Local Sensitivity Research Articles

Plasmonic waveguide-mode based aluminum nanogratings for enhanced chemical and biological sensing in the UV regime

Abstract We have designed and modeled one dimensional narrow-gap aluminum nanogratings for plasmonic sensing of bulk and localized changes in the refractive index in the UV spectral region. The proposed configuration of the plasmonic sensors based on narrow-gap nanogratings allows normally light to be directly coupled into plasmonic waveguide modes in the gaps between the nanogratings, thereby alleviating the problem of employing bulky prism coupling mechanisms. The rigorous coupled wave analysis (RCWA) simulations were performed to optimize all the narrow-gap nanograting parameters such as the periodicity ‘P’, the gap between the nanograting walls ‘W’, and height ‘H’ of the nanogratings such that the plasmonic sensors based on these nanogratings operated in the UV spectral region and had the highest values of sensing performance characteristics. The plasmon resonance related dips in the reflectance spectra of these narrow-groove nanogratings can be tuned in the far-UV and deep-UV wavelength ranges by varying the different structural parameters of these nanogratings. Furthermore, we have defined other performance parameters like FOM bulk * and FOM localized * to account for the depth of the plasmonic resonance dip along with the sensitivity (S) and figure of merit (FOM). These narrow-gap nanogratings have localized regions of high electromagnetic fields inside the gaps between the nanogratings, which results in enhanced sensitivity of the proposed structures. We have calculated the maximum bulk sensitivity (S) of 190 nm RIU−1 with the figure of merit (FOM bulk *) of 2.567 RIU−1 in the UV region. Similarly, the highest localized sensitivity (S s) of 18.2 nm nm−1 and a figure of merit (FOM localized *) of 0.15356 nm−1 was obtained for the narrow-gap aluminum nanograting based plasmonic sensor. The high sensitivity achieved in localized and bulk sensing enables this configuration to be developed into a compact and highly robust sensor for chemical and bio-sensing applications.

A Matlab Toolbox for cardiac electrophysiology simulations on patient-specific geometries.

In this paper, we present CardioMat, a Matlab toolbox for cardiac electrophysiology simulation based on patient-specific anatomies. The strength of CardioMat is the easy and fast construction of electrophysiology cardiac digital twins from segmented anatomical images in a general-purpose software such as Matlab. CardioMat implements a quasi-automatic pipeline that guides the user toward the construction of anatomically detailed cardiac electrophysiology models. Importantly, the CardioMat framework includes the generation of physiologically plausible fiber orientation and Purkinje networks. The main novelty of our framework is its ability to handle voxel-based geometries as produced by segmentation procedures directly, without the need for an unstructured mesh. Indeed, the CardioMat monodomain solver uses a smoothed boundary approach and runs completely on GPU for fast simulations. We employed CardioMat in different application scenarios to show its potentialities and provide preliminary assessment of the feasibility, diagnostic performance, and accuracy of the toolbox. In particular, we showed that CardioMat simulations derived from post-infarction patients hold high sensitivity, specificity, predictive value, and accuracy for localization of deceleration zones in sinus rhythm.

P-1123. Characterization of microbiological isolates in the newborn unit of the San Ignacio university hospital between 2018 and 2022

Abstract Background Identifying local microbial profiles and antibiotic sensitivities is an important part of antimicrobial stewardship programs in order to prevent overuse of antibiotics. The purpose of this study was to characterize the microbiological isolates from patients hospitalized in the newborn care unit of the Hospital Universitario San Ignacio from January 1, 2018 to December 31, 2022. Total number of microorganisms isolated 1 Methods An observational, descriptive, historical single cohort study was conducted. Positive cultures of sterile samples (blood, urine, spinal fluid) from patients hospitalized. Demographic, clinical, and microbiological data of patients with positive cultures were also collected. Results A total of 207 positive cultures of sterile samples were identified (97 blood, 108 urines, 2 spinal fluid). The most frequent pathogens isolated were E. coli 28.9%, S. epidermidis 27%, Enterococcus faecalis 11.5%, Klebsiella pneumoniae 8.2% and Staphylococcus aureus 4.8%. E. coli and S. epidermidis being the main microorganisms isolated 28% each were the main pathogens associated with neonatal sepsis (NS). NS was predominantly late-onset >72 hours 94.6%. Early-onset sepsis was less common 5.3% and was associated with E. coli 5.4% and S. agalactiae 18.1%. Healthcare-associated infections (34.7%), and catheter-associated bloodstream infections (CLABSI) were the most common. Congenital heart disease (P = .000) was the main comorbidity related to CLABSI, as well as gastroschisis (P = 0.002) and intestinal atresia (P = 0.033) Multidrug-resistant (MDR) microorganisms were isolated in 12 instances (5.8%). Extended Spectrum Beta-Lactamase (ESBL) was 50%. Congenital heart disease was the only comorbidity with a statistically significant relationship with infection by MDR microorganisms (P = 0.026). Clinical and sociodemographic characteristics of the multidrug-resistant microorganism population. Conclusion This is the first study in our environment to know the basal factors of infections in a population at risk, the patterns of resistance and possible association with the most prevalent pathologies and, based on this, to define the strategies of antimicrobial stewardship. Disclosures All Authors: No reported disclosures

Impact of antimicrobial resistance on infections in children in Africa.

Antimicrobial resistance is an escalating public health threat in Africa, and an awareness of the devastating impact on children is growing. This review highlights the prevalence and patterns of antimicrobial resistance among children in Africa, focusing on pathogens responsible for bloodstream infections, community-acquired pneumonia, bacterial meningitis, neonatal infections, diarrhea and malaria. Current strategies to tackle antimicrobial resistance in pediatric populations are discussed. Bloodstream infections significantly contribute to child mortality, with high resistance observed in pathogens like Salmonella spp., Klebsiella spp., Escherichia coli, and Staphylococcus aureus. Additionally, rising resistance in pathogens causing community-acquired pneumonia, meningitis and bacterial diarrhea challenges the effectiveness of WHO-recommended therapies. Antibiotics used to treat neonatal infections, such as ampicillin, gentamicin and cefotaxime, are threatened by high resistance in Escherichia coli and Klebsiella spp, contributing to adverse neonatal outcomes. PfKelch 13 mutations linked to artemisinin resistance in parts of Africa raise public health concerns, as malaria remains a major cause of illness and death. Stronger collaborative efforts are needed to enhance surveillance, improve diagnostic capabilities and update treatment protocols based on local pathogen sensitivities. More research is required on pediatric antimicrobial resistance in Africa.

Effect of PCC Material Properties on MEPDG Jointed Plain Concrete Pavement (JPCP) Performance Prediction

This paper focuses on comprehensive sensitivity analyses of various rigid pavement scenarios. Jointed Plain Concrete Pavement (JPCP) sections designed for three traffic levels in each of five climate zones are evaluated in the sensitivity analyses. One-at-a-time (OAT) local sensitivity analysis was implemented using a design limit normalized sensitivity index (NSI) to provide both quantitative and qualitative sensitivity information. All portland cement concrete (PCC) material properties examined showed sensitivity to at least one rigid pavement performance measure. The highest ranked sensitive inputs for JPCP analysis include the PCC strength and stiffness properties and the curling and warping related properties. The findings of this study can provide some practical guidance in PCC material property selection for rigid pavement practitioners in using MEPDG.

On the Practicability of Deep Learning based Anomaly Detection for Modern Online Software Systems: A Pre-Train-and-Align Framework

Operation and maintenance are critical activities in the whole life cycle of modern online software systems, and anomaly detection is a crucial step of these activities. Recent studies mainly develop deep learning techniques to complete this task. Notably, though these techniques have achieved promising results in experimental evaluations, there are still several practicality gaps for them to be successfully applied in a real-world online system, including the scalability gap, availability gap and alignment gap. To bridge these gaps, we propose an anomaly detection framework, namely ShareAD , based on a pre-train-and-align paradigm. Specifically, we argue that pre-training a shared model for anomaly detection is an effective way to bridge the scalability gap and the availability gap. To support this argument, we systematically study the necessity and feasibility of model sharing for online system maintenance. We further propose a novel model based upon Transformer encoder layers and Base layers, which works well for anomaly detection pre-training. Then, to bridge the alignment gap, we propose ShareAD alignment to align the pre-trained model with operator preference by jointly considering the local observation context and sensitivity of each monitor entity. Extensive experiments on two real-world large-scale datasets demonstrate the effectiveness and practicality of ShareAD .

Screening for Structural Heart Defects: A Single-Center Retrospective Cost Analysis for Fetal Echocardiography in Adults with Congenital Heart Disease.

Fetal echocardiography (FE) is recommended for parents with congenital heart disease (pCHD) due to a 3-6% recurrence risk of congenital heart disease (CHD). This study aimed to evaluate the cost of FE for detecting neonatal CHD in pCHD. FE data were collected between 12/2015 and 12/2022. Parents were stratified by CHD complexity: "simple" (class I) and "complex" (class II/III). Cost analysis compared universal FE with selective FE following a positive level II screening anatomical ultrasound (SAU). Primary outcomes included the cost and number needed to screen (NNT) to detect one case of neonatal CHD. Of 419 pCHD cases, 48 were analyzed separately due to additional FE indications. Among the remaining 371 cases (73% maternal, 27% paternal; mean maternal age: 31years), 14 postnatal CHD cases were detected (3.8%). Recurrence rates were 1.9% for simple pCHD (n = 156) and 5.1% for complex pCHD (n = 215). Universal FE increased the cost of detecting neonatal CHD. The cost per detected case was $267,157 for simple CHD (NNT = 560) and $135,125 for complex CHD (NNT = 288). The lower sensitivity of SAU reduced the cost of universal FE. In this single-center cohort, the recurrence risk of CHD in pCHD is higher than in the general population, particularly in complex cases. Universal screening in simple pCHD is costlier with high-sensitivity SAU. Targeted screening in complex pCHD may offer a better cost-to-risk ratio, highlighting the need for early detection to improve outcomes. The cost effectiveness is dependent on local SAU sensitivity rates.

A Bio-Inspired Visual Neural Model for Robustly and Steadily Detecting Motion Directions of Translating Objects Against Variable Contrast in the Figure-Ground and Noise Interference.

(1) Background: At present, the bio-inspired visual neural models have made significant achievements in detecting the motion direction of the translating object. Variable contrast in the figure-ground and environmental noise interference, however, have a strong influence on the existing model. The responses of the lobula plate tangential cell (LPTC) neurons of Drosophila are robust and stable in the face of variable contrast in the figure-ground and environmental noise interference, which provides an excellent paradigm for addressing these challenges. (2) Methods: To resolve these challenges, we propose a bio-inspired visual neural model, which consists of four stages. Firstly, the photoreceptors (R1-R6) are utilized to perceive the change in luminance. Secondly, the change in luminance is divided into parallel ON and OFF pathways based on the lamina monopolar cell (LMC), and the spatial denoising and the spatio-temporal lateral inhibition (LI) mechanisms can suppress environmental noise and improve motion boundaries, respectively. Thirdly, the non-linear instantaneous feedback mechanism in divisive contrast normalization is adopted to reduce local contrast sensitivity; further, the parallel ON and OFF contrast pathways are activated. Finally, the parallel motion and contrast pathways converge on the LPTC in the lobula complex. (3) Results: By comparing numerous experimental simulations with state-of-the-art (SotA) bio-inspired models, we can draw four conclusions. Firstly, the effectiveness of the contrast neural computation and the spatial denoising mechanism is verified by the ablation study. Secondly, this model can robustly detect the motion direction of the translating object against variable contrast in the figure-ground and environmental noise interference. Specifically, the average detection success rate of the proposed bio-inspired model under the pure and real-world complex noise datasets was increased by 5.38% and 5.30%. Thirdly, this model can effectively reduce the fluctuation in this model response against variable contrast in the figure-ground and environmental noise interference, which shows the stability of this model; specifically, the average inter-quartile range of the coefficient of variation in the proposed bio-inspired model under the pure and real-world complex noise datasets was reduced by 38.77% and 47.84%, respectively. The average decline ratio of the sum of the coefficient of variation in the proposed bio-inspired model under the pure and real-world complex noise datasets was 57.03% and 67.47%, respectively. Finally, the robustness and stability of this model are further verified by comparing other early visual pre-processing mechanisms and engineering denoising methods. (4) Conclusions: This model can robustly and steadily detect the motion direction of the translating object under variable contrast in the figure-ground and environmental noise interference.

Simulation and Local Parametric Sensitivity Analysis of a Computational Model of Fructose Metabolism

This research utilized a mathematical model of fructose metabolism within the CellDesigner software package to investigate the effects of varying dietary fructose intake on fat metabolism. By simulating different meal patterns with varying levels of fructose, the model provided valuable insights into the relationship between fructose consumption and hepatic triglyceride accumulation. The results demonstrated a clear correlation between increased fructose intake and elevated hepatic triglycerides. Additionally, a local parametric sensitivity analysis identified glyceraldehyde-3-phosphate and pyruvate as key regulatory factors in this process. Importantly, the model accurately simulated changes in fructose concentration and its metabolites, validating its predictive capabilities. These findings underscore the importance of systems biology in elucidating the complex mechanisms underlying nutrition-related diseases. By integrating computational modeling with experimental data, researchers can gain a deeper understanding of how dietary factors influence metabolic pathways and contribute to health outcomes. Ultimately, systems biology holds the promise of enabling personalized nutrition recommendations tailored to individual needs and genetic predispositions.

An integrated approach to uncertainty and global sensitivity analysis in penstock structural modeling.

Enhanced penstock structural models significantly advance hydropower engineering, yet their increasing complexity introduces challenges. As model interactions intensify, predictability and comprehensibility decrease, complicating the evaluation of model accuracy and alignment with operational performance metrics and safety standards. This issue is particularly pronounced in dynamic modeling, where knowledge gaps hinder straightforward validation via observational data. Traditional techniques for model calibration and validation are becoming impractical, necessitating a strategic approach to prioritize sources of uncertainty related to critical response variability. This study aims to advance our understanding and management of structural variabilities in penstock models by developing a comprehensive, step-by-step Global Sensitivity Analysis (GSA), designed to meet the specific needs of penstock modeling. Illustrated through a free vibration analysis model of a penstock span, this structured methodology begins with Uncertainty Analysis (UA) to identify variabilities, followed by a screening phase using the Morris method to enhance computational efficiency. Subsequent application of multi-method GSA ranks parameter sensitivities, assesses robustness, and provides a comparative evaluation, providing insights into the effective tools for conducting GSA on penstock models. The process culminates with Regional Sensitivity Analysis (RSA), targeting local sensitivities and enhancing understanding of local parameter influences, thereby supporting model adjustments and design optimization. Results from this application characterize model sensitivities for the most prominent mode shapes to specific structural parameters and indicate that these sensitivity outcomes are influenced by variability in parameter spaces and output sub-range definitions. This study provides a practical framework for addressing uncertainties in penstock design, enhancing model accuracy, prioritizing parameters, managing risks, and improving the reliability and efficiency of hydropower infrastructure.

Noninvasive estimation of central blood pressure through fluid-structure interaction modeling.

Central blood pressure (cBP) is considered a superior indicator of cardiovascular fitness than brachial blood pressure (bBP). Even though bBP is easy to measure noninvasively, it is usually higher than cBP due to pulse wave amplification, characterized by the gradual increase in peak systolic pressure during pulse wave propagation. In this study, we aim to develop an individualized transfer function that can accurately estimate cBP from bBP. We first construct a three-dimensional, patient-specific model of the upper limb arterial system using fluid-structure interaction simulations, incorporating variable material properties and complex boundary conditions. Then, we develop an analytical brachial-aortic transfer function based on novel solutions for compliant vessels. The accuracy of this transfer function is successfully validated against numerical simulation results, which effectively reproduce pulse wave propagation and amplification, with key hemodynamic parameters falling within the range of clinical measurements. Further analysis of the transfer function reveals that cBP is a linear combination of bBP and aortic flow rate in the frequency domain, with the coefficients determined by vessel geometry, material properties, and boundary conditions. Additionally, bBP primarily contributes to the steady component of cBP, while the aortic flow rate is responsible for the pulsatile component. Furthermore, local sensitivity analysis indicates that the lumen radius is the most influential parameter in accurately estimating cBP. Although not directly applicable clinically, the proposed transfer function enhances understanding of the underlying physics-highlighting the importance of aortic flow and lumen radius-and can guide the development of more practical transfer functions.

Dynamic Analysis and Optimal Control of the Spread of Tungro Virus Disease in Rice Plants Considering Refugia Planting and Pesticide Application

One of the main obstacles in rice cultivation is tungro disease, caused by Rice Tungro Spherical Virus (RTSV) and Rice Tungro Bacilliform Virus (RTBV), which are transmitted by green leafhopper vectors (Nephotettix virescens). This disease can be controlled by using pesticides and refugia plants. Excessive use of pesticides can have negative impacts and high costs, so it is necessary to control the use of pesticides. In this study, a mathematical model of the spread of tungro virus disease in rice plants was developed by considering the characteristics of the virus, the presence of green leafhoppers and natural enemies, refugia planting, and pesticide use. From this model, dynamic and sensitivity analyses were carried out, and the optimal control theory was searched using the Pontryagin minimum principle. The analysis results showed three equilibriums: two non-endemic equilibriums (when plant and vector populations exist and when plant, vector, and natural enemy populations exist) and one endemic equilibrium. The non-endemic equilibrium will be asymptotically stable locally if R0<1. At the same time, the parameters that greatly influence the spread of this disease are parameters μ, μ2, and ϕ for local sensitivity analysis and α, a, β, b, ϕ, and μ2 for global sensitivity analysis. The results of the numerical simulation show that control using combined control is more effective in reducing the intensity of the spread of tungro disease in rice plants than control in the form of planting refugia plants as a source of food for natural enemies. The use of pesticides is sufficient for only four days, so the costs incurred are quite effective in controlling the spread of this disease.

Local damage-sensing method for structural members based on stress correlation characteristics

Abstract Structures are subjected to various external factors during the service life, the structure may have potential safety hazards caused by local damage. In order to accurately perceive the structural safety and identify issues in real-time, it is essential to develop local damage-sensing method for structural members using monitoring data. Therefore, this study proposes a local damage-sensing method for structural components based on stress correlation characteristics. This method relies on stress monitoring data, takes temperature influence into account, and achieves high accuracy in identifying local damage sensitivity. First, a strain response expression method is proposed based on the unit impulse response function and the external excitation function. The strain correlation function for any two measurement points on the structure is derived, and the correlation function between the strain at the measurement point and the strain at a reference point is calculated. The correlation function magnitude vector is constructed from its absolute values, and a component-state perception index is proposed based on the residuals of the magnitude vector of the stress correlation function. The monitoring data of Zhuhai Grand Theatre for 30 d within 5 months are analysed. By applying the proposed method to the Zhuhai Grand Theatre, the slowly varying temperature effects are effectively separated, the local damage identification result is in accordance with the presupposed situation, where the feasibility and accuracy of the proposed method is confirmed.

Adaptasi Strategi Pemasaran Global Starbucks di Indonesia

Starbucks Corporation, a global leader in the coffee industry, has successfully adapted its global marketing strategies to meet the needs of the Indonesian market. This study aims to analyze how Starbucks integrates product innovation, customer experience, technology utilization, strategic location selection, and loyalty programs to align with local preferences. Using a qualitative approach, this research examines the challenges and opportunities in adapting global strategies within Indonesia’s unique cultural and consumer landscape. The findings reveal that localization and cultural sensitivity are key drivers of Starbucks' success in Indonesia, enabling the company to maintain market leadership amidst increasing competition.

Pelvic floor muscle activation in response to pressure stimuli applied to the vulvar vestibule: an observational study comparing women with and without provoked vestibulodynia.

The nature of pelvic floor muscle (PFM) involvement in provoked vestibulodynia (PVD) is poorly understood. We aimed to determine if PFM electromyographic (EMG) activity in anticipation of or response to pressure applied to the posterior vaginal fourchette differs between those with and without PVD, and if the magnitude of PFM response is associated with pressure pain sensitivity, psychological or psychosexual function. This was an observational case-control study. Forty-two volunteers with PVD and 43 controls with no vulvar pain participated. Five on-line questionnaires were completed, then participants underwent a laboratory-based evaluation of vulvar pain sensitivity. EMG activation of the PFMs, hip adductor, and upper trapezius muscles was measured before, during, and after pressure stimuli (low, moderate) were applied, in random order, to the posterior vaginal fourchette and the posterior thigh (control site). EMG amplitude of the pubovisceralis (PV), bulbocavernosus (BC), and external anal sphincter (EAS) muscles. Secondary outcomes were EMG activation of the hip adductor brevis and upper trapezius muscles, questionnaire scores reflecting psychological/psychosexual outcomes, pressure pain threshold (PPT) at the vulvar vestibule, pain reported on a tampon test, and heart rate/heart rate variability. Compared to controls, EMG activation of the PV and EAS, but not the BC, was higher in anticipation of the pressure applied to the vaginal fourchette, was higher in all PFMs while the pressure was applied, and remained higher than baseline after the pressure was removed among those with PVD. EMG response amplitudes were modulated by the intensity of the pressure applied, with the largest responses reaching over 40% MVC in the EAS among those with PVD. PFM EMG amplitudes were associated with greater pain sensitivity and lower sexual function, but not with pain catastrophizing, central sensitization, depression, anxiety, or stress. While some anticipatory activation was observed, EMG responses were primarily observed during and after the application of the pressure. Among those with PVD, digital assessment of PFM tone might reflect PFM responses to pain at the vulvar vestibule, and interventions to reduce local pain sensitivity may be an important first step to successful improvements in vaginal function. This study includes a robust analysis of EMG activation. However, the cross-sectional design precludes the determination of causal relationships. Those with PVD demonstrate higher PFM responses and a higher prevalence of anticipatory activation in the PV and EAS muscles than controls in response to pressure applied at the vulvar vestibule.

Modeling water balance components of conifer species using the Noah-MP model in an eastern Mediterranean ecosystem

Abstract. Few studies have investigated the performance of land surface models for semiarid Mediterranean forests. This study aims to parameterize and test the performance of the Noah-MP land surface model for an eastern Mediterranean ecosystem. To this end, we calibrated the model for root zone soil moisture and transpiration of two conifer species, Pinus brutia, and Cupressus sempervirens, in a plantation forest on the Mediterranean island of Cyprus. The study area has a long-term average annual rainfall of 315 mm. Observations from 4 sap flow and 48 soil moisture sensors, for the period from December 2020 to June 2022, were used for model parameterization. A local sensitivity analysis found that the surface infiltration (REFKDT), hydraulic conductivity (SATDK), and stomatal resistance (RSMIN) parameters had the highest impacts on the water balance components (soil evaporation, tree transpiration, surface runoff, and drainage). The model performed better during the wetter 9-month validation period (379 mm rain) than during the drier 10-month calibration period (175 mm rain). Average soil moisture in the top 60 cm of the soil profile was reasonably well captured for both species (daily Nash–Sutcliffe efficiency > 0.80 for validation). Among the three soil layers, the second layer (20–40 cm) showed better simulation performance during both periods and for both species. The model exhibited limitations with respect to simulating transpiration, particularly during the drier calibration period. The inclusion of a root distribution function in the model, along with the monitoring of soil moisture below the 60 cm soil depth in the field, could improve the accuracy of model simulations in such water-limited ecosystems.

Analysis of the Validity of P2D Models for Solid-State Batteries in a Large Parameter Range

Simulation models are nowadays indispensable to efficiently assess or optimize novel battery cell concepts during the development process. Electro-chemo-mechano models are widely used to investigate solid-state batteries during cycling and allow the prediction of the dependence of design parameters like material properties, geometric properties, or operating conditions on output quantities like the state of charge. One possibility of classification of these physics-based models is their level of geometric resolution, including three-dimensionally resolved models and geometrically homogenized models, known as Doyle-Fuller-Newman or pseudo two-dimensional models. Within this study, the advantages and drawbacks of these two types of models are identified within a wide range of the design parameter values. Therefore, the sensitivity of an output quantity of the models on one or a combination of parameters is compared. In particular, the global sensitivity, i.e. the sensitivity in a wide range of parameter values, is computed by using the Sobol indexes as a measure. Furthermore, the local sensitivity of the difference in the output quantities of both models is evaluated to identify regions of parameter values in which they contain significant deviations. Finally, remarks on the potential interplay between both models to obtain fast and reliable results are given.

Sensitivity Analysis of Relative Permeability on Unsteady-State Core Flooding via Experimental Data

The use of computational reservoir simulation plays a key role in understanding fluid flow in geological formations, enabling the development of advanced models and accurate predictions. These simulations rely heavily on parametric constitutive relations to represent essential properties such as relative permeability (Krel), which is required for modeling continuous-scale multiphase flow in porous media. However, the majority of models described in the existing literature contain numerous empirical parameters that must be determined. Regardless of how complex their parameterization is, these parameters have no direct relationship to the problem's underlying physics. As a result, there is a need to estimate these parameters and regularly assess the associated uncertainties. Nevertheless, achieving satisfactory uncertainty quantification requires a preliminary investigation into sensitivity and linear dependence, a step that is often overlooked, especially in the context of core flood experiments. Given this issue, this study aims to analyze the reduced sensitivity coefficient of relative permeability parameters, parameterized using the LET model, in unsteady-state core flooding experiments, considering both bump and no bump conditions, while also taking into account the capillary pressure parameterized via LET. In these experiments, a plug saturated with oil undergoes axial water injection at one end, resulting in oil (and water after breakthrough) being produced at the opposite end. Experimental data includes the pressure difference between the water inlet and the oil (and water) outlet, along with the accumulated volume of produced oil. Based on experimental data, computational simulations were conducted using the Cydar® software to optimize the relative permeability and capillary pressure. The dynamic behavior of the relative permeability parameters' local sensitivity is showcased, alongside their dimensional comparison.

Local and global sensitivity analysis for railway upgrading between hydrogen fuel cell and electrification

Local and global sensitivity analysis for railway upgrading between hydrogen fuel cell and electrification

Impacts of Climate Change on Energy-Saving Sensitivity of Residential Building Envelope Design Parameters in Three Hot-Dry Cities

Warming climatic conditions are projected to intensify extreme heat events, especially in hot climates, affecting the energy-saving effectiveness of various building envelope design parameters. However, limited studies exist on the energy-saving sensitivity of building envelope design parameters for residential buildings in hot and dry climates under future climate change. This study aims to fill this gap by examining typical residential buildings in three cities in Iraq, classified as hot desert, hot semiarid, and Mediterranean. First, the most common type of residential building was selected as a case study, and its energy modelling was validated using measured energy bills. Second, future climatic weather files were constructed using the latest general circulation models to assess their impacts on building energy consumption. Third, the energy-saving sensitivity of applicable building envelope design parameters under different future climatic conditions was obtained through local and global sensitivity analysis methods. The results of global sensitivity analysis indicate that the solar heat gain coefficient of windows, the specific heat of exterior walls, and the projection of side fins are the most important factors, with standardized regression coefficients (SRC) ranging from -0.92 to 0.62. Notably, the SRC of windows' solar heat gain coefficient is expected to increase under future climate scenarios. In contrast, the significance of the specific heat of exterior walls depends on local climates. Although this study only included envelope design parameters and excluded economic analysis, it provides insights for policymakers and architects to prioritize building envelope design strategies that enhance the climate resilience of residential buildings.