System-level Behavior Research Articles

Dynamics of spindle assembly and position checkpoints: Integrating molecular mechanisms with computational models.

Mitotic checkpoints orchestrate cell division through intricate molecular networks that ensure genomic stability. While experimental research has uncovered key aspects of checkpoint function, the complexity of protein interactions and spatial dynamics necessitates computational modeling for a deeper, system-level understanding. This review explores mathematical frameworks-from ordinary differential equations to stochastic simulations, which reveal checkpoint dynamics across multiple scales, encompassing models ranging from simple protein interactions to whole-system simulations with thousands of parameters. These approaches have elucidated fundamental properties, including bistable switches driving spindle assembly checkpoint (SAC) activation, spatial organization principles underlying spindle position checkpoint (SPOC) signaling, and critical system-level features ensuring checkpoint robustness. This study evaluates diverse modeling approaches, from rule-based models to chemical organization theory, highlighting their successful application in predicting protein localization patterns and checkpoint response dynamics validated through live-cell imaging. Contemporary challenges persist in integrating spatial and temporal scales, refining parameter estimation, and enhancing spatial modeling fidelity. However, recent advances in single-molecule imaging, data-driven algorithms, and machine learning techniques, particularly deep learning for parameter optimization, present transformative opportunities for improving model accuracy and predictive power. By bridging molecular mechanisms with system-level behaviors through validated computational frameworks, this review offers a comprehensive perspective on the mathematical modeling of cell cycle control, with practical implications for cancer research and therapeutic development.

Optimal performance objectives in the highly conserved bone morphogenetic protein signaling pathway

Throughout development, complex networks of cell signaling pathways drive cellular decision-making across different tissues and contexts. The transforming growth factor β (TGF-β) pathways, including the BMP/Smad pathway, play crucial roles in determining cellular responses. However, as the Smad pathway is used reiteratively throughout the life cycle of all animals, its systems-level behavior varies from one context to another, despite the pathway connectivity remaining nearly constant. For instance, some cellular systems require a rapid response, while others require high noise filtering. In this paper, we examine how the BMP-Smad pathway balances trade-offs among three such systems-level behaviors, or “Performance Objectives (POs)”: response speed, noise amplification, and the sensitivity of pathway output to receptor input. Using a Smad pathway model fit to human cell data, we show that varying non-conserved parameters (NCPs) such as protein concentrations, the Smad pathway can be tuned to emphasize any of the three POs and that the concentration of nuclear phosphatase has the greatest effect on tuning the POs. However, due to competition among the POs, the pathway cannot simultaneously optimize all three, but at best must balance trade-offs among the POs. We applied the multi-objective optimization concept of the Pareto Front, a widely used concept in economics to identify optimal trade-offs among various requirements. We show that the BMP pathway efficiently balances competing POs across species and is largely Pareto optimal. Our findings reveal that varying the concentration of NCPs allows the Smad signaling pathway to generate a diverse range of POs. This insight identifies how signaling pathways can be optimally tuned for each context.

Plastic hinge length in reinforced HPFRCC beams and columns

The use of ductile concrete materials such as High Performance Fiber Reinforced Cementitious Composites (HPFRCCs) within plastic hinge regions of structural components has garnered research interest in order to improve the seismic resistance of reinforced concrete structures. While experimental and numerical results appear promising in reducing component damage and probability of system level collapse, accurate nonlinear analysis tools capable of capturing the influence of axial load well into a component’s inelastic regime is needed. In this study, a series of 180 high fidelity numerical simulations of HPFRCC beam–column elements are simulated and used to calibrate new plastic hinge length expressions for concentrated and distributed plasticity models for use in system level structural analysis. The numerical models cover a range of HPFRCC material properties, reinforcement ratios, shear span lengths, and axial load levels. The ability of the newly developed expressions to predict component inelastic rotations are subsequently compared to hinge length expressions in the literature and the inelastic rotations of 47 experimental components. The results of this study provide new insights into the effects of axial load on the plastic hinge behavior of HPFRCC components, significantly improves on the accuracy of past plastic hinge length expressions allowing for more accurate modeling of HPFRCC component responses and system level behavior.

Dimension reduction approach for understanding resource-flow resilience to climate change

Networked dynamics are essential for assessing the resilience of lifeline infrastructures. The dimension-reduction approach was designed as an efficient way to map the high-dimensional dynamics to a low-dimensional representation capturing system-level behavior while taking into consideration network structure. However, its application to socio-technical systems has not been considered yet. Here, we extend the dimension-reduction approach to resource-flow dynamics in multiplex networks. We apply it to the San Francisco fuel transportation network, considering the flow between refineries, terminals and gas stations. We capture the aggregated dynamics between the facilities of each type and identify macroscopic conditions for the system to supply a given demand of fuel. By considering multiple sea level rise scenarios between 2020 and 2100, we address the impact of coastal flooding due to climate change on the maximum suppliable demand. Finally, we analyze the system’s transient response to production failures, investigating the temporary interruption in production and the duration it takes for complete demand satisfaction to become unachievable after the interruption.

Multidomain Device-Level Fuel-Cell Modeling and Real-Time Hardware Emulation for Marine Research Vessel Power System

Marine research vessels (MRVs) are redesigned and refurbished to meet higher energy conversion efficiency and modular integration schemes while conforming to stronger environmental regulations. The proton exchange membrane fuel cell (PEMFC) is currently regarded as a potential power source for marine transportation applications due to its advantages of stability, sustainability, and zero emissions. This paper proposes a hierarchical scheme for the real-time hardware emulation of the MRV’s power system and a comprehensive multi-domain model for PEMFCs. The PEMFC model is presented in the electrochemical, hydration, and thermal domains by ordinary differential equations considering the interactions and dynamics of each domain. Meanwhile, the multi-domain PEMFC model considers the implications of the fluctuating supply of the onboard hydrogen circulation system. Moreover, the dynamics of the lithium-ion battery stacks are represented by an equivalent circuit model which considers heat flux phenomena. In the case study, the DC-AC grid of the MRV’s power system and electric propulsion system is configured using extensive electrification technology with an average model. The real-time hardware emulation is conducted on the Xilinx® UltraScale+™ VCU118 FPGA platform to execute the device-level and system-level behavior transients of the MRV. The results of the real-time hardware emulation have been validated against the full-scale hybrid MRV power system emulation over a wide operating range.

Limitations of XAI Methods for Process-Level Understanding in the Atmospheric Sciences

Abstract Explainable artificial intelligence (XAI) methods are becoming popular tools for scientific discovery in the Earth and atmospheric sciences. While these techniques have the potential to revolutionize the scientific process, there are known limitations in their applicability that are frequently ignored. These limitations include that XAI methods explain the behavior of the AI model and not the behavior of the training dataset, and that caution should be used when these methods are applied to datasets with correlated and dependent features. Here, we explore the potential cost associated with ignoring these limitations with a simple case study from the atmospheric chemistry literature – learning the reaction rate of a bimolecular reaction. We demonstrate that dependent and highly correlated input features can lead to spurious process-level explanations. We posit that the current generation of XAI techniques should largely only be used for understanding system-level behavior and recommend caution when using XAI methods for process-level scientific discovery in the Earth and atmospheric sciences.

Analysis of Battery Testing Protocols in the Transition from the Lab to the Field: A Test Case Using Advanced Zn-MnO2 Batteries in Off-Grid Solar Microgrids

Understanding cycling performance of batteries under varying conditions is crucial for continued development of emerging chemistries. Currently much of this work is focused on cycling single cells in well-controlled lab experiments. There are few module cycling studies in the open literature. Studies of full-sized fielded systems are even rarer. As a result of this data gap, there is limited understanding of how single cell cycling can be used to predict performance of fielded systems prior to their deployment.Sandia National Laboratories (SNL) recently deployed an advanced Zn-MnO2 secondary battery energy storage system in an off-grid solar plus storage system. Using this deployment as a test case, we will detail how performance and degradation for batteries can vary between the lab and the field. We will also discuss ways that single cell and module lab cycling can be improved to better predict operation in fielded systems.In preparation for this deployment, SNL conducted single cell cycling experiments in the lab to simulate operation in the field and determine the expected life of the systems. The first test program utilized elements of the IEC 61427-1 standard for photovoltaic off-grid application to simulate the temperature and predicted state of charge (SOC) range the cells will be cycled during field operation. The simulated field cycling targeted SOC ranges of 10 to 40% and 80-100% over 150 cycles with temperature varied between -4 and 35oC to match the ambient conditions in the field during each season. The second type of cycling test was a temperature dependence study to determine how temperature impacted cell degradation. Cells were cycled at C/10 at their full SOC range and at a single temperature until they reached 40% capacity retention. Both cycling studies predicted that low temperature operation would increase the rate of degradation.The system was deployed in May of 2022, and since that time the SNL team has collected a significant amount of performance data to help analyze system level behaviors as compared to observations from cell level testing. These include battery interactions with the solar charge controller, limitations on power inverter settings, and variance between the average system level state of charge and the test protocol. Additionally, the team is analyzing conditions found in the field, with regards to ambient temperature, solar insolation and battery charging rates, and customer usage patterns for comparison with cell-level testing protocol.Observations of the deployed system’s performance indicate there is a need for more nuanced testing protocols for lab testing that can better approximate field conditions. These may include dynamic temperature ranges that vary throughout a charge/discharge cycle, and solar charging assumptions that account for reduced insolation due to cloud cover. This can help further inform the development of charge control algorithms, system hardware and software that can help improve the overall performance of solar microgrid energy storage systems.Sandia National Laboratories is a multi-mission laboratory managed and operated by National Technology and Engineering Solutions of Sandia, LLC., a wholly owned subsidiary of Honeywell International, Inc., for the U.S. Department of Energy’s National Nuclear Security Administration under contract DE-NA0003525. SAND2023-02502A

Using network analysis to identify leverage points based on causal loop diagrams leads to false inference

Network analysis is gaining momentum as an accepted practice to identify which factors in causal loop diagrams (CLDs)—mental models that graphically represent causal relationships between a system’s factors—are most likely to shift system-level behaviour, known as leverage points. This application of network analysis, employed to quantitatively identify leverage points without having to use computational modelling approaches that translate CLDs into sets of mathematical equations, has however not been duly reflected upon. We evaluate whether using commonly applied network analysis metrics to identify leverage points is justified, focusing on betweenness- and closeness centrality. First, we assess whether the metrics identify the same leverage points based on CLDs that represent the same system but differ in inferred causal structure—finding that they provide unreliable results. Second, we consider conflicts between assumptions underlying the metrics and CLDs. We recognise six conflicts suggesting that the metrics are not equipped to take key information captured in CLDs into account. In conclusion, using betweenness- and closeness centrality to identify leverage points based on CLDs is at best premature and at worst incorrect—possibly causing erroneous identification of leverage points. This is problematic as, in current practice, the results can inform policy recommendations. Other quantitative or qualitative approaches that better correspond with the system dynamics perspective must be explored.

Local Assortativity Affects the Synchronizability of Scale-Free Network

Synchronization is critical for system-level behavior in physical, chemical, biological, and social systems. Empirical evidence has shown that the network topology strongly impacts the synchronizability of the system, and the analysis of their relationship remains an open challenge. We know that the eigenvalue distribution determines a network's synchronizability, but analytical expressions that connect network topology and all relevant eigenvalues (e.g., the extreme values) remain elusive. Here, we accurately determine its synchronizability by proposing an analytical method to estimate the extreme eigenvalues using perturbation theory. Our analytical method exposes the role that global and local topology combine to influence synchronizability. We show that the smallest nonzero eigenvalue <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$\lambda ^{(2)}$</tex-math></inline-formula> , which determines synchronizability, is estimated by the smallest degree augmented by the inverse degree difference in the least connected nodes. From this, we can conclude that there exists a clear negative relationship between <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$\lambda ^{(2)}$</tex-math></inline-formula> and the local assortativity of nodes with the smallest degree value. We validate the accuracy of our framework within the setting of a scale-free network and can be driven by commonly used ordinary differential equations (e.g., 3-D Rosler dynamics or Hindmarsh–Rose neuronal circuit). From the results, we demonstrate that the synchronizability of the network can be tuned by rewiring the connections of these particular nodes while maintaining the general degree profile of the network.

System-level analysis of a self-centring moment-resisting frame under post-earthquake fire

Post-earthquake fire is a multi-hazard combination with cascading effects, of which catastrophic consequences are not only caused by the earthquake but exacerbated by the triggered fire. Only a few studies focused on the system-level structural analysis in earthquake-fire sequence, mostly on the conventional moment-resisting frame. Despite the self-centring system being a novel structure with excellent seismic performance, its post-earthquake fire response is still unclear due to limited research. Accordingly, this study aims to investigate the system-level behaviour of a self-centring system under post-earthquake fire. A numerical model of the six-storey prototype frame is established, and a two-stage bilinear material model is proposed to reflect the combined effects of pre-induced damage and temperature on material properties. A total of 11 ground motions (DBE and MCE levels) followed by 3 fires on different storeys compose the post-earthquake fire scenarios. A welded moment-resisting frame with reduced beam sections (WR-MRF) is selected for comparison, with the DBE response designed the same as the self-centring frame (SC-MRF). Results show that the SC-MRF exhibits smaller responses to the preceding earthquake and subsequent fire than the WR-MRF. Post-earthquake fire mainly affects two inter-storey drift ratios in each scenario, while it has a negligible effect on others which remain unchanged from the residual status after the earthquake. An obvious increase in structural responses can be found from fire-only (FO) to post-MCE fire (P-MCE-F) scenarios, and deformation is slightly larger in multi-floor fires than in single-floor ones. The findings unveil the post-earthquake fire responses of a seismic-resilient system with self-centring mechanism and provide a comparative assessment against the conventional structure. The methodology, including the proposed material model, can be further extended for analysis on other systems to understand and enhance the comprehensive performance in earthquake-fire sequence.

Seismic collapse assessment of archetype frames with ductile concrete beam hinges

Highly ductile cement-based materials have emerged as alternatives to conventional concrete materials to improve the seismic resistance of reinforced concrete (RC) structures. While experimental and numerical research on the behavior of individual components has provided significant knowledge on element-level response, relatively little is known about how ductile cement-based materials influence system-level behavior in seismic applications. This study uses recently developed lumped-plasticity models to simulate the unique failure characteristics and ductility of reinforced ductile-cement-based materials in beam hinges and applies them in the assessment of archetype frame structures. Numerous story heights (four, eight, and twelve), frame configurations (perimeter vs. space), materials (conventional vs. ductile concrete), and replacement mechanisms within the beam hinges are considered in the seismic analysis of the archetype structures. Results and comparisons are made in terms of the probability of collapse at 2% in 50-year ground motion, mean annual frequency of collapse, and adjusted collapse margin ratio (ACMR) across archetype structures. The results show that engineered HPFRCCs in beam plastic-hinge regions can improve the seismic safety of moment frame buildings with higher collapse margin ratios, lower probability of collapse, and the ability to withstand large deformations. Data is also reported on how ductile concrete materials can reduce concrete volume and longitudinal reinforcement tonnage across frame configurations and story heights while maintaining or improving seismic resistance of the structural system. Results demonstrate future research needs to assess life-cycle costs, predict column hinge behavior, and develop code-based design methods for structural systems using highly ductile concrete materials.

A deep generative model for feasible and diverse population synthesis

An agent-based model (ABM) simulates actions and interactions of the synthetic agents to understand the system-level behaviour. The synthetic population, the key input to ABM, mimics the distribution of the individual-level attributes in the actual population. Since individual-level attributes of the entire population are unavailable, small-scale samples are generally used for population synthesis. Synthesizing the population by directly sampling from the small-scale samples ignores the possible attribute combinations that are observed in the actual population but do not exist in the small-scale samples, called ‘sampling zeros’. A deep generative model (DGM) can potentially synthesize the sampling zeros but at the expense of falsely generating the infeasible attribute combinations that should be ‘zero’ in the generated data but exist, called ‘structural zeros’. This study proposes a novel method to ensure that the generation of structural zeros is minimal while recovering the ignored sampling zeros. Two loss functions for regularizing the DGMs are devised to customize the training and applied to a generative adversarial network (GAN) and a variational autoencoder (VAE). The adopted metrics for feasibility and diversity of the synthetic population indicate the capability of generating sampling and structural zeros – lower generation probability of structural zeros and lower generation probability of sampling zeros indicate the higher feasibility and the lower diversity, respectively. Results show that the proposed loss functions achieve considerable performance improvement in the feasibility and diversity of the synthesized population over traditional models. The proposed VAE additionally generated 23.5 % of the population ignored by the sample with 79.2 % precision (i.e., the generation ratio of structural zeros and the total samples is 20.8 %), while the proposed GAN generated 18.3 % of the ignored population with 89.0 % precision. The proposed improvement in DGM generates a more feasible and diverse synthetic population. Since synthesizing the population is the first stage of ABM, the proposed approach improves the overall accuracy of the ABM by circumventing the error propagation to later modelling stages.

Analysis methodology and design verification for strengthening moment-resisting caisson foundations

Many existing monopole towers rely on caisson foundations to resist the effects of large overturning moments. Increased loading on these towers, coupled with material deterioration and more stringent code requirements, often results in the overloading of the existing caissons. The strengthening of these caissons for additional overturning resistance presents many challenges and typically requires the installation of new piles. A pile cap is commonly used to connect the existing caisson to the new piles. The proper design of the pile cap has a critical importance in ensuring an effective stress transfer and creating a resilient foundation system. While a number of strengthening design approaches are repeatedly used in industry, there is a dearth of guidance and proven analysis methods to verify the performance achieved with these approaches. This study proposes a well-defined analysis methodology for the system-level structural design verification of the strengthened foundation systems. The methodology provides two routes, using either the strut-and-tie or nonlinear finite element analysis methods, to quantify the load, deformation, cracking, and failure responses. The methodology helps expose and address the weak aspects of the design using an iterative design improvement process. A common strengthening design approach from industry, involving a moment-resisting caisson strengthened with four concrete piles and a pile cap, is used as a case study to demonstrate the application of the methodology. The results demonstrate that the methodology provides significant insight into the system-level behavior both at the serviceability and ultimate limit states. The analysis results are found useful in identifying the locations where modified bar detailing could significantly improve the system performance. The improved design from the methodology provided a 56 % increase in the load capacity for the case-study foundation while still using the same amounts of reinforcing steel and concrete as compared to the initial design before the application of the proposed methodology. This paper presents the studies undertaken, conclusions reached, and recommendations made for developing resilient pile cap designs for strengthening moment-resisting caisson foundations.

Achieving reinforcement learning in a three-active-terminal neuromorphic device based on a 2D vdW ferroelectric material.

Currently, for most three-terminal neuromorphic devices, only the gate terminal is active. The inadequate modes and freedom of modulation in such devices greatly hinder the implementation of complex neural behaviors and brain-like thinking strategies in hardware systems. Taking advantage of the unique feature of co-existing in-plane (IP) and out-of-plane (OOP) ferroelectricity in two-dimensional (2D) ferroelectric α-In2Se3, we construct a three-active-terminal neuromorphic device where any terminal can modulate the conductance state. Based on the co-operation mode, controlling food intake as a complex nervous system-level behavior is achieved to carry out positive and negative feedback. Specifically, reinforcement learning as a brain-like thinking strategy is implemented due to the coupling between polarizations in different directions. Compared to the single modulation mode, the chance of the agent successfully obtaining the reward in the Markov decision process is increased from 68% to 82% under the co-operation mode through the coupling effect between IP and OOP ferroelectricity in 2D α-In2Se3 layers. Our work demonstrates the practicability of three-active-terminal neuromorphic devices in handling complex tasks and advances a significant step towards implementing brain-like learning strategies based on neuromorphic devices for dealing with real-world challenges.

Microgravity two-phase flow research in the context of vapor compression cycle experiments on parabolic flights

Research on two-phase channel flow in microgravity has extensively investigated flow regimes, pressure drop, and heat transfer during evaporation and condensation processes over several decades. This literature has been primarily motivated by the goal of enabling future space applications for two-phase thermal management systems, among them vapor compression refrigeration cycles. However, relative to the number of two-phase channel flow experiments, research on vapor compression cycles in microgravity has been almost non-existent. This paper reviews two-phase flow research and compares key outcomes with those from system-level measurements obtained from recent testing of a vapor compression cycle at varying inclination angles and on parabolic flights. Previous two-phase channel flow experiments have resulted in several microgravity-specific flow regime maps and have also generally found reduced condensation heat transfer coefficients in microgravity as compared to normal gravity. The flow pattern map of Jayawardena et al. (1997) and generally reduced condensation heat transfer are confirmed by the vapor compression cycle experiments. Regarding evaporation heat transfer coefficients and friction factors, there is not a consensus in the literature on the effects of microgravity in two-phase channel flows. Similarly, the system-level experiments do not show repeatable increases or decreases for those parameters. Lastly, the dependence of system-level behavior on orientation with respect to gravity was tested by performing dynamic inclination experiments, where the inclination angle was changed every 2 min by a 90-degree increment. These tests reveal an increased relative dependence of the vapor compression cycle to orientation with decreasing mass fluxes, in line with two-phase channel flow research that has generally shown that increasing the flow inertia acts to mitigate various flow instabilities and dependence on orientation. However, inclination testing with longer locking times per angle showed a constant relative dependence of the vapor compression cycle to orientation changes.

Attack Detection and Mitigation in MEC-Enabled 5G Networks for AIoT

In recent years, AIoT (Artificial Intelligence of Things) applications have received lots of attention since it exploits widely deployed IoT devices to collect data and perform an action while leveraging AI to obtain knowledge and insights. When deploying AIoT in 5G networks, Multi-access Edge Computing (MEC) is appropriate for enabling local management of IoT devices and computation of machine learning (ML) algorithms. Facing the multitude of threats in the ML data layer, IoT service layer, and 5G communications layer, MEC should enable corresponding detection and mitigation schemes to protect AIoT applications. In this article, we examine the existing security solutions located in each layer and discuss the interrelated challenges. For example, a network-traffic-based IDS solution in MEC might capture IoT malware but fail to identify a growing file-less attack. We suggest that firmware emulation in IoT endpoint should be included to provide system-level behaviors to network-based detectors in MEC so that file-less attacks can be distinguished. The potential of a backdoor attack aiming to poison data or corrupt ML models cannot be ignored in AIoT applications, and a corresponding detector should be implemented in MEC. Due to the rise of low-cost Software-Defined Radio (SDR), malicious attacks using rogue base stations (BSs) have become more popular. It implies that security protection at MEC in the communications layer is necessary. This article, therefore, proposes a novel platform, M3Inspector, where inspectors located in mobiles and AIoT machines collect information from surrounding BSs and provide them to MEC. MEC determines the rogue BS and makes a notification to users subscribing to the local service. A realistic 5G experimental platform with rogue BSs is developed. The results demonstrate that attack detection and mitigation can be implemented in the MEC paradigm to significantly improve the security protection of AIoT from the perspectives of rogue BS attacks and file-less attacks.

Evolution of Combined Arms Tactics in Heterogeneous Multi-Agent Teams

Multi-agent systems research is concerned with the emergence of system-level behaviors from relatively simple agent interactions. Multi-agent systems research to date is primarily concerned with systems of homogeneous agents, with member agents both physically and behaviorally identical. Systems of heterogeneous agents with differing physical or behavioral characteristics may be able to accomplish tasks more efficiently than homogeneous teams, via cooperation between mutually complementary agent types. In this article, we compare the performance of homogeneous and heterogeneous teams in combined arms situations. Combined arms theory proposes that the application of heterogeneous forces, en masse, can generate effects far greater than outcomes achieved by homogeneous forces or the serial use of individual arms. Results from experiments show that combined arms tactics can emerge from simple agent interactions.

Experimental and numerical study of bending-induced buckling of stiffened composite plate assemblies

Despite their importance in benchmarking numerical simulations, buckling tests still feature compromises between component-level and high-fidelity large-scale tests. For example, compression-induced buckling tests cannot capture the through-thickness or span-wise stress gradients in wing skins. Consequently, the results obtained often require careful interpretation and conservative considerations before applying to a structure. Alternatively, a system-level large-scale test can be used, yet at considerably increased time and expense. There has been little progression towards capturing system-level behaviour in a simplified test. Herein, for buckling behaviour assessment, a three-point bending test is used, which is quick, simple to implement, and cost-effective compared to existing conventional methods. The proposed method relies on subjecting a panel with auxiliary stiffeners to bending to introduce compression-induced buckling in the skin. The three-point bend test is used, because it provides readily controllable loading and boundary conditions. The location of the neutral plane can be tailored via design of the stiffeners, thus allowing for control of the through-thickness stress gradient induced in the skin. This method is applicable to buckling of stiffened structures subject to bending (e.g., aircraft wingboxes). Numerical models are used to explore the limits of the proposed method and comparing it against traditional coupon and full-scale structural level tests. The test method is experimentally demonstrated for capturing the buckling behaviour of a thermoplastic composite panel made via automated fibre placement. The proposed approach is shown to reliably capture the buckling behaviour of a large-scale test using a simpler and more time and cost-efficient setup than conventional methods.

A kinetic mechanism for systems-level behavior in GTPase signaling

GTPase switches are hubs for multiple distinct cell signaling inputs and outputs. In a new study combining genetic and biochemical methods, Perica, Mathy et al. identify an unexpected connection between the kinetics of a GTPase switch cycle and functional specificity.

Social Network Word-of-Mouth Integrated Into Agent-Based Design for Market Systems Modeling

Abstract Improving engineering design in the context of market systems requires a deep understanding of the decision-making processes of multiple interacting stakeholders and how they affect the success of new products. One key group of stakeholders in this system is consumers, who make purchase choices that directly influence each product’s market share and profits. Since real-world individual decisions are influenced by social communications, supporting product development efforts with social network analysis can enable producers to predict demand much more accurately.This article presents an agent-based modeling (ABM) framework for design for market systems analysis that incorporates social network word-of-mouth (WOM) recommendations. To investigate influences of homophily-driven WOM and network structures on consumer preferences and the prediction of market demand, the random and small-world networks are generated based on the concept of homophily to study the differences in the emergent system-level behaviors. We compare the output of the models against a similar model that excludes WOM influences, using a case study of the top-selling midsize sedans in the US automobile industry. The results show that the addition of WOM improves the ability to accurately forecast consumer demand in a statistically significant way. This suggests that producers who invest in supporting their product development efforts with design for market systems analyses that account for social networks may be able to better optimize their decision-making and increase their market success.