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The emphasis of this study is the numerical analysis via D2Q9-based Lattice Boltzmann Method of free convection MHD non-Darcy flow in hybrid nanofluid (TiO2/Cu–water) occupying a differentially heated square enclosure. A key novelty of the work is the inclusion of a response surface methodology (RSM)-based sensitivity analysis. The current investigation has been done considering the variation in Rayleigh number (103 ≤ Ra ≤ 106), Darcy number (0.0001 ≤ Da ≤ 0.1), and Hartmann number (0 ≤ Ha ≤ 50). From RSM, high Darcy parameter, low volumetric fraction, and low Hartmann number are identified as the optimum functioning conditions for heat transfer rates. [ABSTRACT FROM AUTHOR]

AbstractIn this article, I propose a tractable approach to Bayesian inference in a simple linear regression model for which the standard exogeneity assumption does not hold. By specifying a beta prior for the squared correlation between an error term and regressor, I demonstrate that the implied prior for a bias parameter is t-distributed. If the posterior distribution for the identified regression coefficient is normal, this implies that the posterior distribution for the unidentified treatment effect is the convolution of a normal distribution and a t-distribution. This result is closely related to the literatures on unidentified regression models, imperfect instrumental variables, and sensitivity analysis. [ABSTRACT FROM AUTHOR]

Single-point electromechanical (EM) cable subsurface mooring (EMC-SSM) is an innovative subsea structure used to measure several ocean parameters throughout a water column in real time. However, designing an EMC-SSM that can adapt to rough sea conditions is challenging owing to the relative fragility of EM cables. In this study, an optimisation design method based on parameter sensitivity analysis was proposed to improve the responses of EMC-SSM. The unreasonable design and optimisation value were determined by evaluating the slopes of the sensitivity curves of four response parameters: the minimum and maximum tensions, maximum declination angle, and maximum curvature to the net buoyancy of the main float and big sensor package (BSP); drag; added mass coefficient of the BSP; and net weight and hydrodynamic coefficient of small sensor packages. We found that the proposed optimisation design method is effective in identifying and optimising the unreasonable configurations of mooring similar to the EMC-SSM. [ABSTRACT FROM AUTHOR]

In the condition of actual obstacle avoidance, where longitudinal deceleration is large and variable, it is difficult for the autonomous vehicle to simultaneously track longitudinal velocity and lateral motion. In addition, when the planned trajectory exceeds the adhesion of the road, the vehicle is also prone to losing stability. Most studies of the obstacle avoidance process only consider constant velocity. This paper presents a novel control architecture for four-wheel driving (4WD) and four-wheel steering (4WS) autonomous vehicles to track predefined trajectory and velocity. It is proposed to use preview technology to obtain the desired state of the vehicle and a controller based on multi-input multi-output nonlinear model predictive control (MIMO-NMPC) that considers the deceleration-steering combination condition. According to the sensitivity analysis of the influence of tyre slip rate on tyre lateral force, the optimal working boundary of the tyre is determined. The output of the controller is constrained by estimating tyre force to prevent tyre force saturation and maximise tyre adhesion limit utilisation. The simulation results demonstrate that the proposed controller can better track the trajectory and longitudinal velocity with a faster response on the high-adhesion road. The overshoot of lateral displacement and the maximum error of longitudinal velocity are less than 0.03m and 0.2m/s, respectively. In addition, when the planned trajectory exceeds the adhesion limit of the road, it can still follow the desired trajectory as closely as possible while maintaining stability on low-adhesion roads. [ABSTRACT FROM AUTHOR]

In the framework of risk assessment in nuclear accidents, simulation tools are widely used to understand and model physical phenomena. These simulation tools take into account a large number of uncertain input parameters. We often use Monte Carlo–type methods to explore their range of variation: The input space is randomly sampled, and a code run is performed on each sampled point. However, some of these code runs may fail to converge. Analyzing these code failures to understand which of the inputs have the most influence on them leads to a better understanding of how the code works. It also intends to improve the robustness of the simulation software and code computations. For this purpose, we propose two complementary approaches performing a statistical analysis of the code failures. The first approach is based on goodness-of-fit tests and compares conditional probability distributions according to code failures to a reference one. A second approach, based on a dependence measure named the Hilbert-Schmidt Independence Criterion, provides another way to measure the global dependence between the inputs and the code failures. The development of this methodology is carried out in the context of severe nuclear accidents. More especially, the presented methods are applied for the study of the simulation code MC3D, which simulates the fuel-coolant interaction in a severe nuclear accident context. [ABSTRACT FROM AUTHOR]

Dynamic crop models are powerful tools for evaluating crop responses to alternative management options. These models consist of various input parameters that need calibration before being implemented in a new environment. Sensitivity analysis (SA) has proven to be an efficient method for identifying dominant model parameters. This study aimed to determine influential parameters of the AquaCrop model for basil and also quantify output uncertainty under different nitrogen fertilizer rates. The Sobol' SA method was employed to compute both first-order and total-order sensitivity indices. Results indicated that normalized water productivity (WP*) and crop coefficient (KcTr,x) were the most influential parameters across all treatments. Furthermore, as fertility stress increased, the interaction effects among parameters decreased. The temporal analysis of model output (biomass) revealed that parameter sensitivities were highly time-dependent. In addition, the uncertainty of predicted biomass was determined using combined violin and box plots. Results demonstrated that the optimal nitrogen fertilizer application rate extended the distribution of model output. In summary, these findings offer valuable insights for model simplification and effective decision-making. [ABSTRACT FROM AUTHOR]

Estimating an individual treatment effect (ITE) is essential to personalized decision making. However, existing methods for estimating the ITE often rely on unconfoundedness, an assumption that is fundamentally untestable with observed data. To assess the robustness of individual-level causal conclusion with unconfoundedness, this article proposes a method for sensitivity analysis of the ITE, a way to estimate a range of the ITE under unobserved confounding. The method we develop quantifies unmeasured confounding through a marginal sensitivity model, and adapts the framework of conformal inference to estimate an ITE interval at a given confounding strength. In particular, we formulate this sensitivity analysis as a conformal inference problem under distribution shift, and we extend existing methods of covariate-shifted conformal inference to this more general setting. The resulting predictive interval has guaranteed nominal coverage of the ITE and provides this coverage with distribution-free and nonasymptotic guarantees. We evaluate the method on synthetic data and illustrate its application in an observational study. for this article are available online. [ABSTRACT FROM AUTHOR]

The aim of this study is to develop an inland terminal location selection methodology. This methodology is viewed from the perspective of the shipping line designing the inland transport chain while also taking the objectives of multiple other stakeholders into account. To that end, we develop a consensus model for a group Best-Worst Method (BWM) in order to aggregate the evaluations of the various stakeholders. The proposed method is applied to a real-life case study involving the Maersk shipping line, in which nine experts representing three different types of stakeholders assess six possible locations. After the evaluation, the market volume potential is identified as one of the most important criteria. Furthermore, a sensitivity analysis indicates that a varying influx of the container volume has no impact on the most desirable location. [ABSTRACT FROM AUTHOR]

Building-Stock Energy Models (BSEMs) have grown in popularity, implementation, scale and complexity. Yet, BSEM quality assurance processes have lagged behind. This article proposes a scalable methodology to apply Uncertainty (UA) and Sensitivity Analysis (SA) to BSEMs and studies the performance of eleven common UA-SA methods (OAT, SRC, SRRC, FFD, Morris, Sobol', eFAST, FAST-RBD, DMIM, PAWN, DGSM) for three UA-SA targets: screening, ranking and indices. Applying UA and SA to BSEMs requires a two-step input parameter sampling that samples 'across stocks' and 'within stocks'. To make efficient use of computational resources, practitioners should (i) distinguish between three UA-SA targets and (ii) choose a method based on the aimed UA-SA target. The computational cost varies according to the UA-SA target and method; (i) for screening: OAT, SRC, SRRC, FFD and Morris; (ii) for ranking: SRC, SRRC and Morris and (iii) for indices: Sobol' is the most efficient, among the tested UA-SA methods. [ABSTRACT FROM AUTHOR]

AbstractIntroducing pulsatility in LVADs is known to reduce complications such as stagnation and thrombosis, but it is an ongoing topic of research on what the optimal form is. We present a framework consisting of parametrized full-order simulations, reduced-order models, and sensitivity analysis to systematically quantify the effects of parametrized pulsatility on washout. As a sample problem, we study the washout in an idealized 2D left ventricle and a parametrized sinusoidal LVAD flow rate. The framework yields speed-ups proportional to the number of samples required in the sensitivity analysis. In our setting, we find that short, intense pulses wash out the left ventricle best, while the time between consecutive pulses does not play a significant role. [ABSTRACT FROM AUTHOR]

This article aims to simulate natural gas vehicle (NGVs) refueling station located in Bucharest, which includes an on-board cylinder, reservoir tanks, connecting pipes, and a reciprocating compressor using thermodynamic analysis. Tanks' pressures during the filling process were changed: 180-200, 200-220, and 220-235 bar for low, average and high pressure respectively. Furthermore, sensitivity analyses were implemented to study changing the cylinder initial pressure and the influence of vehicles' number alteration. The simulation result for mass flow rate is close to the experimental measurements. Additionally, the higher the initial pressure inside vehicle tank, the lower mass delivered and the shorter loading time. Finally, this study helps the government to increase the environmentally friendly fuel in the local market. [ABSTRACT FROM AUTHOR]

In order to study the influence of the space linkage system on the stability of the straddle-type monorail vehicle with single-axle bogies, the sensitivity analysis of the space linkage system is identified based on the Pearson coefficient method to determine the importance of each component of the single-axle bogies. The dynamics model of straddle-type monorail vehicle with single-axle bogies is established by the multi-rigid body dynamics. The Pearson analysis method is used to identify the key parameters of the space linkage system. Based on that, dynamic response optimization model of space bar system is developed to carry out a multi-objective suspension design optimization process. The results show that the maximum sensitivity parameter for the RMS value of yaw acceleration and pitch angle acceleration is the length of the longitudinal tow bar; there is a correlation of approximately 0.088 between the stiffness K and the damping coefficient C of the hydraulic shock absorber. After optimization the root mean square of lateral acceleration of the optimized single-axle bogie vehicle is reduced by 6.0%. [ABSTRACT FROM AUTHOR]

Sensitivity analysis methods have found extensive use in nuclear criticality safety applications for understanding the impact of uncertain nuclear data on eigenvalue estimates. Significant uncertainty exists in nuclear data and nuclear physics models for photon and electron transport applications, and the goal of this work is to explore whether recently developed adjoint-based, first-order generalized perturbation theory reaction rate sensitivity methods can be extended to coupled Monte Carlo radiation transport simulations. This paper presents a rigorous theoretical derivation for this extended sensitivity analysis method, which is then implemented in a one-dimensional test Monte Carlo code. The adjoint-based sensitivity coefficients are found to agree well with reference direct perturbation and deterministic SENSMG sensitivity coefficients for a simple test problem. [ABSTRACT FROM AUTHOR]

This work focuses on the effect of wrinkle defects within curved laminate composites under compressive load. In the study, 22 curved specimens with different levels of maximum misalignment angles – ranging from 1.5° to 17° – were manufactured. The specimens with embedded defects were tested under compression to assess the effect of defects on the compressive strength. Then, specimens were modeled using the Discrete Ply Model strategy and simulations were run. The numerical model showed a relevant correlation with the experimental results and was used to distinguish the influence of different failure mechanisms (matrix cracking, delamination and fiber failure) on the global failure of the specimen. Finally, a numerical sensitivity analysis of the compressive strength of curved specimens in relation with the misalignment angle was carried out. [ABSTRACT FROM AUTHOR]

Most of the reported optimizations of micro-channel evaporators focus on specific variables, their priority has not been revealed. In the present study, a numerical model for a louvered micro-channel evaporator is established. The operating patterns under different cases are first revealed. Then, sensitivity analysis of operational and structural parameters is analyzed; sensitivity coefficients are additionally revealed to enumerate the priority. The results indicate that the influence mechanism and sensibility of different operating factors are distinct. The log-mean temperature difference (LMTD) increases first and then decreases with the air speed while the overall heat transfer coefficient increases first and then decreases with the inlet dryness. In other cases, the LMTD, heat transfer rate, entropy generation, and overall heat transfer coefficient display positive or negative variation trends with the operating factors. On the whole, the air speed, initial air temperature, and mass flow rate of the refrigerant play positive roles while the cross-sectional size and inlet dryness play opposite roles in determining the heat transfer performance. Of which, the heat transfer rate is positively determined by the initial air temperature with the maximum value of 0.85 while negatively determined by the inlet dryness with the value of −0.23; the overall heat transfer coefficient is positively determined by the air speed with the value of 0.83 while negatively determined by the cross-sectional size with the maximum value of −0.41. The mass flow rate of the refrigerant makes no difference in determining the heat transfer performance. [ABSTRACT FROM AUTHOR]

The eigenvalues and eigenvectors of the Fisher Information Matrix (FIM) can reveal the most and least sensitive directions of a system and it has wide application across science and engineering. We present a symplectic variant of the eigenvalue decomposition for the FIM and extract the sensitivity information with respect to two-parameter conjugate pairs. The symplectic approach decomposes the FIM onto an even-dimensional symplectic basis. This symplectic structure can reveal additional sensitivity information between two-parameter pairs, otherwise concealed in the orthogonal basis from the standard eigenvalue decomposition. The proposed sensitivity approach can be applied to naturally paired two-parameter distribution parameters, or a decision-oriented pairing via regrouping or re-parameterization of the FIM. It can be used in tandem with the standard eigenvalue decomposition and offer additional insights into the sensitivity analysis at negligible extra cost. for this article are available online. [ABSTRACT FROM AUTHOR]

The textile supply chains (TSCs) are exposed to unprecedented risks which can disrupt the sustainable process of the manufacturing industries. It is necessary to analyze the impact of risks and to create reliability in the end-to-end supply chain. The aim of this research is to identify the supply chain reliability (SCR)-related risks and proposed a risk mitigation model based on a systematic toolkit of strategies for handling the risks. For this purpose, a total of 10 key SCR-related risks have been recognized and prioritized using a detailed review of the literature and industrial expert's opinions. A risk mitigation methodology is presented for improving SCR through its relevant criteria, i.e. risks factors, based on the intensity of importance. This study focuses in determining and evaluating the key potential risks through the blend of mathematical multi-criteria decision-making (MCDM) methods namely fuzzy FMEA, fuzzy AHP, fuzzy TOPSIS, and sensitivity analysis. This study develops a novel approach for objectively projecting future supply chain risks that can be managed timely in achieving SCR. This study also contributed to fill the existing literature gap for prioritization of key risks in the TSCs. The results highlight how postponement in supply chain risks is identified as highly prioritized whereas, manufacturing risks are ranked as second highly prioritized, and disruption risk is ranked as the last one. The concerned stakeholders can adopt the proposed structured methodology as a way forward in formulating risk mitigation strategies and tackling the SC risks in achieving the optimal level of supply reliability. [ABSTRACT FROM AUTHOR]

The use of simulation-based sensitivity analyses is fundamental for evaluating and comparing candidate designs of future clinical trials. In this context, sensitivity analyses are especially useful to assess the dependence of important design operating characteristics with respect to various unknown parameters. Typical examples of operating characteristics include the likelihood of detecting treatment effects and the average study duration, which depend on parameters that are unknown until after the onset of the clinical study, such as the distributions of the primary outcomes and patient profiles. Two crucial components of sensitivity analyses are (i) the choice of a set of plausible simulation scenarios and (ii) the list of operating characteristics of interest. We propose a new approach for choosing the set of scenarios to be included in a sensitivity analysis. We maximize a utility criterion that formalizes whether a specific set of sensitivity scenarios is adequate to summarize how the operating characteristics of the trial design vary across plausible values of the unknown parameters. Then, we use optimization techniques to select the best set of simulation scenarios (according to the criteria specified by the investigator) to exemplify the operating characteristics of the trial design. We illustrate our proposal in three trial designs. for this article are available online. [ABSTRACT FROM AUTHOR]

Growing concerns about the climate change accompanied with energy crisis due to the unrest in the world have pushed the energy market to utilize sustainable and secure alternatives such as solar photovoltaics (PV). However, for residential consumers, the primary concerns are the reliability of the supply and the cost of PV-generated electricity. This study reports on the economic feasibility and technical analysis of PV systems in megacities worldwide. Nine mega cities, Beijing, Berlin, Istanbul, London, New Delhi, New York, Paris, Seoul, and Tokyo, have been selected based on their significant population sizes and the pronounced demand for electricity independence among residents. The annual production, load matching, and PV-electricity and battery dispatch are discussed, with Lazard's levelized cost of electricity (LCOE) and net present value (NPV) employed as economic indicators. While calculated LCOEs ranged from $0.084/kWh to $0.27/kWh across different cities, the NPVs were predominantly negative, varying approximately between –$2,000 and –$20,000, with Berlin as an exception. Finally, a sensitivity analysis was conducted, revealing that the LCOE is more sensitive to financial parameters than to module efficiency and irradiation. [ABSTRACT FROM AUTHOR]

Parallel process latent growth curve mediation models (PP-LGCMMs) are frequently used to longitudinally investigate the mediation effects of treatment on the level and change of outcome through the level and change of mediator. An important but often violated assumption in empirical PP-LGCMM analysis is the absence of omitted confounders of the relationships among treatment, mediator, and outcome. In this study, we analytically examined how omitting pretreatment confounders impacts the inference of mediation from the PP-LGCMM. Using the analytical results, we developed three sensitivity analysis approaches for the PP-LGCMM, including the frequentist, Bayesian, and Monte Carlo approaches. The three approaches help investigate different questions regarding the robustness of mediation results from the PP-LGCMM, and handle the uncertainty in the sensitivity parameters differently. Applications of the three sensitivity analyses are illustrated using a real-data example. A user-friendly Shiny web application is developed to conduct the sensitivity analyses. [ABSTRACT FROM AUTHOR]

This study investigates biochar as an attractive option for removing pharmaceuticals from wastewater streams utilizing data from various literature sources and also explores the sensitivity of the characteristics and implementation of biochar. ANN 1 was designed to determine the optimal biochar characteristics (Surface Area, Pore Volume) to achieve the maximum percentage removal of pharmaceuticals in wastewater streams. ANN 2 was developed to identify the optimal biomass feedstock composition, pyrolysis conditions (temperature and time), and chemical activation (acid or base) to produce the optimal biochar from ANN 1. ANN 3 was developed to investigate the effectiveness of the biochar produced in ANN 1 and 2 in removing dye from water. Biomass feedstock with a high lignin content and high volatile matter at a high pyrolysis temperature, whether using an acid or base, achieves a high mesopore volume and high surface area. The biochar with the highest surface area and mesopore volume achieved the highest removal percentage. Regardless of hydrophobicity conditions, at low dosages (0.2), a high surface area and pore volume are required for a high percent removal. And with a higher dosage, a lower surface area and pore volume is necessary to achieve a high percent removal. [ABSTRACT FROM AUTHOR]

This work investigates linear and non-linear parametric reduced order models (ROM) capable of replacing computationally expensive high-fidelity simulations of human body models (HBM) through a non-intrusive approach. Conventional crash simulation methods pose a computational barrier that restricts profound analyses such as uncertainty quantification, sensitivity analysis, or optimization studies. The non-intrusive framework couples dimensionality reduction techniques with machine learning-based surrogate models that yield a fast responding data-driven black-box model. A comparative study is made between linear and non-linear dimensionality reduction techniques. Both techniques report speed-ups of a few orders of magnitude with an accurate generalization of the design space. These accelerations make ROMs a valuable tool for engineers. [ABSTRACT FROM AUTHOR]

The Philippines heavily relies on crop production, with a significant portion cultivated in coastal agricultural areas. However, these farmlands are impacted by salinity intrusion, which adversely affects agricultural productivity. This study assessed the coastal agricultural barangays in Kalibo (Aklan, Philippines) using the exposure, sensitivity, and adaptive capacity indicators, and following the Intergovernmental Panel on Climate Change (IPCC) framework. It aimed to enhance local and national stakeholders' understanding of vulnerability to seawater intrusion. Physical, agroecological, and socioeconomic parameters were used as indicators, with weights determined through the Analytic Hierarchy Process (AHP). Various aggregation methods were used, and datasets from participatory methods and remote sensing technologies, including LiDAR, were integrated to evaluate vulnerability on a 500 x 500-meter grid. The assessment focused on coastal barangays selected by local officials and agricultural experts. Caano was the most vulnerable, while Bachaw Norte was the least. The exposure map highlighted Nalook, Estancia, and Caano as areas with the highest levels of exposure. The results were compared with the knowledge of city agricultural experts. A sensitivity analysis was conducted to assess the model for exposure indicators. This research contributes to planning and management approaches in Kalibo and advances knowledge of critical indicators in addressing the current challenges. [ABSTRACT FROM AUTHOR]

This paper investigates the availability of a system incorporating uncertainty in the inspection period, including multiple failure modes (FMs). We decrease or increase the inspection period depending on the occurrence or nonoccurrence of system failure during any particular inspection, i.e. if, during an inspection, a defect is detected, we reduce the inspection period for the next inspection, and if no defect is found, we increase the inspection period. Also, the proposed model is examined under a pre-defined critical repair time incorporating the Gumbel-Hougaard copula. This paper considers a repairable system having α number of FMs. Random failure times are associated with every single FM, and whenever the system collapses from the ath FM, a subsequent corrective repair is conducted, which requires a random amount of time ψ a (a = 1, 2,..., α). Then the point and long-run availability of the proposed model are derived, and a sensitivity analysis is done to examine how inspection time affects the system's availability. Additionally, the system's average long-run cost rate is analyzed, and the model is employed to determine the optimal inspection period that optimizes the system's ALRCR. Lastly, the results are verified via a numerical illustration of a power transformer. [ABSTRACT FROM AUTHOR]

We conducted a Mendelian randomization study to evaluate the potential causal effects of attending religious groups on the prevalence of depression, generalized anxiety disorders, anxiety disorders, and suicide and self-harm. Data from the UK Biobank and the FinnGen project were processed by inverse variance weighting (IVW), weighted median, and MR-Egger regression methods. Despite certain pleiotropic risks, we found that attending religious groups was potentially associated with a reduced risk of anxiety disorders (OR = 0.213, p =.028). The pleiotropies were largely controlled and the effect on anxiety disorders became more significant in our sensitivity analysis (OR = 0.162; p =.006). Additionally, attending religious groups was associated with a reduced risk of suicide and self-harm (OR = 0.231, p =.0006). However, we did not observe any substantial protection against depression. In conclusion, our study adds to the existing literature and sheds light on the potential health benefits of religious activities from a novel perspective. [ABSTRACT FROM AUTHOR]

The mechanical performance caused by fluid pressure or uneven temperature in heat exchangers is important to predict dangerous locations and guide the manufacture or installation. Under the background of coal-water slurry (CWS) preheating technology, using fluid-structure interaction, the distribution law, and mechanism of von Mises stress and deformation for CWS preheaters under different loads were studied, and sensitivity analysis combining metamodel of prognosis was carried out to determine the most influential parameters on mechanical performance. The results illustrate maximum von Mises stress under coupled pressure and temperature occurs at the perforated location on the baffle closed to the shell outlet, but maximum total deformation is located at the outer edge of the baffle near the shell inlet. When the folding angle, folding ratio, relative height is 30°, 0.4, and 0.5, respectively, under coupled loads, the thermal stress is dominant at lower velocity, and total deformation caused by the pressure is dominant at all different shell-inlet velocities. With increasing shell-inlet velocity, maximum von Mises stress decreases first and then increases, and maximum total deformation increases. The shell-inlet velocity and relative height are the most influential and non-influential parameters, respectively, for maximum total deformation. The research lays the foundation of mechanical analysis for material failure, structure destruction, and parameter screening of design variables for CWS preheaters. [ABSTRACT FROM AUTHOR]

The high thermal efficiency of supercritical water makes it a promising alternative to water cooled reactors. This study employs numerical analysis, utilizing the SST k-ω turbulence model, to investigate the heat transfer performance of supercritical water (SCW) in various tube configurations and fluid flow conditions across Reynolds numbers ranging from 8,000 to 20,000. This research examines how different geometrical parameters, such as the helical direction, number of lobes, and cross-sectional shape, impact the flow physics and heat transfer performance of different spiral tubes. The outcomes specify that increasing the lobed number in the tube improves the heat flux by about 5%–16%. Furthermore, introducing two direction changes in the twisted tube will cause a slight increase (about 20%) on heat transfer enhancement. Finally, the sensitivity analysis of heat flux and Nusselt number to each of the effective parameters in the heat transfer of supercritical water in twisted tubes has been accomplished and the Response Surface Method (RSM) utilizes the central composite design (face-centered) approach. According to the findings of these two studies, it has been established that the Reynolds number of the fluid flow is the most influential parameter in determining the extent of heat transfer. Specifically, it exerts an effectiveness of 26% and 76% on the Nusselt number and heat flux, respectively. Furthermore, the inscribed circle diameter of tube by 8% effectivity and minor axis length by 5% effectivity on heat flux are more effective than others. [ABSTRACT FROM AUTHOR]

Carbon-dioxide (CO2) centrifugal compressors are machines with high potential of usage in power generation plants and oil industry as it achieves high thermal efficiency in the Brayton cycles and contributes to oil production through the capture and storage of CO2 (CCS) in EOR (Enhanced Oil Recovery) systems. High levels of static pressure at the outlet are desired for EOR applications, which are usually obtained through the insertion of vanes in the diffuser. This work intends to increase vaneless-diffuser static pressure recovery by modifying only its meridional profile, ensuring a broader range of off-design operation when compared to vaned diffusers and attending to the fluctuations of mass flow and rotation expected in EOR practical applications. Therefore, a parametric optimization through surrogate model coupled to CFD was performed with three different objective functions that were submitted to single-optimization through the NSGA-II method: Maximize total-to-total polytropic efficiency, minimize total pressure loss coefficient or maximize static pressure recovery coefficient. Additionally, a sensitivity analysis was conducted using Morris Elementary Effects and SS-ANOVA. The results indicated that the optimized geometries increased the total-to-total polytropic efficiency by 2.9%, reduced the total pressure loss coefficient by 24.0% and increased the static pressure recovery coefficient at the design point by 11.4%, which is discussed in detail after a careful phenomenology assessment. The strategy adopted in the present work through a combination of Sensitivity Analysis, surrogate models and CFD increased the vaneless-diffuser static pressure recovery without the need of inserting vanes in the diffuser, which avoid instabilities in the equipment and would restrict its range of off-design operation. [ABSTRACT FROM AUTHOR]

Solar irradiance and temperature are two primary factors that affect the energy generation efficiency of solar photovoltaic (PV) systems, meaning that climate change may significantly impact the production of solar energy in the future. In this study, a two-way sensitivity analysis is carried out to determine the energy generation potential under future climate change conditions, and conditions of shadow covering are also considered. The simulation results indicate that the impact of solar irradiance causes the most variation in energy generation, and that shadow covering has a weak impact on the output. In the one-way sensitivity analysis, solar irradiance makes a ± 18% difference in energy generation, whereas shadow covering represents about ± 2%. In the future trend of climate change in the world and Taiwan, the energy generation potential can decrease up to 21% and increase 8%, respectively, mainly corresponded by solar irradiance. We suggest that compared with the issue of temperature, the efficiency of solar irradiance usage is more important, especially in view of the global trend toward solar irradiance loss. Taiwan also suffers the issue, but the different trend of climate change impact may make the challenge different. [ABSTRACT FROM AUTHOR]

The heat extraction process of the Enhanced Geothermal System (EGS) faces the problem of mechanical property degradation caused by artificial cracks and surrounding rock caused by water-cooling shock. However, there are few studies considering the effect of water-cooling shock on fracture deformation and heat production performance. Therefore, a comprehensive numerical model considering water-cooling shock was established to simulate the heat extraction process of EGS. Four different scenarios were designed to analyze the influence of water-cooling shock on the heat production performance of EGS. The sensitivity analysis of several important variables affecting fracture aperture and heat production performance is carried out. The results show that compared with EGS without considering water-cooling shock, the heat extraction performance of EGS considering water-cooling shock is more reasonable. For a reservoir with an initial temperature of 300°C, the decrease of production temperature after 40 years is 3.35°C. Water-cooling shock changes the fracture aperture by degrading the elastic modulus of rocks and fractures, which affects the heat production performance of EGS. The decrease of fracture elastic modulus means that the fracture aperture increases under the same injection water pressure and confining pressure. The decrease of elastic modulus of bedrock leads to the decrease of stress sensitivity index, which inhibits the expansion of fracture aperture. Comprehensively considering water-cooling shock on reservoirs and fractures will more reasonably predict the heat production performance of EGS. [ABSTRACT FROM AUTHOR]

In ship construction, material costs constitute a substantial portion of the overall expenses. With the surging steel prices, the shipbuilding industry faces a pressing challenge. To counterbalance this issue, optimizing the structural components of ships has emerged as a viable solution. Genetic Algorithm (GA) methods, known for their application in structural optimization, have demonstrated their potential. However, the protracted computational time associated with GA remains a limiting factor. This research introduces a novel approach by merging GA with Finite Element Method (FEM) for optimizing plate sizes, resulting in a hybrid GA system. Moreover, the study incorporates sensitivity analysis (SA) due to its proven efficacy in enhancing optimization processes involving multiple variables. The SA component investigates plate interrelationships and effectively clusters them. After this grouping, the hybrid GA executes parallel optimization of plates that influence each other under tension. By integrating SA, the optimization process becomes faster and more time-efficient, while preserving optimal manufacturing costs. Remarkably, this methodology culminates in a substantial reduction in computational time when contrasted with the hybrid GA approach devoid of SA, all the while maintaining a parallel manufacturing cost trajectory. In conclusion, this study presents an innovative hybrid GA approach, supplemented with SA, as an effective strategy for mitigating the escalating steel costs in shipbuilding. The amalgamation of GA, FEM and SA synergistically simplifies the optimization process, ensuring optimal results in a faster way. [ABSTRACT FROM AUTHOR]

The market of courier, express and parcel services (CEP) is characterized by the presence of requests for the transfer of goods and documents. During the COVID-19 pandemic, the global postal and CEP infrastructure played a key role in enabling governments to respond to the emerging challenges. In this paper, the competitiveness and the rank of four observed CEP service providers are analyzed using ratio indicators, the Ordinal Priority Approach method (OPA) and the Weighted Sum Method (WSM). We use the OPA method because there is no need for a pairwise comparison matrix and no need for normalization. The paper aims to examine the liquidity, economy and profitability of CEP service providers to get a true picture of the situation in the market of postal and CEP services in the Serbia. In the analysis of business performance and the implementation of the OPA method, the opinions of experts were taken into account. This paper yields a key insight: the identification of the top-performing provider within the group of service providers is accomplished through the utilization of both the OPA and WSM methods. Sensitivity analysis is shedding light on the varying impact of the provider's rank on the final ranking of the CEP providers. [ABSTRACT FROM AUTHOR]

It is essential to effectively identify the sources and health risks of heavy metals in groundwater in rural areas. Therefore, the sources and health risks of heavy metals in groundwater in rural areas of Huaibei Plain were investigated. The results showed that the mean concentrations of Cr, Fe, Mn, Cu, Zn, Ni, As, Cd, and Pb were 0.38, 86.48, 90.94, 0.20, 0.97, 1.18, 0.009 and 0.16 μg/L. Multivariate statistical analysis combined with positive matrix factorization (PMF) was used to quantitatively identify four sources (agricultural activities (24.01%), industrial activities (22.27%), natural sources (26.45%), and traffic sources (27.28%)). The probabilistic health risk assessment results based on Monte Carlo simulation showed that the non-carcinogenic risk for adults and children was negligible. However, 17.52% of children and 1.99% of adults exceeded the maximum acceptable safety limit (1.0E-6) for cancer risk. Sensitivity analysis showed that daily intake, Cr, and Fe were the main factors influencing the health risk assessment results. These results have important implications for groundwater management in rural areas of the Huaibei Plain. [ABSTRACT FROM AUTHOR]

Our aim is to explore novel optical solitons within the context of an extended higher-order nonlinear Schrödinger equation that governs the behaviour of propagating light waves. Primarily, this research finds abundant types of solitons, such as singular, kink and trigonometric functions subject to certain constraint conditions. We have utilized G /(bG + G + a)- expansion method, which has proved effective in retrieving these solitons and presenting them for further analysis. The model’s dynamic behaviour is then investigated through bifurcation, quasi-periodic oscillations, chaotic behaviour, and sensitivity. These include methods like phase portrait rendering, time series scrutiny, Lyapunov exponents calculation, and the assessment of multi-stability. Finally, sensitivity analysis is conducted at three distinct initial conditions, revealing that the model displays a high level of sensitivity, with substantial alterations occurring in response to minor changes in the initial conditions. The results of this study are revolutionary, intriguing, and possess crucial theoretical importance in evolution disruptions. [ABSTRACT FROM AUTHOR]

This article provides a set of methods for quantifying the robustness of treatment effects estimated using the unconfoundedness assumption. Specifically, we estimate and do inference on bounds for various treatment effect parameters, like the Average Treatment Effect (ATE) and the average effect of treatment on the treated (ATT), under nonparametric relaxations of the unconfoundedness assumption indexed by a scalar sensitivity parameter c. These relaxations allow for limited selection on unobservables, depending on the value of c. For large enough c, these bounds equal the no assumptions bounds. Using a nonstandard bootstrap method, we show how to construct confidence bands for these bound functions which are uniform over all values of c. We illustrate these methods with an empirical application to the National Supported Work Demonstration program. We implement these methods in the companion Stata module tesensitivity for easy use in practice. [ABSTRACT FROM AUTHOR]

To investigate the influences of structural parameters on output performances and further quantify the degree of influences, a one-dimensional transient multiphase proton exchange membrane fuel cell (PEMFC) model and a global sensitivity analysis model were established. After rigorous model validation, structural parameters such as the thickness of proton exchange membrane (MEM), catalytic layer (CL), micro-porous layer (MPL), and gas diffusion layer (GDL) as well as the porosity of CL, MPL, and GDL were studied. The ionomer volume fraction of cathode and anode were also studied. Increasing the thickness of MEM and CL decreases the membrane water content and increases the ohmic voltage loss. The increase of thickness leads to longer reaction gas transfer path and increase of transfer resistance. The increase of porosity increases the concentration of the reaction gas. However, too high porosity will reduce the effective ion conductivity. The increase of ionomer volume fraction is beneficial to electrochemical reaction. Based on quantitative sensitivity analysis, the MEM thickness and the anode ionomer volume fraction are defined as highly sensitive parameters. The MPL thickness and the GDL porosity are defined as insensitive parameters. The sensitivity of MEM thickness, CL thickness, and anode ionomer volume fraction become more prominent in large current density regions. [ABSTRACT FROM AUTHOR]

In this study, the optimal design of step-stress accelerated degradation tests is focused. An optimization model is proposed where an improved accelerated degradation model is involved to comprehensively consider the influence of accelerated stress and the measurement error. Then, a novel optimal design method is constructed, where multiple decision variables can be simultaneously optimized based on neural network and genetic algorithm. An effective sensitivity analysis method is further established to quantitively illustrate the influence of the predetermined model parameters on the optimal results. Finally, a case study is implemented, and a series of comparisons are implemented to demonstrate the effectiveness and rationality of the proposed method. [ABSTRACT FROM AUTHOR]

This paper examines the interval-valued availability of a standby redundant system with imperfect switching, incorporating uncertainty, i.e. the probability of successful operation of the switch is not known with accuracy. The standby system undergoes periodic inspections and is vulnerable to a number of failure modes (FMs). Every FM has a random failure time assigned to it, and once the system fails from the ath FM, a corrective repair is executed promptly. The corrective repair takes a random amount of time a ða = 1; 2; . . . aÞ. Incorporating the aforesaid points, expressions for the upper and lower bounds of the model's point and long-run availability are derived, and a sensitivity analysis is done to evaluate the impact of inspection time on the system's availability. Lastly, the derived theorems are established by using a numerical example of a combi-boiler standby system with an imperfect switch. [ABSTRACT FROM AUTHOR]

The selection of the suitable production parameters for dry machining remains a challenging task which impacts the productivity, the system degradation and the quality of the output product. This study proposes an integrated production-maintenance policy allowing the simultaneous consideration of the production parameters, mainly the cutting speed, the production time and cost, the preventive maintenance period and the quality levels and the selling prices of the output product. A special feature of this strategy, defined on a finite time span, is to allow a change of the cutting speed at a certain instant to be determined in an optimal way concurrently with the preventive maintenance period, such as to maximise the total expected profit per time unit. A mathematical model is developed to express the average profit per time unit as a function of the decision variables. A solving procedure is also proposed. A numerical example and a sensitivity analysis are presented in order to show the impact of some major input parameters on the optimal policy. [ABSTRACT FROM AUTHOR]

We consider an Economic Order Quantity (EOQ) model with imperfect quality items. All the items undergo a quality screening process for which both the cost and the defect detection rate depend on the time devoted to this operation. Contrary to most previous research in the field, the speed of screening is taken as a decision variable in the presented model jointly with the order quantity. Additionally, we introduce various functional forms for the screening cost and defect detection functions. In particular, nonlinear forms lead to nonconvexities in the total cost function consisting of multiple cost components, e.g. inspection cost, penalty costs, etc. We solve the model analytically for a selected set of assumptions and provide numerical examples and sensitivity analysis. The results provide interesting insights. While lower screening costs motivate longer screening times, the effectiveness of this operation is only obtained in combination with the reliability of the defect detection method. It is also observed that reducing return rates and improving customer satisfaction can be achieved by adjusting the operational parameters, without a significant increase in system cost or new investments. [ABSTRACT FROM AUTHOR]

Product configuration involves selecting common components/modules of individualised products. This is done according to customer requirements under mass customisation. However, the strategies adopted by most existing product configurators are often extreme, with either total diversity (mass personalisation) or standardised products (a limited set of products). In order to investigate the intermediate case in this situation, a new uncertain decision model is proposed in this paper. The aim is to find the optimal product configuration using a redundancy and standardisation strategy that minimises the total costs. In this model, customer requirements are defined and assembly sequences are initially constructed. Then, a manufacturing approach of modular multi-platform assembly is employed to increase uniqueness in mass customisation. Next, the uncertain decision model for product configuration is linearised and solved by uncertainty theory using CPLEX 12.8. Finally, a sensitivity analysis is conducted to suggest optimal platform number and producing strategies and determine the final customised product specification. From a case study of the mobile phone, we found that standardisation performs better than the redundancy strategy and a flexible platform strategy effectively reduces production costs. [ABSTRACT FROM AUTHOR]

Compared with non-renewing warranty, renewing warranty is more attractive to the customers. To reduce the possible cost pressure of the manufacturers, this study proposes a novel two-stage renewing warranty model with a threshold of maintenance times. Based on a preset allocation ratio, the warranty period is divided into two stages, i.e. the renewing warranty stage and the minimal repair warranty stage. In the first stage, when the number of maintenance actions exceeds the given threshold, the failed item will be replaced with a new one and attached with a new warranty period; otherwise, the product will enter the second stage when its operating time exceeds the time span of the renewing warranty stage. When the product fails in the second stage, only minimal repair is performed. On this basis, four types of maintenance schemes are proposed, and the corresponding warranty cost and profit models are developed to optimise the warranty policy. A numerical example is provided to illustrate the effectiveness of the proposed model, and sensitivity analysis is conducted. The results show that by considering the threshold of maintenance times, the warranty cost can be reduced and the manufacturer's expected profit can be improved simultaneously. [ABSTRACT FROM AUTHOR]

Optimal fixture configuration is critical to minimise the machining distortion of the workpiece. This study presented an approach for optimisation of fixturing configuration in flexible machining fixtures. Herein, a flexible fixture system composed of adaptive clamping unit, locating unit, and auxiliary locating unit was investigated. Response surface methodology (RSM) was employed to describe the relationship between fixturing parameters (distance between two zero-point fixtures m1, the clamping force of float fixture m2, and quantity of float fixture m3) and machining distortion. Furthermore, sensitivity analysis was implemented to evaluate the effects of fixturing parameters on machining distortion. Adaptive penalty function (APF) was constructed to search optimal settings of fixturing parameters. Comparative analysis of different optimal fixturing parameters obtained by RSM and APF was conducted. Single-sided component was adopted to validate the effectiveness of the proposed approach. Machining distortion was found to first decrease and then increase with the augment of m1 and it reduced with the increment of m2 and m3. Besides, m3 exhibited the greatest influence on machining distortion, 61.78%. The interaction between m2 and m3 resulted in the lowest influence, 0.019003%. Furthermore, the maximum machining distortion through optimised flexible clamping was only 0.312 mm, which reduced by 57.6% compared to traditional clamping. [ABSTRACT FROM AUTHOR]

AbstractThermosolutal natural convection originates in a fluid when the density changes due to the presence of two distinct components with varying rates of diffusion. This convection is driven by buoyancy resulting from concurrent temperature and concentration gradients. This study focuses on the thermosolutal free convection flow within a parallelogrammic enclosure. The functioning fluid under consideration is non-Newtonian and formulated by the viscosity model (power-law). The top and bottom walls of the chamber are adiabatic and are assumed to be impermeable. The temperature and concentration on the left side are maintained at a high level, while the conditions on the right side are cold and low. The controlling parameters for the present case include Rayleigh number (105≤Ra≤106), Lewis number (Le = 5, 10), Prandtl number (Pr = 6.2), buoyancy ratio (N = 0.8, −0.8) along with power-law index (n = 0.6, 1, 1.4). The results obtained from this numerical study are validated with existing results and indicate the correctness of the code. The output of the study is shown in terms of velocity and temperature profiles, streamlines, isotherms, and iso-concentrations, the average rate of heat and mass transmission in terms of the average Nusselt number (Nu¯) and the average Sherwood number (Sh¯). The correlation equation derived from the RSM approach demonstrates the relationship between the output responses and the input parameters. It concludes that n negatively affects both heat and mass transfer, while Ra affects positively. The findings of this study could be helpful for understanding of thermosolutal natural convection behavior of non-Newtonian (power-law) fluid in an enclosure and therefore accelerate the industrial application of such fluid in the relevant field, such as HVAC (heating, ventilation, and air conditioning) system. [ABSTRACT FROM AUTHOR]

AbstractIn this study, a novel sinusoidal higher-order shear deformation thin-walled beam theory is presented to examine the effects of material properties and external load uncertainty on static responses of laminated composite thin-walled beams with open sections. The solution for the deterministic flexural analysis is based on Hamilton’s principle and Ritz-type exponential shape function series. Several mechanical parameters of laminated composite materials are randomized and plugged into the beam solver to investigate the thin-walled beam’s stochastic flexural behaviors. The computational cost and accuracy of the polynomial chaos expansion (PCE) method with both projection and linear regression approaches are presented and evaluated by comparing its results with crude Monte Carlo simulation (MCS). This comparison allows for a thorough assessment of the PCE method’s performance. Additionally, a sensitivity analysis is conducted to compare the relative significance of the uncertainty in material properties and loads on the stochastic responses. The supervised training of the artificial neural network based on the MCS beam data is also conducted and compared to the PCE and MCS methods. The findings about the stochastic outputs are introduced in various statistical metrics and illustrations to demonstrate the influences of material properties’ randomness on different laminated composite thin-walled beam configurations. [ABSTRACT FROM AUTHOR]

AbstractMeasles, a member of the Paramyxoviridae family and the Morbillivirus genus, is an infectious disease caused by the measles virus that is extremely contagious and can be prevented through vaccination. When a person with the measles coughs or sneezes, the virus is disseminated by respiratory droplets. Normally, the appearance of measles symptoms takes 10–14 d following viral exposure. Conjunctivitis, a high temperature, a cough, a runny nose, and a distinctive rash are some of the symptoms. Despite the measles vaccination being available, it is still widespread worldwide. To eradicate measles, the Reproduction Number (i.e. R0<1) must remain less than unity. This study examines a SEIVR compartmental model in the caputo sense using a double dose of vaccine to simulate the measles outbreak. The reproduction number R0 and model properties are both thoroughly examined. Both the local and global stabilities of the proposed model are determined for R0 less and greater than 1. To achieve the model’s global stability, the Lyapunov function is used while the existence and uniqueness of the proposed model are demonstrated In addition to the calculated and fitted biological parameters, the forward sensitivity indices for R0 are also obtained. Simulations of the proposed fractional order (FO) caputo model are performed in order to analyse their graphical representations and the significance of FO derivatives to illustrate how our theoretical findings have an impact. The graphical results show that the measles outbreak is reduced by increasing vaccine dosage rates. [ABSTRACT FROM AUTHOR]

Malaria is a serious health problem in the world. Nearly half of the world’s population is at risk of disease. In this study, a mathematical model was developed to understand and analyze the dynamics of the malaria parasite within host cells with cell-mediated and antibody-mediated immune systems. The model categorizes the population into six compartments. The study provides a concise representation of the interaction between the parasite and red blood cells, as well as the responses of the immune system, including both antibody-mediated and cell-mediated responses. The research examines the biological visibility and well-posedness of the model, ensuring that the solutions remain bounded and positive. The basic reproduction number, which indicates the potential spread of the parasite is determined. The stability analysis of the parasite-free equilibrium point was also conducted, revealing that the parasite-free state is locally and globally stable if the basic reproduction number, ${R_0} \lt 1$R0<1. Furthermore, bifurcation and sensitivity analysis are performed. Finally, to complement the theoretical findings and illustrate the impact of immunological effectiveness, numerical simulations are conducted under different scenarios. The result shows that the cell-mediated and antibody-mediated immune response helps to eliminate the parasite within the human host. [ABSTRACT FROM AUTHOR]

In this study, a high-power synchronous reluctance motor (SynRM) was designed for the traction motor of electric vehicle (EV) and its double-stage optimization was performed. Genetic algorithm and sensitivity analysis methods were used to obtain the best design parameters. Double-stage optimization was carried out to minimize the torque ripple and obtain the targeted torque, speed, and power values of the SynRM. In the first stage, the genetic algorithm method was used to improve the design parameters of the stator and rotor. With the improved design parameters, it was observed that the torque ripple decreased. In the second stage, the sensitivity analysis method was used. In this method, the effect of changing the skew angle of the stator on the torque ripple was investigated. The performance of the designed motor was examined in the optimization process. It was observed that the targeted torque, power, speed, efficiency, and torque ripple minimization values are successfully achieved with the best stator and rotor parameters. The results showed that SynRM produces high torque and high power with high efficiency and low torque ripple over wide speed range. It is quite proper to use the designed SynRM as a traction motor of new generation electric vehicles. [ABSTRACT FROM AUTHOR]

Energy resources are decreasing in the world and the use of electrical machines is increasing. Motor design and development studies are carried out to reduce the losses and increase the efficiency of motors, which are widely used especially in industry. In this study, Line Start Synchronous Reluctance Motor (LS-SynRM) was designed by taking an induction motor, one of the most widely used motors in the industrial field, as a reference. With its rotor cage structure feature, it can be line-start without the need of a driver. The rotor has a lot of design variables due to slots and barriers. Sensitivity analysis was used to determine the effect of design variables on performance criteria such as power factor, efficiency and torque ripple. Subsequently, Multi-Objective Genetic Algorithm (MOGA) optimization was used to find the values of the design variables that bring the motor performance closer to the optimum result. The results of the reference induction motor, initial LS-SynRM, and the optimized LS-SynRM are compared. It has been taken care to preserve the synchronization feature of the LS-SynRM and the related results are given in the analyses. It is shown that the optimized motor is in IE4 efficiency class and operates with low torque ripple. [ABSTRACT FROM AUTHOR]

Energy demand is growing exponentially due to industrialization and population growth, and existing conventional energy systems (CES) cannot meet it totally due to the limited availability of power supply. Furthermore, CES has a substantial environmental impact. In comparison to CES, hybrid renewable energy systems (HRES) are a suitable combination of renewable and non-renewable energy systems that consider the system's benefits, allowing for lower system implementation and maintenance costs, lower emissions levels, and improved overall system reliability, and so forth. Therefore, this article offers an extensive review of the research conducted on numerous aspects that must be considered while developing and implementing an HRES. The numerous aspects of HRES considered are configuration, modeling, storage technologies, design parameters, sizing methodologies, energy management, control, and sensitivity analysis. This research assists the designer in choosing the best HRES configuration, appropriate energy management and HRES control. In addition, the selection of various design parameters, and sensitivity parameters for performance evaluation of HRES. Further the selection of mode of operation (off-grid or grid-connected modes) of HRES depending on the location's requirements. [ABSTRACT FROM AUTHOR]