Machine Learning-Enabled Regional Multi-Hazards Risk Assessment Considering Social Vulnerability

The regional multi-hazards risk assessment poses difficulties due to data access challenges, and the potential interactions between multi-hazards and social vulnerability. For better natural hazards risk perception and preparedness, it is important to study the nature-hazards risk distribution in different areas, specifically a major priority in the areas of high hazards level and social vulnerability. We propose a multi-hazards risk assessment method which considers social vulnerability into the analyzing and utilize machine learning-enabled models to solve this issue. The proposed methodology integrates three aspects as follows: (1) characterization and mapping of multi-hazards (Flooding, Wildfires, and Seismic) using five machine learning methods including Naïve Bayes (NB), K-Nearest Neighbors (KNN), Logistic Regression (LR), Random Forest (RF), and K-Means (KM); (2) evaluation of social vulnerability with a composite index tailored for the case-study area and using machine learning models for classification; (3) risk-based quantification of spatial interaction mechanisms between multi-hazards and social vulnerability. The results indicate that RF model performs best in both hazard-related and social vulnerability datasets. The most cities at multi-hazards risk account for 34.12% of total studied cities (covering 20.80% land). Additionally, high multi-hazards level and socially vulnerable cities account for 15.88% (covering 4.92% land). This study generates a multi-hazards risk map which show a wide variety of spatial patterns and a corresponding understanding of where regional high hazards potential and vulnerable areas are. It emphasizes an urgent need to implement information-based prioritization when natural hazards coming, and effective policy measures for reducing natural-hazards risks in future

GraphLLM: Boosting Graph Reasoning Ability of Large Language Model

The advancement of Large Language Models (LLMs) has remarkably pushed the boundaries towards artificial general intelligence (AGI), with their exceptional ability on understanding diverse types of information, including but not limited to images and audio. Despite this progress, a critical gap remains in empowering LLMs to proficiently understand and reason on graph data. Recent studies underscore LLMs' underwhelming performance on fundamental graph reasoning tasks. In this paper, we endeavor to unearth the obstacles that impede LLMs in graph reasoning, pinpointing the common practice of converting graphs into natural language descriptions (Graph2Text) as a fundamental bottleneck. To overcome this impediment, we introduce GraphLLM, a pioneering end-to-end approach that synergistically integrates graph learning models with LLMs. This synergy equips LLMs with the ability to proficiently interpret and reason on graph data, harnessing the superior expressive power of graph learning models. Our empirical evaluations across four fundamental graph reasoning tasks validate the effectiveness of GraphLLM. The results exhibit a substantial average accuracy enhancement of 54.44%, alongside a noteworthy context reduction of 96.45% across various graph reasoning tasks

Protective Effect of Anthocyanin on Neurovascular Unit in Cerebral Ischemia/Reperfusion Injury in Rats

Treating cerebral ischemia continues to be a clinical challenge. Studies have shown that the neurovascular unit (NVU), as the central structural basis, plays a key role in cerebral ischemia. Here, we report that anthocyanin, a safe and natural antioxidant, could inhibit apoptosis and inflammation to protect NVU in rats impaired by middle cerebral artery occlusion/reperfusion (MCAO/R). Administration of anthocyanin significantly reduced infarct volume and neurological scores in MCAO/R rats. Anthocyanin could also markedly ameliorate cerebral edema and reduce the concentration of Evans blue (EB) by inhibiting MMP-9. Moreover, anthocyanin alleviated apoptotic injury resulting from MCAO/R through the regulation of Bcl-2 family proteins. The levels of inflammation-related molecules including tumor necrosis factor-α (TNF-α), interleukin-1β (IL-1β), and interleukin-6 (IL-6), which were over-expressed with MCAO/R, were decreased by anthocyanin. In addition, Nuclear factor-kappa B (NF-κB) and the NLRP3 inflammasome pathway might be involved in the anti-inflammatory effect of anthocyanin. In conclusion, anthocyanin could protect the NVU through multiple pathways, and play a protective role in cerebral ischemia/reperfusion injury

Resource availability modulates biodiversity-invasion relationships by altering competitive interactions

Community diversity affects the survival of newly introduced species via resource competition. Competitive interactions can be modulated by resource availability and we hypothesized that this may alter biodiversity-invasion relationships. To study this, we assessed the growth of a bacterial invader, Ralstonia solanacearum, when introduced into communities comprised of one to five closely related resident species under different resource concentrations. The invader growth was then examined as a function of resident community richness, species composition and resource availability. We found that the relative density of the invader was reduced by increasing resident community richness and resource availability. Mechanistically, this could be explained by changes in the competitive interactions between the resident species and the invader along the resource availability gradient. At low resource availability, resident species with a high catabolic similarity with the invader efficiently reduced the invader relative density, while at high resource availability, fast-growing resident species became more important for the invader suppression. These results indicate that the relative importance of different resident community species can change dynamically along to resource availability gradient. Diverse communities could be thus more robust to invasions by providing a set of significant species that can take suppressive roles across different environments

Resource stoichiometry shapes community invasion resistance via productivity-mediated species identity effects

The diversity-invasion resistance relationships are often variable and sensitive to environmental conditions such as resource availability. Resource stoichiometry, the relative concentration of different elements in the environment, has been shown to have strong effects on the physiology and interactions between different species. Yet, its role for diversity-invasion resistance relationships is still poorly understood. Here we explored how the ratio of nitrogen and phosphorus affect the productivity and invasion resistance of constructed microbial communities by plant pathogenic bacterium, Ralstonia solanacearum. We found that resource stoichiometry and species identity effects affected the invasion resistance of communities. Both high nitrogen concentration and resident community diversity constrained invasions, and two resident species, in particular, had strong negative effects on the relative density of the invader and the resident community productivity. While resource stoichiometry did not affect the mean productivity of the resident community, it favored the growth of two species that strongly constrained invasions turning the slope of productivity-invasion resistance relationship more negative. Together our findings suggest that alterations in resource stoichiometry can change the community resistance to invasions by having disproportionate effects on species growth 37 potentially explaining changes in microbial community composition under 38 eutrophication

Flexible Energy Conversion Control Strategy for Brushless Dual-Mechanical-Port Dual-Electrical-Port Machine in Hybrid Vehicles

Due to the advantages of high torque density and compact mechanical structure, brushless dual-mechanical-port dual-electrical-port (BLDD) PM machine has become a promising-alternative in series-parallel HEVs. However, two sets of windings in the same core may also result in flux cross coupling, which deteriorate control performance. Through special design of the magnetic circuit, the two sets of windings in the stator side are decoupled. In this paper, the mathematical model of BLDD-PM machine is analyzed firstly. Then, based on energy management system and the model of machine, the decoupled vector control algorithm for outer and inner rotor is developed. Next, common operation states under city road condition for hybrid vehicles have been summarized. According to operation state of HEV, the power flow analysis in the BLDD-PM machine system has been done. To validate the analysis results, experimental test under different operation condition has been conducted. Test results show good control performance of the BLDD-PM prototype and verify the correctness of analysis. Index Terms-BLDD-PM machine, power flow analysis, HEV

Machine learning-enabled regional multi-hazards risk assessment considering social vulnerability

Abstract The regional multi-hazards risk assessment poses difficulties due to data access challenges, and the potential interactions between multi-hazards and social vulnerability. For better natural hazards risk perception and preparedness, it is important to study the nature-hazards risk distribution in different areas, specifically a major priority in the areas of high hazards level and social vulnerability. We propose a multi-hazards risk assessment method which considers social vulnerability into the analyzing and utilize machine learning-enabled models to solve this issue. The proposed methodology integrates three aspects as follows: (1) characterization and mapping of multi-hazards (Flooding, Wildfires, and Seismic) using five machine learning methods including Naïve Bayes (NB), K-Nearest Neighbors (KNN), Logistic Regression (LR), Random Forest (RF), and K-Means (KM); (2) evaluation of social vulnerability with a composite index tailored for the case-study area and using machine learning models for classification; (3) risk-based quantification of spatial interaction mechanisms between multi-hazards and social vulnerability. The results indicate that RF model performs best in both hazard-related and social vulnerability datasets. The most cities at multi-hazards risk account for 34.12% of total studied cities (covering 20.80% land). Additionally, high multi-hazards level and socially vulnerable cities account for 15.88% (covering 4.92% land). This study generates a multi-hazards risk map which show a wide variety of spatial patterns and a corresponding understanding of where regional high hazards potential and vulnerable areas are. It emphasizes an urgent need to implement information-based prioritization when natural hazards coming, and effective policy measures for reducing natural-hazards risks in future

RheologyNet: A Physics-Informed Neural Network Solution to Evaluate the Thixotropic Properties of Cementitious Materials

Thixotropic behaviors can be predicted by rheological partial differential equations (PDEs) of cementitious materials. The ability to solve the rheological PDEs of viscous fluids accurately and efficiently has become an emerging interest in research. However, due to the growing number of parameters in rheological constitutive equations and the non-ideal behavior of materials from experiments, solving the rheological PDEs becomes computationally costive and error-prone. We propose a physics-informed neural network (PINN)-based framework, RheologyNet, as a surrogate solution to predict the general thixotropic behavior of cementitious materials. The complex PDEs are embedded in the well-designed RheologyNet architecture to link macroscopic viscous flow behaviors and microstructural changes. Numerical experiments suggested that RheologyNet can accurately and efficiently predict the rheological properties of cementitious materials compared to the traditional Fully-connected Neural Network (FNN) and mechanistic Finite Element Analysis (FEA). Particularly, RheologyNet demonstrated great promise for simulating history-dependent thixotropic behaviors