Combined Sensitivity Analysis Research Articles

Lithography mask thermal and stress effect for the machine overlay compensation

With the continuous advancement of integrated circuit technology nodes, the degree of chip integration is increasing, and the requirements for critical dimensions and overlay errors are becoming more and more stringent. In recent years, near-field lithography technologies such as nanoimprint lithography and plasmonic lithography have made rapid progress; however, the challenge of compensating for their overlay has yet to be solved or systematic reports are lacking. This work offers an overlay compensation approach based on the theory of overlay and mask stress mechanics and thermodynamics by providing stress to the mask. The overlay analysis model and correction feedback mechanism based on mask compensation technology is carried out theoretically. This work establishes the relationship between the overlay compensation parameters and the mask stress by using strict calculation methods, quantitative characterization, and combining sensitivity analysis methods. Theoretical verification of various overlay distribution patterns demonstrates the efficiency of this feedback compensation strategy as well as the quantitative analytical calculation. It is verified that, under ideal conditions, the feedback technique may minimize the overlay error caused by mask thermal effects to ∼1.5 nm. This research presents a quantitative control mechanism for reducing overlay for near-field lithography, and it has substantial guiding implications for traditional lithography quantitative research on the influence of mask stress or temperature on overlay.

A combined uncertainty and sensitivity analysis of melting in a cubical cavity, Part A: Model validation

Latent heat thermal energy storage systems exploit the solid–liquid phase transition of a material to store thermal energy almost isothermally with high energy density. Numerical methods are increasingly being used for the targeted design of these storage systems. The problem, however, is that the results obtained with these methods – despite a frequently shown near-perfect agreement between validation experiments and simulation results – are subject to large uncertainty. To illustrate the extent of this problem and to show approaches to solving it, a combined uncertainty and sensitivity analysis of the melting process of a paraffin (octadecane) in a cuboid heated from one side is carried out in this two-paper series. In part A, we present the model and validate it; in part B we use it to carry out the combined uncertainty and sensitivity analysis. The validation of the model is performed with the help of experimental results. For this purpose, we measure the liquid phase fraction, the position of the phase boundary, the velocity field in the liquid and the temperature inside the test capsule. We make sure that the experimental results are reproducible. Systematic deviations are discussed and, if possible, the results are corrected for their value. When measuring the velocity field using particle image velocimetry (PIV), the greatest systematic deviation is due to the inhomogeneous refractive index field in the liquid phase. Finally, it is shown that, in the present case, the influence is very low if constant material properties evaluated at the mean temperature are taken for each phase compared to a temperature-dependent implementation. An exception is the density. If the density change during melting is not taken into account the deviation of the melt fraction lies between 6 and 8%. In summary, the validation of the numerical model is very satisfactory and demonstrates the suitability of the model for performing the investigations of the part B paper.

Bayesian hybrid-kernel machine-learning-assisted sensitivity analysis and sensitivity-relevant inverse modeling for groundwater DNAPL contamination

Accurate source characterization and transport parameter estimation is important when seeking to predict the spatiotemporal distribution of dense non-aqueous phase liquid (DNAPL) contaminants in groundwater. However, this is a complex multimodal search problem prone to equifinality and premature convergence, which leads to considerable error. To address this, a sensitivity-relevant dynamic swarm intelligence (SRD-SI) algorithm embedded in a homotopy-variation mechanism is proposed in the present study to rationally balance the inversion processes of sensitivity-varied source characteristics and DNAPL transport parameters. In this approach, global optima are progressively approached in conjunction with the homotopy variation of the search space. Furthermore, to avoid computationally expensive numerical model repetition during the sensitivity analysis and inverse iterations, a Bayesian-based optimization framework that combines multiple kernel functions in a kernel extreme learning machine (KELM) model is designed considering the complex site conditions and statistical characteristics of the input variables, thus creating the Bayesian hybrid KELM (BHK-ELM) model for the reliable surrogate modeling of numerical DNAPL-transport simulations. The results show that the BHK-ELM model recognizes and effectively reconstructs the complex input − output mapping of the numerical model by increasing the determination coefficient R2 to 0.9988 while improving the computational efficiency approximately 4500-fold. Because source characteristics and boundary conditions are far more sensitive than transport parameters to the contaminant distribution, conventional inverse modeling methods struggle to accurately identify these transport parameters. In contrast, the proposed inverse modeling system combining sensitivity analysis, swarm intelligence, and homotopy variation is more stable and provides significantly more accurate estimations for all unknown variables. Compared with the traditional SI algorithm, the homotopy-variation SRD-SI reduced the maximum inversion relative error from 46.22 % to 9.53 %, while the mean inversion relative error was reduced from 11.09 % to 3.90 %.

Robustly estimating the demographic contribution of immigration: Simulation, sensitivity analysis and seals.

Identifying important demographic drivers of population dynamics is fundamental for understanding life-history evolution and implementing effective conservation measures. Integrated population models (IPMs) coupled with transient life table response experiments (tLTREs) allow ecologists to quantify the contributions of demographic parameters to observed population change. While IPMs can estimate parameters that are not estimable using any data source alone, for example, immigration, the estimated contribution of such parameters to population change is prone to bias. Currently, it is unclear when robust conclusions can be drawn from them. We sought to understand the drivers of a rebounding southern elephant seal population on Marion Island using the IPM-tLTRE framework, applied to count and mark-recapture data on 9500 female seals over nearly 40 years. Given the uncertainty around IPM-tLTRE estimates of immigration, we also aimed to investigate the utility of simulation and sensitivity analyses as general tools for evaluating the robustness of conclusions obtained in this framework. Using a Bayesian IPM and tLTRE analysis, we quantified the contributions of survival, immigration and population structure to population growth. We assessed the sensitivity of our estimates to choice of multivariate priors on immigration and other vital rates. To do so we make a novel application of Gaussian process priors, in comparison with commonly used shrinkage priors. Using simulation, we assessed our model's ability to estimate the demographic contribution of immigration under different levels of temporal variance in immigration. The tLTRE analysis suggested that adult survival and immigration were the most important drivers of recent population growth. While the contribution of immigration was sensitive to prior choices, the estimate was consistently large. Furthermore, our simulation study validated the importance of immigration by showing that our estimate of its demographic contribution is unlikely to result as a biased overestimate. Our results highlight the connectivity between distant populations of southern elephant seals, illustrating that female dispersal can be important in regulating the abundance of local populations even when natal site fidelity is high. More generally, we demonstrate how robust ecological conclusions may be obtained about immigration from the IPM-tLTRE framework, by combining sensitivity analysis and simulation.

Heterogeneity analysis of main driving factors affecting potential evapotranspiration changes across different climate regions

Exploring the influencing factors of potential evapotranspiration (PET) is of great significance for further understanding the causes of climate change and improving agricultural irrigation efficiency. In this study, modified Mann–Kendall analysis was used to elucidate the temporal variation characteristics of meteorological factors and PET based on a dataset from 710 meteorological stations in China. Furthermore, we revealed the main factors that influence the temporal and climate heterogeneity of PET by combining sensitivity analysis with the contribution analysis method. The results showed that 1) climate factors and PET exhibited trend changes on a yearly scale, with slope variation ranges of temperature (T), relative humidity (RH), net radiation (RN), wind speed (U) and PET of 0.03–0.04 °C/a, 0.03–0.08 %/a, 0.001–0.007[MJ/(m2/day)]/a, −0.005 to −0.012(m/s)/a and −0.30–0.38 mm/a, respectively. 2) The sensitivity coefficient fluctuated greatly inter-annually, but the trend was more pronounced inter-annually. Most sensitive factor for PET was RN in hyperarid (HAR), arid (AR) and semiarid regions (SAR), while it changed to RH in semihumid (SHR) and humid regions (HR). PET was more sensitive to RN in dry and relatively wet hot seasons, while it changed to RH during wet and relatively dry cold seasons. 3) PET changes were determined by the relative changes and the sensitivity coefficient, and significant temporal heterogeneity was observed. In HAR, AR, SAR and SHR, the relative changes in T and U result in higher contributions. In HR, PET changes were primarily caused by its higher sensitivity to RH and RN. 4) In dry region and humid-cold seasons, the bigger relative changes of climate factors were the main drivers affecting PET changes, but in humid region and arid-hot seasons, the they were determined by the strong nonlinear relationship between PET and factors. This finding holds great significance for the scientific understanding of the evolution mechanism of PET under changing environments.

Four Fundamental Questions to Evaluate Land Change Models with an Illustration of a Cellular Automata–Markov Model

Numerous models exist for users to simulate land change to communicate with an audience concerning future land change. This article raises four fundamental questions to help model users decide whether to use any model: (1) Can the user understand the model? (2) Can the audience understand the model? (3) Can the user control the model? (4) Does the model address the goals of the specific application? This article applies these questions to the popular cellular automata–Markov (CA–Markov) model as IDRISI’s CA–Markov module expresses. Sensitivity analysis examines 120 ways to set the module’s parameters. Verification compares the module’s behavior to the software’s documentation. Results show that the cellular automata’s allocation fails to follow the quantity of change that the Markov module computes. The module’s behavior is likely to cause users to misinterpret the validation metrics and to miscommunicate with audiences. Thus, the answers to the four questions were not satisfactory for this article’s case study. This article’s framework helps users to judge a model’s appropriateness for a specific application by combining sensitivity analysis with verification in a manner that helps to interpret validation. Users should answer the four questions as they decide whether to use any software’s modules.

Identification of Factors Influencing the Operational Effect of the Green Wave on Urban Arterial Roads Based on Association Analysis

Green wave control is an important technology that synchronizes traffic signals to improve traffic flow on urban arterial roads. Current research has focused on optimizing and evaluating control schemes; however, their operational effect is easily affected by a variety of traffic and travel factors. This means it is important to study methods to identify the factors influencing the operational effect of the green wave on arterial roads. In this study, we conducted innovative research to identify these factors and made breakthroughs in optimizing and evaluating schemes of green wave control. We use the number of stops, travel time, and delays as representative evaluation indicators to assess the effects of four influencing factors: design speed, signal timing, pedestrian crossing, and heavy vehicles. An association analysis that combines sensitivity analysis and grey relational analysis was used to rank these factors in their degree of correlation. A case study was conducted based on the traffic data on Eshan Road in Wuhu City to verify the proposed method. The results of simulations in Vissim 7.0 showed that pedestrian crossing and heavy vehicles were the more important factors influencing the operational effect of the green wave. Moreover, implementing measures related to traffic management helped improve the effect to an extent greater than by optimizing the scheme for green wave control alone. Additionally, optimizing control schemes in the context of implementing measures related to traffic management significantly improved the operational effect of the green wave.

Energy-saving potential assessment of a classroom building envelope through sensitivity analysis and multi-objective optimization under different climate types

The present work aims to optimize the thermal behavior of a building envelope by combining sensitivity analysis (SA) and multi-objective optimization (MOO). An existing classroom located in Marrakech city was considered a case study building. The building model was analyzed under six Moroccan climate zones. The SA was applied on 16 design variables and performed using the Morris method implemented in the tool Simlab to rank each design variable based on its influence on the objective function (overall energy demand). The SA results showed that the solar absorptance of the internal roof, wall, and ground floor and the ground hollow core slab thickness impacted less the overall energy demand. Therefore, the only remaining variables showing the most relevant effect will be optimized afterward. The optimization phase was conducted by coupling the generic optimization tool GenOpt with TRNSYS. The optimum solution was selected based on the Pareto front approach. The obtained results assessed the effectiveness of the adopted methodological approach in significant minimization of the required thermal loads. Furthermore, the values of each optimum design variables set differ from one climate zone to another; leading to energy demand reduction varying from 30 to 42%, in comparison with the original design building.

Research on Risk Propagation in Complex Supply Chain Networks Based on SEIR Model

The complexity of supply chain network structure makes the risk propagation among the enterprises in the supply chain more and more complicated. This paper constructs a SEIR risk propagation model for complex supply chain networks based on the analytical idea of contagion model, which reveals the risk propagation mechanism of supply chain networks from a macro perspective. The article verifies the existence of risk propagation threshold and the stability of different equilibrium points by analyzing the basic regeneration number, equilibrium point and stability of the model; explores the effectiveness of different parameters on the basic regeneration number by combining sensitivity analysis; and verifies them by using MATLAB simulation software. The results show that the basic regeneration number is a key factor affecting the risk propagation, and the risk diffusion in this system can be effectively curbed by controlling the basic regeneration number.

Optimization of parameters in coherent spin dynamics of radical pairs in quantum biology.

Identification of the external electromagnetic fields and internal hyperfine parameters which optimize the quantum singlet-triplet yield of simplified radical pairs modeled by Schrödinger system with spin Hamiltonians given by the sum of Zeeman interaction and hyperfine coupling interaction terms are analyzed. A method that combines sensitivity analysis with Tikhonov regularization is implemented. Numerical results demonstrate that the quantum singlet-triplet yield of the radical pair system can be significantly reduced if optimization is pursued simultaneously for both external magnetic fields and internal hyperfine parameters. The results may contribute towards understanding the structure-function relationship of a putative magnetoreceptor to manipulate and enhance quantum coherences at room temperature and leveraging biofidelic function to inspire novel quantum devices.

Optimization of key energy and performance metrics for drug product manufacturing

During the development of pharmaceutical manufacturing processes, detailed systems-based analysis and optimization are required to control and regulate critical quality attributes within specific ranges, to maintain product performance. As discussions on carbon footprint, sustainability, and energy efficiency are gaining prominence, the development and utilization of these concepts in pharmaceutical manufacturing are seldom reported, which limits the potential of pharmaceutical industry in maximizing key energy and performance metrics. Based on an integrated modeling and techno-economic analysis framework previously developed by the authors (Sampat et al., 2022), this study presents the development of a combined sensitivity analysis and optimization approach to minimize energy consumption while maintaining product quality and meeting operational constraints in a pharmaceutical process. The optimal input process conditions identified were validated against experiments and good agreement resulted between simulated and experimental data. The results also allowed for a comparison of the capital and operational costs for batch and continuous manufacturing schemes under nominal and optimized conditions. Using the nominal batch operations as a basis, the optimized batch operation results in a 71.7% reduction of energy consumption, whereas the optimized continuous case results in an energy saving of 83.3%.

Characterization of sialylation-related long noncoding RNAs to develop a novel signature for predicting prognosis, immune landscape, and chemotherapy response in colorectal cancer.

Aberrant sialylation plays a key biological role in tumorigenesis and metastasis, including tumor cell survival and invasion, immune evasion, angiogenesis, and resistance to therapy. It has been proposed as a possible cancer biomarker and a potential therapeutic target of tumors. Nevertheless, the prognostic significance and biological features of sialylation-related long noncoding RNAs (lncRNAs) in colorectal cancer (CRC) remain unclear. This study aimed to develop a novel sialylation-related lncRNA signature to accurately evaluate the prognosis of patients with CRC and explore the potential molecular mechanisms of the sialylation-related lncRNAs. Here, we identified sialylation-related lncRNAs using the Pearson correlation analysis on The Cancer Genome Atlas (TCGA) dataset. Univariate and stepwise multivariable Cox analysis were used to establish a signature based on seven sialylation-related lncRNAs in the TCGA dataset, and the risk model was validated in the Gene Expression Omnibus dataset. Kaplan-Meier curve analysis revealed that CRC patients in the low-risk subgroup had a better survival outcome than those in the high-risk subgroup in the training set, testing set, and overall set. Multivariate analysis demonstrated that the sialylation-related lncRNA signature was an independent prognostic factor for overall survival, progression-free survival, and disease-specific survival prediction. The sialylation lncRNA signature-based nomogram exhibited a robust prognostic performance. Furthermore, enrichment analysis showed that cancer hallmarks and oncogenic signaling were enriched in the high-risk group, while inflammatory responses and immune-related pathways were enriched in the low-risk group. The comprehensive analysis suggested that low-risk patients had higher activity of immune response pathways, greater immune cell infiltration, and higher expression of immune stimulators. In addition, we determined the sialylation level in normal colonic cells and CRC cell lines by flow cytometry combined with immunofluorescence, and verified the expression levels of seven lncRNAs using real-time quantitative polymerase chain reaction. Finally, combined drug sensitivity analysis using the Genomics of Drug Sensitivity in Cancer, Cancer Therapeutics Response Portal, and Profiling Relative Inhibition Simultaneously in Mixtures indicated that the sialylation-related lncRNA signature could serve as a potential predictor for chemosensitivity. Collectively, this is the first sialylation lncRNA-based signature for predicting the prognosis, immune landscape, and chemotherapeutic response in CRC, and may provide vital guidance to facilitate risk stratification and optimize individualized therapy for CRC patients.

Hybrid Method for Optimal Placement of DC-PFCs to Enhance Static Security of VSC-HVdc Grids

This article proposes a hybrid method to find the optimal place of dc power flow controllers (DC-PFC)s in HVdc grids. This hybrid method consists of max flow-min cut theory with Ford–Fulkerson augmenting paths and combining sensitivity analysis to find the most important HVdc lines and the best placement. In this article, series DC-PFC is modeled and get placed. A significant advantage of this method is, reducing the computational burden of calculations by finding the most important and susceptible HVdc lines to get overloaded without focusing on other unnecessary HVdc lines. The static security of the power system strongly depends on the placement of DC-PFCs, which has become an important issue because of rapid-developing of HVdc grids. Static security studies have been done on an eight-terminal HVdc grid incorporating double scenarios.

Combined sensitivity analysis for multiple failure modes

Sensitivity analysis identifies the influential parameters and it is a key step in the design process. Focusing on reliability based design, the probability of a failure mode and its sensitivity to input uncertainties are obtained in a single Monte Carlo simulation using the Likelihood Ratio method. For correlated multiple failure modes, a sensitivity matrix is proposed. The dominant singular vectors of the sensitivity matrix are found to indicate the most important parameters for combined sensitivity of multiple failure modes. Therefore, the sensitivity matrix can guide resources allocation so that most uncertainties can be reduced for system reliabilities.

A meteorologically adjusted ensemble Kalman filter approach for inversing daily emissions: A case study in the Pearl River Delta, China

The conventional Ensemble Kalman filter (EnKF), which is now widely used to calibrate emission inventories and to improve air quality simulations, is susceptible to simulation errors of meteorological inputs, making accurate updates of high temporal-resolution emission inventories challenging. In this study, we developed a novel meteorologically adjusted inversion method (MAEInv) based on the EnKF to improve daily emission estimations. The new method combines sensitivity analysis and bias correction to alleviate the inversion biases caused by errors of meteorological inputs. For demonstration, we used the MAEInv to inverse daily carbon monoxide (CO) emissions in the Pearl River Delta (PRD) region, China. In the case study, 60% of the total CO simulation biases were associated with sensitive meteorological inputs, which would lead to the overestimation of daily variations of posterior emissions. Using the new inversion method, daily variations of emissions shrank dramatically, with the percentage change decreased by 30%. Also, the total amount of posterior CO emissions estimated by the MAEInv decreased by 14%, indicating that posterior CO emissions might be overestimated using the conventional EnKF. Model evaluations using independent observations revealed that daily CO emissions estimated by MAEInv better reproduce the magnitude and temporal patterns of ambient CO concentration, with a higher correlation coefficient (R, +37.0%) and lower normalized mean bias (NMB, -17.9%). Since errors of meteorological inputs are major sources of simulation biases for both low-reactive and reactive pollutants, the MAEInv is also applicable to improve the daily emission inversions of reactive pollutants.

Parameter sensitivity analysis of a physico-chemical lithium-ion battery model with combined discharge voltage and electrochemical impedance data

Physico-chemical battery models are capable of better simulation of lithium-ion cell behaviour due to their more physically descriptive modelling approach using many physical parameters. Accuracy of simulations highly depend on the correct identification of parameters. However, large parameter estimation uncertainties and model complexities increase the model parameterization difficulties. Therefore, pre-analysis of parameter sensitivities to model outputs can greatly help determining the best conditions and techniques for identifying many parameters with better accuracy. This work demonstrates a next step in improving the parameter identification accuracy by combined parameter sensitivity analysis of both discharge voltage and EIS data. A commercially available NMC-graphite based lithium-ion cell is modelled and 26 physical parameters are analysed for their sensitivity. For discharge voltage, capacity-related and non-capacity-related parameters clearly show different sensitivity patterns at different C-rates and depth of discharge regions. Moreover, non-dimensional EIS spectra states are introduced for correct identification of impedance-based parameter sensitivities and accurate parameter sensitivities are observed matching the frequency bands truly affected by the parameter kinetics. The ranked analysis of the parameter sensitivities clearly demonstrate the ability to identify many discharge voltage insensitive parameters using impedance measurements, which would greatly increase the number of identifiable parameters of a physico-chemical battery model.

Integrated Analysis of MATH-Based Subtypes Reveals a Novel Screening Strategy for Early-Stage Lung Adenocarcinoma.

Lung adenocarcinoma (LUAD) is a frequently diagnosed cancer type, and many patients have already reached an advanced stage when diagnosed. Thus, it is crucial to develop a novel and efficient approach to diagnose and classify lung adenocarcinoma at an early stage. In our study, we combined in silico analysis and machine learning to develop a new five-gene–based diagnosis strategy, which was further verified in independent cohorts and in vitro experiments. Considering the heterogeneity in cancer, we used the MATH (mutant-allele tumor heterogeneity) algorithm to divide patients with early-stage LUAD into two groups (C1 and C2). Specifically, patients in C2 had lower intratumor heterogeneity and higher abundance of immune cells (including B cell, CD4 T cell, CD8 T cell, macrophage, dendritic cell, and neutrophil). In addition, patients in C2 had a higher likelihood of immunotherapy response and overall survival advantage than patients in C1. Combined drug sensitivity analysis (CTRP/PRISM/CMap/GDSC) revealed that BI-2536 might serve as a new therapeutic compound for patients in C1. In order to realize the application value of our study, we constructed the classifier (to classify early-stage LUAD patients into C1 or C2 groups) with multiple machine learning and bioinformatic analyses. The 21-gene–based classification model showed high accuracy and strong generalization ability, and it was verified in four independent validation cohorts. In summary, our research provided a new strategy for clinicians to make a quick preliminary assisting diagnosis of early-stage LUAD and make patient classification at the intratumor heterogeneity level. All data, codes, and study processes have been deposited to Github and are available online.

Dynamic Security Control in Heat and Electricity Integrated Energy System With an Equivalent Heating Network Model

Intensified interaction between electric power system (EPS) and district heating network (DHN) has raised an urgent requirement of security control to ensure the regular operation of the heat and electricity integrated energy system (HE-IES). However, the implicit expressions of thermal dynamics remain an obstacle to building the quantitative relationships between the EPS and the DHN. To solve this, this paper proposes a novel dynamic model of the district heating network, which contains the equivalent pipe model and the analytical thermal source-load function (TSLF). Then, the interaction between EPS and DHN is divided into the energy flow calculation at the control points and the dynamic simulation during the control intervals to investigate the coupling effect. On this basis, the dynamic characteristics in HE-IES are employed to quantitatively establish the security control strategy using the combined sensitivity analysis. Case studies verify the accuracy and efficiency of the proposed method.

Sensitivity of an alkali-silica reaction kinetics model to diffusion and reactive mechanisms parameters

Alkali-silica reaction (ASR) expansion is due to a combination of chemo-mechanical mechanisms. To obtain realistic predictions, modelling developed at the material scale has to consider reactive transport and mechanical issues. Numerous input variables concerning aggregate and cement paste properties are thus necessary. The uncertainties that affect such variables make the prediction of the ASR phenomenon random and thus need to be considered in a probabilistic context. To reduce the stochastic dimension for a further probabilistic analysis, a sensitivity analysis using the Morris method is conducted here, at different dates, on an ASR model developed at the material scale. It is illustrated by a combined sensitivity analysis of both the total volume of ASR products formed over time and the corresponding expansion. The work shows the relative impact of transport and reactive mechanisms on ASR kinetics. Moreover, the most significant parameters are not the same for laboratory accelerated expansion tests as for real structures under low temperatures. This highlights the relative impact of ASR mechanisms according to temperature.

Theory of temperature-dependent consumer-resource interactions.

Changes in temperature affect consumer-resource interactions, which underpin the functioning of ecosystems. However, existing studies report contrasting predictions regarding the impacts of warming on biological rates and community dynamics. To improve prediction accuracy and comparability, we develop an approach that combines sensitivity analysis and aggregate parameters. The former determines which biological parameters impact the community most strongly. The use of aggregate parameters (i.e., maximal energetic efficiency, ρ, and interaction strength, κ), that combine multiple biological parameters, increases explanatory power and reduces the complexity of theoretical analyses. We illustrate the approach using empirically derived thermal dependence curves of biological rates and applying it to consumer-resource biomass ratio and community stability. Based on our analyses, we generate four predictions: (1) resource growth rate regulates biomass distributions at mild temperatures, (2) interaction strength alone determines the thermal boundaries of the community, (3) warming destabilises dynamics at low and mild temperatures only and (4) interactions strength must decrease faster than maximal energetic efficiency for warming to stabilise dynamics. We argue for the potential benefits of directly working with the aggregate parameters to increase the accuracy of predictions on warming impacts on food webs and promote cross-system comparisons.