**Author details**

Iuliana Ceausu1 \*, Dominic Iliescu2 , Cristian Poalelungi1 , Cristian Posea1 , Nicolae Bacalbasa1 , Dragos Dobritoiu1 and Liana Ples3

\*Address all correspondence to<EMAIL_ADDRESS>
1 Department of Obstetrics–Gynecology, University of Medicine and Pharmacy "Carol Davila," "Dr. I. Cantacuzino" Hospital, Bucharest, Romania

2 Department of Obstetrics–Gynecology, University of Medicine and Pharmacy, Craiova, Romania

3 Department of Obstetrics–Gynecology, University of Medicine and Pharmacy "Carol Davila," "Sf. Ioan" Emergency Hospital, Bucharest, Romania


**5. Conclusions**

This study is the first attempt to probe into long-term climate change (warm/wet versus cold/dry) and its association with the hot spots of di fferent types of war (all war, agri-nomadic conflict, and rebellion) in imperial China. Looking at the connection from a spatiotemporal perspective with the aid of a few quantitative and visualization techniques, we conclude the following:

**Supplementary Materials:** The following are available online at http://www.mdpi.com/2073-4433/11/4/378/s1, Figure S1: Comparisons of battle number counted within the grids of (a) 50 km, (b) 100 km, and (c) 200 km for each side of a cell, Figure S2: Geographical features of China mentioned in the main text, Table S1: History of China in the study period, Table S2a: N–S confrontation within China Proper during warm and cold phase, 5–1911 CE, Table S2b: N–S confrontation within China Proper during wet and dry phase, 1–1911 CE, Table S3: The Global Moran's *I* for di fferent categories of war and climatic stages based on the grids of 50, 100, and 200 km.

**Author Contributions:** Conceptualization, D.D.Z.; Data curation, S.Z. and D.D.Z.; Formal analysis, S.Z.; Funding acquisition, D.D.Z.; Investigation, S.Z.; Methodology, S.Z. and D.D.Z.; Project administration, D.D.Z.; Resources, D.D.Z.; Supervision, D.D.Z. and J.L.; Validation, S.Z., D.D.Z., and J.L.; Visualization, S.Z.; Writing—original draft, S.Z.; Writing—review and editing, S.Z., D.D.Z., and J.L. All authors have read and agreed to the published version of the manuscript.

**Funding:** This research was funded by the Fund for Key Platform Construction Project–Special Project of High Level University Construction at Guangzhou University, grant number 290020363.

**Acknowledgments:** The authors thank the anonymous reviewers for their valuable comments on this manuscript. **ConflictsofInterest:**Theauthorsdeclarenoconflictof interest.


**8. SOC determination and model application**

The SOC behavior in the soil can be analyzed using different models [17, 18, 49]. The CENTURY soil organic matter model environment is a FORTRAN model. It is used in the plant–soil ecosystem. It is represented by C. It is a software design for nutrient dynamism in a different ecosystem. This includes grassland, forest, crop, and savannah [17, 18, 49]. The EPIC model establishes a relationship that exists between the soil and climate. It is a process-based model well-known. The Roth C-26.3 can be used to analyze soil type, temperature, moisture content, and plant cover on the turnover process. It can determine the turnover of organic carbon in non-waterlogged soil [17, 18, 49]. Some of the required parameters for analyzing SOM and SOC were weather, sowing procedures, plan of work on the different fields, fertilization etc. This information is based on different algorithms [17, 18, 49]. STIC model was also noted for a good approximation of water requirement. However, the use of multiple linear regression (MLR) was noted for its multidimensional functions [17, 18, 49]. MLR is capable of modeling soil properties after the application of organic manure. MLR can be used accomplished to the relationship between SOC stock and other soil properties in any region of the world. It can provide a relationship between liable C form, soil properties, and management practices. The use of MLR was found efficient and reliable [17, 18, 49].


**2. Thermal runaway at the single-cell level**

Utilizing a multi-physics and multi-scale model is essential for comprehensively understanding the intricate dynamics of battery performance. Xu and Hendricks [34] developed a model of a lithium-ion battery (LIB) to investigate both its electrochemical and thermal-mechanical processes. The model's accuracy was confirmed through comparison with experimental data obtained from large-format battery cells subjected to diverse load and boundary conditions [35]. The simulation model configuration progresses through several stages:

The multi-physics model is simulated by solving a series of individual physics consecutively, and the obtained results will be passed on to the next module to account for the coupling effect. This is done to ensure that the interdependencies and interactions between different physical phenomena within the battery are appropriately accounted for. This physics focuses on different areas such as fluid dynamics, heat transfer, and solid mechanics to individually address heat generation, thermal expansion, and structural deformation.

**Figure 1(a)** showcases the outcome of the overcharging simulation, providing insights into the temperature distribution within the lithium-ion battery (LIB). By the end of the time-dependent simulation, specifically at 3960 seconds, the highest temperature is localized at the core of the cell, gradually diminishing toward the outer circumference.

**Figure 1(b)** presents the simulation results under overcharging conditions, delineating the structural deformation and stress distribution within the LIB cell. At 3960 seconds, signifying the conclusion of the time-dependent simulation, stress concentration emerges along the junction between the upper and side walls of the cell, attaining its maximum intensity. Moreover, **Figure 1** highlights that the upper portion of the LIB cell experiences the most pronounced deformation, in line with empirical observations. Additionally, the simulation indicates that the entire battery cell expands due to heightened internal pressure, as depicted in **Figure 1**.

**Figure 1(c)** exhibits the outcome illustrating the temperature distribution within the LIB cell, with a focus on the overheating test simulation where exothermic

#### **Figure 1.**

*(a) Temperature distribution of the LIB in the overcharging simulation at 3960 s; (b) stress distribution and structural deformation of the LIB in the overcharging simulation at 3960 s; (c) temperature distribution of the LIB in the oven heating test without exothermic reaction at 8560 s; (d) stress distribution and structural deformation of the LIB in the oven heating test without exothermic reactions at 8550 s.*