Rapid Iteration Research Articles

To What Extent Have LLMs Reshaped the Legal Domain So Far? A Scoping Literature Review

Understanding and explaining legal systems is very challenging due to their complex structure, specialized terminology, and multiple interpretations. Legal AI models are currently undergoing drastic advancements due to the development of Large Language Models (LLMs) that have achieved state-of-the-art performance on a wide range of tasks and are currently undergoing very rapid iterations. As an emerging field, the application of LLMs in the legal field is still in its early stages, with multiple challenges that need to be addressed. Our objective is to provide a comprehensive survey of legal LLMs, not only reviewing the models themselves but also analyzing their applications within the legal systems in different geographies. The paper begins by providing a high-level overview of AI technologies in the legal field and showcasing recent research advancements in LLMs, followed by practical implementations of legal LLMs. Two databases (i.e., SCOPUS and Web of Science) were considered alongside additional related studies that met our selection criteria. We used the PRISMA for Scoping Reviews (PRISMA-ScR) guidelines as the methodology to extract relevant studies and report our findings. The paper discusses and analyses the limitations and challenges faced by legal LLMs, including issues related to data, algorithms, and judicial practices. Moreover, we examine the extent to which such systems can be effectively deployed. The paper summarizes recommendations and future directions to address challenges, aiming to help stakeholders overcome limitations and integrate legal LLMs into the judicial system.

Identification of Supply Chain Disruption Risks and Strategies in the Electric Vehicle Industry Based on Fuzzy Analytic Hierarchy Process (FAHP)

In recent years, the electric vehicle industry has witnessed unprecedented development opportunities. However, with rapid technological iterations and fluctuating market demands, the vulnerability and complexity of the electric vehicle supply chain have significantly increased the potential risk of supply chain disruptions. Against this backdrop, this study adopts the Fuzzy Analytic Hierarchy Process (FAHP) to systematically analyze the risks of supply chain disruptions in the electric vehicle industry. By constructing a judgment matrix through industry data surveys and expert scoring, and conducting consistency checks, the weights of various risk factors are determined. The study finds that technological quality risk, demand forecast deviation, and logistics disruption are the primary factors affecting the stability of the electric vehicle supply chain. Additionally, although the probability is relatively low, the potential destructiveness of exogenous risks is equally undeniable. Accordingly, the study will propose targeted management strategies to enhance the resilience of the new energy vehicle supply chain at both the enterprise and industry levels in response to these risks.

GAME DEVELOPMENT PROJECT MANAGEMENT USING SCRUM FRAMEWORK: HYPERCASUAL GAME CASE STUDY 'RUSH RUNNER'

The research aims to explore and implement an effective Project Management approach in Game Development using the Scrum Framework. A case study was conducted on the development of a hypercasual game titled "Rush Runner." With a focus on rapid iterations, continuous feedback, and adaptation to changes, the Scrum methodology was used to manage the game development workflow from planning to publication. Concrete steps were taken to adapt to user needs and evolving demands as an integral part of the development process. The research findings indicate that the implementation of Scrum had a positive impact on productivity, quality, and user satisfaction in hypercasual game development. Furthermore, the integration of Project Management and the Scrum approach optimized overall time, cost, and quality. These findings provide valuable insights for practitioners and researchers in the field of game development to understand the importance of an adaptive and responsive Project Management approach to market changes and user needs.

Application of Lean Startup Principles in the Improvement of Digital Platforms: a Case Study in a Solar Energy Company

Objective: The objective of this study is to improve a solar energy company's website by utilizing the principles of the Lean Startup methodology to meet customer expectations. Theoretical Framework: This study is based on the concepts of digital entrepreneurship and the Lean Startup methodology, emphasizing the importance of adapting to the digital environment and applying agile methodologies for technological innovation. Method: The research adopted a case study with a qualitative and descriptive approach. Data collection was carried out through semi-structured interviews with customers and potential customers, as well as prototyping to validate the hypotheses raised. Results and Discussion: The results revealed that the main improvement needs include adding information about the company, adjusting visual elements, and incorporating interactive tools. The application of Lean Startup principles allowed for rapid iterations on the improvements, resulting in an increased perception of the website's credibility and usability by users. Research Implications: This research provides practical insights into the implementation of agile methodologies for the enhancement of digital products, highlighting the importance of continuous validation with users to achieve satisfactory results. Originality/Value: This study contributes to the literature by demonstrating the applicability of the Lean Startup methodology in a real context of digital product improvement, offering a replicable model for other companies seeking continuous innovation and customer satisfaction.

A Hybrid Prototype Method Combining Physical Models and Generative Artificial Intelligence to Support Creativity in Conceptual Design

Conceptual design is an essential stage in the design process, and its ultimate success largely depends on designers’ creativity. Both physical and digital prototypes are commonly adopted by designers to support ideation and creativity, providing intuitive perception and rapid iteration, respectively. In recent advancements, large-scale generation models are able to offer data-enabled creativity support by generating high-quality solutions comparable to human designers. This opens up an imaginary space for designers and brings new possibilities for design tools. In this study, we proposed a hybrid prototype method that synergistically combines physical models and generative artificial intelligence (AI) in the conceptual design stage. Correspondingly, we developed a hybrid prototype system to implement the proposed method. We conducted a comparative user study with 45 designers who completed a design task using the physical prototype method, standalone generative AI, and the hybrid prototype method, respectively. Our results verified the effectiveness of the hybrid prototype method and investigated its mechanism in supporting creativity. Finally, we discussed the application value and optimisation space of the hybrid prototype method.

A fixed point evolution algorithm based on expanded Aitken rapid iteration method for global numeric optimization

Evolution algorithms based on mathematical models whose reproduction operators are derived from mathematical models are a promising branch of metaheuristic algorithms. Aitken rapid iteration method, as a fixed point iteration technique for solving nonlinear equations, performs a procedure of progressive display of a root and generates an iterative sequence that exhibits a convergent trend. Inspired by the idea that an iterative sequence gradually converges to the optimal point during the progressive display procedure of a fixed point of an equation, a fixed point evolution algorithm based on the expanded Aitken rapid iteration method (FPEea) is proposed. To develop FPEea, an expanded Aitken rapid model is first constructed. Then, three polynomials which are derived from the expanded Aitken rapid model are used as the reproduction operator of FPEea to produce offspring. The performance of FPEea is investigated on CEC2019 and CEC2020 benchmark function sets, as well as four engineering design problems. Experimental results show that FPEea is an effective and competitive algorithm compared with several classical evolution algorithms and state-of-the-art algorithms.

Monthly industrial added value monitoring model with multi-source big data

Introduction: With the increasing fluctuations in the current domestic and international economic situation and the rapid iteration of macroeconomic regulation and control demands, the inadequacy of the existing economic data statistical system in terms of agility has been exposed. It has become a primary task to closely track and accurately predict the domestic and international economic situation using effective tools and measures to compensate for the inadequate economic early warning system and promote stable and orderly industrial production.Methods: Against this background, this paper takes industrial added value as the forecasting object, uses electricity consumption to predict industrial added value, selects factors influencing industrial added value based on grounded theory, and constructs a big data forecasting model using a combination of “expert interviews + big data technology” for economic forecasting.Results: The forecasting accuracy on four provincial companies has reached over 90%.Discussion: The final forecast results can be submitted to government departments to provide suggestions for guiding macroeconomic development.

A Data-Driven Modeling Framework for Lithium Metal Battery Health Estimation

The Achilles' heel of LMB, despite its high theoretical energy density, has been its limited lifespan and variability in aging trajectories due to coupling of multiple failure mechanisms. Effective health estimation could enable timely maintenance actions before failure and a rapid LMB development iteration. Data-driven methods capture a wide spectrum of failure mechanisms and incorporates aging behavior variability. Recent work around data-driven LMB health estimation rely on empirical correlations observed specific to the datasets [1][2], and lack adaptability for a wide variety of experimental conditions and cell behaviors, especially those with a flat discharge capacity retention followed by abrupt decline.In this work, we cycled 36 LMB cells across various temperatures and charge/discharge rates. The examined cells exhibit significant cell-to-cell variabilities even under identical experimental conditions, and consistent abrupt capacity fade behaviors. We extracted an extensive inventory of non-invasive features based on cycle-to-cycle variation of LMB external voltage-current measurements throughout aging, and managed to accurately track LMB capacity fade with quantified confidence intervals using these features combined with machine learning tools. The identified features provide not only reliable and robust LMB health monitoring performance, but also starting points for further first-principal modeling and experimental investigation to demystify LMB aging mechanisms. The abundance of discovered features, along with an automatic and time-efficient correlation mining pipeline, enables the generalizability of our proposed framework toward a wide variety of LMB cells.

Highly Active Nano-Porous Thin-Films for Water Electrolysis Using an Automated Print Technology Based on Spark Ablation

Accelerating hydrogen (H2) deployment via renewable electricity is a key G7 strategy for sustainable transitions in hard-to-decarbonize sectors (e.g., circular-plastics, -steel, -fertilizers,shipping, and aviation) therefore, Alkaline (AWE) and Polymer Electrolyte Membrane water electrolysis (PEMWE) play a crucial role amongst low-temperature power-to-X technologies for establishing a hydrogen network. Assuming all hydrogen will be green by 2030 (with ca. 150 million metric tons), a significant increase in electrolyser capacity to 3,000 gigawatts is necessary, given the current capacity of 0.3 gigawatts. However, the adoption of electrolysers faces a bottleneck due to scarce platinum group materials (PGMs) used as catalyst, specifically Iridium and Platinum. The commercial PGM loading per membrane electrode assembly (MEA) is at 3 mg/cm2, requiring substantial efforts to redesign electrocatalyst materials through a nano-architecture, necessitating a thousandfold increase in surface area (µm to nm scale) and a tenfold reduction in mass loading (mg to µg). The realization of this objective is conceivable through innovative surface engineering and manufacturing techniques, exemplified by. VSPARTICLE is actively advancing its manufacturing technology for green H2, bridging academia and industry . We developed a unique nano-printing technology which utilizes spark ablation for generating nano-porous electrocatalyst thin films across diverse elements and alloys. Unlike traditional methods, it eliminates the need for chemical precursors or binders, reducing waste and extra pre- / post-process steps. The VSP-P1 nano-printer serves as a crucial R&D tool for dry-synthesis, enabling both the production of complex electrocatalyst layers and also sample arrays high-throughput screening. This accelerates the discovery of new catalysts, fostering the creation and application of novel nanomaterials in specialized setups. Our oral presentation will unveil the latest findings concerning oxygen evolution electrodes, employing Ir-based and Ni-based catalysts produced through the innovative VSP-P1 nano-printing technology, which exhibit notable activity in acidic and alkaline water electrolysis, respectively, even at low loadings, such as 0.4 mg/cm2. Therefore, our oral presentation aims to show the latest findings on accelerated electrocatalysts discovery concerning oxygen evolution reaction (OER) produced through the innovative VSP-P1 nano-printing technology. In parallel to comprehensive flow-cell experiments, meticulous control tests and characterization techniques (XRD, XRF, HR-SEM) on proxy samples were conducted, leveraging the versatility of VSP-P1 to create identical nano-porous films on various substrates such as Si-wafer, conductive glass, and glassy carbon disks. The scalability of this dry manufacturing process, coupled with the rapid iteration capability for tuning material parameters and test-beds, promises a tenfold acceleration in electrocatalyst utilization. In summary, this presentation intends to reveal a methodology that not only shows an efficient strategy for catalyst utilization through nano-printed layers, but also shortens the time required to bring novel materials to market. Figure 1

Using a board game to explain a concept model: Experience from multimodal traffic management

IntroductionIn the near future, traffic has to be managed in new ways to facilitate a more resilient transport system and integration of connected and autonomous vehicles and vessels. This paper presents our experiences with designing and using a serious board game as a tool for spreading knowledge and getting feedback on a concept model for future multimodal traffic management designed to handle these challenges. MethodsOur research has been done within a design science framework, where a table-top board game was the main artefact designed and tested in several quick iterations. The relevance of the board game was evaluated through two workshops where data were collected using a digital questionnaire and through observations and audio recording of the game play sessions. ResultsOne main result is the board game with game elements that can be mapped to the concept model, and a description of how the game evolved through the iterations of design and testing. This can serve as an example for others on how a board game can be developed based on a concept model. In addition, results from the questionnaire show that most of the respondents agree or strongly agree that the board game made central concepts more concrete and understandable and that it triggered good discussions, and this is also supported by the observations and recordings. ConclusionWe conclude that a table-top board game can support the spreading of knowledge from a concept model to stakeholders in the transport domain. A board game suitable for such use can be developed using moderate resources by mapping concepts from the model to the game, using rapid iterations of prototyping and testing, and maintaining a focus on what you want the participants to learn and discuss.

Artifacts-free lensless on-chip tomography empowered by three-dimensional deconvolution

A lensless holographic microscope based on in-line holograms and optical diffraction tomography is an ideal imaging system for label-free 3D biological samples and can achieve large-volume imaging with single-cell resolution in a convenient way. However, due to the phase information loss and the missing cone problem, the imaging quality is significantly degraded by the reconstructed artifacts of twin images and out-of-focus images, which severely hinders the identification and interpretation of the objects. We propose an artifacts-free lensless on-chip tomography certified by three-dimensional deconvolution, which facilitates the extraction of real object morphology through straightforward yet effective computation. Initially, a globally valid systemic point spread function (PSF) is generated by simulating the imaging output of an ideal point light source positioned at the origin of the object space coordinate. Subsequently, an iterative three-dimensional deconvolution process is applied to the primitive imaging outcome of the lensless on-chip tomography using this PSF. Through rapid iterations, the optimized imaging result is swiftly obtained. Both the simulated and experimental results indicate that the artifacts-free lensless on-chip tomography can effectively circumvent the reconstructed artifacts and retrieve the real object morphology, which is critical for detailed observation and further quantitative analysis. In addition, we anticipate that the proposed approach has the potential to be transferred to other 3D imaging systems in systemic artifacts removal after corresponding modifications.

Implementation Web-Based Qr-Code Information System Design in Warehouse Inventory Management System Using Rapid Application Development (RAD) Method at PT Dharma Precision Parts

This research aims to implement a web-based information system using QR-codes in warehouse inventory management using the Rapid Application Development (RAD) method. The implementation of QR-code technology is expected to increase efficiency and accuracy in the process of tracking and managing goods in warehouses. The RAD method was chosen because of its ability to speed up the system development process through rapid iteration and prototyping. The results of this implementation show significant improvements in warehouse operational efficiency as well as reduced human error in inventory recording and tracking. With a web-based information system, users can easily access and manage inventory data in real-time, increasing transparency and accuracy of information

Determination of the defrosting duration ratio for defrosting performance evaluation of air source heat pump

Defrosting performance evaluation is an important aspect for pre-delivery inspection of air source heat pumps (ASHPs). According to GB/T 25127-2020 and ANSI/AHRI 550/590–2023, defrosting duration ratio (α) should not exceed 20 % of the frosting-defrosting cycle. This value has remained unchanged for over 20 years. However, with the rapid iteration of defrosting technology, the α of new ASHPs has been decreased significantly. Hence, the applicability of this index value deserves to be deliberated. To solve this issue, a method for determining the optimal α was proposed firstly. Then, the α under different characteristic index (CICO) values at 2/1 °C and 2/0 °C were investigated at the optimal and original defrosting initiating time point (topt, tori), using twelve ASHPs from nine manufacturers. Results indicates the frosting duration (tf) is directly proportional to defrosting duration (tdf), at the same ambient air condition and CICO. For Unit C, when tf is increased from 30.0 min to 60.0 min and 90.0 min at the CICO of 10.0 × 106, tdf increases from 2.43 min to 3.20 min and 4.03 min at 2/1 °C, respectively. No matter at topt nor tori, the range of α is 1.4%–9.9 %. It is suggested that the 10 % is used as the new index for defrosting performance evaluation of ASHP.

Hybrid additive-subtractive manufacturing of high-density copper-based flexible transparent circuits based on side-etch process

High-density flexible transparent circuits (FTCs) are widely used in 5G/6G flexible transparent antennas, wearable devices, flexible transparent electronics and other fields. However, the rapid iteration and upgrading of electronic circuits present significant challenges to the manufacturing of both high-density and high-precision circuits (especially copper-based circuits) at room temperature, in an efficient and flexible manner. Here, we employ the side-etching phenomenon, which is typically avoided, to propose a novel method for hybrid additive-subtractive fabricating high-density copper-based FTCs that combines electric field-driven (EFD) microjet printing and wet etching technology. Firstly, the customized photoresist mask was flexibly printed on the copper-clad flexible substrate by EFD microjet printing, and then the copper circuit with less than the mask resolution was obtained by the side etching process of the wet etching process. By optimizing process parameters and precisely controlling the speed of the side etching stage during the etching process, the manufacturing of high-precision copper-based FTCs with a minimum line width of 2.4 μm and a minimum pitch of 4 μm was achieved. The typical sample is manufactured with a resistivity of 4 × 10−6 Ω·cm, a sheet resistance of 3.58 Ω/sq at a transmittance of up to 87.65 % (including the substrate), and it exhibits excellent mechanical stability. The prepared high-density flexible transparent interdigital electrode has excellent sensitivity, and can detect the concentration change of dilute H2SO4 solution at a minimum of 1 nmol/L at a frequency of 100–10,000 Hz. This method provides a new solution for the fabrication of copper-based FTCs at room temperature with high efficiency and low cost, and shows a good prospect for industrial application.

Analysis of ZARA's Social Media Marketing Strategy in China

The rapid iteration of the fashion industry and the influx of numerous niche emerging fast fashion brands into the market have driven continuous innovation in social media marketing strategies, with new promotional methods constantly emerging. This article, utilizing literature analysis and observational methods, examines ZARA's social media marketing strategy in China. This analysis is beneficial for companies in the fast fashion industry, particularly those looking to expand their influence in the Chinese market. After a detailed analysis of the characteristics and marketing methods of Chinese social media platforms, the impact of external factors on Chinese consumers, and ZARA's social media presence, the article provides detailed recommendations for social media marketing strategies. ZARA, as a pioneer in the fast fashion industry, has established a certain market presence on Chinese social media platforms. However, its promotional strategy still shows potential for improvement. On platforms like Weibo and Little Red Book, ZARA's content strategy lacks specificity and diversity. In contrast, its live streaming marketing on platforms like TikTok demonstrates excellent visual effects and interactivity. This article provides several specific recommendations for ZARA's social media marketing strategy in China, such as strengthening brand storytelling and product showcases on Little Red Book, hiring celebrity endorsements, and regularly hosting styling challenges on TikTok. In summary, by implementing these strategies, ZARA is poised to achieve more effective brand building and market expansion in China's social media landscape.

Explor-A-Thora: A Novel Three-Dimensionally Printed Pleural Simulator.

For procedural education, the shift from the traditional apprenticeship model to simulation-based mastery has become increasingly accepted as the gold standard and has underscored the importance of high-fidelity, cost-effective training options. However, cost-effective pleural procedure simulators providing both realistic haptic feedback and ultrasound compatibility are lacking. We aimed to create a pleural procedure simulator with characteristics of human tissue, at low cost and with ultrasound compatibility. This work used design-based research principles and a collaborative rapid iteration approach in collaboration with the University of California, San Francisco, Makers Lab and design-based researchers at the University of California, Berkeley, which led to the creation of a three-dimensionally printed pleural procedure simulator. The needs assessment indicated significant discomfort with pleural procedures and a request for more accessible simulation opportunities. Iterative prototyping resulted in a three-dimensionally printed rib cage and a series of innovations in the fluid pocket and skin layers to provide realistic tactile feedback and ultrasound imaging compatibility. The final model costs significantly less than commercial simulators, with durable components and replaceable parts that can be reused multiple times. The development of a low-cost, high-fidelity pleural procedure simulator addresses the current limitations of commercially available pleural simulators. By integrating three-dimensional printing technology and easily accessible materials, we were able to produce a simulator that closely replicates the feel of human tissue, allows ultrasound use, and is adaptable for different patient anatomies and clinical scenarios. This novel simulator is a scalable solution to elevate the standard of procedural education and ultimately positively affect patient care.

Fed4UL: A Cloud–Edge–End Collaborative Federated Learning Framework for Addressing the Non-IID Data Issue in UAV Logistics

Artificial intelligence and the Internet of Things (IoT) have brought great convenience to people’s everyday lives. With the emergence of edge computing, IoT devices such as unmanned aerial vehicles (UAVs) can process data instantly at the point of generation, which significantly decreases the requirement for on-board processing power and minimises the data transfer time to enable real-time applications. Meanwhile, with federated learning (FL), UAVs can enhance their intelligent decision-making capabilities by learning from other UAVs without directly accessing their data. This facilitates rapid model iteration and improvement while safeguarding data privacy. However, in many UAV applications such as UAV logistics, different UAVs may perform different tasks and cover different areas, which can result in heterogeneous data and add to the problem of non-independent and identically distributed (Non-IID) data for model training. To address such a problem, we introduce a novel cloud–edge–end collaborative FL framework, which organises and combines local clients through clustering and aggregation. By employing the cosine similarity, we identified and integrated the most appropriate local model into the global model, which can effectively address the issue of Non-IID data in UAV logistics. The experimental results showed that our approach outperformed traditional FL algorithms on two real-world datasets, CIFAR-10 and MNIST.

Technology Focus: Reservoir Simulation (July 2024)

The early days of artificial intelligence (AI) and machine learning (ML) were filled with promise, but now we’re witnessing a wave of practical applications transforming reservoir engineering. The integration of AI/ML with traditional workflows marks a turning point, unleashing the immense potential of these proven techniques to address our everyday challenges in reservoir simulation. These advancements lead to more-accurate models and faster simulation turnaround times, allowing us to model various scenarios more efficiently. These techniques enable us to understand and simulate reservoir behavior with greater fidelity and focus on the underlying physics. This deeper understanding translates to more-confident decisions regarding reservoir management and development, with a clearer picture of potential risks. Physics-informed machine learning emerges as a significant step forward, improving our understanding and providing better models while boosting runtimes, convergence, and overall performance. Paper IPTC 23730 highlights its application in tackling computationally intensive tasks such as critical temperature prediction with high accuracy, leading to significant speed-ups in simulations, particularly for complex compositional models undergoing miscible gas injection. Paper IPTC 23935 presents a fundamentally different approach to reservoir simulation studies. Adaptive models use multiple smaller, specialized models instead of one giant model. These models can be developed and run independently, allowing for parallel workflows and significantly reduced turnaround times. Adaptive models foster a culture of rapid experimentation and iteration, aligning perfectly with the fail-fast approach—prioritizing the quick evaluation of ideas and discarding those that don’t show promise early on. Paper SPE 214855 provides a comprehensive overview of a complex problem—wellbore modeling. This paper offers practical insights directly relevant to my work. These advancements represent a significant leap forward, paving the way for more-powerful and -versatile reservoir models. As research and development continue, these techniques are poised to revolutionize how we manage and optimize oil and gas reservoirs. I hope you enjoy reading this selection of papers and find them enlightening. Recommended additional reading at OnePetro: www.onepetro.org. SPE 216722 Developing Consistent Relative Permeability and Capillary Pressure Models for Reservoir Simulation of Carbon Capture and Storage Projects by L.S. Lun, ExxonMobil, et al.

Machine learning assisted prediction for hydrogen production of advanced photovoltaic technologies

The photovoltaic (PV) water electrolysis method currently stands as the most promising approach for green hydrogen production. The rapid iteration of photovoltaic technologies has significantly affected on the technical and economic evaluation for photovoltaic hydrogen production. In this work, the photovoltaic hydrogen production of three most advanced silicon photovoltaic technologies is systematically compared for the first time under the climatic conditions of the Kucha region. All-weather stable hydrogen production control system with optimal charging and discharging strategies is constructed to realize stable and efficient hydrogen energy production. Seven machine learning (ML) algorithms are used to forecast the performance in power generation and hydrogen production of a 100 ​MW photovoltaic hydrogen production and energy storage (PH-S) system throughout its operational life. The long short-term memory (LSTM) algorithm exhibits the best performance, achieving mean absolute error (MAE) of 0.0415, root mean square error (RMSE) of 0.0891, and coefficient of determination (R2) of 0.8402. In terms of cost-effectiveness, heterojunction with intrinsic thin layer (HJT) PV technology achieves the lowest levelized cost of electricity (LCOE) and hydrogen (LCOH) at 0.025 $/kWh and 6.95 $/kg, respectively. According to the sensitivity analysis, when the cost of proton exchange membrane electrolysis (PEMEC) reduced 50%, the LCOH for PH-S system decreased 21.40%. This study provides valuable insights for the practical implementation of large-scale photovoltaic hydrogen production and cost reduction in PH-S systems.

THE IMPACT OF CRISIS ON INTERECOSYSTEMIC INNOVATION: DYNAMICS OF ENEL’S HYDROGEN ECOSYSTEM

Crisis highlights conflicts but also helps disparate organisations rally around common objectives through forming ecosystemic relationships. To improve future crisis response, we study the influence of crisis-driven transitions on innovation in ecosystems. We illustrate the study with the dynamics of hydrogen innovation at Enel. Here, innovation caters to multiple value propositions; therefore, we define interecosystemic innovation as an innovation that addresses the value propositions of more than one ecosystem. The unexpected disruption caused by the energy crisis focuses attention on a single value proposition, thus fomenting the dynamic, temporal sequencing of interecosystemic innovation. This increases activity in the ecosystem core as well as interaction with the ecosystem periphery, allowing more rapid innovation iterations throughout the ecosystem. When operating in the intercept of ecosystems, firms need to consider the multiple value propositions of ecosystem participants to identify and mitigate tensions that may impede innovation.