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The robotic live-cube compact storage system can provide a higher storage capacity and more flexible throughput than other automated warehousing systems. The extant studies usually fix the input/output point at the bottom-left of such storage systems and have not investigated other configurations of the input/output point. In this study, we propose six policies for the configuration of the input/output point and evaluate their performance by using an analytical model and simulations. A sensitivity analysis is carried out by considering different shape-related factors, system heights, and energy consumption to explore the optimal configuration of the input/output point. For most input/output point policies, a shape factor (length divided by depth) of 0.5 results in good system performance in terms of retrieval time. When using optimal system dimension, the input/output point located in the upper-middle part along the length and depth performs best compared with other policies. Specifically, this configuration reduces the retrieval time by 40.23% and energy consumption by 42.31% compared with the bottom-left corner input/output point. We find that there exists a trade-off among the cost of setup, number of empty grids, and retrieval time when varying the number of input/output points. [ABSTRACT FROM AUTHOR]

Lot splitting is one of the effective technologies of time-based strategy and has been widely studied in a variety of production environments. Nevertheless, the literature on its application in seru production has been highly scarce until now. Seru system is composed of simple equipment and multi-skilled workers, which can be quickly converted from the traditional assembly line to seru units. As an innovative production mode, seru production inevitably allows applying lot splitting in the real world. Therefore, a multi-objective model is studied for line–cell conversion with lot splitting, aiming at determining the trade-off among makespan, inter-seru system balancing, and intra-seru system balancing. Due to the proposed model's NP-hard nature, an improved NSGA-II was developed to solve it. Finally, extensive numerical simulations are conducted. Compared to no lot splitting, lot splitting is improved by 4.2% and 3.7% in terms of inter-seru system balancing and makespan respectively. The better efficiency and effectiveness of the improved NSGA-II are proved by comparisons with other state-of-the-art algorithms. Additionally, a sensitivity analysis is conducted to ascertain the degree of contribution of the model parameters towards the value of objectives, which provides management implications to support the decision-making of seru production for the enterprise. [ABSTRACT FROM AUTHOR]

We considered two periodic review perishable inventory systems. The first system is controlled by a periodic review (T , S) policy, where orders are triggered at each review period T and when the stock is less than S. The second one is controlled by a periodic review (s , S) policy, where orders are triggered only if the stock level is below s. For both models, the lifetime of each product was assumed to follow a general distribution, whereas the lead time was constant. The demands followed a Poisson process, and all unsatisfied demand was lost. Using an approximate solution of the steady-state probabilities of the inventory level, we obtained a closed form of cost expression. Next, we conducted a simulation study to identify our solution accuracy. Finally, we performed a sensitivity analysis to evaluate the effect of the lifetime variation and cost parameters on the best policy and its cost. The obtained results were close to those of the optimal policy with an average accuracy of 3.7% for the periodic review (T , S) policy and 3.8% for the periodic review (s , S) policy. For the worst cases, where the accuracy is higher than 7%, we provided an analytical approach to improve the system accuracy. [ABSTRACT FROM AUTHOR]

With the development of information technology, big data analysis has been highlighted in operations and management. From this viewpoint, this paper studies a buyer's optimal purchasing decisions in combined procurement. For combined procurement, a buyer first signs a long-term contract with a supplier to guarantee a certain level of commodity supply, and can then replenish the commodities from the spot market if necessary. The optimal purchasing quantity in the long-term contract is examined to maximise the buyer's expected profit from combined procurement. In view of the imperfectness in the spot market, the spot trading liquidity is considered in the buyer's optimal purchasing decision. The properties of the two optimal purchasing quantities are examined and several interesting results are obtained. For example, it is illustrated that a buyer's expected profit may decrease in the spot capacity, a result that has never appeared in the existing literature, which reveals the importance of a buyer's optimal order decision in the presence of spot replenishment. Numerical results and sensitivity analysis are performed to verify the results. Management insights are suggested for a buyer's optimal purchasing decisions in combined procurement with a long-term contract and spot replenishment. [ABSTRACT FROM AUTHOR]

Computational and machine learning frameworks have greatly contributed to early diagnosis and rapid therapeutic interventions due to breakthroughs in science and technology. This study aims to develop a mathematical model that utilizes machine learning and integrates both innate and adaptive immunological components to examine the progression of breast cancer. The proposed framework utilizes a set of ordinary differential equations to represent the interactions between breast cancer cells, cytotoxic T lymphocytes, T-helper cells, and macrophages. We used machine learning optimization techniques to enhance the computational framework, enabling accurate modeling of the dynamic alterations occurring in the tumor microenvironment. This approach also considers the different properties and responses of immune cells. Our validated results, obtained using metaheuristic algorithms and sensitivity analysis, provide significant insights into the correlation between the advancement of breast cancer and the innate and adaptive immune systems. This study supports the theory that advanced programming tools may enhance healthcare systems by offering reliable techniques for understanding and possibly controlling the progression of cancer via the manipulation of the immune system. [ABSTRACT FROM AUTHOR]

AbstractThe global population has encountered significant challenges throughout history due to infectious diseases. To comprehensively study these dynamics, a novel deterministic mathematical model, TCD IL2 Z, is developed for the early detection and treatment of lung cancer. This model incorporates IL2 cytokine and anti-PD-L1 inhibitors, enhancing the immune system’s anticancer response within five epidemiological compartments. The TCD IL2Z model is analyzed qualitatively and quantitatively, emphasizing local stability given the limited data-a critical component of epidemic modeling. The model is systematically validated by examining essential elements such as equilibrium points, the reproduction number (R0), stability, and sensitivity analysis. Next-generation techniques based on R0 that track disease transmission rates across the sub-compartments are fed into the system. At the same time, sensitivity analysis helps model how a particular parameter affects the dynamics of the system. The stability on the global level of such therapy agents retrogrades individuals with immunosuppression or treated with IL2 and anti-PD-L1 inhibitors admiring the Lyapunov functions’ applications. NSFD scheme based on the implicit method is used to find the exact value and is compared with Euler’s method and RK4, which guarantees accuracy. Thus, the simulations were conducted in the MATLAB environment. These simulations present the general symptomatic and asymptomatic consequences of lung cancer globally when detected in the middle and early stages, and measures of anticancer cells are implemented including boosting the immune system for low immune individuals. In addition, such a result provides knowledge about real-world control dynamics with IL2 and anti-PD-L1 inhibitors. The studies will contribute to the understanding of disease spread patterns and will provide the basis for evidence-based intervention development that will be geared toward actual outcomes. [ABSTRACT FROM AUTHOR]

This study proposed a framework using artificial neural networks (ANNs), the designed residual regression method, and probabilistic indexing, for drought sensitivity shift analysis, associated with future yield loss risk in Anthropocene. The temporal assessment of the drought index (standardized precipitation evapotranspiration index, SPEI) illustrated prevalent occurrence of mild to moderate drought with a fluctuating trend. Furthermore, the optimal ANN model, with R2 = 0.86 for training and 0.7 for testing, was used to project yield for future analysis. The projected spatial changes in drought sensitivity patterns suggest most of the area will experience an increased sensitivity to drought in the far future (2036–2050). The estimated probability loss under the different risk levels defined by the yield reduction rate was 6.2–55.5%. Furthermore, the spatial distribution of the designed risk index (IR) revealed the districts with the highest probability of vulnerability and showed a probable increase in future vulnerability. [ABSTRACT FROM AUTHOR]

Groundwater is one of the most important sources of freshwater, contributing significantly for drinking and other domestic activities around the world. However, its quality is deteriorating over time due to overexploitation and anthropogenic activities. Rib watershed is located in the Tana basin (Ethiopia), is intensively cultivated and urbanizing area. Therefore, this study attempted to assess groundwater vulnerability to pollution using the GIS-based Modified DRASTIC Model. The modified DRASTIC model was selected due to it incorporates both hydrogeological parameters and anthropogenic factors for assessment. The necessary data were collected from the field, and downloaded from websites and laboratory experiments (No3-). The results showed that more than 73.24% of the watershed is under medium to high vulnerability. Highly vulnerable areas of the watershed (22.48%) was confined to the Southern parts of the watershed (under built-up and cultivated areas). Based on single parameter sensitivity analysis, its vulnerability was highly influenced by aquifer media (24.1%), net recharge (21.75%), land use/land cover (15.1%), and depth of groundwater table (13.6%). The Modified drastic model was validated based on the observed data of nitrate concentration in groundwater. Based on the observed data of nitrate concentration, high-vulnerable areas were more contaminated than medium and low-vulnerable areas. The modified drastic index has a strong correlation with nitrate concentration with (r = 0.76) and (P < 0.043). Therefore, the result indicated that the area is vulnerable to contamination calling for appropriate groundwater management. Hence, this finding helps to plan and minimize future contamination of groundwater by considering its vulnerability before high-risk activities are allowed. [ABSTRACT FROM AUTHOR]

AbstractNuclear energy and renewables, both being low-carbon energy sources that are likely to play an increasingly larger role in energy systems of the future, are increasingly being considered from an integrated standpoint. However, the deployment of baseload nuclear reactors as part of such integrated systems may present some challenges in long-term planning, such as surplus energy generation, inflexibility, and increased energy storage requirements. On the other hand, flexible advanced nuclear reactors can be utilized to tackle the limitations of baseload reactors, such as inflexibility of load-following and high initial capital cost.This paper aims to investigate from techno-economic aspects whether energy modelers should use a flexible nuclear reactor model, specifically a small modular pressurized water reactor technology, or a baseload reactor model of comparable size in long-term integrated nuclear renewable (NR) integrated energy system planning simulations. We mathematically develop an off-grid NR integrated system in a MATLAB environment. An advanced small modular reactor is incorporated in this study and is operated in a baseload and flexible mode of operation for comparative analysis. Two metaheuristic optimization algorithms, pelican optimization algorithm and particle swarm optimization, are employed to obtain and validate the optimal configurations of two different NR systems (e.g. baseload reactor system and flexible reactor system). A sensitivity analysis is conducted to reinforce the key research findings.The results indicate that a flexible nuclear reactor reduces the total annualized cost by a very small amount (roughly 2%) and the energy storage sizing by around 3% for NR integrated system planning, compared to a baseload reactor. This study provides insights into the operational assumptions (e.g. baseload or flexible operation) to consider during the modeling of a long-term planning problem of for NR integrated systems. [ABSTRACT FROM AUTHOR]

Shapley value is originally a concept in econometrics to fairly distribute both gains and costs to players in a coalition game. In the recent decades, its application has been extended to other areas such as marketing, engineering and machine learning. For example, it produces reasonable solutions for problems in sensitivity analysis, local model explanation toward the interpretable machine learning, node importance in social network, attribution models, etc. However, it could be very expensive to compute the Shapley value. Specifically, in a d-player coalition game, calculating a Shapley value requires the evaluation of d ! or 2 d marginal contribution values, depending on whether we are taking the permutation or combination formulation of the Shapley value. Hence, it becomes infeasible to calculate the Shapley value when d is reasonably large. A common remedy is to take a random sample of the permutations to surrogate for the complete list of permutations. We find an advanced sampling scheme can be designed to yield much more accurate estimation of the Shapley value than the simple random sampling (SRS). Our sampling scheme is based on combinatorial structures in the field of design of experiments (DOE), particularly the order-of-addition experimental designs for the study of how the orderings of components would affect the output. We show that the obtained estimates are unbiased, and can sometimes deterministically recover the original Shapley value. Both theoretical and simulations results show that our DOE-based sampling scheme outperforms SRS in terms of estimation accuracy. Surprisingly, it is also slightly faster than SRS. Lastly, real data analysis is conducted for the C. elegans nervous system and the 9/11 terrorist network. for this article are available online. [ABSTRACT FROM AUTHOR]

The method of difference-in-differences (DID) is widely used to study the causal effect of policy interventions in observational studies. DID employs a before and after comparison of the treated and control units to remove bias due to time-invariant unmeasured confounders under the parallel trends assumption. Estimates from DID, however, will be biased if the outcomes for the treated and control units evolve differently in the absence of treatment, namely if the parallel trends assumption is violated. We propose a general identification strategy that leverages two groups of control units whose outcomes relative to the treated units exhibit a negative correlation, and achieves partial identification of the average treatment effect for the treated. The identified set is of a union bounds form that involves the minimum and maximum operators, which makes the canonical bootstrap generally inconsistent and naive methods overly conservative. By using the directional inconsistency of the bootstrap distribution, we develop a novel bootstrap method to construct confidence intervals for the identified set and parameter of interest when the identified set is of a union bounds form, and we theoretically establish the asymptotic validity of the proposed method. We develop a simple falsification test and sensitivity analysis. We apply the proposed strategy for bracketing to study whether minimum wage laws affect employment levels. for this article are available online. [ABSTRACT FROM AUTHOR]

In this work, we propose an approach for assessing sensitivity to unobserved confounding in studies with multiple outcomes. We demonstrate how prior knowledge unique to the multi-outcome setting can be leveraged to strengthen causal conclusions beyond what can be achieved from analyzing individual outcomes in isolation. We argue that it is often reasonable to make a shared confounding assumption, under which residual dependence amongst outcomes can be used to simplify and sharpen sensitivity analyses. We focus on a class of factor models for which we can bound the causal effects for all outcomes conditional on a single sensitivity parameter that represents the fraction of treatment variance explained by unobserved confounders. We characterize how causal ignorance regions shrink under additional prior assumptions about the presence of null control outcomes, and provide new approaches for quantifying the robustness of causal effect estimates. Finally, we illustrate our sensitivity analysis workflow in practice, in an analysis of both simulated data and a case study with data from the National Health and Nutrition Examination Survey (NHANES). for this article are available online. [ABSTRACT FROM AUTHOR]

To be convincing, an observational or nonrandomized study of causal effects must demonstrate that its conclusions cannot be readily explained by a small unmeasured bias in the way individuals were assigned to treatment or control. The Bahadur relative efficiency of a sensitivity analysis compares the performance of different test statistics or different research designs when sensitivity to unmeasured bias is appraised: better statistics and better designs exhibit insensitivity to larger biases. Bahadur efficiency is relevant here because, unlike Pitman efficiency, it can depict efficiency with an effect that is not trivially small: every trivially small treatment effect is sensitive to trivially small biases. The Bahadur efficiency of a sensitivity analysis has been used by various authors in the simple case of matched pairs, but the technical issues are more complex in the case of blocks larger than pairs, and they are developed here for the first time. Choosing a better test statistic for a block design, or choosing a better block size—larger than pairs—can result in enormous increases in the efficiency of a sensitivity analysis. An adaptive choice of test statistic can ensure the better Bahadur efficiency of two competing statistics. An R package weightedRank implements the methods, contains the example and reproduces its analysis. for this article are available online. [ABSTRACT FROM AUTHOR]

The fuzzy safety index (FSI) is put forward in this article under the assumption that the fuzzy inputs are independently and identically distributed. Then, the failure possibility-based constraints in original possibility-based design optimization (PBDO) are equivalently converted to the FSI-based constraints, and the fuzzy safety index approach (FSIA) is presented for solving PBDO. Based on the definition of FSI and the relationship between FSI and the fuzzy most possible failure point (F-MPP), the analytical expression is derived for the local sensitivity of FSI (LS-FSI) with respect to the design parameters, and it is used to provide the exact gradient for FSIA. An efficient iterative method is proposed for solving the FSI, which can estimate LS-FSI as a by-product of analysing the FSI without any additional computational cost. Finally, five numerical examples are demonstrated to show the feasibility of the derived analytical expression of LS-FSI and the proposed FSIA for PBDO. [ABSTRACT FROM AUTHOR]

A full-station blackout at a nuclear power plant can lead to fuel failures and radiological release to the environment if there is a breach of the reactor vessel. For Canada Deuterium Uranium (CANDU) reactors, the expected failure mechanism is through thermal stress concentration at the calandria vessel wall, and is thus influenced by local heat flux values. The current study uses computational fluid dynamics to simulate heat transfer, fluid flow, and crust formation within a CANDU geometry. Sensitivity to critical parameters, including the volumetric decay heat generation rate and the percentage of Zr oxidation is explored. The results show that as the volumetric heat generation rate decreases, the crust is thicker, and the wall heat flux is lower. This suggests that the activation of mitigating measures that delay the accident progression result in more favorable outcomes. The percentage of Zr oxidation primarily influences the thermal conductivity, which impacts the crust formation and wall heat flux rates. Specifically, corium with a lower percentage of Zr oxidation has higher thermal conductivity, and thus lower heat transfer resistance. This results in lower corium temperatures, which reduces the radiation heat transfer from the top surface and also increases crust thickness. Higher rates of heat removal from the vessel wall thus occur. In contrast, a higher percentage of Zr oxidation results in lower thermal conductivity, which leads to lower wall heat flux and a thinner crust at the vessel wall. Overall, these findings highlight the importance of considering the effects of sensitivity parameters on the heat flux distribution in the event of severe accidents. [ABSTRACT FROM AUTHOR]

Dynamic origin–destination (OD) flow is a fundamental input for dynamic network models and simulators. Numerous studies have conducted dynamic OD estimations based on fixed detectors, where a high device coverage rate and data quality are often required to accomplish the desired results. Several existing methods have used probe vehicle trajectories as an additional data source, and generalized least squares (GLS) is commonly recognized as an effective framework. However, the prior matrices used in these models either came from historical data or data obtained by uniform scaling that neglected the variation in penetration rates and suffer from sparsity issues. Moreover, the microscopic information contained in the high-resolution probe vehicle trajectories has not been fully utilized. The possibility of estimating OD flows using only vehicle trajectories without external information is rarely discussed in current literature. Therefore, this paper introduces a dynamic OD flow estimation model solely using probe vehicle trajectories. In the proposed model, two methods based on probe OD pair distribution are proposed to infer prior OD flows. Then the GLS framework is extended by including link travel times as another objective term, and the solution algorithm is adapted to deal with uncertain priors. To validate the proposed model, extensive experiments were conducted on a simulation network. The results show that the proposed model could reliably estimate dynamic OD flows and showed superiority to two existing models. In sensitivity analysis concerning the penetration rate and degree of saturation, the proposed model presented satisfactory performance and could adapt to various conditions. [ABSTRACT FROM AUTHOR]

Due to the complexity of spill fire, predicting heat release rate (HRR) is a challenging aspect, therefore, identifying key contributors to uncertainty is essential to develop reliable models for fire risk assessment. We propose a framework that couples Artificial-Neural-Network (ANN) and Monte Carlo Uncertainty Quantification (UQ) to quantify the uncertainties of spill fire parameters on peak HRR variance. The ANN spill fire model, trained on experimental spill fire data, shows good agreement with measured HRR values. By coupling with Dakota for Monte Carlo uncertainty propagation, the framework identifies major contributors to peak HRR uncertainty for fixed and continuous unconfined spill fires. This is achieved by investigating two uncertainty sources: data scarcity and input parameters. UQ results indicate that the confidence intervals widen at points where data are scarcer. Sensitivity analysis shows that in the case of fixed quantity spills, the fuel amount and properties parameters contribute to 54.4% and 22.6% of peak HRR variance, respectively. For continuous spills, the discharge rate and fuel properties parameters account for 41.8% and 39.3% of peak HRR variance, respectively. This work can be utilized in developing more advanced predictive ANN models for spill fire scenarios, ultimately enhancing fire probabilistic safety analysis in nuclear power plants. [ABSTRACT FROM AUTHOR]

This paper presents a comprehensive procedure for the parametric design and analysis of a underwater vehicle rotary docking skirt (RDS) structure. Different types of software were integrated to improve the design efficiency, and different techniques were used to establish an approximate model of the RDS structure with high fitting accuracy. Nondominated sorting genetic algorithm II (NSGA-II) was improved by dynamic adjustment of the population size according to the number of iterations and new crossover and mutation operations (i.e. INSGA-II), which was then applied to a multi-objective optimisation model of the RDS structure. A Pareto solution set was obtained, and a sensitivity analysis was performed on the design variables and objective functions of four representative optimisation schemes. These results can be used to provide a richer selection for designers to reference when designing RDS structures. Abbreviations: RDS; Rotary docking skirt; NSGA-II; Nondominated sorting genetic algorithm II [ABSTRACT FROM AUTHOR]

The transparency, reproducibility, and ethics movement has helped promote efforts to improve the rigour and validity of social science research. However, replication research, where the original data is used to check the validity and robustness of the estimations and recommendations in the original paper, has not been widely used as a tool in this field. This article outlines a checklist and standardised process that can guide replication researchers in creating and justifying their replication plans. To create the checklist, we reviewed 31 resources that were identified through unstructured keyword searches. We also reviewed and coded replication studies from 3ie's Replication Program which includes more than 20 3ie-funded replication papers and guidance on our replication process. There were five categories of potential validation tests and robustness checks that researchers could include. The checklist attributes range from testing specific assumptions for select impact evaluation methods to data transformations or dealing with heterogeneity. We hope that this checklist helps stimulate demand for replication research and promotes the use of replication research as a tool within transparency, reproducibility and ethics. [ABSTRACT FROM AUTHOR]

Columns in traditional timber structures are commonly seen resting on stone bases and are very important in resisting lateral loads. This paper numerically modeled the lateral resistance of the columns combined with experimental investigation. Different numerical models were developed, based on which sensitivity analyses were performed. A practical numerical modelling strategy was further proposed and verified. The analysis results indicated that instead of material properties and contact area, the columns' lateral performance was much more sensitive to the variation of surface curvature at bottom surface. Without consideration of the surface curvature, the modeling error in the initial stiffness and peak load of a column was more than 607% and 8%, respectively. By best matching the load–displacement curves of tested column specimens, the optimal surface curvature was identified as 1/20306.5 mm−1. Then, a practical finite numerical model, characterized by the optimal curvature at the bottom surface, was proposed. This modelling method was validated by use of existing test results of traditional timber columns with different diameters and vertical compression loads. The modelling load–displacement curves agreed well with experimental curves both in terms of the initial lateral stiffness and peak load. Detailed simulation results based on the practical modelling strategy were presented and discussed. [ABSTRACT FROM AUTHOR]

It has been recognised that the fiscal multiplier is a function of structural features of the economy and policy reaction parameters. Moreover, the debate on the magnitude of the multiplier along the business cycle has also been the subject of disputed debates. On these grounds, we look at the Greek case by calibrating a NUTS-2 interregional general equilibrium model using data for distinct states of the Greek economy during the development of the recent crisis. Whether this matters for local and nationwide multipliers depends on qualitative differences in the numerical structure of the model. As governments have to decide how to conduct fiscal policy, our results on the Greek case provide insights to policymakers on the reach of their actions in a spatially integrated system. [ABSTRACT FROM AUTHOR]

The objective of the current work is to explore the most appropriate temperature processing technologies (TPTs) and make their assessment based on the desired performance characteristics (assessment criteria) in the context of Indian cold chain (CC) industries. The assessment criteria and explored TPTs have been framed into a model for their theoretical and empirical assessment. The developed model has been analysed using an integrated approach based on Best Worst Method (BWM) and Evaluation Based on the Distance from Average Solution (EDAS) methods. The key findings of the work show that the criteria 'capability to achieve desired temperature' and 'Compatibility with environmental standards' play a crucial role in selecting a TPT for CC applications. Meanwhile, the refrigeration technologies such as 'solar energy-driven tri-generation refrigeration system' and 'passive cold devices as refrigeration technologies' can be adopted to achieve the optimum performance level of CC. To check the robustness of the results, sensitivity analysis has been performed. In addition, the study offers sound theoretical and managerial implications enriching the literature content into the subject domain and suggesting the most suitable TPT for temperature maintenance in CC, which might help the management to make decisions towards organisational growth. [ABSTRACT FROM AUTHOR]

AbstractIn this study, the structural parameters of porous Functionally Graded (FG) plates are regarded as random fields with spatial variability, and their impact on static bending and free vibration, are investigated along with sensitivity analysis. Higher-order shear deformation theories (HSDT) are employed to establish the displacement field of the plate. Various parameters, including thickness, Young’s modulus, density, volume fraction index, and porosity parameter are treated as random fields, which are discretized by Karhunen-Loève (KL) method. The analysis of uncertainties concerning structural bending deflections and natural frequencies is conducted using modified perturbation stochastic method (MPSM) in conjunction with radial point interpolation method (RPIM). The investigation extends to exploring the sensitivities of the structural responses to these random fields. The results indicate that porous FG plates exhibit heightened sensitivity to variations in thickness, ceramic Young’s modulus, and volume fraction index; alterations in external conditions do not exert an influence on these sensitivities; the porosity distribution type significantly impacts the sensitivities. [ABSTRACT FROM AUTHOR]

This paper attempts to analyse the effect of insurance development on sophistication measured by the economic complexity index. Empirical analyses are carried out on a sample of 36 African countries over the period 2003-2019. In order to limit potential endogeneity biases, we run the two-step system Generalized Method of Moments and the Lewbel estimator. Empirical results, robust to a set of sensitivity analyses, show that insurance development can foster economic complexity in Africa. Moreover, when distinguishing between life and non-life insurance, empirical results remain globally unchanged, with a greater effect of non-life insurance as compared to that of life insurance. Furthermore, the mediation analysis enabled us to identify innovations and investments as some transmission channels through which insurance development may affect economic complexity in Africa. Thus, African leaders would do well to improve the regulation of the insurance business to enable it to participate effectively in financing the economy and managing risks, thereby stimulating economic complexity. [ABSTRACT FROM AUTHOR]

The paper gives a directive toward assessment of resilience of building designed to comply the nearly zero energy standard in the current conditions and also points out the gap in the existing approach for evaluating the effect that the climate changes could have on building material behavior and systems degradation. A method is introduced for evaluating how the variation of energy need and in-situ production can modify the energy balance under the representative concentration pathway of the intermediate (RCP 4.5) and the worst emissions scenario (RCP 8.5). The results for a case study indicate that under RCP 4.5 scenario, the polyethylene foam insulation has the higher increment of heating demand (+ 9.8%) instead the cooling demand decreases by −4.2% (RCP 8.5) and −4.6% (RCP 4.5). The innovative materials are less affected (<1%) by temperature variations. On the other hand, the PV system degradation causes the reduction of in-situ production from −6.9% (RCP 8.5) and −8.0% (RCP 4.5). However, the analysis of the hourly balance suggests that the building resilience would be not compromised. Indeed, by evaluating the ratio between the self-consumption and the total request, it increases compared to the current conditions of around 2%. [ABSTRACT FROM AUTHOR]

Flood wave propagation involves complex flow variable dependencies. Continuous in situ hydrograph peak magnitude and timing data provide the most relevant information for understanding these dependencies. New acoustic instruments can produce experimental evidence by extracting usable signals from noisy datasets. This study presents a new screening protocol to smoothen streamflow data from unwanted influences and noise generated by flow perturbations and observational fluctuations. The protocol combines quantitative (statistical fitness parameters) and qualitative (domain expert judgments) evaluations. It is tested with 18 smoothing methods to identify optimal data conditioning candidates. Sensitivity analyses assess the validity, generality, and scalability of the procedures. The goal of this analysis is to set a mathematical foundation for empirical results that can lead to unified, general conclusions on principles or protocols for unsteady flows propagating in open channels, formulating practical guidance for future data acquisition and processing, and using in situ data to better support data-driven modeling efforts. [ABSTRACT FROM AUTHOR]

Container ports are increasingly deploying electric trucks (ETs) for inter-terminal transportation (ITT). However, en-route recharging activities, which reduce ITT efficiency, are necessary for ETs because of limited battery capacity. This study proposes a real-time strategy to schedule the en-route recharging activities for ETs. The strategy dynamically monitors the ITT system, and simultaneously designates charging piles for ETs and determines their positions in the service sequences. An ET may be designated to a remote but shorter-queued pile. The total queue time of the ETs served by a designated pile can be reduced by adjusting the service sequence. The effectiveness is demonstrated in comparison with an alternative strategy in a real-world case. The results indicate that the proposed strategy effectively reduces container throughput time by 21.8% and ET energy consumption by 12.3%. Sensitivity analyses suggest that the strategy is applicable for ports with multiple charging stations and moderate ET fleet size. [ABSTRACT FROM AUTHOR]

We present a new calibration strategy guide for coupled surface–subsurface hydrologic models. The goal is to reduce bias in environmental modeling and make calibration processes more consistent. The strategy guide enhances model efficiency and accuracy by focusing on changes to channel, soil, landscape, and aquifer properties. This approach is particularly beneficial when simulating important hydrologic features like baseflow and peaks and across different watersheds. Developed using the Soil and Water Assessment Tool (SWAT+) and the new gwflow module in six diverse US watersheds, each with unique hydrologic characteristics, the guide encourages improved calibration through routine use. It addresses specific issues related to the temporal distribution of streamflow and offers a robust method for enhancing model performance across a wide range of hydrologic conditions. By employing parameter estimation and sensitivity analysis, the approach ensures rigorous and objective calibration of SWAT+ and other models that include land, soil, aquifer, and water processes, leading to more accurate hydrologic modeling. [ABSTRACT FROM AUTHOR]

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

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

The in-wheel motor (IWM) is a promising advancement in electric vehicle propulsion since it offers greater packaging efficiency and torque vectoring capabilities than a chassis-mounted motor. However, the increased unsprung mass degrades the vehicle dynamics by increasing body vertical acceleration (BVA) and tyre dynamic load (TDL), worsening ride comfort and road-holding respectively. Another important consideration is magnetic gap deformation (MGD), which results from the deflection between the motor stator and rotor. Reducing MGD improves the motor reliability and longevity. This paper addresses these issues by incorporating inerters into an existing IWM model. They are incorporated into the strut and bushes to control BVA, TDL and MGD. The system optimised for MGD resulted in a deflection reduction of over 98% without compromising performance of the other indices. Optimising the strut and bushes for BVA and TDL reduced them by 20% and 9% respectively without degrading MGD. A sensitivity analysis of the results is presented. Here the damping and inertance values are varied by 20% from the optimum, and the resulting performance still improves upon the default. It is concluded that there are significant potential improvements where inerters are included in an in-wheel motor system. [ABSTRACT FROM AUTHOR]

AbstractDifferent pile penetration methods will cause different pile penetration results. Therefore, it is very necessary to study the pile penetration mechanism under different pile penetration methods. In this paper, numerical simulations using the discrete element method (DEM) are carried out to investigate the meso-scopic mechanism of pipe pile penetration under jacking and driving methods. The mechanism is comprehensively compared and analyzed in terms of particle movement, changes in the force chain, displacement changes, resistance of penetration pile, soil lateral stress and soil vertical stress. Results show that the height of the soil plug in the jacked pile is lower than that in the driven pile. The degree of contact force chain at the bottom of the soil plug in the driven pile is slightly higher than that at the bottom of the soil plug in the pile of the jacked pile. The total resistance of the driven pile greater than that of the jacked pile. Within a certain distance below the pile tip, the jacked pile affects the vertical stresses of the soil in this area to a greater extent than the driven pile. Then, parametric sensitivity analysis is performed, which captures the effects of the penetration velocity in the jacking method and the hammering frequency in the driving method. This study can provide a reference for the selection of the penetration method of pipe piles in actual projects. [ABSTRACT FROM AUTHOR]

While sensitivity analysis improves the transparency and reliability of mathematical models, its uptake by modelers is still scarce. This is partially explained by its technical requirements, which may be hard to understand and implement by the nonspecialist. Here we propose a sensitivity analysis approach based on the concept of discrepancy that is as easy to understand as the visual inspection of input-output scatterplots. First, we show that some discrepancy measures are able to rank the most influential parameters of a model almost as accurately as the variance-based total sensitivity index. We then introduce an ersatz-discrepancy whose performance as a sensitivity measure is similar that of the best-performing discrepancy algorithms, is simple to implement, easier to interpret and orders of magnitude faster. [ABSTRACT FROM AUTHOR]

In the current study, the sensitivity analysis for the combined convection boundary layer flow over a horizontal circular cylinder embedded in a nanofluid has been made. The governing partial differential equations are transformed into a dimensionless form by utilizing a suitable similarity transformation. The Keller-box technique is utilize to get a numerical solution. The numerical outcomes of the Sherwood number, skin friction coefficients, and Nusselt number have been shown graphically. Lewis number (5 ≤ Le ≤ 10) , Brownian motion parameter (0.1 ≤ N b ≤ 0.3) , thermophoresis motion parameter (0.1 ≤ N t ≤ 0.3) are the important physical parameter for the current research work. Sensitivity analysis is carried out after applying the RSM to develop a correlation between dependent variables and significant physical parameters after designing the best-fitting model. Numerical results show that the skin friction coefficient is more prominent at the center of the cylinder and skin friction is highly sensitive with Lewis number, Nusselt number is highly sensitive with the Brownian motion parameter. [ABSTRACT FROM AUTHOR]

The paper proposes a joint use of two global sensitivity analysis methods to evaluate the influence of model inputs on spatially explicit outputs. The analysis is conducted on spatial outputs of geodiversity assessment. First, the screening method is used to identify inputs with low importance and interactions, which can be discarded in later analysis. A simplified model is compared with a complete model using the mean and variance calculated on spatial outputs to evaluate outcome uncertainty. Finally, the variance decomposition method computes two sensitivity index maps per input. The results are mapped using bivariate classification, allowing for the simultaneous identification of regions with higher geodiversity scores and their corresponding variability. The research reveals that the streamlined approach preserves the overall patterns of outcome uncertainty and sensitivity observed in an original model while simultaneously reducing the number of outcome maps to interpret. Additionally, bivariate mapping proves efficient in concurrently depicting spatial sensitivity and interaction effects. The analytic efficiencies afforded by the presented approach and bivariate mapping enhance the operational knowledge of spatially explicit uncertainty and sensitivity analysis by simplifying the computational procedure and offering an alternative way of visualizing and interpreting input factors driving the sensitivity of model output. KEY POLICY HIGHLIGHTS: Spatially explicit uncertainty and sensitivity analysis extends the geodiversity assessment methods and comprises the next step in improving the accuracy of geodiversity assessments for land management decisions. An integrated uncertainty and sensitivity analysis helps focus on high-priority areas (high sensitivity and low uncertainty) to improve assessment reliability. The uncertainty and sensitivity analysis in models with spatially explicit output results depicted on maps can help reduce field research expenses by identifying a particular area or a specific natural component that warrants better-quality data. Bivariate maps employed to visualize the results of spatially explicit uncertainty and sensitivity analysis can potentially reduce the cognitive effort of visually switching between multiple single-variable (theme) maps. [ABSTRACT FROM AUTHOR]

AbstractBackgroundImmune cells may have a significant role in the pathophysiology of asthma, according to increasing evidence, although it is yet unclear how immune cells cause asthma. Therefore, we aimed to use Mendelian randomization (MR) methods to investigate this causal relationship.Methods：This study explored the causal effects between immune cells and asthma using a two-sample Mendelian randomization technique. Using publicly available genetic data, the causal connection between asthma risk and 731 immune cell phenotypes was investigated. Sensitivity analysis guaranteed the results' stability. To further evaluate the existence of reverse causality, we employed reverse MR analysis.Results：According to the inverse-variance weighted (IVW) method, five immune cell phenotypes were found to be statistically significantly associated with asthma risk (p < 0.001). Among them, TCRgd %T cell (OR = 0.968,95%CI = 0.951-0.986), TCRgd %lymphocyte (OR = 0.978,95%CI = 0.965-0.991), HLA DR + NK AC (OR = 0.966,95% CI = 0.947-0.986) and CD3 on CD4 Treg (OR = 0.956,95%CI= 0.931-0.981), four phenotypes that resulted in a decreased risk of asthma.CD25 on transitional (OR = 1.033, 95%CI = 1.014-1.052) resulted in an increased risk of asthma. Reverse MR analysis revealed that asthma increases HLA DR + NK AC levels (p < 0.05).Conclusion：The results of MR analysis showed a causal relationship between immune cell phenotype and asthma risk, which provides a direction for future asthma treatment. [ABSTRACT FROM AUTHOR]

Owing to the peculiarities of the design environment that structural engineers have to face – an environment where prototypes are designed and erected without intensive and purposeful development and testing – case studies of failures and/or near misses are an indispensable source of experience and knowledge and form the basis for enhancing and updating the set of technical rules and regulations. By discussing actual and historic case studies, the development of the tools and methods of forensic engineering as well as of the findings from these for the design, erection, supervision, inspection and maintenance of civil engineering structures will be addressed. The interface problems, when different parties are involved, are highlighted, and the necessity of establishing a good and reliable co-ordination mechanism is carved out. Because of the increasing fragmentation of the design process, in combination with the use of complex software tools and inevitable pressure of time, often a comprehensive design review process is the last line of defence to avoid incidents due to minor lapses or serious misjudgements. [ABSTRACT FROM AUTHOR]

Currently, there is no mature method for calculating the probability of human failure that occurs in multi-unit. In this study, the single-unit HRA is expanded and improved, and we have compiled a method flow suitable for human reliability analysis in multi-unit. This method considers the communication of commands between different organizations and analyzes the equipment shared by multiple units. This research demonstrates the feasibility of the THERP+HCR method to calculate the human-caused failure probability of multiple units. In this paper, the human failure probability of the fifth emergency diesel engine is investigated during a station blackout at a multi-unit site. The calculated failure probability of personnel deploying a fifth diesel-engine between two units is 0.1061, which is higher than the failure probability of a single unit, and the consequences of failure of a dual-unit invocation would be more severe. Due to the uncertainty of some conditions and parameters, the variation of the failure probability after sensitivity analysis ranges from–0.0846–0.2083. Involvement of more organizations during the accident increases the risk of the plant site. Among the most influential factors on the probability of failure are the presence of inspection clauses in the operating procedures and the level of tension of personnel. [ABSTRACT FROM AUTHOR]

To address the challenge of quantitatively predicting the wellbore pressure and the force of the tubing string below the packer during the perforation detonation, a prediction model for the wellbore pressure and the axial force of the tubing string is established. The evolution laws of the wellbore pressure and the axial force of the tubing are analyzed. The sensitivity analysis of four primary controlling factors is further studied. The obtained results indicate that the peak pressure of the wellbore at the bottom of the perforating gun is the cause of the tubing string failure. The relative error of the predicted peak pressure of the wellbore is 2.22%. The major sensitive parameters affecting the peak pressure of the wellbore at the bottom of the perforating gun and the peak axial force at the bottom of the tubing are ranked as follows: the initial wellbore pressure, the shot density, the explosive mass, and the perforating fluid density. The work presented can provide a theoretical foundation and technological basis for the quantification of the wellbore pressure and the force of the tubing string during the perforation detonation, which is critical for well completion design and wellbore safety evaluation. [ABSTRACT FROM AUTHOR]

AbstractIn order to improve the safety of on-board hydrogen storage tanks during collision, a protective shell based on a negative Poisson’s ratio (NPR) core is designed in this paper. After analyzing and comparing the crashworthiness of three typical honeycomb structures, the concave hexagonal negative Poisson’s ratio honeycomb is selected as the energy-absorbing inner core of the hydrogen storage tank protection structure. The sensitivity analysis of the structural parameters of the NPR shell is conducted through orthogonal tests to identify parameters with a significant impact on crash performance. These parameters are then used as experimental variables for subsequent optimization design. Subsequently, a response surface approximation model between the optimization objective and the structural parameters is established based on the response surface method. Finally, the adaptive simulated annealing algorithm (ASA), neighborhood cultivation genetic algorithm (NCGA), and non-dominated sorting genetic algorithm-II (NSGA-II) are used to optimize the structural parameters, and the optimization results are verified by the whole-vehicle crash simulation. The simulation results demonstrate that all three algorithms achieve better optimization results, among which NCGA is more advantageous in improving the overall performance of the protective shell. After NCGA optimization, the specific energy absorption of the protective shell increases by 19.75%, the maximum collision force of the rigid wall decreases by 23.63%, and the maximum stress of the hydrogen storage tank body decreases by 22.77%. These findings indicate that the designed protection shell effectively improves the crashworthiness of the hydrogen storage tank during collisions. [ABSTRACT FROM AUTHOR]

Objectives. Construction worker safety behavior (CWSB) may be affected by a confluence of multilevel and interrelated factors. Cultivating and maintaining CWSB is vital for improving construction safety. Current studies focus on organization-level or individual-level CWSB antecedents. However, few studies have examined the influence of psychological factors on CWSB, thereby reducing the joint effects of multilevel factors on CWSB. Methods. To determine effective strategies for strengthening CWSB, this study adopted the Bayesian network technique to explore the interrelationships between CWSB and its antecedent factors. A Bayesian belief network model was developed and trained with data collected from Chinese construction workers, which connected organizational, individual and psychological factors with CWSB. Results. According to the sensitivity analysis, safety knowledge, safety climate and psychological capital are the three most significant influencing factors for CWSB. A combined strategy that enhances safety knowledge, safety climate and communication competence simultaneously is the most effective option for strengthening CWSB. The validation and robustness of the network showed good accuracy for safety behavior judgment. Conclusion. This study proposes an alternative way to improve safety behavior by identifying its interactive causes and illustrates the importance of initiating systematic safety measures, which may help to mitigate the problem of safety plateau. [ABSTRACT FROM AUTHOR]

This paper optimizes a ground replenishment strategy for large-scale LEO satellite constellations, aiming for ensuring high economic efficiency and performance requirements. Firstly, the uncertainties of influential elements are analyzed and satellite lifetime distributions are estimated to determine satellite operating statuses. Secondly, satellite statuses are used for obtaining real-time constellation status and simulating data package transmission paths. Performance degradation function is then derived by selecting time delay as the key performance. To confront with the performance degradation, an optimization model of ground replenishment strategy is established with the objective of minimizing the overall replenishment cost under the time delay and loading capacity constraints. The model is solved by a Dichotomous-domain algorithm. Finally, a case study of a large-scale STARLINK constellation is adopted to validate the effectiveness of the model and sensitivity analyses are conducted to verify its robustness. [ABSTRACT FROM AUTHOR]

Industry 4.0 is potentially innovative in the workers' role, which is becoming increasingly involved in smart activities. In this situation, it is necessary to improve highly repetitive uncomfortable working postures to reduce physical risks. This study intends to assess physical risks during VR interaction for manufacturing work. Posture-related physical risk levels were calculated using ergonomic risk assessment tools RULA, REBA, and OWAS. Three task conditions were considered for the experiment in a VR-based car-assembly environment. An analysis of variance was applied to investigate significant differences between task conditions, and it suggested that a higher risk level was obtained while working in the overhead position for RULA and REBA, whereas the squatting position obtained a higher risk level for OWAS. Sensitivity analysis identified that the upper arm and neck were responsible for the highest risk level for RULA, the upper arm, neck, and trunk for REBA, and the back posture parameter for OWAS. [ABSTRACT FROM AUTHOR]

In recent years, some interested data can be recorded only if the values fall within an interval range, and the responses are often subject to censoring. Attempting to perform effective statistical analysis with censored, especially heavy-tailed and asymmetric data, can be difficult. In this paper, we develop a novel linear regression model based on the proposed skewed generalized t distribution for censored data. The likelihood-based inference and diagnostic analysis are established using the Expectation/Conditional Maximization Either algorithm in conjunction with smoothing approximate functions. We derive relevant measures to perform global influence for this novel model and develop local influence analysis based on the conditional expectation of the complete-data log-likelihood function. Some useful perturbation schemes are discussed. We illustrate the finite sample performance and the robustness of the proposed method by simulation studies. The proposed model is compared with other procedures based on a real dataset, and a sensitivity analysis is also conducted. [ABSTRACT FROM AUTHOR]

The model divides the population into five sub-populations of humans and three vectors. Boundedness, non-negativity, and continuous dependence of solutions on initial data prove their well-posedness. An explicit expression of the basic reproduction number and stability analysis of equilibria offer insights into disease mitigation and persistence. The Disease-Free Equilibrium is globally stable when the basic reproduction number is below 1, while the Endemic Equilibrium is globally stable when it is greater than 1. Sensitivity analysis identifies key mitigation parameters, and bifurcation analysis reveals the bifurcation parameter. Optimal control strategies are identified through the maximum principle, and numerical simulations exemplify the analytical results. [ABSTRACT FROM AUTHOR]

In this paper, we put forth an analysis of sensitivity which aims to discern individual from merely statistical evidence. We argue that sensitivity is not to be understood as a factive concept, but as a purely epistemic one. Our resulting analysis of epistemic sensitivity gives rise to an account of legal proof on which a defendant is only found liable based on epistemically sensitive evidence. [ABSTRACT FROM AUTHOR]

Mass rapid transit (MRT) maintenance systems enable owners to make optimal decisions for site selection and preventive maintenance plans while considering the total operating costs. To ensure that the MRT planning system is effective, it is essential to construct a maintenance cost plan. This study implements the mathematical planning method to establish a comprehensive plan for route maintenance and preventive maintenance for the Taoyuan MRT based on the minimum total operating cost as the target, and meets the practical emergency repair time limit, site location distance and other relevant constraints. This study developed and evaluated novel heuristic algorithms with CPLEX software. Sensitivity analysis was also conducted to test the model's effectiveness. The proposed model establishes the optimum maintenance cost and finds the critical parameters that affect overall cost within 1% error, maintaining service quality. The proposed model is an accurate tool for engineers to reduce time-consuming procedures. [ABSTRACT FROM AUTHOR]

Dimension reduction techniques have long been an important topic in statistics, and active subspaces (AS) have received much attention this past decade in the computer experiments literature. The most common approach toward estimating the AS is to use Monte Carlo with numerical gradient evaluation. While sensible in some settings, this approach has obvious drawbacks. Recent research has demonstrated that active subspace calculations can be obtained in closed form, conditional on a Gaussian process (GP) surrogate, which can be limiting in high-dimensional settings for computational reasons. In this article, we produce the relevant calculations for a more general case when the model of interest is a linear combination of tensor products. These general equations can be applied to the GP, recovering previous results as a special case, or applied to the models constructed by other regression techniques including multivariate adaptive regression splines (MARS). Using a MARS surrogate has many advantages including improved scaling, better estimation of active subspaces in high dimensions and the ability to handle a large number of prior distributions in closed form. In one real-world example, we obtain the active subspace of a radiation-transport code with 240 inputs and 9372 model runs in under half an hour. for this article are available online. [ABSTRACT FROM AUTHOR]

Classification of driving events is a crucial stage in driving behavior monitoring using smartphone sensory data. It has not been previously explored that to what extent classification performance depends on the classifier type and input data characteristics. To fill this gap, a real-world experiment is designed for supervised data collection. Then the effects of different machine learning (ML) classifiers, data sampling rates, and sensor combinations on the final classification accuracy are demonstrated. A considerable number of labeled events (4114) containing 11 types of driving maneuvers are collected using base sensors (accelerometer and gyroscope) and composite sensors (linear accelerometer and rotation vector) available in smartphones. Several models using 23 ML algorithms are trained. The sensitivity of these models is analyzed by changing the characteristics of the input data concerning the type of ML classifier, data sampling rate, and the bundle of mobile sensors. It is demonstrated that: (1) F1 scores vary from 70 to 96% for different ML classifiers, (2) F1 scores drop 30–40% depending on the classifier type when reducing the data sampling rate, and (3) using all four sensors as a bundle for classifying driving events is not reasonable since an approximate equal F1 score is achievable by a three-sensor bundle which includes an accelerometer and a linear accelerometer. [ABSTRACT FROM AUTHOR]

The study examines the magnetohydrodynamic flow of engineered fluid past an extending cylinder, analyzing the effects of non-linear radiation and an inclined magnetic field. The physical problem generates a system of non-linear coupled partial differential equations, and the system is transformed into ordinary differential equations using similarity transformation and solved using the Runge-Kutta method and shooting technique. Also, sensitivity analysis is taken into account to observe the input variables effects on skin friction and heat transfer rate. The study reveals that magnetic field orientation, nonlinear radiation effects, and injecting more core fluid significantly impact heat transfer characteristics. The effects of angle of orientation, magnetic field, suction parameter, and volume concentration on skin friction are significant. Reynolds number significantly affects heat transfer rate by 62.15% for suction. The findings can be applied in physics and engineering to control heat transfer between stretching surfaces and engineered fluids. [ABSTRACT FROM AUTHOR]