and the polygenic liability model were made. In Taiwan, the prevalence rate of pterygium in 2017 was 1.64% for individuals with affected first-degree relatives, higher than the general population (1.34%). The adjusted relative risk (RR) for pterygium was highest for twins of the same sex (15.54), followed by siblings of the same sex (4.69), offsprings (3.39), siblings of the different sex (2.88), spouse (2.12), parents (1.86), twins of the different sex (1.57), respectively. The phenotypic variance of pterygium was 21.6% from additive genetic variance, 24.3% from common environmental factors shared by family members, and 54.1% from non-shared environmental factors, respectively. Sensitivity analysis by restricting those with surgical pterygium reveals that aRRs and the three components were similar to those of the overall pterygium. In summary, the prevalence rate of pterygium was higher for individuals with affected first-degree relatives than for the general population. The non-shared environmental factors account for half of the phenotypic variance of pterygium; genetic and shared environmental factors explain the rest. [ABSTRACT FROM AUTHOR]

The number of Methicillin-resistant Staphylococcus aureus (MRSA) cases in communities and hospitals is on the rise worldwide. In this work, a nonlinear deterministic model for the dynamics of MRSA infection in society was developed to visualize the significance of awareness in interventions that could be applied in the prevention of transmission with and without optimal control. Positivity and uniqueness were verified for the proposed corruption model to identify the level of resolution of infection factors in society. Furthermore, how various parameters affect the reproductive number R 0 and sensitivity analysis of the proposed model was explored through mathematical techniques and figures. The global stability of model equilibria analysis was established by using Lyapunov functions with the first derivative test. A total of seven years of data gathered from a private hospital consisting of inpatients and outpatients of MRSA were used in this model for numerical simulations and for observing the dynamics of infection by using a non-standard finite difference (NSFD) scheme. When optimal control was applied as a second model, it was determined that increasing awareness of hand hygiene and wearing a mask were the key controlling measures to prevent the spread of community-acquired MRSA (CA-MRSA) and hospital-acquired MRSA (HA-MRSA). Lastly, it was concluded that both CA-MRSA and HA-MRSA cases are on the rise in the community, and increasing awareness concerning transmission is extremely significant in preventing further spread. [ABSTRACT FROM AUTHOR]

Evidence from previous studies have demonstrated that gut microbiota are closely associated with occurrence of interstitial cystitis/bladder pain syndrome (IC/BPS), yet the causal link between the two is not well known. In this study, we performed a two-sample Mendelian randomization (MR) analysis to determine the possible causal association between gut microbiota with IC/BPS. Gut microbiota summary level data were derived from the genome-wide association study (GWAS) conducted by MiBioGen and the IC/BPS GWAS summary level data were obtained from the GWAS Catalog. Next, we performed an MR study to investigate the causal link between gut microbiota and IC/BPS. The primary method for causal analysis was the inverse variance weighted (IVW), and the MR results were validated through multiple sensitivity analyses. A positive association was found between IC/BPS and eight gut microbial taxa, including genus Bacteroides, genus Haemophilus, genus Veillonella, genus Coprococcus1, genus Butyricimonas, family Bacteroidaceae, family Christensenellaceae, and order Lactobacillales. Sensitivity analysis revealed lack of significant pleiotropy or heterogeneity in the obtained results. This MR analysis reveals that a causal association exists between some gut microbiota with IC/BPS. This finding may is expected to guide future research and development of IC/BPS preventions and treatments based on the bladder-gut axis. However, given the clinical complexity and diagnostic challenges of IC/BPS, along with the limitations of using large-scale GWAS summary data for analysis, our MR results require further validation through additional research. [ABSTRACT FROM AUTHOR]

Objective: To investigate the relationship between uric acid to high-density lipoprotein cholesterol ratio (UHR) and circulating α-klotho levels in U.S. adults. Methods: A cross-sectional study used data from the National Health and Nutrition Examination Survey (NHANES) from 2007 to 2016. Circulating α-klotho was defined as the dependent variable and UHR was defined as the independent variable. Multivariable linear regression was performed to assess the relationship between the independent and dependent variables. The nonlinear relationship and effect size between UHR and α-klotho were evaluated using smooth curve fitting and threshold effect analysis. Subgroup analysis and sensitivity analysis were conducted to determine the stability of the results. The diagnostic performance of UHR and α-klotho in common elderly diseases was compared using ROC (Receiver Operating Characteristic) analysis. Results: Among 12,849 participants, there was a negative relationship between the UHR and circulating α-klotho. In the fully adjusted overall model, each unit increase in UHR was associated with a decrease of 4.1 pg/mL in α-klotho. The threshold effect analysis showed that before the inflection point of 8.2, each unit increase in UHR was associated with a decrease of 15.0 pg/mL in α-klotho; beyond the inflection point of 8.2, each unit increase in UHR was associated with a decrease of 2.8 pg/mL in α-klotho. Subgroup analyses and sensitivity analysis indicated that the relationship between UHR and α-klotho remained stable across most populations. The ROC diagnostic test indicated that the evaluative efficacy of UHR in diagnosing age-related diseases was comparable to that of α-klotho. Conclusion: This study revealed that the UHR was associated with the circulating α-klotho concentration, with a negative association observed in most cases. This finding suggested that the UHR might be a promising indicator for evaluating circulating α-klotho levels. [ABSTRACT FROM AUTHOR]

Background: Previous observational clinical studies and meta-analyses have yielded inconsistent results regarding the relationship between vitamin D and headache, and the causal relationship remains unclear. The aim of this study was to investigate the causal relationship between vitamin D and headache by bidirectional two-sample Mendelian randomisation (MR) analysis. Methods: The relationship between high levels of vitamin D and headache was investigated by two-sample MR analysis using publicly available genomewide association study (GWAS) data. The primary method was inverse variance weighting (IVW), and secondary methods were weighted median and MR-Egger methods. No heterogeneity or horizontal multidirectionality was found in the MR results. The robustness and validity of the findings were assessed using the leave-behind method. Results: A significant causal relationship was found between high vitamin D levels and headache using the IVW method (OR = 0.848; p = 0.007; 95% CI = 0.752-0.956). However, in a reverse analysis, no evidence of a causal relationship between headache and high levels of vitamin D was found using the IVW method (OR = 1.001; p = 0.906; 95% CI = 0.994-1.006). Our MR analyses showed no significant horizontal multidimensionality or heterogeneity (p > 0.05). Sensitivity analyses confirmed that MR estimates were not affected by single nucleotide polymorphisms (SNPs). Confirmation that our results are robust and valid has been obtained by the leave-one-out method. Conclusion: Our study suggests that high levels of vitamin D prevent the risk of headache. However, there is no evidence of a causal relationship between headache and high levels of vitamin D. Vitamin D may reduce the risk of headache. [ABSTRACT FROM AUTHOR]

Drive-by inspection of bridge damage using a passing vehicle’s dynamic response has great potential in bridge damage identification. Among the current drive-by methodologies, the methods based on bridge modeling have been proposed for the quantitative identification of bridge damage and bridge surface roughness. However, the coupling of bridge damage and unknown surface roughness increases the difficulty of the identification problem and most previous studies conduct the identification task of bridge damage or bridge surface roughness separately. In this paper, a novel method is proposed for the identification of joint bridge damage and bridge surface roughness based on the residual bridge deflections from the front and rear wheels contact points of an instrumented passing vehicle. The vehicle–bridge interaction is analyzed using a half-car model of a four degrees of freedom (4-DOF) with front and rear vehicle wheels. First, the vehicle–bridge unknown forces and displacement of the vehicle axles are identified by the generalized Kalman filter under unknown input (GKF-UI) proposed by the authors. The sensors can be installed conveniently on the vehicle body due to GKF-UI. Then, the residual bridge deflections from the front and rear vehicle wheels contact points are utilized to eliminate the effect of road surface roughness. Afterward, bridge structural damage is identified based on the sensitivity analysis of residual bridge deflections from the front and rear contact points with l1-norm regularization. Finally, bridge road surface roughness is estimated based on the identified unknown forces and updated bridge model with damage. The results of numerical identification examples demonstrate the effectiveness of the proposed method for the identification of joint bridge damage and surface roughness. [ABSTRACT FROM AUTHOR]

Due to the substantial economic and environmental repercussions of improper E-waste management and treatment, the issue has gained indispensable attention. In recent years, the research has been done about the selection of proper E-waste management methods like site selection, disposal method selection, etc. with fuzzy environments. But in real situations, the major reasons for improper e-waste management are lack of awareness and improper disposal with sustainability. In finding a sustainable awareness-creating program and disposal method for E-waste, the uncertainty and its sustainability of the information make challenges to decision makers, so the problem becomes a two-dimensional problem with uncertainty. This paper uses complex fuzzy terms to illustrate this two-dimensional uncertain information, in which amplitude term represents the uncertainty about alternative and phase term represents the uncertainty about the sustainability of an alternative. First, we present a new framework MULTIMOORA under complex fuzzy structure. Then, in order to solve two-dimensional uncertain information about E-waste, the proposed complex fuzzy-MULTIMOORA method is applied, in which E-waste awareness-creating program and treatment technology selection are done. The sensitivity and comparative analysis are further delivered to validate the effectiveness of the proposed technique. [ABSTRACT FROM AUTHOR]

Summary Fast online surveys without sampling frames are becoming increasingly important in survey research. Their recruitment methods result in non‐probability samples. As the mechanism of data generation is always unknown in such samples, the problem of non‐ignorability arises making vgeneralisation of calculated statistics to the population of interest highly questionable. Sensitivity analyses provide a valuable tool to deal with non‐ignorability. They capture the impact of different sample selection mechanisms on target statistics. In 2019, Andridge and colleagues proposed an index to quantify potential (non‐ignorable) selection bias in proportions that combines the effects of different selection mechanisms. In this paper, we validate this index with an artificial non‐probability sample generated from a large empirical data set and additionally applied it to proportions estimated from data on current political attitudes arising from a real non‐probability sample selected via River sampling. We find a number of conditions that must be met for the index to perform meaningfully. When these requirements are fulfilled, the index shows an overall good performance in both of our applications in detecting and correcting present selection bias in estimated proportions. Thus, it provides a powerful measure for evaluating the robustness of results obtained from non‐probability samples. [ABSTRACT FROM AUTHOR]

This study aims to formulate a mathematical framework to examine how the Lassa virus spreads in humans of opposite genders. The stability of the model is analyzed at an equilibrium point in the absence of the Lassa fever. The model's effectiveness is evaluated using real-life data, and all the parameters needed to determine the basic reproduction number are estimated. Sensitivity analysis is performed to pinpoint the crucial parameters significantly influencing the spread of the infection. The interaction between threshold parameters and the basic reproduction number is simulated. Control theory is employed to devise and evaluate strategies, such as awareness campaigns, advocating condom usage, and deploying rodenticides to reduce the possibility of virus transmission efficiently. [ABSTRACT FROM AUTHOR]

Instrumental variable (IV) analysis relies on the exclusion restriction—that the instrument only affects the dependent variable via its relationship with the independent variable and not via other causal routes. However, scholars generally justify the exclusion restriction based on its plausibility. I propose a method for searching for additional violations implied by existing social science studies. I show that the use of weather to instrument different independent variables represents strong prima facie evidence of exclusion‐restriction violations for all weather‐IV studies. A review of 289 studies reveals 194 variables previously linked to weather: all representing potential exclusion‐restriction violations. Using sensitivity analysis, I show that the magnitude of many of these violations is sufficient to overturn numerous existing IV results. I conclude with practical steps to systematically review existing literature to identify and quantify possible exclusion‐restriction violations when using IV designs. [ABSTRACT FROM AUTHOR]

AbstractAccording to the International Atomic Energy Agency’s requirements for the minimization of radioactive waste, nuclear facility components need to be cut into smaller blocks and packaged in waste containers during decommissioning. Therefore, it is important to increase the space utilization (SU) of the cut blocks in the containers while reducing the cutting time (CT). In engineering practice, increasing SU often leads to longer CTs, which poses a great challenge to improve the efficiency of the nuclear decommissioning process. To overcome this challenge and to determine the optimal operational parameters, this paper introduces a multi-objective optimization design method for cutting and packaging nuclear facility components. The goal is to increase SU while concurrently reducing CTs, ultimately minimizing the waste and reducing the decommissioning cost. First, we project the intricate three-dimensional (3D) irregular parts packing problem onto a two-dimensional plane to simplify the complexity inherent in 3D parts packing and to formulate a packing optimization model. Second, we employ the Morris method to perform global sensitivity analysis on critical parameters, including cutting angle, height, component radius, and plate thickness parameters, throughout the cutting process of nuclear facility components. This analysis produces global sensitivity indicators for each parameter, facilitating a precise assessment of the sensitivity values associated with different operational parameters. Finally, we formulate a multi-objective design optimization model for cutting and packing nuclear facility components that yields a series of alternative operating parameter solutions upon solving it. This methodology offers a resolution for the selection of optimal operational parameters in the decommissioning process of nuclear facility components, thereby attaining optimal outcomes in waste disposal and furnishing guidance for subsequent decommissioning activities. [ABSTRACT FROM AUTHOR]

Hydrogen plays a crucial role in global net-zero policies due to its numerous advantages and is widely regarded as a key future fuel. Clean and sustainable hydrogen production technologies are currently a focal point of research, with thermochemical methods showing promise in achieving environmentally friendly hydrogen. This study builds upon previous research conducted by the authors on thermochemical hydrogen production through chlorine family cycles. It conducts a thorough sensitivity analysis of various system variables that significantly impact the overall performance of these processes. The investigation assesses the performance of chlorine family thermochemical cycles from exergetic, exergoeconomic, and exergoenvironmental perspectives. Exergetic performance parameters include net thermal exergies and exergy efficiencies, exergoeconomic parameters encompass hourly levelized and total cost rates, and exergoenvironmental parameters involve exergoenvironmental factors and component-associated environmental impact rates. The system variables under consideration include ambient temperature, system lifetime, annual operating hours, and operation and maintenance cost factor. The analysis reveals that the system's lifetime notably influences both the exergoenvironmental factors and component-associated environmental impact rates (by about 80%). Meanwhile, its impact on both the hourly levelized (around 11.5%) and total cost rates (approximately 1.2%) is comparatively less significant. Similarly, ambient temperature has a marginal effect on the net thermal exergies and exergy efficiencies of the considered cycles. [Display omitted] • A comprehensive sensitivity analysis of chlorine family cycles is presented. • Cycles are studied via exergetic, exergoeconomic & exergoenvironmental perspectives. • Lifetime influences the exergoenvironmental factor by about 80%. • Lifetime influences the hourly levelized cost rate by 11.5% and total cost rate by 1.2%. • Ambient temperature marginally affects net thermal exergies and exergy efficiencies. [ABSTRACT FROM AUTHOR]

Verification, validation, and uncertainty quantification is generally recognized as a standard for assessing the credibility of mechanical models. This is especially evident in biomechanics, with intricate models, such as knee joint models, and highly subjective acquisition of parameters. Propagation of uncertainty is numerically expensive but required to evaluate the model reliability. An alternative to this is to analyze the worst-case models obtained within the specific bounds set on the parameters. The main idea of the paper is to search for two models with the greatest different response in terms of displacement-load curve. Real-Coded Genetic Algorithm is employed to effectively explore the high-dimensional space of uncertain parameters of a 2D dynamic knee model, while Radial Basis Function surrogates reduce the computation by orders of magnitude to near real-time, with negligible impact on the quality. It is expected that the studied knee joint model is very sensitive to uncertainty in the geometrical parameters. The obtained worst-case knee models showcase unrealistic behavior with one of them unable to fully extend, and the other largely overextending. Their relative difference in extension is up to 35% under ±1 mm bound set on the geometry. This unrealistic behavior of knee joint model is confirmed by the large standard deviation obtained from a classical sampling-based sensitivity analysis. The results confirm the viability of the method in assessing the reliability of biomechanical models. The proposed approach is general and could be applied to other mechanical systems as well. [ABSTRACT FROM AUTHOR]

Background: The gut microbiota significantly influences the onset and progression of juvenile idiopathic arthritis (JIA) and associated uveitis (JIAU); however, the causality remains unclear. This study aims to establish a causal link between gut microbiota and JIA or JIAU. Methods: Using publicly available genome-wide association studies (GAWS) summary data, we conducted a two-sample Mendelian randomisation (MR) analysis employing various methods, namely inverse variance weighted (IVW), simple mode, weighted mode, weighted median and MR-Egger regression methods, to assess the causal association between JIA or JIAU and gut microbiota. Sensitivity analyses, including Cochrane’s Q test, MR-Egger intercept test, leave-one-out analysis and MR-PRESSO, were performed to evaluate the robustness of the MR results. Subsequently, reverse MR analysis was conducted to determine causality between gene-predicted gut microbiota abundance and JIA or JIAU. Results: The MR analysis revealed a causal association between gut microbiota abundance variations and JIA or JIAU risk. Specifically, the increased abundance of genus Ruminococcaceae UCG013 (OR: 0.055, 95%CI: 0.006–0.103, p = 0.026) and genus Ruminococcaceae UCG003 (β: 0.06, 95%CI: 0.003–0.117, p = 0.041) correlated with an increased risk of JIA, while genus Lachnospiraceae UCG001 (OR: 0.833, 95%CI: 0.699~0.993, p = 0.042) was associated with a reduced risk of JIA, among others. Sensitivity analysis confirmed MR analysis robustness. Conclusions: This study provides substantial evidence supporting a causal association between genetically predicted gut microbiota and JIA or JIAU. It highlights the significant role of intestinal flora in JIA or JIAU development, suggesting their potential as novel biomarkers for diagnosis and prevention. These findings offer valuable insights to mitigate the impact of JIA or JIAU. [ABSTRACT FROM AUTHOR]

Background: To investigate the causal relationship between major depression and functional dyspepsia using two-sample Mendelian randomization. Methods: Data for major depression and functional dyspepsia were obtained from genome-wide association studies. We selected Single Nucleotide Polymorphisms (SNPs) strongly associated with severe depression. Mendelian randomization analysis was conducted using methods such as Inverse-Variance Weighted (IVW), MR-Egger, and Weighted Median Estimator (WME). Sensitivity analysis was performed to assess the robustness of the results. Results: A total of 31 eligible SNPs were identified as instrumental variables for major depression. IVW analysis indicated a positive causal relationship between the two conditions (ß = 0.328; SE = 0.137; p = 0.017), suggesting that severe depression increases the risk of functional dyspepsia (OR = 1.389; 95% CI: 1.062-1.816). Sensitivity tests showed no evidence of heterogeneity or horizontal pleiotropy (p > 0.05). Conclusion: MR analysis had shown that major depressive disorder is associated with an increased risk of functional dyspepsia. [ABSTRACT FROM AUTHOR]

Background: Previous epidemiological studies have reported an association between Sjögren's syndrome (SS) and Parkinson's disease (PD); however, the causality and direction of this relationship remain unclear. In this study, we aimed to investigate the causal relationship between genetically determined SS and the risk of PD using bidirectional Mendelian randomization (MR). Methods: Summary statistics for Sjögren's syndrome used as exposure were obtained from the FinnGen database, comprising 1,290 cases and 213,145 controls. The outcome dataset for PD was derived from the United Kingdom Biobank database, including 6,998 cases and 415,466 controls. Various MR methods, such as inverse variance weighted (IVW), Mendelian randomization Egger regression (MR-Egger), weighted median (WM), simple mode, weighted mode, MR-pleiotropy residual sum and outlier (MR-PRESSO), and robust adjusted profile score (RAPS), were employed to investigate the causal effects of SS on PD. Instrumental variable strength evaluation and sensitivity analyses were conducted to ensure the reliability of the results. In addition, reverse MR analysis was performed to examine the causal effects of PD on SS. Results: The WM, IVW, RAPS and MR-PRESSO methods demonstrated a significant association between genetically predicted SS and reduced risk of PD (odds ratio ORWM = 0.9988, ORIVW = 0.9987, ORRAPS = 0.9987, ORMRPRESSO = 0.9987, respectively, P < 0.05). None of the MR analyses showed evidence of horizontal pleiotropy (P > 0.05) based on the MR-Egger and MRPRESSO tests, and there was no statistical heterogeneity in the test results of the MR-Egger and IVW methods. The leave-one-out sensitivity analysis confirmed the robustness of the causal relationship between SS and PD. Furthermore, reverse MR analysis did not support any causal effects of PD on SS. Conclusion: Our MR study supports a potential causal association between SS and a reduced risk of PD. Further extensive clinical investigations and comprehensive fundamental research are warranted to elucidate the underlying mechanisms linking SS and PD. [ABSTRACT FROM AUTHOR]

Objective To explore the causal relationship between exercise, sedentary behavior, and the risk of sarcopenia, and to gain a deeper understanding of the pathogenesis of this disease, to provide guidance for prevention, treatment, and rehabilitation. Methods Mendelian randomization (MR) analysis was conducted using data from genome-wide association studies. Single nucleotide polymorphisms that were significantly associated with the exposure and with the outcome were selected, and multiple MR methods were employed, including inverse variance weighting, weighted median, MR-Egger, weighted mode, simple mode, and MR-PRESSO. Heterogeneity, robustness, and consistency of the results were assessed through CochranJ s Q statistic, MR -Egger regression, and leave -one -out sensitivity analysis. Results The inverse variance weighting method showed that low-intensity exercise had a positive causal relationship with the risk of sarcopenia (OR=1.76, 95%CI: 1.09-2.85, P=0.02; OR二0.3& 95%CIi 0.14-1.00, P<0.05; O2-=2.13, 95%CI: 1.75-2.5& P<0.001), while sedentary behavior had a negative causal relationship with the risk of sarcopenia (OR二0.74, 95%CI: 0.64-0.85, P<0,001; OR二 1.51, 95%CI: 1.24-1.83, P<0.001; OR=0.73, 95%CI: 0.70-0,77, P<0,001). Sensitivity analysis showed no evidence of reverse causality, pleiotropy, or heterogeneity. Conclusion The results of this study confirm a reverse causal relationship between low-intensity exercise and sarcopenia, while sedentary behavior has a positive causal relationship with sarcopenia, which provides genetic evidence to support the treatment of sarcopenia. [ABSTRACT FROM AUTHOR]

Objective To investigate the potential causal relationship between seven common unhealthy lifestyles (smoking, alcohol, sugar intake, salt intake, fat intake, sleep, and prolonged sedentary behaviors) and chronic kidney disease (CKD) using Mendelian randomization (MR) method. Methods The instrumental variables were obtained from genome-wide association study (GW AS) databases. Inverse-variance weighted (IVW) method was used as the primary analysis method, and MR-Egger and weighted-median (WM) as supplementary metliods to analyze the potential causal association between exposure and outcome. Sensitivity analyses were also conducted. Results The IVW analysis showed a causal relationship between fat intake (O/-=1.09, 95%CZ: 1.01-1.19, P=0.027) and CKD. Sensitivity analysis results did not reveal notable heterogeneity (-=18.00, P=0.12) or pleiotropy (P=0.27). Smoking, alcohol, sugar intake, salt intake, sleep, and prolonged sedentary behaviors showed no causal relationship with CKD in the IVW analysis. Conclusion There is a positive relationship between fat intake and CKD risk. It is suggested to control fat intake to reduce the risk of CKD. [ABSTRACT FROM AUTHOR]

The design of cyber-physical systems (CPS) is challenging due to the heterogeneity of software and hardware components that operate in uncertain environments (e.g., fluctuating workloads), hence they are prone to performance issues. Software performance antipatterns could be a key means to tackle this challenge since they recognize design problems that may lead to unacceptable system performance. This manuscript focuses on modeling and analyzing a variegate set of software performance antipatterns with the goal of quantifying their performance impact on CPS. Starting from the specification of eight software performance antipatterns, we build a baseline queuing network performance model that is properly extended to account for the corresponding bad practices. The approach is applied to a CPS consisting of a network of sensors and experimental results show that performance degradation can be traced back to software performance antipatterns. Sensitivity analysis investigates the peculiar characteristics of antipatterns, such as the frequency of checking the status of resources, that provides quantitative information to software designers to help them identify potential performance problems and their root causes. Quantifying the performance impact of antipatterns on CPS paves the way for future work enabling the automated refactoring of systems to remove these bad practices. [ABSTRACT FROM AUTHOR]

Due to importance of cell voltage and caustic current efficiency (CCE) in chlor-alkali industry, the necessity of accurate approach for prediction these parameters has become evident. In the current work, an extreme learning machine (ELM) approach is used to this end. Determination of the statistical qualities including R2 and different types of error reveals the fact that ELM method is suitable tool for calculation of CCE and cell voltage. The determined R2 values for CCE and cell voltage are equal to 1. Furthermore, RMSE values are 0.00002 and 1.3 × 10−6 for cell voltage and CCE, respectively. On the other hand, different graphical methods confirmed this acclaim. Moreover a sensitivity analysis is used to show effect of brine concentration, current density, operating temperature, electrolyte velocity, run time and pH on cell voltage and CCE. This analysis concluded to the fact that brine concentration and current density have the most effects on CCE and Cell voltage, respectively. [ABSTRACT FROM AUTHOR]

Background: Certain associations observed in the National Birth Defects Prevention Study (NBDPS) contrasted with other research or were from areas with mixed findings, including no decrease in odds of spina bifida with periconceptional folic acid supplementation, moderately increased cleft palate odds with ondansetron use and reduced hypospadias odds with maternal smoking. Objectives: To investigate the plausibility and extent of differential participation to produce effect estimates observed in NBDPS. Methods: We searched the literature for factors related to these exposures and participation and conducted deterministic quantitative bias analyses. We estimated case‐control participation and expected exposure prevalence based on internal and external reports, respectively. For the folic acid‐spina bifida and ondansetron‐cleft palate analyses, we hypothesized the true odds ratio (OR) based on prior studies and quantified the degree of exposure over‐ (or under‐) representation to produce the crude OR (cOR) in NBDPS. For the smoking‐hypospadias analysis, we estimated the extent of selection bias needed to nullify the association as well as the maximum potential harmful OR. Results: Under our assumptions (participation, exposure prevalence, true OR), there was overrepresentation of folic acid use and underrepresentation of ondansetron use and smoking among participants. Folic acid‐exposed spina bifida cases would need to have been ≥1.2× more likely to participate than exposed controls to yield the observed null cOR. Ondansetron‐exposed cleft palate cases would need to have been 1.6× more likely to participate than exposed controls if the true OR is null. Smoking‐exposed hypospadias cases would need to have been ≥1.2 times less likely to participate than exposed controls for the association to falsely appear protective (upper bound of selection bias adjusted smoking‐hypospadias OR = 2.02). Conclusions: Differential participation could partly explain certain associations observed in NBDPS, but questions remain about why. Potential impacts of other systematic errors (e.g. exposure misclassification) could be informed by additional research. [ABSTRACT FROM AUTHOR]

Computational models of the cardiovascular system are increasingly used for the diagnosis, treatment, and prevention of cardiovascular disease. Before being used for translational applications, the predictive abilities of these models need to be thoroughly demonstrated through verification, validation, and uncertainty quantification. When results depend on multiple uncertain inputs, sensitivity analysis is typically the first step required to separate relevant from unimportant inputs, and is key to determine an initial reduction on the problem dimensionality that will significantly affect the cost of all downstream analysis tasks. For computationally expensive models with numerous uncertain inputs, sample‐based sensitivity analysis may become impractical due to the substantial number of model evaluations it typically necessitates. To overcome this limitation, we consider recently proposed Multifidelity Monte Carlo estimators for Sobol' sensitivity indices, and demonstrate their applicability to an idealized model of the common carotid artery. Variance reduction is achieved combining a small number of three‐dimensional fluid–structure interaction simulations with affordable one‐ and zero‐dimensional reduced‐order models. These multifidelity Monte Carlo estimators are compared with traditional Monte Carlo and polynomial chaos expansion estimates. Specifically, we show consistent sensitivity ranks for both bi‐ (1D/0D) and tri‐fidelity (3D/1D/0D) estimators, and superior variance reduction compared to traditional single‐fidelity Monte Carlo estimators for the same computational budget. As the computational burden of Monte Carlo estimators for Sobol' indices is significantly affected by the problem dimensionality, polynomial chaos expansion is found to have lower computational cost for idealized models with smooth stochastic response. [ABSTRACT FROM AUTHOR]

In order to improve the problem of poor sedimentation and easy leakage when applied to MR devices of magnetorheological (MR) fluid, this work made a magnetorheological grease (MRG) material with 70 % carbonyl iron powder and used Anton Paar rheometer to test its flow curve under different currents. The Bingham model is selected and the Origin software is used for fitting calculation. In addition, due to the advantages of rheological properties of MRG, the double rod MRG damper applied to the vibration reduction system was designed. The calculation of the main component parameters and the dimensions of the magnetic field Road size were conducted during the design process. After the physical completion of the MRG damper, its mechanical performance was tested, and the results showed that the maximum damping force reached 1.1 kN when the current was 1.8 A. Subsequently, the Bouc Wen model and Dahl model were introduced to describe the nonlinear behavior of the magnetorheological grease damper. The prediction accuracy of the two models was compared using a coefficient of determination, and it was found that the Bouc Wen model can more accurately and efficiently simulate the dynamic characteristics of the magnetorheological grease damper, with a coefficient of determination value above 0.99. Finally, the relationship between model parameters and current was established through least squares fitting, and most of the parameters were fitted with a cubic polynomial relationship with current. [ABSTRACT FROM AUTHOR]

A cost-effective approach using metamodel-based design optimization has been employed to determine the optimal configuration of input parameters for a desired output in an industrial engineering problem, with a case study of a bio-filtration system. Inhomogeneous multiphase flow around a simplified bio-filtration system under backwashing conditions was simulated based on parameter sampling through the design of experiments. Metamodeling was conducted along with a sensitivity analysis of input parameters for desired outputs, which helped filter out unimportant parameters and achieve computational benefits during the modeling process. Finally, optimization was performed using the metamodel and an evolutionary algorithm to determine the optimal parameter configuration for the desired outputs, i.e., evenly distributed filter-bed mass flow. The numerical approach can yield well-predicted results with significantly reduced computational time compared to full computational fluid dynamics simulations, potentially reducing the number of expensive simulations required for various industrial engineering problems. [ABSTRACT FROM AUTHOR]

More than two-thirds of the installed solar power in Brazil comes from distributed mini-generation and microgeneration (DMMG), mostly residential and for local self-consumption of solar PV. DMMG had its legal framework approved in 2022, gradually introduces charges for distribution network use, previously exempt. To understand these implications, this article compiles recent legislative and normative changes, particularly for solar PV DMMG projects. In addition, the present study focuses on the use of DMMG systems in the city of Fortaleza in the Northeast Region. The study includes an analysis of the integration of photovoltaic plants in homes in Fortaleza and their economic viability under the new conditions established after 2020. Two residential photovoltaic systems, with varying power levels common in the residential sector of Fortaleza, were studied. Each plant underwent three analyses: one with vested rights; another with 100% taxation of FIO Bl and a third for the current scenario, factoring in the growing FIO B charge from 15% in 2023 to 100% in 2029. Customers who obtained the system by cash payment or financing were considered. An economic feasibility study factored in a 5.79% yearly inflation rate and an 11.35% annual tariff readjustment across the two plants, including sensitivity analysis for this variable. [ABSTRACT FROM AUTHOR]

Power systems are complex and often subject to faults, requiring accurate mathematical models for a thorough analysis. Traditional time-domain models are employed to evaluate the dynamic response of power system elements during transmission system faults. However, only the positive sequence components are considered for unbalanced faults, so the small-signal stability analysis is no longer accurate when assuming balanced conditions for asymmetrical faults. The dynamic phasor approach extends traditional models by representing synchronous machines with harmonic sequence components, making it suitable for an unbalanced condition analysis and revealing dynamic couplings not evident in conventional methods. By modeling electrical and mechanical equations with harmonic sequence components, the study implements an eigenvalue sensitivity analysis and participation factor analysis to identify the variable with significant participation in the critical modes and consequently in the dynamic response of synchronous machines during asymmetric faults, thereby control strategies can be proposed to improve system stability. The article validates the dynamic phasor model through simulations of a single-phase short circuit, demonstrating its accuracy and effectiveness in representing the transient and dynamic behavior of synchronous machines, and correctly identifies the harmonic sequence component with significant participation in the critical modes identified by the eigenvalue sensitivity to the rotor angular velocity and rotor angle. [ABSTRACT FROM AUTHOR]

An integral equation is constructed relating the three-parameter Mittag-Leffler function with a perturbed one, from which a new property is derived. The latter helps in finding new sums of series of Mittag-Leffler functions. Also, a bound is obtained for the Mittag-Leffler function, allowing to perform a sensitivity analysis, being vital in the solution of fractional differential equations. The bound is also tested on a numerical example. [ABSTRACT FROM AUTHOR]

The pricing problem of European option is investigated under the generalized fractional jump-diffusion model. First of all, the generalized fractional jump-diffusion model is proposed, with the assumption that the underlying asset price follows this model, and the explicit solution is derived using the Itô formula. Then, the partial differential equation (PDE) of the European option price is obtained by using the delta-hedging strategy, and the analytical solutions of the European call and put option prices are obtained through the risk-neutral pricing principle. Moreover, the accuracy of closed-form formula for European option pricing is verified by the Monte Carlo simulation. Furthermore, the properties of the pricing formulas are discussed and the impact of main parameters on the option pricing model are analyzed via calculations of Greeks. Finally, the rationality and validity of the established option pricing model are verified by numerical analysis. [ABSTRACT FROM AUTHOR]

Phonation onset is characterized by the unstable growth of vocal fold (VF) vibrations that ultimately results in self-sustained oscillation and the production of modal voice. Motivated by histological studies, much research has focused on the role of the layered structure of the vocal folds in influencing phonation onset, wherein the outer “cover” layer is relatively soft and the inner “body” layer is relatively stiff. Recent research, however, suggests that the body-cover (BC) structure over-simplifies actual stiffness distributions by neglecting important spatial variations, such as inferior–superior (IS) and anterior–posterior gradients and smooth transitions in stiffness from one histological layer to another. Herein, we explore sensitivity of phonation onset to stiffness gradients and smoothness. By assuming no a priori stiffness distribution and considering a second-order Taylor series sensitivity analysis of phonation onset pressure with respect to stiffness, we find two general smooth stiffness distributions most strongly influence onset pressure: a smooth stiffness containing aspects of BC differences and IS gradients in the cover, which plays a role in minimizing onset pressure, and uniform increases in stiffness, which raise onset pressure and frequency. While the smooth stiffness change contains aspects qualitatively similar to layered BC distributions used in computational studies, smooth transitions in stiffness result in higher sensitivity of onset pressure than discrete layering. These two general stiffness distributions also provide a simple, low-dimensional, interpretation of how complex variations in VF stiffness affect onset pressure, enabling refined exploration of the effects of stiffness distributions on phonation onset. [ABSTRACT FROM AUTHOR]