Sep 2026· International Communications in Heat and Mass Transfer· Vol 180, pp. 112653· 16 references
Fire effects on ecosystems
Abstract
This study introduces a physics-based mathematical model to predict the injury and mortality of tree stems during prescribed burns and in wildland fires. The two-dimensional transient model is developed in polar coordinates employing the finite volume method with an implicit time integration scheme. The model includes a source term in the heat transfer equation to account for the volumetric heat absorbed due to desiccation and pyrolysis processes. The thermophysical properties such as thermal conductivity, density, and specific heat are set to vary as a function of temperature. The model is verified against existing experimental and numerical studies (1D and 2D). The temperature history just underneath the inner bark shows that the temperatures predicted by the present model align closely with experimental data. The necrotic depth and viability factor of the tree stem are determined from the predicted temperature distribution in the tree stem. A parametric study is conducted by varying the area exposed to the heat flux, moisture content, and tree species. The depth of stem injury and mortality rate are analyzed for these cases. Results show that the coupled effect of thermo-property variation and moisture content in different layers can significantly affect the heat diffusion and the predicted temperature distribution within the inner layers of the stem. The model serves as a valuable tool for predicting tree mortality rates during wildfires and prescribed burns and helps land managers and fire managers foster better forestry management and ecosystem conservation strategies.
The results are packaged in the Greenfield Startup Model (GSM), which explains the priority of startups to release the product as quickly as possible, and the need to shorten time-to-market, by speeding up the development through low-precision engineering activities.
Carmine Giardino, Nicolò Paternoster, M. Unterkalmsteiner et al.· IEEE Transactions on Softwar...· 178 citations· ⚡14
Software startup companies develop innovative, software-intensive products within limited timeframes and with few resources, searching for sustainable and scalable business models.
M. Unterkalmsteiner, P. Abrahamsson, Xiaofeng Wang et al.· e-Informatica Software Engin...· 157 citations· ⚡17
This study conducts a case survey study based on the secondary data of the major pivots happened in 49 software startups, and demonstrates that customer need pivot is the most common among all pivot types.
Sohaib Shahid Bajwa, Xiaofeng Wang, Anh Nguyen-Duc et al.· Empirical Software Engineeri...· 127 citations· ⚡15
The comparison of adopter and non-adopter sample reveals three potential adoption inhibitor, security, data privacy, and portability, which underlines the importance of the technical and security perspectives for research investigating the adoption of technology.
Nattakarn Phaphoom, Xiaofeng Wang, S. Samuel et al.· Journal of Systems and Softw...· 111 citations· ⚡8
This study investigates how Lean internal startup facilitates software product innovation in large companies and identifies its enablers and inhibitors, and shows the potential of the method-in-action framework to investigate the Lean startup approach in non-startup context.
Henry Edison, Nina M. Smørsgård, Xiaofeng Wang et al.· Journal of Systems and Softw...· 78 citations· ⚡6
The application of agile software methods and more recently the integration of Lean practices contribute to the trend of continuous improvement in the software industry. One such area warranting proper empirical evidence is a project’s operational efficiency when using the Kanban method. This short paper takes a new an...
Marko Ikonen, Petri Kettunen, Nilay V. Oza et al.· EUROMICRO Conference on Soft...· 67 citations· ⚡9
Learn how Google DeepMind and GovTech Singapore uses lightweight on-device Gemma models to intercept conversational scams in private chats with zero cloud data egress.
Microsoft Research Blog· microsoft.comOct 12, 2026
StudentSim is a research system for evaluating and improving AI tutoring tools before they are tested with learners. It models how individuals may engage with tutors, giving feedback to improve tutor performance across subjects like chess, writing, and mathematics. The post Simulating student learning for better AI tutoring appeared first on Microsoft Research.